Research ArticleLeveraging Innovation Resources for Modular Products underOpen Innovation Scenarios Using Fuzzy Distance Method

Hai-jun Wang and Chao-hui Shu

School of Management Shenyang University of Technology Shenyang 110870 China

Correspondence should be addressed to Hai-jun Wang hjwangsuteducn

Received 3 July 2020 Revised 10 August 2020 Accepted 29 August 2020 Published 14 September 2020

Academic Editor Lifei Chen

Copyright copy 2020 Hai-jun Wang and Chao-hui Shu +is is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in anymedium provided the original work isproperly cited

In an open innovation environment it is meaningful for manufacturing enterprises targeting global markets to integrate qualifiedinnovation resources In this paper the linkage between product modularity and open innovation is first discussed revealing arole that modular product architecture plays in linking enterprisesrsquo innovation requirements and innovation resources as externalinnovation inputs Next indices for evaluating external innovation resources are developed An evaluation method based on fuzzydistance is then proposed which is intended to select optimal resources for the core modules of modular product architecture Amodular product of Haier Group is used as a typical case to verify the proposed method Consistent evaluation results ofinnovation resources are achieved for different decision-making attitudes Another finding regarding the case enterprise is that theresource management mechanisms it employs lead to a win-win cooperative relationship with its partners

1 Introduction

As the industrial chain is growing more open than ever thetraditional vertical integration model where a product ismanufactured by a single enterprise independently is nolonger the trend +e significance of an open innovationsystem for corporate technical innovations is becomingunquestionably evident [1] In their attempts at improvingmarket competitiveness with rapidly manufactured and low-cost products or services a growing number of enterpriseshave crossed their organizational border for technical in-novations and embarked on joint RampD activities by takingadvantage of external resources [2] Against this backdropan external resource network consisting of universitiesscientific research institutes and suppliers now evolves as anincreasingly important source for corporate innovations

In the open innovation theory of Chesbrough [3] anenterprise may seek innovations by introducing and ab-sorbing diverse external resources +e significance of anopen innovation system for corporate technical innovationshas been well documented [1 2] As an example statisticsshow that in the period from 2001 to 2006 about 50 of the

products of Procter amp Gamble stemmed from cooperativeresearch and development with its external partners

Schmitt pointed out that in an interdependent rela-tionship between supply and demand sides the establish-ment of a long-term mechanism contributes substantially toresource sharing and continued coordination [4] Howeverresearch gaps exist regarding reasonable selection and in-tegration of heterogeneous module innovation resources Inparticular issues due to the subjective and ill-targeted in-troduction of external partner resources and homogeneity ofsuch resources can be serious Furthermore there is ageneral lack of research on the design of an effectivemechanism for managing evaluated and selected partners

In addition with the development of economic glob-alization large manufacturing enterprises involved in globalmarkets pursue a more professional division of labor andcoordination Complicated products are decomposed intomultiple functional modules while external innovationresources such as design houses and modular suppliers areintegrated into corresponding stages of product designprocurement manufacturing and service with the assistanceof standardized module interfaces thus forming a modular

HindawiDiscrete Dynamics in Nature and SocietyVolume 2020 Article ID 5281638 10 pageshttpsdoiorg10115520205281638

innovation network Based on the above discussion thisstudy explores an effective method aiming at leveraginginnovation resources for modular products of largemanufacturing enterprises under open innovation scenarios+e remainder of this article is organized as follows Section2 presents a method of evaluating and selecting innovationresources To verify suchmethod Section 3 introduces a casestudy of a large Chinese manufacturing enterprise Section 4concludes this paper

2 Method

21Modularity andOpen Innovation Modularity refers to aprocess where a complicated system is broken down into anumber of modules with standard interfaces [5] +e key ofmodularity is to incorporate usersrsquo demands into a productdesign process and to generate modular products [6 7]Modular product architecture (MPA) is a unified form ofarchitecture with loosely coupled modules as the basic el-ements MPA allows enterprises to realize product modu-larity to decompose corresponding innovation tasks and toassign individual innovation resources to such tasks (Fig-ure 1) In this way MPA can play the role of linking thedemand and supply sides of innovation [8] +rough thecoordination role of the standardized interface the inter-dependence between technical modules and resources theyrely on can be reduced [9]

In an open innovation environment driven by productmodularity the supply and demand sides as innovationstakeholders are able to rapidly align their new innovationtasks and requirements with each other based on ldquotrans-parent design rulesrdquo of modules Meanwhile they caneliminate barriers at innovation interfaces possibly resultingfrom information asymmetries and subjective differences+is in turn leads to optimal quantity and quality of externalresources as well as reduced cost of communication andcoordination

22 Indices for Evaluating Module Innovation ResourcesFor enterprises implementing product modularity there aremultiple possible forms of collaboration with external re-sources like ldquouniversity-enterpriserdquo ldquoresearch institute-en-terpriserdquo and ldquosupplier-enterpriserdquo Such collaboration isexpected to promote enterprise innovation competitivenessthrough the interaction of elements such as knowledge andtechnology [10] By referring to previous studies aboutcategories of innovation resources under open innovationscenarios [3 11] we further classify innovation resourcesinto nine primary types university (M1) research institute(M2) strategic supplier (M3) testing laboratory (M4)technology innovation agent (M5) industrial association(M6) consulting firm (M7) venture capital firm (M8) andindividual expert (M9) In particular strategic suppliers aremodule suppliers that can not only respond to enterprisesrsquomodule procurement requirements in a timely way but alsotake part in early research and development of theseenterprises

Scholars have demonstrated that basic qualificationperformance and availability are critical for the evaluationand selection of external resources [12] Technological in-novation ability and resource complementarity are regardedas key indicators for assessing partners and coordinationbetween external resources and an enterprise is an importantfactor affecting the latterrsquos innovation performance [13 14]Shaikh pointed out that the selection of open innovationresources is even more complicated [15] and the evaluationof open innovation resources should pay more attention tovalue creation In addition knowledge matching andcomplementarity between external resources and an en-terprise also play an important role in improving the latterrsquosproduct innovation performance [16]

According to our survey and observation the evaluationand selection of external resources by an enterpriseimplementing product modularity are not merely based ontechnology innovation and organization management as-pects +e abilities of external resources to improve theperformance of the module value chain need to be con-sidered as well Hence this paper defines evaluation indicesfor external innovation resources including basic qualifi-cation and reputation (P1) technology innovation ability(P2) complementarity (P3) ability to perform a contract(P4) integration difficulty (P5) management and controldifficulty (P6) (P5 describes the difficulty to integrate ex-ternal resources into open innovation activities while P6 is ameasure of the difficulty for an enterprise to coordinate withresources in a cooperative innovation project) cost per-formance (P7) ability to provide value-added services (P8)and ability to manage and control subresources (P9)

23 Evaluation and Selection ofModule InnovationResourcesConsidering the nature of the evaluation and selection ofstrategic innovation resources for core modules (systems) afuzzy method is usually adopted for ranking comprehensiveperformances of candidate resources However there existcertain shortcomings of traditional fuzzy ranking methods[17 18] (1) their calculation processes are complex (2) theyare sometimes in conflict with human intuition or lack

Modular productarchitectureCore module

Generic module

Core moduleresource

Generic moduleresource

Generic moduleresource

Figure 1 Linkage between innovation resources and modularproduct architecture

2 Discrete Dynamics in Nature and Society

discrimination (3) the information of decision elements likethe weight used for modeling is accurate which is not thecase in practice To address these shortcomings this paperproposes a novel fuzzy ranking method featuring a fuzzydistance comparison +e method includes the followingspecific steps

231 Definition of Evaluation Indices and Fuzzy Mem-bership Function +e fuzzy linguistics made by evaluationexperts is converted to triangular fuzzy numbers For in-stance five levels of fuzzy linguistics ie very low (VL) low(L) moderate (M) high (H) and very high (VH) can beemployed to describe the importance of different indices(Figure 2) In this regard a triangular fuzzy function can beused to express the linguistics of such fuzzy evaluations Forexample an index with an importance level of moderate (M)may be denoted by a triangular fuzzy number (02 05 08)Next seven levels of fuzzy linguistics ie very good (VG)good (G) relatively good (RG) moderate (M) relativelypoor (RP) poor (P) and very poor (VP) can be employed toevaluate the performance of external module innovationresources (Figure 3)

232 Evaluating Fuzzy Weights and Each CandidateResource It is assumed that there are M external moduleinnovation resources to be rated and N experts engaged inrating By using ak

ij to indicate a rating value assigned by anexpert Ek(k 1 2 N) to an external module innovationresource Mi for the index Pj(j 1 2 9) the fuzzyevaluation matrix A can be expressed as

A aij1113960 1113961Mtimes9

a11 a12 middot middot middot a19

a21 a22 middot middot middot a29

middot middot middot middot middot middot middot middot middot middot middot middot

aM1 aM2 middot middot middot aM9

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(1)

where aij is a comprehensive evaluation fuzzy number of theexternal module innovation resource Mi(i 1 2 M)

for the index Pj Naturally there is a conversion relationbetween aij and ak

ij

aij 1Notimes a

1ij oplus a

2ijoplus middot middot middot oplus a

Nij1113872 1113873 (2)

A combination of significance values assigned bydifferent experts to the index gives the fuzzy weight factorB b1 b2 b91113864 1113865 On this basis the final evaluationresults of all candidate resources can be derived bymultiplying the fuzzy evaluation matrix A by the fuzzyweight vector B of the index

R AotimesBT

r1

r2

⋮

rM

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

a11 otimes b1oplusa12 otimes b2oplus middot middot middotoplusa19 otimes b9

a21 otimes b1oplusa22 otimes b2oplus middot middot middotoplusa29 otimes b9

⋮

aM1 otimes b1oplusaM2 otimes b2oplus middot middot middotoplusaM9 otimes b9

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(3)

where ri is the fuzzy rating value assigned to an externalmodule innovation resource Mi ri aij times bj bj is the fuzzyweight value for index Pj and otimes and oplus are fuzzy operators+e fuzzy sum and product of triangular fuzzy numbers 1113957A

(aL a aR) and 1113957B (bL b bR) can be expressed as

1113957Aoplus1113957B aL a aR( 1113857oplus bL b bR( 1113857 aL + bL a + b bR + bR( 1113857

1113957Aotimes 1113957B aL a aR( 1113857otimes bL b bR( 1113857 aLbL ab aRbR( 1113857

⎧⎨

⎩

(4)

233 Ranking Each Candidate Resource Using Fuzzy Dis-tance Measurement Method Since the above evaluationassignment process for external module innovation re-sources still leads to fuzzy numbers it is not yet possible todetermine the final ranking of module innovation re-sources A fuzzy distance measurement method istherefore adopted for further comparison of the fuzzynumbers [19] +e sequence of fuzzy numbers is deter-mined by comparing the distance from every fuzzynumber to predefined maximum and minimum

M Hμ(x)

x

1

01002 03 05 07 08

VL L VH

Figure 2 Membership functions for ranking indices

x

μ(x)

1

005 07 0903 0402 100601 08

VP RG G VGP RP M

Figure 3 Membership functions for ranking resources

Discrete Dynamics in Nature and Society 3

distinguishing points +e fuzzy number closest to themaximum distinguishing point (Dmax) and most distantfrom the minimum distinguishing point (Dmin) ranks firstin the sequence and is regarded as the strategic innovation

resource for the core module (system) of an enterprise infurther cooperation

Following the theory of fuzzy distance measurementmethod [19] we can define a distance between two randomfuzzy numbers (A and B) as

D2(A B f) 1113946

1

0

AL(α) + AU(α)

21113888 1113889 minus

BL(α) + BU(α)

21113888 11138891113890 1113891

2

+13

AL(α) + AU(α)

21113888 1113889

2

+BL(α) + BU(α)

21113888 1113889

2⎡⎣ ⎤⎦

⎧⎨

⎩

⎫⎬

⎭ timesf(α)dα

111393810 f(α)dα

(5)

where AL(α) AU(α) BL(α) andBU(α) represent an α cutset interval of random fuzzy numbers A and B and

f(α) ατ((0le αle 1) represents a weight function and itis a continuous positive function defined in the interval [0 1]

Hence Dmax and Dmin ie the distances from any tri-angular fuzzy number to the maximum distinguishing point(max) and minimum distinguishing point (min) can bederived

D2(r M) r2 minus M( 1113857

2+

1τ + 2

r2 minus M( 1113857 r3 + r1( 1113857 minus 2r21113858 1113859

+2

3(τ + 2)(τ + 3)r3 minus r2( 1113857

2+ r2 minus r1( 1113857

21113960 1113961

minus2

3(τ + 2)(τ + 3)r3 minus r2( 1113857 r2 minus r1( 11138571113858 1113859

(6)

where r is a triangular fuzzy number to be ratedr (r1 r2 r3) M is the maximum or minimum dis-tinguishing point and τ is the weight coefficient

Different f(α) values correspond to different weightsused during the calculation of fuzzy distance For examplewhen τ 1 a larger value of α in [0 1] corresponds to ahigher weight assigned to it When τ 0 all α values in [0 1]have the same weight Different τ values reflect differentdecision-making attitudes If a decision maker tends to beneutral then τ 1 is more reasonable τ gt 1 is preferred for aconservative decision maker and τ 0 or a decreasingfunction is preferred for an aggressive decision maker +emaximum and minimum distinguishing points can be de-rived from the following equation

max(I)ge sup 1113947I

i1s 1113957Ai1113872 1113873⎛⎝ ⎞⎠

min(I)le inf 1113947

I

i1s 1113957Ai1113872 1113873⎛⎝ ⎞⎠

(7)

Based on the above analysis the following characteristicsof the proposed innovation resource evaluation methodbased on fuzzy distance measurement can be observed Firstcompared with traditional evaluation and ranking methodseg the analytic hierarchy process (AHP) the calculation

process of the proposed method is simpler and visualizedSecond various t values can reflect different decision atti-tudes thereby leading to good robustness and flexibilityFinally fuzzy linguistics are used to evaluate the weight ofeach index By combining the performance of candidateinnovation resources such linguistics are more in line withreal situations

3 Case Study and Results

31 Case Background Haier Group a typical Chinesemanufacturing enterprise evolving rapidly in the time ofReform and Opening is selected for a case study to verify theproposed method+ere are four major reasons for selectingHaier as the case sample (1) Haier is now a leader in theChinese home appliance industry and also one of the largepioneering Chinese enterprises that have strategically builttheir worldwide RampD production marketing and salesnetworks (2) Despite its rapid development in recent de-cades Haier realizes that it has become impossible to meetever-increasing product design complexity and customerdemands by relying only on its own RampD capabilitiesConsequently Haier adopts an open innovation strategy toexploit innovation potential via external partners Mean-while Haier has established an open innovation platformHaier Open Partnership Ecosystem (HOPE) on which itinteracts with external resources to come up with innovativesolutions and fulfill global usersrsquo requirements (3) In 2011Haier switched from traditional component design to aproduct modularity model for its large home appliance linesModularity has thus become a key pillar for the company toleverage product innovations and pursue business modeltransformations

+e Chinese air-conditioning industry has entered a highlycompetitive stage in recent years On the one hand the tasks ofenergy conservation and emission reduction are demandingand real estate regulation and control policies suppress furtherexpansion of the industry With the release of energy-savingproduct subsidy policies the production cost of air conditionersis gradually rising On the other hand too many homogeneousChinese air-conditioning products lacking technological in-novation are competing more intensely than before in themarket Realizing that the in-house RampD force alone would nolonger completely meet global customer needs Haier CEO

4 Discrete Dynamics in Nature and Society

Zhang Ruimin calls for a move from a closed organization to anopen one

Our analysis involves a high-end air conditioner product(T-AC) of Haier Group that features a modular design +isproduct is a response to a survey of over 600000 consumersIt is mainly intended to address issues or needs related to air-conditioning diseases cold air natural air and remotecontrol

32 Overview of Case Product Modularity +e modularproduct architecture of T-AC was created using the MFDmethodology given by Ericsson and Erixon [5] and iscomposed of 28 modules with standardized interfaces Tofurther promote commonality among different modules andoptimize the number of external innovation resources Haierworked out seven module systems based on the functionaland supply chain correlations among modules (Figure 4)+erefore the modular product architecture can be depictedas seven module systems plus ten individual modules Ontop of that the core module (system) within the modularproduct architecture is defined according to Haierrsquos tech-nological and marketing strategies as well +e integration ofexternal innovation resources for the core module (system)was well considered

Being regarded as one core module (system) the air-supplying module system is closely related to air output airsupply distance and air supply range of T-AC (Figure 5)Due to product modularity Haier released modular inter-face standards with external innovation resources withoutdisclosing too much detailed information such as technicalspecifications drawings and inspection standards Ac-cordingly technical secrets and intellectual properties of

T-AC as well as its modules (systems) have been wellprotected though it was necessary to design proper methodsto integrate qualified competent and suitable external in-novation resources during development

33 Evaluation and Selection ofModule InnovationResourcesWith the evaluation method described in Section 22 anoptimum module innovation resource is selected from thecandidate resources (Mi i 1 2 9) collected fromaround the world and made available on the HOPE (HaierOpen Partnership Ecosystem) platform In this process theparticipating evaluators first assign a value to each indexbased on five levels of fuzzy language very low (VL) low (L)moderate (M) high (H) and very high (VH) Ratings of thesignificance of the module innovation resource indices bythree experts are given in Table 1 +e triangular fuzzynumbers corresponding to the fuzzy language are VL (0 003) L (0 03 05) M (02 05 08) H (05 07 10)and VH (07 10 10)

Seven levels of fuzzy language ie very good (VG) good(G) relatively good (RG) moderate (M) relatively poor(RP) poor (P) and very poor (VP) are then used by threeexperts to evaluate each innovation resource for differentindices +e fuzzy evaluation matrix of external modularresources based on the evaluation by three experts is given inTable 2 +e triangular fuzzy numbers of fuzzy language areVP (0 0 02) P (01 02 03) RP (02 04 05)M (04 05 06) RG (05 07 08) G (07 08 09) andVG (08 10 10)

+e fuzzy rating values of all module innovation re-sources can be derived from (1)fd1

R 1113957r1 1113957r2 1113957r9( 1113857

(19056 39989 62822) (18867 37056 58456)

(19167 43467 67478) (16433 38700 63100)

(08422 21733 41178) (14956 25233 45167)

(16833 27867 44400) (14678 35278 58344)

(16211 34600 58689)

⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝

⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠

(8)

By selecting τ 0 12 1 2 the ranking values of allexternal module innovation resources can be obtained asshown in Table 3 It can be seen from Figures 6ndash9 that fordifferent decision-making attitudes (ie at different values ofτ) the distances from the evaluation fuzzy numbers forM1minusM9 to maximum and minimum distinguishing pointsare consistent +e fuzzy rating number of module inno-vation resource M5 (strategic suppliers) is closest to themaximum distinguishing point and most remote to theminimum distinguishing point M5 can therefore be de-termined as the optimum module innovation resource

During the cooperation with a selected strategic supplier(a bid winner) Haier implemented resource managementmechanisms First a contract-binding mechanism was usedto regulate the rights and responsibilities of both parties

leading to well-defined delivery times prices and sup-porting services for innovative air supply module servicesSecond a dynamic optimization mechanism was used tokeep the strategic supplier competitive driving its capabilityupgrading +ird Haier focused on a practical incentivemechanism called ldquobig resources in exchange for big re-sourcesrdquo For example large-volume module purchase or-ders were placed to stimulate adequate innovation inputsfrom the strategic supplier during its cooperation withHaier

Driven by these mechanisms the open innovationnetwork was operated in a self-organized way Followingthe implementation of its corporate transition strategy inrecent years Haier has concluded that it must rely on suchoperation mechanisms to improve the quality and

Discrete Dynamics in Nature and Society 5

performance of innovation resources so as to boost openinnovation

331 Barrier-Free Resource Inclusion and ExclusionMechanism In response to Haierrsquos technology require-ments a number of global resources have been included incollaborative innovation and they are evaluated under equalcompetition and access conditions On this basis a rea-sonable innovative solution may be proposed by anyqualified innovation source in accordance with modularrequirements Previously the majority of external resourcesof Haier conditioners were based in China Starting from2000 Haier has carried out the innovation strategy ofldquotaking the world as our RampD centerrdquo +is has driven theresource integration process of Haier toward globaliza-tion Under the right circumstances networking toolsallow resources in other countriesregions to participatein the fair bidding procedures of Haier and to interactwith the company easily On the other hand due to the

function of the innovation resource filtering mechanismthe innovation resources in the circles of collaborativeinnovation have to continuously enhance their capabil-ities and adjust their service strategies based on thedynamic needs of Haierrsquos T-AC In addition the ability tosecure qualified innovation resources has been includedin the assessment of Key Performance Indexes (KPIs) ofrelevant employees

332 Participation Constraint and Incentive CompatibilityMechanism In order to manage innovation resourceswithin a controllable scope their behaviors are wellregulated so as to avoid possible risks during collabo-rative innovation Specifically strict clauses relating toparticipation qualifications are defined in contracts be-tween Haier and innovation resources thus eliminatingthe possibility of a deliberate withdrawal from cooper-ation and misconduct +rough the use of incentivecompatibility measures innovation resources are able to

Module Module systemEvaporator module Evaporating module systemElectrical heating moduleMotor module (internal)

Air supplyingmodule system

Fan module (internal)Skeleton moduleAir induction moduleStepping motor moduleSelf-cleaning module ndashFan curtain module Casing module

system Display modulePanel moduleAnion generator module ndashElectrical control box (internal) Electrical control module

system Computer board (internal)PM25 scavenger module ndashformaldehyde scavenger module ndashExhaust module ndashFixed frequency control module Fixed frequency control

module system Electrical parts box moduleIntelligent sensing module ndashCondenser module ndashMotor module (external) Exhaustmodule systemFan module (external)rottling depressurization module ndashElectrical control box (external) Converter control module

system Computer board (external)Structural components module ndashFresh air module ndash

Mod

ular

des

ign

Figure 4 +e modular product architecture of T-AC

Air inductionmodule

Motor module

Stepping motormodule

Skeleton moduleFan module

Air supplyingmodule system

Figure 5 Composition of the air-supplying module system of T-AC

6 Discrete Dynamics in Nature and Society

Tabl

e1

Fuzzyevaluatio

nmatrixof

evaluatio

nindices

Index

P1P2

P3P4

P5P6

P7P8

P9

Expert

E1VH

(071

01

0)

H(050

71

0)

H(050

71

0)

M(020

50

8)

H(050

71

0)

VH

(071

01

0)

H(050

71

0)

L(00

30

5)

VL(00

03)

E2H

(050

71

0)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

M(020

50

8)

H(050

71

0)

M(020

50

8)

M(020

50

8)

L(00

30

5)

E3H

(050

71

0)

VH

(071

01

0)

VH

(071

01

0)

L(00

30

5)

L(00

30

5)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

VL(00

03)

Average

(057

08

10)

(063

09

10)

(047

0730

93)

(013

0430

7)

(023

05

077)

(063

09

10)

(030

57

087)

(013

0430

7)

(00

10

37)

Discrete Dynamics in Nature and Society 7

Table 2 Fuzzy evaluation matrix of module innovation resources

ResourcesIndices

P1 P2 P3 P4 P5 P6 P7 P8 P9M1 G VG G RP RP M VG GG VG M P RG RG M M RG VG RG G RG RG RG VG G VG G VGM2 RG M RG M M M G VG G VP VP P P P VP G RG RG M RP M RG VG VG G G GM3 VG VG G RP P RP VG VG G G G G VG RG VG M G M G VG VG RG M RG M M GM4 RG G M M RP RP M M G RG M RG G RG RG M M RG RG G VG RG G RG M M GM5 VP VP P RG G VG M M RG RP RP VP RG RG M P M M RG G G VG VG VG P P VPM6 P P VP G RG M RG G RG VP M VP M M RG P M P RG RG G G M RG P RP PM7 P VP P RG G M RG RG M VP P P RG M RG RP RP RP G G RG G RG RG P P RPM8 RP M P G RG M G G VG M M RG G G VG RG VG G RG G G G M RG M M PM9 M RG M RG M RG M G G P P P RG M M G RG VG P VP VP G RG M VG G RG

41117 38432

44135 39998

23915 28137 29611

36627 36723

30425 32815

27797 31736

47060 42674 41024

34875 34715

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 6 Visualization of the evaluation of module innovation resources at τ 0

4081937919

4391839654

2341427512

29224

36277 36149

30214 3255927414

31539

4726443057 41215

34757 34825

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 7 Visualization of the evaluation of module innovation resources at τ 05

Table 3 Evaluation of module innovation resources using the distance measurement method

τ Dmax Dmin M1 M2 M3 M4 M5 M6 M7 M8 M9

τ 0 Dmin 41117 38432 44135 39998 23915 28137 29611 36627 36723Dmax 30425 328148 27797 31736 47060 42674 41024 34875 34715

τ 12 Dmin 40819 37919 43918 39654 23414 27512 29224 36277 36149Dmax 30214 32559 27414 31539 47264 43057 41215 34757 34825

τ 1 Dmin 40635 37882 43791 39442 23092 27102 28974 36059 35907Dmax 30140 32745 27184 31430 47408 43319 41349 34696 34498

τ 2 Dmin 40425 37633 43654 39200 22705 26600 28670 32810 35529Dmax 30000 32757 26805 31281 47719 43866 41636 37629 34977

8 Discrete Dynamics in Nature and Society

tap their potential effectively for the purpose of profit-sharing For example a Chinese Southern manufacturerSunwill was originally only responsible for supplying thefan module of T-AC but later it became the designer andsupplier of the whole air supply module system thanks toHaierrsquos incentive compatibility mechanism A purchaseorder of more than 100000 units (module systems) perquarter was given to Sunwill together with a contractcommission for at least two years As a result Sunwill nowmanages other module suppliers of the air supply modulesystem including those offering motors and winddeflectors

333 Distributed Resource Management Mechanism +ecore modules (air supply module system evaporatormodule system etc) of the modular product architectureof T-AC were developed in the headquartersrsquo RampD centerof Haier At this center Haier utilizes its RampD capacitiesand interacts with innovation resources to strengthen itsinnovation competence With high-qualification re-quirements for standard modules among such innovationresources resource optimization frequency is low Haierseeks to establish a strategic cooperation relationship with

a number of excellent selected candidates Individualizedmodulessystems (formaldehyde elimination moduledouble-way fresh air module etc) are developed in areasclose to target markets +e benefits include more synergieswith innovation resources lower cost and higher serviceefficiency To cater for varying user needs the optimizationfrequency of the innovation resources in these areas isrelatively high

4 Conclusions

41 eoretical Contribution To fill the gaps in existingstudies the relationship between modularity and openinnovation is first analyzed Module innovation resourcesin an open environment are then grouped In order toselect qualified innovation resources for core modules ofmodular product architecture nine evaluation indices areconstructed in this study Next a fuzzy distance mea-surement method is presented to compare fuzzy numbersand decide which innovation resource is optimal By usinga case study of one of Haier Grouprsquos modular productsconsistent evaluation and selection results are achievedfor different decision-making attitudes proving that themethod proposed in this paper is robust

40635 37882

43791 39442

23092 27102 28974

36059

30140 32745

27184 31430

47408 43319 41349

34696

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

35907 34498

Figure 8 Visualization of the evaluation of module innovation resources at τ 1

40425 37633 43654

39200

22705 26600 28670

32810 35529

30000 32757 26805

31281

47719 43866 41636

37629 34977

0

1

2

3

4

5

6

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 9 Visualization of the evaluation of module innovation resources at τ 2

Discrete Dynamics in Nature and Society 9

42 Empirical Conclusions Based on the case study of theHaier Group modular product architecture plays a vitalrole in linking the enterprisersquos innovation requirementswith external innovation resources Meanwhile it isdiscovered that strategic suppliers not only collaboratewith Haier in terms of procurement but also actively getinvolved in core module system innovation +rough theimplementation of strategic supplier managementmechanisms such as contract binding dynamic optimi-zation and big resources in exchange for big resources astrategic supplier is able to improve its capabilities con-tinuously thus engaging in open innovation with thecompany in a win-win context

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that they have no conflicts of interest

Acknowledgments

+is research was supported by the Chinese NationalFunding of Social Sciences (19BGL045) Liaoning PublicWelfare Research Project (2020JH410100023) and theProject is sponsored by Liaoning BaiQianWan TalentsProgram (2020921084) +e authors herewith show ap-preciation for their support

References

[1] D Vrontis A +rassou G Santoro and A Papa ldquoAmbi-dexterity external knowledge and performance in knowledge-intensive firmsrdquo e Journal of Technology Transfer vol 42no 2 pp 374ndash388 2016

[2] M Candi D L Roberts T Marion and G Barczak ldquoSocialstrategy to gain knowledge for innovationrdquo British Journal ofManagement vol 29 no 4 pp 731ndash749 2018

[3] H Chesbrough Open Innovation e New Imperative forCreating and Profiting for Technology Harvard BusinessSchool Press Cambridge MA USA 2003

[4] R W Schmitt ldquoConflict or synergy University-industry re-search relationsrdquo Accountability in Research vol 5 no 4pp 251ndash254 2011

[5] A Ericsson and G Erixon Controlling Design VariantsModular Product Platforms American Society of MechanicalEngineers New York NY USA 1999

[6] R Sanchez and J T Mahoney ldquoModularity flexibility andknowledge management in product and organization de-signrdquo Strategic Management Journal vol 17 no S2pp 63ndash76 1996

[7] C Y Baldwin and K B Clark ldquo+e architecture of partici-pation Does code architecture mitigate free riding in the opensource development modelrdquo Management Science vol 52no 7 pp 1116ndash1127 2006

[8] H J Wang J Cheng and R S Zou ldquoStudy on the coordi-nating mechanism of knowledge transfer of IURU

collaborative innovation under modularity scenariordquo Studiesin Science of Science vol 36 no 7 pp 1274ndash1283 2018

[9] A Tiwana ldquoDoes interfirm modularity complement igno-rance A field study of software outsourcing alliancesrdquoStrategic Management Journal vol 29 no 11 pp 1241ndash12522008

[10] H J Wang and Y Zhang ldquoCorporate modularity-basedcollaborative innovation and innovation resources manage-ment analysis on an exploratory case of haierrdquo Science ampTechnology Progress and Policy vol 35 no 21 pp 97ndash1052018

[11] H Chesbrough S Kim and A Agogino ldquoChez PanisseBuilding an open innovation ecosystemrdquo California Man-agement Review vol 56 no 4 pp 144ndash171 2014

[12] J D Lewis ldquoMaking strategic alliances workrdquo Research-Technology Management vol 33 no 6 pp 12ndash15 1990

[13] J-F Ding and G-S Liang ldquoUsing fuzzy MCDM to selectpartners of strategic alliances for liner shippingrdquo InformationSciences vol 173 no 1ndash3 pp 197ndash225 2005

[14] L Li and Y Xi ldquo+e design and application study of theevaluation index system of research project collaborativeinnovation performancerdquo Science amp Technology Progress andPolicy vol 31 no 1 pp 123ndash129 2014

[15] S Maha and L Natalia ldquoSelecting an open innovationcommunity as an alliance partner looking for healthy com-munities and ecosystemsrdquo Research Policy vol 48 no 8Article ID 103766 2019

[16] J Jian M Wang L Li J Su and T Huang ldquoA partner se-lection model for collaborative product innovation from theviewpoint of knowledge collaborationrdquo Kybernetes vol 49no 6 pp 1623ndash1644 2019

[17] C Gu Y Wang and L Jia ldquoRanking fuzzy numbers based onfuzzy distancerdquo Journal of Henan Institute of Education(Natural Science Edition) vol 23 pp 17ndash21 2014

[18] Z Wang H Nie and H Zhao ldquoAn extended GEDM methodwith heterogeneous reference points of decision makers and anew hesitant fuzzy distance formulardquo Computers amp IndustrialEngineering vol 146 Article ID 106533 2020

[19] L Tran and L Duckstein ldquoComparison of fuzzy numbersusing a fuzzy distance measurerdquo Fuzzy Sets and Systemsvol 130 no 3 pp 331ndash341 2002

10 Discrete Dynamics in Nature and Society

discrimination (3) the information of decision elements likethe weight used for modeling is accurate which is not thecase in practice To address these shortcomings this paperproposes a novel fuzzy ranking method featuring a fuzzydistance comparison +e method includes the followingspecific steps

231 Definition of Evaluation Indices and Fuzzy Mem-bership Function +e fuzzy linguistics made by evaluationexperts is converted to triangular fuzzy numbers For in-stance five levels of fuzzy linguistics ie very low (VL) low(L) moderate (M) high (H) and very high (VH) can beemployed to describe the importance of different indices(Figure 2) In this regard a triangular fuzzy function can beused to express the linguistics of such fuzzy evaluations Forexample an index with an importance level of moderate (M)may be denoted by a triangular fuzzy number (02 05 08)Next seven levels of fuzzy linguistics ie very good (VG)good (G) relatively good (RG) moderate (M) relativelypoor (RP) poor (P) and very poor (VP) can be employed toevaluate the performance of external module innovationresources (Figure 3)

232 Evaluating Fuzzy Weights and Each CandidateResource It is assumed that there are M external moduleinnovation resources to be rated and N experts engaged inrating By using ak

ij to indicate a rating value assigned by anexpert Ek(k 1 2 N) to an external module innovationresource Mi for the index Pj(j 1 2 9) the fuzzyevaluation matrix A can be expressed as

A aij1113960 1113961Mtimes9

a11 a12 middot middot middot a19

a21 a22 middot middot middot a29

middot middot middot middot middot middot middot middot middot middot middot middot

aM1 aM2 middot middot middot aM9

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(1)

where aij is a comprehensive evaluation fuzzy number of theexternal module innovation resource Mi(i 1 2 M)

for the index Pj Naturally there is a conversion relationbetween aij and ak

ij

aij 1Notimes a

1ij oplus a

2ijoplus middot middot middot oplus a

Nij1113872 1113873 (2)

A combination of significance values assigned bydifferent experts to the index gives the fuzzy weight factorB b1 b2 b91113864 1113865 On this basis the final evaluationresults of all candidate resources can be derived bymultiplying the fuzzy evaluation matrix A by the fuzzyweight vector B of the index

R AotimesBT

r1

r2

⋮

rM

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

a11 otimes b1oplusa12 otimes b2oplus middot middot middotoplusa19 otimes b9

a21 otimes b1oplusa22 otimes b2oplus middot middot middotoplusa29 otimes b9

⋮

aM1 otimes b1oplusaM2 otimes b2oplus middot middot middotoplusaM9 otimes b9

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(3)

where ri is the fuzzy rating value assigned to an externalmodule innovation resource Mi ri aij times bj bj is the fuzzyweight value for index Pj and otimes and oplus are fuzzy operators+e fuzzy sum and product of triangular fuzzy numbers 1113957A

(aL a aR) and 1113957B (bL b bR) can be expressed as

1113957Aoplus1113957B aL a aR( 1113857oplus bL b bR( 1113857 aL + bL a + b bR + bR( 1113857

1113957Aotimes 1113957B aL a aR( 1113857otimes bL b bR( 1113857 aLbL ab aRbR( 1113857

⎧⎨

⎩

(4)

233 Ranking Each Candidate Resource Using Fuzzy Dis-tance Measurement Method Since the above evaluationassignment process for external module innovation re-sources still leads to fuzzy numbers it is not yet possible todetermine the final ranking of module innovation re-sources A fuzzy distance measurement method istherefore adopted for further comparison of the fuzzynumbers [19] +e sequence of fuzzy numbers is deter-mined by comparing the distance from every fuzzynumber to predefined maximum and minimum

M Hμ(x)

x

1

01002 03 05 07 08

VL L VH

Figure 2 Membership functions for ranking indices

x

μ(x)

1

005 07 0903 0402 100601 08

VP RG G VGP RP M

Figure 3 Membership functions for ranking resources

Discrete Dynamics in Nature and Society 3

distinguishing points +e fuzzy number closest to themaximum distinguishing point (Dmax) and most distantfrom the minimum distinguishing point (Dmin) ranks firstin the sequence and is regarded as the strategic innovation

resource for the core module (system) of an enterprise infurther cooperation

Following the theory of fuzzy distance measurementmethod [19] we can define a distance between two randomfuzzy numbers (A and B) as

D2(A B f) 1113946

1

0

AL(α) + AU(α)

21113888 1113889 minus

BL(α) + BU(α)

21113888 11138891113890 1113891

2

+13

AL(α) + AU(α)

21113888 1113889

2

+BL(α) + BU(α)

21113888 1113889

2⎡⎣ ⎤⎦

⎧⎨

⎩

⎫⎬

⎭ timesf(α)dα

111393810 f(α)dα

(5)

where AL(α) AU(α) BL(α) andBU(α) represent an α cutset interval of random fuzzy numbers A and B and

f(α) ατ((0le αle 1) represents a weight function and itis a continuous positive function defined in the interval [0 1]

Hence Dmax and Dmin ie the distances from any tri-angular fuzzy number to the maximum distinguishing point(max) and minimum distinguishing point (min) can bederived

D2(r M) r2 minus M( 1113857

2+

1τ + 2

r2 minus M( 1113857 r3 + r1( 1113857 minus 2r21113858 1113859

+2

3(τ + 2)(τ + 3)r3 minus r2( 1113857

2+ r2 minus r1( 1113857

21113960 1113961

minus2

3(τ + 2)(τ + 3)r3 minus r2( 1113857 r2 minus r1( 11138571113858 1113859

(6)

where r is a triangular fuzzy number to be ratedr (r1 r2 r3) M is the maximum or minimum dis-tinguishing point and τ is the weight coefficient

Different f(α) values correspond to different weightsused during the calculation of fuzzy distance For examplewhen τ 1 a larger value of α in [0 1] corresponds to ahigher weight assigned to it When τ 0 all α values in [0 1]have the same weight Different τ values reflect differentdecision-making attitudes If a decision maker tends to beneutral then τ 1 is more reasonable τ gt 1 is preferred for aconservative decision maker and τ 0 or a decreasingfunction is preferred for an aggressive decision maker +emaximum and minimum distinguishing points can be de-rived from the following equation

max(I)ge sup 1113947I

i1s 1113957Ai1113872 1113873⎛⎝ ⎞⎠

min(I)le inf 1113947

I

i1s 1113957Ai1113872 1113873⎛⎝ ⎞⎠

(7)

Based on the above analysis the following characteristicsof the proposed innovation resource evaluation methodbased on fuzzy distance measurement can be observed Firstcompared with traditional evaluation and ranking methodseg the analytic hierarchy process (AHP) the calculation

process of the proposed method is simpler and visualizedSecond various t values can reflect different decision atti-tudes thereby leading to good robustness and flexibilityFinally fuzzy linguistics are used to evaluate the weight ofeach index By combining the performance of candidateinnovation resources such linguistics are more in line withreal situations

3 Case Study and Results

31 Case Background Haier Group a typical Chinesemanufacturing enterprise evolving rapidly in the time ofReform and Opening is selected for a case study to verify theproposed method+ere are four major reasons for selectingHaier as the case sample (1) Haier is now a leader in theChinese home appliance industry and also one of the largepioneering Chinese enterprises that have strategically builttheir worldwide RampD production marketing and salesnetworks (2) Despite its rapid development in recent de-cades Haier realizes that it has become impossible to meetever-increasing product design complexity and customerdemands by relying only on its own RampD capabilitiesConsequently Haier adopts an open innovation strategy toexploit innovation potential via external partners Mean-while Haier has established an open innovation platformHaier Open Partnership Ecosystem (HOPE) on which itinteracts with external resources to come up with innovativesolutions and fulfill global usersrsquo requirements (3) In 2011Haier switched from traditional component design to aproduct modularity model for its large home appliance linesModularity has thus become a key pillar for the company toleverage product innovations and pursue business modeltransformations

+e Chinese air-conditioning industry has entered a highlycompetitive stage in recent years On the one hand the tasks ofenergy conservation and emission reduction are demandingand real estate regulation and control policies suppress furtherexpansion of the industry With the release of energy-savingproduct subsidy policies the production cost of air conditionersis gradually rising On the other hand too many homogeneousChinese air-conditioning products lacking technological in-novation are competing more intensely than before in themarket Realizing that the in-house RampD force alone would nolonger completely meet global customer needs Haier CEO

4 Discrete Dynamics in Nature and Society

Zhang Ruimin calls for a move from a closed organization to anopen one

Our analysis involves a high-end air conditioner product(T-AC) of Haier Group that features a modular design +isproduct is a response to a survey of over 600000 consumersIt is mainly intended to address issues or needs related to air-conditioning diseases cold air natural air and remotecontrol

32 Overview of Case Product Modularity +e modularproduct architecture of T-AC was created using the MFDmethodology given by Ericsson and Erixon [5] and iscomposed of 28 modules with standardized interfaces Tofurther promote commonality among different modules andoptimize the number of external innovation resources Haierworked out seven module systems based on the functionaland supply chain correlations among modules (Figure 4)+erefore the modular product architecture can be depictedas seven module systems plus ten individual modules Ontop of that the core module (system) within the modularproduct architecture is defined according to Haierrsquos tech-nological and marketing strategies as well +e integration ofexternal innovation resources for the core module (system)was well considered

Being regarded as one core module (system) the air-supplying module system is closely related to air output airsupply distance and air supply range of T-AC (Figure 5)Due to product modularity Haier released modular inter-face standards with external innovation resources withoutdisclosing too much detailed information such as technicalspecifications drawings and inspection standards Ac-cordingly technical secrets and intellectual properties of

T-AC as well as its modules (systems) have been wellprotected though it was necessary to design proper methodsto integrate qualified competent and suitable external in-novation resources during development

33 Evaluation and Selection ofModule InnovationResourcesWith the evaluation method described in Section 22 anoptimum module innovation resource is selected from thecandidate resources (Mi i 1 2 9) collected fromaround the world and made available on the HOPE (HaierOpen Partnership Ecosystem) platform In this process theparticipating evaluators first assign a value to each indexbased on five levels of fuzzy language very low (VL) low (L)moderate (M) high (H) and very high (VH) Ratings of thesignificance of the module innovation resource indices bythree experts are given in Table 1 +e triangular fuzzynumbers corresponding to the fuzzy language are VL (0 003) L (0 03 05) M (02 05 08) H (05 07 10)and VH (07 10 10)

Seven levels of fuzzy language ie very good (VG) good(G) relatively good (RG) moderate (M) relatively poor(RP) poor (P) and very poor (VP) are then used by threeexperts to evaluate each innovation resource for differentindices +e fuzzy evaluation matrix of external modularresources based on the evaluation by three experts is given inTable 2 +e triangular fuzzy numbers of fuzzy language areVP (0 0 02) P (01 02 03) RP (02 04 05)M (04 05 06) RG (05 07 08) G (07 08 09) andVG (08 10 10)

+e fuzzy rating values of all module innovation re-sources can be derived from (1)fd1

R 1113957r1 1113957r2 1113957r9( 1113857

(19056 39989 62822) (18867 37056 58456)

(19167 43467 67478) (16433 38700 63100)

(08422 21733 41178) (14956 25233 45167)

(16833 27867 44400) (14678 35278 58344)

(16211 34600 58689)

⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝

⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠

(8)

By selecting τ 0 12 1 2 the ranking values of allexternal module innovation resources can be obtained asshown in Table 3 It can be seen from Figures 6ndash9 that fordifferent decision-making attitudes (ie at different values ofτ) the distances from the evaluation fuzzy numbers forM1minusM9 to maximum and minimum distinguishing pointsare consistent +e fuzzy rating number of module inno-vation resource M5 (strategic suppliers) is closest to themaximum distinguishing point and most remote to theminimum distinguishing point M5 can therefore be de-termined as the optimum module innovation resource

During the cooperation with a selected strategic supplier(a bid winner) Haier implemented resource managementmechanisms First a contract-binding mechanism was usedto regulate the rights and responsibilities of both parties

leading to well-defined delivery times prices and sup-porting services for innovative air supply module servicesSecond a dynamic optimization mechanism was used tokeep the strategic supplier competitive driving its capabilityupgrading +ird Haier focused on a practical incentivemechanism called ldquobig resources in exchange for big re-sourcesrdquo For example large-volume module purchase or-ders were placed to stimulate adequate innovation inputsfrom the strategic supplier during its cooperation withHaier

Driven by these mechanisms the open innovationnetwork was operated in a self-organized way Followingthe implementation of its corporate transition strategy inrecent years Haier has concluded that it must rely on suchoperation mechanisms to improve the quality and

Discrete Dynamics in Nature and Society 5

performance of innovation resources so as to boost openinnovation

331 Barrier-Free Resource Inclusion and ExclusionMechanism In response to Haierrsquos technology require-ments a number of global resources have been included incollaborative innovation and they are evaluated under equalcompetition and access conditions On this basis a rea-sonable innovative solution may be proposed by anyqualified innovation source in accordance with modularrequirements Previously the majority of external resourcesof Haier conditioners were based in China Starting from2000 Haier has carried out the innovation strategy ofldquotaking the world as our RampD centerrdquo +is has driven theresource integration process of Haier toward globaliza-tion Under the right circumstances networking toolsallow resources in other countriesregions to participatein the fair bidding procedures of Haier and to interactwith the company easily On the other hand due to the

function of the innovation resource filtering mechanismthe innovation resources in the circles of collaborativeinnovation have to continuously enhance their capabil-ities and adjust their service strategies based on thedynamic needs of Haierrsquos T-AC In addition the ability tosecure qualified innovation resources has been includedin the assessment of Key Performance Indexes (KPIs) ofrelevant employees

332 Participation Constraint and Incentive CompatibilityMechanism In order to manage innovation resourceswithin a controllable scope their behaviors are wellregulated so as to avoid possible risks during collabo-rative innovation Specifically strict clauses relating toparticipation qualifications are defined in contracts be-tween Haier and innovation resources thus eliminatingthe possibility of a deliberate withdrawal from cooper-ation and misconduct +rough the use of incentivecompatibility measures innovation resources are able to

Module Module systemEvaporator module Evaporating module systemElectrical heating moduleMotor module (internal)

Air supplyingmodule system

Fan module (internal)Skeleton moduleAir induction moduleStepping motor moduleSelf-cleaning module ndashFan curtain module Casing module

system Display modulePanel moduleAnion generator module ndashElectrical control box (internal) Electrical control module

system Computer board (internal)PM25 scavenger module ndashformaldehyde scavenger module ndashExhaust module ndashFixed frequency control module Fixed frequency control

module system Electrical parts box moduleIntelligent sensing module ndashCondenser module ndashMotor module (external) Exhaustmodule systemFan module (external)rottling depressurization module ndashElectrical control box (external) Converter control module

system Computer board (external)Structural components module ndashFresh air module ndash

Mod

ular

des

ign

Figure 4 +e modular product architecture of T-AC

Air inductionmodule

Motor module

Stepping motormodule

Skeleton moduleFan module

Air supplyingmodule system

Figure 5 Composition of the air-supplying module system of T-AC

6 Discrete Dynamics in Nature and Society

Tabl

e1

Fuzzyevaluatio

nmatrixof

evaluatio

nindices

Index

P1P2

P3P4

P5P6

P7P8

P9

Expert

E1VH

(071

01

0)

H(050

71

0)

H(050

71

0)

M(020

50

8)

H(050

71

0)

VH

(071

01

0)

H(050

71

0)

L(00

30

5)

VL(00

03)

E2H

(050

71

0)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

M(020

50

8)

H(050

71

0)

M(020

50

8)

M(020

50

8)

L(00

30

5)

E3H

(050

71

0)

VH

(071

01

0)

VH

(071

01

0)

L(00

30

5)

L(00

30

5)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

VL(00

03)

Average

(057

08

10)

(063

09

10)

(047

0730

93)

(013

0430

7)

(023

05

077)

(063

09

10)

(030

57

087)

(013

0430

7)

(00

10

37)

Discrete Dynamics in Nature and Society 7

Table 2 Fuzzy evaluation matrix of module innovation resources

ResourcesIndices

P1 P2 P3 P4 P5 P6 P7 P8 P9M1 G VG G RP RP M VG GG VG M P RG RG M M RG VG RG G RG RG RG VG G VG G VGM2 RG M RG M M M G VG G VP VP P P P VP G RG RG M RP M RG VG VG G G GM3 VG VG G RP P RP VG VG G G G G VG RG VG M G M G VG VG RG M RG M M GM4 RG G M M RP RP M M G RG M RG G RG RG M M RG RG G VG RG G RG M M GM5 VP VP P RG G VG M M RG RP RP VP RG RG M P M M RG G G VG VG VG P P VPM6 P P VP G RG M RG G RG VP M VP M M RG P M P RG RG G G M RG P RP PM7 P VP P RG G M RG RG M VP P P RG M RG RP RP RP G G RG G RG RG P P RPM8 RP M P G RG M G G VG M M RG G G VG RG VG G RG G G G M RG M M PM9 M RG M RG M RG M G G P P P RG M M G RG VG P VP VP G RG M VG G RG

41117 38432

44135 39998

23915 28137 29611

36627 36723

30425 32815

27797 31736

47060 42674 41024

34875 34715

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 6 Visualization of the evaluation of module innovation resources at τ 0

4081937919

4391839654

2341427512

29224

36277 36149

30214 3255927414

31539

4726443057 41215

34757 34825

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 7 Visualization of the evaluation of module innovation resources at τ 05

Table 3 Evaluation of module innovation resources using the distance measurement method

τ Dmax Dmin M1 M2 M3 M4 M5 M6 M7 M8 M9

τ 0 Dmin 41117 38432 44135 39998 23915 28137 29611 36627 36723Dmax 30425 328148 27797 31736 47060 42674 41024 34875 34715

τ 12 Dmin 40819 37919 43918 39654 23414 27512 29224 36277 36149Dmax 30214 32559 27414 31539 47264 43057 41215 34757 34825

τ 1 Dmin 40635 37882 43791 39442 23092 27102 28974 36059 35907Dmax 30140 32745 27184 31430 47408 43319 41349 34696 34498

τ 2 Dmin 40425 37633 43654 39200 22705 26600 28670 32810 35529Dmax 30000 32757 26805 31281 47719 43866 41636 37629 34977

8 Discrete Dynamics in Nature and Society

tap their potential effectively for the purpose of profit-sharing For example a Chinese Southern manufacturerSunwill was originally only responsible for supplying thefan module of T-AC but later it became the designer andsupplier of the whole air supply module system thanks toHaierrsquos incentive compatibility mechanism A purchaseorder of more than 100000 units (module systems) perquarter was given to Sunwill together with a contractcommission for at least two years As a result Sunwill nowmanages other module suppliers of the air supply modulesystem including those offering motors and winddeflectors

333 Distributed Resource Management Mechanism +ecore modules (air supply module system evaporatormodule system etc) of the modular product architectureof T-AC were developed in the headquartersrsquo RampD centerof Haier At this center Haier utilizes its RampD capacitiesand interacts with innovation resources to strengthen itsinnovation competence With high-qualification re-quirements for standard modules among such innovationresources resource optimization frequency is low Haierseeks to establish a strategic cooperation relationship with

a number of excellent selected candidates Individualizedmodulessystems (formaldehyde elimination moduledouble-way fresh air module etc) are developed in areasclose to target markets +e benefits include more synergieswith innovation resources lower cost and higher serviceefficiency To cater for varying user needs the optimizationfrequency of the innovation resources in these areas isrelatively high

4 Conclusions

41 eoretical Contribution To fill the gaps in existingstudies the relationship between modularity and openinnovation is first analyzed Module innovation resourcesin an open environment are then grouped In order toselect qualified innovation resources for core modules ofmodular product architecture nine evaluation indices areconstructed in this study Next a fuzzy distance mea-surement method is presented to compare fuzzy numbersand decide which innovation resource is optimal By usinga case study of one of Haier Grouprsquos modular productsconsistent evaluation and selection results are achievedfor different decision-making attitudes proving that themethod proposed in this paper is robust

40635 37882

43791 39442

23092 27102 28974

36059

30140 32745

27184 31430

47408 43319 41349

34696

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

35907 34498

Figure 8 Visualization of the evaluation of module innovation resources at τ 1

40425 37633 43654

39200

22705 26600 28670

32810 35529

30000 32757 26805

31281

47719 43866 41636

37629 34977

0

1

2

3

4

5

6

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 9 Visualization of the evaluation of module innovation resources at τ 2

Discrete Dynamics in Nature and Society 9

42 Empirical Conclusions Based on the case study of theHaier Group modular product architecture plays a vitalrole in linking the enterprisersquos innovation requirementswith external innovation resources Meanwhile it isdiscovered that strategic suppliers not only collaboratewith Haier in terms of procurement but also actively getinvolved in core module system innovation +rough theimplementation of strategic supplier managementmechanisms such as contract binding dynamic optimi-zation and big resources in exchange for big resources astrategic supplier is able to improve its capabilities con-tinuously thus engaging in open innovation with thecompany in a win-win context

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that they have no conflicts of interest

Acknowledgments

+is research was supported by the Chinese NationalFunding of Social Sciences (19BGL045) Liaoning PublicWelfare Research Project (2020JH410100023) and theProject is sponsored by Liaoning BaiQianWan TalentsProgram (2020921084) +e authors herewith show ap-preciation for their support

References

[1] D Vrontis A +rassou G Santoro and A Papa ldquoAmbi-dexterity external knowledge and performance in knowledge-intensive firmsrdquo e Journal of Technology Transfer vol 42no 2 pp 374ndash388 2016

[2] M Candi D L Roberts T Marion and G Barczak ldquoSocialstrategy to gain knowledge for innovationrdquo British Journal ofManagement vol 29 no 4 pp 731ndash749 2018

[3] H Chesbrough Open Innovation e New Imperative forCreating and Profiting for Technology Harvard BusinessSchool Press Cambridge MA USA 2003

[4] R W Schmitt ldquoConflict or synergy University-industry re-search relationsrdquo Accountability in Research vol 5 no 4pp 251ndash254 2011

[5] A Ericsson and G Erixon Controlling Design VariantsModular Product Platforms American Society of MechanicalEngineers New York NY USA 1999

[6] R Sanchez and J T Mahoney ldquoModularity flexibility andknowledge management in product and organization de-signrdquo Strategic Management Journal vol 17 no S2pp 63ndash76 1996

[7] C Y Baldwin and K B Clark ldquo+e architecture of partici-pation Does code architecture mitigate free riding in the opensource development modelrdquo Management Science vol 52no 7 pp 1116ndash1127 2006

[8] H J Wang J Cheng and R S Zou ldquoStudy on the coordi-nating mechanism of knowledge transfer of IURU

collaborative innovation under modularity scenariordquo Studiesin Science of Science vol 36 no 7 pp 1274ndash1283 2018

[9] A Tiwana ldquoDoes interfirm modularity complement igno-rance A field study of software outsourcing alliancesrdquoStrategic Management Journal vol 29 no 11 pp 1241ndash12522008

[10] H J Wang and Y Zhang ldquoCorporate modularity-basedcollaborative innovation and innovation resources manage-ment analysis on an exploratory case of haierrdquo Science ampTechnology Progress and Policy vol 35 no 21 pp 97ndash1052018

[11] H Chesbrough S Kim and A Agogino ldquoChez PanisseBuilding an open innovation ecosystemrdquo California Man-agement Review vol 56 no 4 pp 144ndash171 2014

[12] J D Lewis ldquoMaking strategic alliances workrdquo Research-Technology Management vol 33 no 6 pp 12ndash15 1990

[13] J-F Ding and G-S Liang ldquoUsing fuzzy MCDM to selectpartners of strategic alliances for liner shippingrdquo InformationSciences vol 173 no 1ndash3 pp 197ndash225 2005

[14] L Li and Y Xi ldquo+e design and application study of theevaluation index system of research project collaborativeinnovation performancerdquo Science amp Technology Progress andPolicy vol 31 no 1 pp 123ndash129 2014

[15] S Maha and L Natalia ldquoSelecting an open innovationcommunity as an alliance partner looking for healthy com-munities and ecosystemsrdquo Research Policy vol 48 no 8Article ID 103766 2019

[16] J Jian M Wang L Li J Su and T Huang ldquoA partner se-lection model for collaborative product innovation from theviewpoint of knowledge collaborationrdquo Kybernetes vol 49no 6 pp 1623ndash1644 2019

[17] C Gu Y Wang and L Jia ldquoRanking fuzzy numbers based onfuzzy distancerdquo Journal of Henan Institute of Education(Natural Science Edition) vol 23 pp 17ndash21 2014

[18] Z Wang H Nie and H Zhao ldquoAn extended GEDM methodwith heterogeneous reference points of decision makers and anew hesitant fuzzy distance formulardquo Computers amp IndustrialEngineering vol 146 Article ID 106533 2020

[19] L Tran and L Duckstein ldquoComparison of fuzzy numbersusing a fuzzy distance measurerdquo Fuzzy Sets and Systemsvol 130 no 3 pp 331ndash341 2002

10 Discrete Dynamics in Nature and Society

distinguishing points +e fuzzy number closest to themaximum distinguishing point (Dmax) and most distantfrom the minimum distinguishing point (Dmin) ranks firstin the sequence and is regarded as the strategic innovation

resource for the core module (system) of an enterprise infurther cooperation

Following the theory of fuzzy distance measurementmethod [19] we can define a distance between two randomfuzzy numbers (A and B) as

D2(A B f) 1113946

1

0

AL(α) + AU(α)

21113888 1113889 minus

BL(α) + BU(α)

21113888 11138891113890 1113891

2

+13

AL(α) + AU(α)

21113888 1113889

2

+BL(α) + BU(α)

21113888 1113889

2⎡⎣ ⎤⎦

⎧⎨

⎩

⎫⎬

⎭ timesf(α)dα

111393810 f(α)dα

(5)

where AL(α) AU(α) BL(α) andBU(α) represent an α cutset interval of random fuzzy numbers A and B and

f(α) ατ((0le αle 1) represents a weight function and itis a continuous positive function defined in the interval [0 1]

Hence Dmax and Dmin ie the distances from any tri-angular fuzzy number to the maximum distinguishing point(max) and minimum distinguishing point (min) can bederived

D2(r M) r2 minus M( 1113857

2+

1τ + 2

r2 minus M( 1113857 r3 + r1( 1113857 minus 2r21113858 1113859

+2

3(τ + 2)(τ + 3)r3 minus r2( 1113857

2+ r2 minus r1( 1113857

21113960 1113961

minus2

3(τ + 2)(τ + 3)r3 minus r2( 1113857 r2 minus r1( 11138571113858 1113859

(6)

where r is a triangular fuzzy number to be ratedr (r1 r2 r3) M is the maximum or minimum dis-tinguishing point and τ is the weight coefficient

Different f(α) values correspond to different weightsused during the calculation of fuzzy distance For examplewhen τ 1 a larger value of α in [0 1] corresponds to ahigher weight assigned to it When τ 0 all α values in [0 1]have the same weight Different τ values reflect differentdecision-making attitudes If a decision maker tends to beneutral then τ 1 is more reasonable τ gt 1 is preferred for aconservative decision maker and τ 0 or a decreasingfunction is preferred for an aggressive decision maker +emaximum and minimum distinguishing points can be de-rived from the following equation

max(I)ge sup 1113947I

i1s 1113957Ai1113872 1113873⎛⎝ ⎞⎠

min(I)le inf 1113947

I

i1s 1113957Ai1113872 1113873⎛⎝ ⎞⎠

(7)

Based on the above analysis the following characteristicsof the proposed innovation resource evaluation methodbased on fuzzy distance measurement can be observed Firstcompared with traditional evaluation and ranking methodseg the analytic hierarchy process (AHP) the calculation

process of the proposed method is simpler and visualizedSecond various t values can reflect different decision atti-tudes thereby leading to good robustness and flexibilityFinally fuzzy linguistics are used to evaluate the weight ofeach index By combining the performance of candidateinnovation resources such linguistics are more in line withreal situations

3 Case Study and Results

31 Case Background Haier Group a typical Chinesemanufacturing enterprise evolving rapidly in the time ofReform and Opening is selected for a case study to verify theproposed method+ere are four major reasons for selectingHaier as the case sample (1) Haier is now a leader in theChinese home appliance industry and also one of the largepioneering Chinese enterprises that have strategically builttheir worldwide RampD production marketing and salesnetworks (2) Despite its rapid development in recent de-cades Haier realizes that it has become impossible to meetever-increasing product design complexity and customerdemands by relying only on its own RampD capabilitiesConsequently Haier adopts an open innovation strategy toexploit innovation potential via external partners Mean-while Haier has established an open innovation platformHaier Open Partnership Ecosystem (HOPE) on which itinteracts with external resources to come up with innovativesolutions and fulfill global usersrsquo requirements (3) In 2011Haier switched from traditional component design to aproduct modularity model for its large home appliance linesModularity has thus become a key pillar for the company toleverage product innovations and pursue business modeltransformations

+e Chinese air-conditioning industry has entered a highlycompetitive stage in recent years On the one hand the tasks ofenergy conservation and emission reduction are demandingand real estate regulation and control policies suppress furtherexpansion of the industry With the release of energy-savingproduct subsidy policies the production cost of air conditionersis gradually rising On the other hand too many homogeneousChinese air-conditioning products lacking technological in-novation are competing more intensely than before in themarket Realizing that the in-house RampD force alone would nolonger completely meet global customer needs Haier CEO

4 Discrete Dynamics in Nature and Society

Zhang Ruimin calls for a move from a closed organization to anopen one

Our analysis involves a high-end air conditioner product(T-AC) of Haier Group that features a modular design +isproduct is a response to a survey of over 600000 consumersIt is mainly intended to address issues or needs related to air-conditioning diseases cold air natural air and remotecontrol

32 Overview of Case Product Modularity +e modularproduct architecture of T-AC was created using the MFDmethodology given by Ericsson and Erixon [5] and iscomposed of 28 modules with standardized interfaces Tofurther promote commonality among different modules andoptimize the number of external innovation resources Haierworked out seven module systems based on the functionaland supply chain correlations among modules (Figure 4)+erefore the modular product architecture can be depictedas seven module systems plus ten individual modules Ontop of that the core module (system) within the modularproduct architecture is defined according to Haierrsquos tech-nological and marketing strategies as well +e integration ofexternal innovation resources for the core module (system)was well considered

Being regarded as one core module (system) the air-supplying module system is closely related to air output airsupply distance and air supply range of T-AC (Figure 5)Due to product modularity Haier released modular inter-face standards with external innovation resources withoutdisclosing too much detailed information such as technicalspecifications drawings and inspection standards Ac-cordingly technical secrets and intellectual properties of

T-AC as well as its modules (systems) have been wellprotected though it was necessary to design proper methodsto integrate qualified competent and suitable external in-novation resources during development

33 Evaluation and Selection ofModule InnovationResourcesWith the evaluation method described in Section 22 anoptimum module innovation resource is selected from thecandidate resources (Mi i 1 2 9) collected fromaround the world and made available on the HOPE (HaierOpen Partnership Ecosystem) platform In this process theparticipating evaluators first assign a value to each indexbased on five levels of fuzzy language very low (VL) low (L)moderate (M) high (H) and very high (VH) Ratings of thesignificance of the module innovation resource indices bythree experts are given in Table 1 +e triangular fuzzynumbers corresponding to the fuzzy language are VL (0 003) L (0 03 05) M (02 05 08) H (05 07 10)and VH (07 10 10)

Seven levels of fuzzy language ie very good (VG) good(G) relatively good (RG) moderate (M) relatively poor(RP) poor (P) and very poor (VP) are then used by threeexperts to evaluate each innovation resource for differentindices +e fuzzy evaluation matrix of external modularresources based on the evaluation by three experts is given inTable 2 +e triangular fuzzy numbers of fuzzy language areVP (0 0 02) P (01 02 03) RP (02 04 05)M (04 05 06) RG (05 07 08) G (07 08 09) andVG (08 10 10)

+e fuzzy rating values of all module innovation re-sources can be derived from (1)fd1

R 1113957r1 1113957r2 1113957r9( 1113857

(19056 39989 62822) (18867 37056 58456)

(19167 43467 67478) (16433 38700 63100)

(08422 21733 41178) (14956 25233 45167)

(16833 27867 44400) (14678 35278 58344)

(16211 34600 58689)

⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝

⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠

(8)

By selecting τ 0 12 1 2 the ranking values of allexternal module innovation resources can be obtained asshown in Table 3 It can be seen from Figures 6ndash9 that fordifferent decision-making attitudes (ie at different values ofτ) the distances from the evaluation fuzzy numbers forM1minusM9 to maximum and minimum distinguishing pointsare consistent +e fuzzy rating number of module inno-vation resource M5 (strategic suppliers) is closest to themaximum distinguishing point and most remote to theminimum distinguishing point M5 can therefore be de-termined as the optimum module innovation resource

During the cooperation with a selected strategic supplier(a bid winner) Haier implemented resource managementmechanisms First a contract-binding mechanism was usedto regulate the rights and responsibilities of both parties

leading to well-defined delivery times prices and sup-porting services for innovative air supply module servicesSecond a dynamic optimization mechanism was used tokeep the strategic supplier competitive driving its capabilityupgrading +ird Haier focused on a practical incentivemechanism called ldquobig resources in exchange for big re-sourcesrdquo For example large-volume module purchase or-ders were placed to stimulate adequate innovation inputsfrom the strategic supplier during its cooperation withHaier

Driven by these mechanisms the open innovationnetwork was operated in a self-organized way Followingthe implementation of its corporate transition strategy inrecent years Haier has concluded that it must rely on suchoperation mechanisms to improve the quality and

Discrete Dynamics in Nature and Society 5

performance of innovation resources so as to boost openinnovation

331 Barrier-Free Resource Inclusion and ExclusionMechanism In response to Haierrsquos technology require-ments a number of global resources have been included incollaborative innovation and they are evaluated under equalcompetition and access conditions On this basis a rea-sonable innovative solution may be proposed by anyqualified innovation source in accordance with modularrequirements Previously the majority of external resourcesof Haier conditioners were based in China Starting from2000 Haier has carried out the innovation strategy ofldquotaking the world as our RampD centerrdquo +is has driven theresource integration process of Haier toward globaliza-tion Under the right circumstances networking toolsallow resources in other countriesregions to participatein the fair bidding procedures of Haier and to interactwith the company easily On the other hand due to the

function of the innovation resource filtering mechanismthe innovation resources in the circles of collaborativeinnovation have to continuously enhance their capabil-ities and adjust their service strategies based on thedynamic needs of Haierrsquos T-AC In addition the ability tosecure qualified innovation resources has been includedin the assessment of Key Performance Indexes (KPIs) ofrelevant employees

332 Participation Constraint and Incentive CompatibilityMechanism In order to manage innovation resourceswithin a controllable scope their behaviors are wellregulated so as to avoid possible risks during collabo-rative innovation Specifically strict clauses relating toparticipation qualifications are defined in contracts be-tween Haier and innovation resources thus eliminatingthe possibility of a deliberate withdrawal from cooper-ation and misconduct +rough the use of incentivecompatibility measures innovation resources are able to

Module Module systemEvaporator module Evaporating module systemElectrical heating moduleMotor module (internal)

Air supplyingmodule system

Fan module (internal)Skeleton moduleAir induction moduleStepping motor moduleSelf-cleaning module ndashFan curtain module Casing module

system Display modulePanel moduleAnion generator module ndashElectrical control box (internal) Electrical control module

system Computer board (internal)PM25 scavenger module ndashformaldehyde scavenger module ndashExhaust module ndashFixed frequency control module Fixed frequency control

module system Electrical parts box moduleIntelligent sensing module ndashCondenser module ndashMotor module (external) Exhaustmodule systemFan module (external)rottling depressurization module ndashElectrical control box (external) Converter control module

system Computer board (external)Structural components module ndashFresh air module ndash

Mod

ular

des

ign

Figure 4 +e modular product architecture of T-AC

Air inductionmodule

Motor module

Stepping motormodule

Skeleton moduleFan module

Air supplyingmodule system

Figure 5 Composition of the air-supplying module system of T-AC

6 Discrete Dynamics in Nature and Society

Tabl

e1

Fuzzyevaluatio

nmatrixof

evaluatio

nindices

Index

P1P2

P3P4

P5P6

P7P8

P9

Expert

E1VH

(071

01

0)

H(050

71

0)

H(050

71

0)

M(020

50

8)

H(050

71

0)

VH

(071

01

0)

H(050

71

0)

L(00

30

5)

VL(00

03)

E2H

(050

71

0)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

M(020

50

8)

H(050

71

0)

M(020

50

8)

M(020

50

8)

L(00

30

5)

E3H

(050

71

0)

VH

(071

01

0)

VH

(071

01

0)

L(00

30

5)

L(00

30

5)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

VL(00

03)

Average

(057

08

10)

(063

09

10)

(047

0730

93)

(013

0430

7)

(023

05

077)

(063

09

10)

(030

57

087)

(013

0430

7)

(00

10

37)

Discrete Dynamics in Nature and Society 7

Table 2 Fuzzy evaluation matrix of module innovation resources

ResourcesIndices

P1 P2 P3 P4 P5 P6 P7 P8 P9M1 G VG G RP RP M VG GG VG M P RG RG M M RG VG RG G RG RG RG VG G VG G VGM2 RG M RG M M M G VG G VP VP P P P VP G RG RG M RP M RG VG VG G G GM3 VG VG G RP P RP VG VG G G G G VG RG VG M G M G VG VG RG M RG M M GM4 RG G M M RP RP M M G RG M RG G RG RG M M RG RG G VG RG G RG M M GM5 VP VP P RG G VG M M RG RP RP VP RG RG M P M M RG G G VG VG VG P P VPM6 P P VP G RG M RG G RG VP M VP M M RG P M P RG RG G G M RG P RP PM7 P VP P RG G M RG RG M VP P P RG M RG RP RP RP G G RG G RG RG P P RPM8 RP M P G RG M G G VG M M RG G G VG RG VG G RG G G G M RG M M PM9 M RG M RG M RG M G G P P P RG M M G RG VG P VP VP G RG M VG G RG

41117 38432

44135 39998

23915 28137 29611

36627 36723

30425 32815

27797 31736

47060 42674 41024

34875 34715

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 6 Visualization of the evaluation of module innovation resources at τ 0

4081937919

4391839654

2341427512

29224

36277 36149

30214 3255927414

31539

4726443057 41215

34757 34825

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 7 Visualization of the evaluation of module innovation resources at τ 05

Table 3 Evaluation of module innovation resources using the distance measurement method

τ Dmax Dmin M1 M2 M3 M4 M5 M6 M7 M8 M9

τ 0 Dmin 41117 38432 44135 39998 23915 28137 29611 36627 36723Dmax 30425 328148 27797 31736 47060 42674 41024 34875 34715

τ 12 Dmin 40819 37919 43918 39654 23414 27512 29224 36277 36149Dmax 30214 32559 27414 31539 47264 43057 41215 34757 34825

τ 1 Dmin 40635 37882 43791 39442 23092 27102 28974 36059 35907Dmax 30140 32745 27184 31430 47408 43319 41349 34696 34498

τ 2 Dmin 40425 37633 43654 39200 22705 26600 28670 32810 35529Dmax 30000 32757 26805 31281 47719 43866 41636 37629 34977

8 Discrete Dynamics in Nature and Society

tap their potential effectively for the purpose of profit-sharing For example a Chinese Southern manufacturerSunwill was originally only responsible for supplying thefan module of T-AC but later it became the designer andsupplier of the whole air supply module system thanks toHaierrsquos incentive compatibility mechanism A purchaseorder of more than 100000 units (module systems) perquarter was given to Sunwill together with a contractcommission for at least two years As a result Sunwill nowmanages other module suppliers of the air supply modulesystem including those offering motors and winddeflectors

333 Distributed Resource Management Mechanism +ecore modules (air supply module system evaporatormodule system etc) of the modular product architectureof T-AC were developed in the headquartersrsquo RampD centerof Haier At this center Haier utilizes its RampD capacitiesand interacts with innovation resources to strengthen itsinnovation competence With high-qualification re-quirements for standard modules among such innovationresources resource optimization frequency is low Haierseeks to establish a strategic cooperation relationship with

a number of excellent selected candidates Individualizedmodulessystems (formaldehyde elimination moduledouble-way fresh air module etc) are developed in areasclose to target markets +e benefits include more synergieswith innovation resources lower cost and higher serviceefficiency To cater for varying user needs the optimizationfrequency of the innovation resources in these areas isrelatively high

4 Conclusions

41 eoretical Contribution To fill the gaps in existingstudies the relationship between modularity and openinnovation is first analyzed Module innovation resourcesin an open environment are then grouped In order toselect qualified innovation resources for core modules ofmodular product architecture nine evaluation indices areconstructed in this study Next a fuzzy distance mea-surement method is presented to compare fuzzy numbersand decide which innovation resource is optimal By usinga case study of one of Haier Grouprsquos modular productsconsistent evaluation and selection results are achievedfor different decision-making attitudes proving that themethod proposed in this paper is robust

40635 37882

43791 39442

23092 27102 28974

36059

30140 32745

27184 31430

47408 43319 41349

34696

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

35907 34498

Figure 8 Visualization of the evaluation of module innovation resources at τ 1

40425 37633 43654

39200

22705 26600 28670

32810 35529

30000 32757 26805

31281

47719 43866 41636

37629 34977

0

1

2

3

4

5

6

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 9 Visualization of the evaluation of module innovation resources at τ 2

Discrete Dynamics in Nature and Society 9

42 Empirical Conclusions Based on the case study of theHaier Group modular product architecture plays a vitalrole in linking the enterprisersquos innovation requirementswith external innovation resources Meanwhile it isdiscovered that strategic suppliers not only collaboratewith Haier in terms of procurement but also actively getinvolved in core module system innovation +rough theimplementation of strategic supplier managementmechanisms such as contract binding dynamic optimi-zation and big resources in exchange for big resources astrategic supplier is able to improve its capabilities con-tinuously thus engaging in open innovation with thecompany in a win-win context

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that they have no conflicts of interest

Acknowledgments

+is research was supported by the Chinese NationalFunding of Social Sciences (19BGL045) Liaoning PublicWelfare Research Project (2020JH410100023) and theProject is sponsored by Liaoning BaiQianWan TalentsProgram (2020921084) +e authors herewith show ap-preciation for their support

References

[1] D Vrontis A +rassou G Santoro and A Papa ldquoAmbi-dexterity external knowledge and performance in knowledge-intensive firmsrdquo e Journal of Technology Transfer vol 42no 2 pp 374ndash388 2016

[2] M Candi D L Roberts T Marion and G Barczak ldquoSocialstrategy to gain knowledge for innovationrdquo British Journal ofManagement vol 29 no 4 pp 731ndash749 2018

[3] H Chesbrough Open Innovation e New Imperative forCreating and Profiting for Technology Harvard BusinessSchool Press Cambridge MA USA 2003

[4] R W Schmitt ldquoConflict or synergy University-industry re-search relationsrdquo Accountability in Research vol 5 no 4pp 251ndash254 2011

[5] A Ericsson and G Erixon Controlling Design VariantsModular Product Platforms American Society of MechanicalEngineers New York NY USA 1999

[6] R Sanchez and J T Mahoney ldquoModularity flexibility andknowledge management in product and organization de-signrdquo Strategic Management Journal vol 17 no S2pp 63ndash76 1996

[7] C Y Baldwin and K B Clark ldquo+e architecture of partici-pation Does code architecture mitigate free riding in the opensource development modelrdquo Management Science vol 52no 7 pp 1116ndash1127 2006

[8] H J Wang J Cheng and R S Zou ldquoStudy on the coordi-nating mechanism of knowledge transfer of IURU

collaborative innovation under modularity scenariordquo Studiesin Science of Science vol 36 no 7 pp 1274ndash1283 2018

[9] A Tiwana ldquoDoes interfirm modularity complement igno-rance A field study of software outsourcing alliancesrdquoStrategic Management Journal vol 29 no 11 pp 1241ndash12522008

[10] H J Wang and Y Zhang ldquoCorporate modularity-basedcollaborative innovation and innovation resources manage-ment analysis on an exploratory case of haierrdquo Science ampTechnology Progress and Policy vol 35 no 21 pp 97ndash1052018

[11] H Chesbrough S Kim and A Agogino ldquoChez PanisseBuilding an open innovation ecosystemrdquo California Man-agement Review vol 56 no 4 pp 144ndash171 2014

[12] J D Lewis ldquoMaking strategic alliances workrdquo Research-Technology Management vol 33 no 6 pp 12ndash15 1990

[13] J-F Ding and G-S Liang ldquoUsing fuzzy MCDM to selectpartners of strategic alliances for liner shippingrdquo InformationSciences vol 173 no 1ndash3 pp 197ndash225 2005

[14] L Li and Y Xi ldquo+e design and application study of theevaluation index system of research project collaborativeinnovation performancerdquo Science amp Technology Progress andPolicy vol 31 no 1 pp 123ndash129 2014

[15] S Maha and L Natalia ldquoSelecting an open innovationcommunity as an alliance partner looking for healthy com-munities and ecosystemsrdquo Research Policy vol 48 no 8Article ID 103766 2019

[16] J Jian M Wang L Li J Su and T Huang ldquoA partner se-lection model for collaborative product innovation from theviewpoint of knowledge collaborationrdquo Kybernetes vol 49no 6 pp 1623ndash1644 2019

[17] C Gu Y Wang and L Jia ldquoRanking fuzzy numbers based onfuzzy distancerdquo Journal of Henan Institute of Education(Natural Science Edition) vol 23 pp 17ndash21 2014

[18] Z Wang H Nie and H Zhao ldquoAn extended GEDM methodwith heterogeneous reference points of decision makers and anew hesitant fuzzy distance formulardquo Computers amp IndustrialEngineering vol 146 Article ID 106533 2020

[19] L Tran and L Duckstein ldquoComparison of fuzzy numbersusing a fuzzy distance measurerdquo Fuzzy Sets and Systemsvol 130 no 3 pp 331ndash341 2002

10 Discrete Dynamics in Nature and Society

Zhang Ruimin calls for a move from a closed organization to anopen one

Our analysis involves a high-end air conditioner product(T-AC) of Haier Group that features a modular design +isproduct is a response to a survey of over 600000 consumersIt is mainly intended to address issues or needs related to air-conditioning diseases cold air natural air and remotecontrol

32 Overview of Case Product Modularity +e modularproduct architecture of T-AC was created using the MFDmethodology given by Ericsson and Erixon [5] and iscomposed of 28 modules with standardized interfaces Tofurther promote commonality among different modules andoptimize the number of external innovation resources Haierworked out seven module systems based on the functionaland supply chain correlations among modules (Figure 4)+erefore the modular product architecture can be depictedas seven module systems plus ten individual modules Ontop of that the core module (system) within the modularproduct architecture is defined according to Haierrsquos tech-nological and marketing strategies as well +e integration ofexternal innovation resources for the core module (system)was well considered

Being regarded as one core module (system) the air-supplying module system is closely related to air output airsupply distance and air supply range of T-AC (Figure 5)Due to product modularity Haier released modular inter-face standards with external innovation resources withoutdisclosing too much detailed information such as technicalspecifications drawings and inspection standards Ac-cordingly technical secrets and intellectual properties of

T-AC as well as its modules (systems) have been wellprotected though it was necessary to design proper methodsto integrate qualified competent and suitable external in-novation resources during development

33 Evaluation and Selection ofModule InnovationResourcesWith the evaluation method described in Section 22 anoptimum module innovation resource is selected from thecandidate resources (Mi i 1 2 9) collected fromaround the world and made available on the HOPE (HaierOpen Partnership Ecosystem) platform In this process theparticipating evaluators first assign a value to each indexbased on five levels of fuzzy language very low (VL) low (L)moderate (M) high (H) and very high (VH) Ratings of thesignificance of the module innovation resource indices bythree experts are given in Table 1 +e triangular fuzzynumbers corresponding to the fuzzy language are VL (0 003) L (0 03 05) M (02 05 08) H (05 07 10)and VH (07 10 10)

Seven levels of fuzzy language ie very good (VG) good(G) relatively good (RG) moderate (M) relatively poor(RP) poor (P) and very poor (VP) are then used by threeexperts to evaluate each innovation resource for differentindices +e fuzzy evaluation matrix of external modularresources based on the evaluation by three experts is given inTable 2 +e triangular fuzzy numbers of fuzzy language areVP (0 0 02) P (01 02 03) RP (02 04 05)M (04 05 06) RG (05 07 08) G (07 08 09) andVG (08 10 10)

+e fuzzy rating values of all module innovation re-sources can be derived from (1)fd1

R 1113957r1 1113957r2 1113957r9( 1113857

(19056 39989 62822) (18867 37056 58456)

(19167 43467 67478) (16433 38700 63100)

(08422 21733 41178) (14956 25233 45167)

(16833 27867 44400) (14678 35278 58344)

(16211 34600 58689)

⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝

⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠

(8)

By selecting τ 0 12 1 2 the ranking values of allexternal module innovation resources can be obtained asshown in Table 3 It can be seen from Figures 6ndash9 that fordifferent decision-making attitudes (ie at different values ofτ) the distances from the evaluation fuzzy numbers forM1minusM9 to maximum and minimum distinguishing pointsare consistent +e fuzzy rating number of module inno-vation resource M5 (strategic suppliers) is closest to themaximum distinguishing point and most remote to theminimum distinguishing point M5 can therefore be de-termined as the optimum module innovation resource

During the cooperation with a selected strategic supplier(a bid winner) Haier implemented resource managementmechanisms First a contract-binding mechanism was usedto regulate the rights and responsibilities of both parties

leading to well-defined delivery times prices and sup-porting services for innovative air supply module servicesSecond a dynamic optimization mechanism was used tokeep the strategic supplier competitive driving its capabilityupgrading +ird Haier focused on a practical incentivemechanism called ldquobig resources in exchange for big re-sourcesrdquo For example large-volume module purchase or-ders were placed to stimulate adequate innovation inputsfrom the strategic supplier during its cooperation withHaier

Driven by these mechanisms the open innovationnetwork was operated in a self-organized way Followingthe implementation of its corporate transition strategy inrecent years Haier has concluded that it must rely on suchoperation mechanisms to improve the quality and

Discrete Dynamics in Nature and Society 5

performance of innovation resources so as to boost openinnovation

331 Barrier-Free Resource Inclusion and ExclusionMechanism In response to Haierrsquos technology require-ments a number of global resources have been included incollaborative innovation and they are evaluated under equalcompetition and access conditions On this basis a rea-sonable innovative solution may be proposed by anyqualified innovation source in accordance with modularrequirements Previously the majority of external resourcesof Haier conditioners were based in China Starting from2000 Haier has carried out the innovation strategy ofldquotaking the world as our RampD centerrdquo +is has driven theresource integration process of Haier toward globaliza-tion Under the right circumstances networking toolsallow resources in other countriesregions to participatein the fair bidding procedures of Haier and to interactwith the company easily On the other hand due to the

function of the innovation resource filtering mechanismthe innovation resources in the circles of collaborativeinnovation have to continuously enhance their capabil-ities and adjust their service strategies based on thedynamic needs of Haierrsquos T-AC In addition the ability tosecure qualified innovation resources has been includedin the assessment of Key Performance Indexes (KPIs) ofrelevant employees

332 Participation Constraint and Incentive CompatibilityMechanism In order to manage innovation resourceswithin a controllable scope their behaviors are wellregulated so as to avoid possible risks during collabo-rative innovation Specifically strict clauses relating toparticipation qualifications are defined in contracts be-tween Haier and innovation resources thus eliminatingthe possibility of a deliberate withdrawal from cooper-ation and misconduct +rough the use of incentivecompatibility measures innovation resources are able to

Module Module systemEvaporator module Evaporating module systemElectrical heating moduleMotor module (internal)

Air supplyingmodule system

Fan module (internal)Skeleton moduleAir induction moduleStepping motor moduleSelf-cleaning module ndashFan curtain module Casing module

system Display modulePanel moduleAnion generator module ndashElectrical control box (internal) Electrical control module

system Computer board (internal)PM25 scavenger module ndashformaldehyde scavenger module ndashExhaust module ndashFixed frequency control module Fixed frequency control

module system Electrical parts box moduleIntelligent sensing module ndashCondenser module ndashMotor module (external) Exhaustmodule systemFan module (external)rottling depressurization module ndashElectrical control box (external) Converter control module

system Computer board (external)Structural components module ndashFresh air module ndash

Mod

ular

des

ign

Figure 4 +e modular product architecture of T-AC

Air inductionmodule

Motor module

Stepping motormodule

Skeleton moduleFan module

Air supplyingmodule system

Figure 5 Composition of the air-supplying module system of T-AC

6 Discrete Dynamics in Nature and Society

Tabl

e1

Fuzzyevaluatio

nmatrixof

evaluatio

nindices

Index

P1P2

P3P4

P5P6

P7P8

P9

Expert

E1VH

(071

01

0)

H(050

71

0)

H(050

71

0)

M(020

50

8)

H(050

71

0)

VH

(071

01

0)

H(050

71

0)

L(00

30

5)

VL(00

03)

E2H

(050

71

0)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

M(020

50

8)

H(050

71

0)

M(020

50

8)

M(020

50

8)

L(00

30

5)

E3H

(050

71

0)

VH

(071

01

0)

VH

(071

01

0)

L(00

30

5)

L(00

30

5)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

VL(00

03)

Average

(057

08

10)

(063

09

10)

(047

0730

93)

(013

0430

7)

(023

05

077)

(063

09

10)

(030

57

087)

(013

0430

7)

(00

10

37)

Discrete Dynamics in Nature and Society 7

Table 2 Fuzzy evaluation matrix of module innovation resources

ResourcesIndices

P1 P2 P3 P4 P5 P6 P7 P8 P9M1 G VG G RP RP M VG GG VG M P RG RG M M RG VG RG G RG RG RG VG G VG G VGM2 RG M RG M M M G VG G VP VP P P P VP G RG RG M RP M RG VG VG G G GM3 VG VG G RP P RP VG VG G G G G VG RG VG M G M G VG VG RG M RG M M GM4 RG G M M RP RP M M G RG M RG G RG RG M M RG RG G VG RG G RG M M GM5 VP VP P RG G VG M M RG RP RP VP RG RG M P M M RG G G VG VG VG P P VPM6 P P VP G RG M RG G RG VP M VP M M RG P M P RG RG G G M RG P RP PM7 P VP P RG G M RG RG M VP P P RG M RG RP RP RP G G RG G RG RG P P RPM8 RP M P G RG M G G VG M M RG G G VG RG VG G RG G G G M RG M M PM9 M RG M RG M RG M G G P P P RG M M G RG VG P VP VP G RG M VG G RG

41117 38432

44135 39998

23915 28137 29611

36627 36723

30425 32815

27797 31736

47060 42674 41024

34875 34715

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 6 Visualization of the evaluation of module innovation resources at τ 0

4081937919

4391839654

2341427512

29224

36277 36149

30214 3255927414

31539

4726443057 41215

34757 34825

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 7 Visualization of the evaluation of module innovation resources at τ 05

Table 3 Evaluation of module innovation resources using the distance measurement method

τ Dmax Dmin M1 M2 M3 M4 M5 M6 M7 M8 M9

τ 0 Dmin 41117 38432 44135 39998 23915 28137 29611 36627 36723Dmax 30425 328148 27797 31736 47060 42674 41024 34875 34715

τ 12 Dmin 40819 37919 43918 39654 23414 27512 29224 36277 36149Dmax 30214 32559 27414 31539 47264 43057 41215 34757 34825

τ 1 Dmin 40635 37882 43791 39442 23092 27102 28974 36059 35907Dmax 30140 32745 27184 31430 47408 43319 41349 34696 34498

τ 2 Dmin 40425 37633 43654 39200 22705 26600 28670 32810 35529Dmax 30000 32757 26805 31281 47719 43866 41636 37629 34977

8 Discrete Dynamics in Nature and Society

tap their potential effectively for the purpose of profit-sharing For example a Chinese Southern manufacturerSunwill was originally only responsible for supplying thefan module of T-AC but later it became the designer andsupplier of the whole air supply module system thanks toHaierrsquos incentive compatibility mechanism A purchaseorder of more than 100000 units (module systems) perquarter was given to Sunwill together with a contractcommission for at least two years As a result Sunwill nowmanages other module suppliers of the air supply modulesystem including those offering motors and winddeflectors

333 Distributed Resource Management Mechanism +ecore modules (air supply module system evaporatormodule system etc) of the modular product architectureof T-AC were developed in the headquartersrsquo RampD centerof Haier At this center Haier utilizes its RampD capacitiesand interacts with innovation resources to strengthen itsinnovation competence With high-qualification re-quirements for standard modules among such innovationresources resource optimization frequency is low Haierseeks to establish a strategic cooperation relationship with

a number of excellent selected candidates Individualizedmodulessystems (formaldehyde elimination moduledouble-way fresh air module etc) are developed in areasclose to target markets +e benefits include more synergieswith innovation resources lower cost and higher serviceefficiency To cater for varying user needs the optimizationfrequency of the innovation resources in these areas isrelatively high

4 Conclusions

41 eoretical Contribution To fill the gaps in existingstudies the relationship between modularity and openinnovation is first analyzed Module innovation resourcesin an open environment are then grouped In order toselect qualified innovation resources for core modules ofmodular product architecture nine evaluation indices areconstructed in this study Next a fuzzy distance mea-surement method is presented to compare fuzzy numbersand decide which innovation resource is optimal By usinga case study of one of Haier Grouprsquos modular productsconsistent evaluation and selection results are achievedfor different decision-making attitudes proving that themethod proposed in this paper is robust

40635 37882

43791 39442

23092 27102 28974

36059

30140 32745

27184 31430

47408 43319 41349

34696

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

35907 34498

Figure 8 Visualization of the evaluation of module innovation resources at τ 1

40425 37633 43654

39200

22705 26600 28670

32810 35529

30000 32757 26805

31281

47719 43866 41636

37629 34977

0

1

2

3

4

5

6

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 9 Visualization of the evaluation of module innovation resources at τ 2

Discrete Dynamics in Nature and Society 9

42 Empirical Conclusions Based on the case study of theHaier Group modular product architecture plays a vitalrole in linking the enterprisersquos innovation requirementswith external innovation resources Meanwhile it isdiscovered that strategic suppliers not only collaboratewith Haier in terms of procurement but also actively getinvolved in core module system innovation +rough theimplementation of strategic supplier managementmechanisms such as contract binding dynamic optimi-zation and big resources in exchange for big resources astrategic supplier is able to improve its capabilities con-tinuously thus engaging in open innovation with thecompany in a win-win context

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that they have no conflicts of interest

Acknowledgments

+is research was supported by the Chinese NationalFunding of Social Sciences (19BGL045) Liaoning PublicWelfare Research Project (2020JH410100023) and theProject is sponsored by Liaoning BaiQianWan TalentsProgram (2020921084) +e authors herewith show ap-preciation for their support

References

[1] D Vrontis A +rassou G Santoro and A Papa ldquoAmbi-dexterity external knowledge and performance in knowledge-intensive firmsrdquo e Journal of Technology Transfer vol 42no 2 pp 374ndash388 2016

[2] M Candi D L Roberts T Marion and G Barczak ldquoSocialstrategy to gain knowledge for innovationrdquo British Journal ofManagement vol 29 no 4 pp 731ndash749 2018

[3] H Chesbrough Open Innovation e New Imperative forCreating and Profiting for Technology Harvard BusinessSchool Press Cambridge MA USA 2003

[4] R W Schmitt ldquoConflict or synergy University-industry re-search relationsrdquo Accountability in Research vol 5 no 4pp 251ndash254 2011

[5] A Ericsson and G Erixon Controlling Design VariantsModular Product Platforms American Society of MechanicalEngineers New York NY USA 1999

[6] R Sanchez and J T Mahoney ldquoModularity flexibility andknowledge management in product and organization de-signrdquo Strategic Management Journal vol 17 no S2pp 63ndash76 1996

[7] C Y Baldwin and K B Clark ldquo+e architecture of partici-pation Does code architecture mitigate free riding in the opensource development modelrdquo Management Science vol 52no 7 pp 1116ndash1127 2006

[8] H J Wang J Cheng and R S Zou ldquoStudy on the coordi-nating mechanism of knowledge transfer of IURU

collaborative innovation under modularity scenariordquo Studiesin Science of Science vol 36 no 7 pp 1274ndash1283 2018

[9] A Tiwana ldquoDoes interfirm modularity complement igno-rance A field study of software outsourcing alliancesrdquoStrategic Management Journal vol 29 no 11 pp 1241ndash12522008

[10] H J Wang and Y Zhang ldquoCorporate modularity-basedcollaborative innovation and innovation resources manage-ment analysis on an exploratory case of haierrdquo Science ampTechnology Progress and Policy vol 35 no 21 pp 97ndash1052018

[11] H Chesbrough S Kim and A Agogino ldquoChez PanisseBuilding an open innovation ecosystemrdquo California Man-agement Review vol 56 no 4 pp 144ndash171 2014

[12] J D Lewis ldquoMaking strategic alliances workrdquo Research-Technology Management vol 33 no 6 pp 12ndash15 1990

[13] J-F Ding and G-S Liang ldquoUsing fuzzy MCDM to selectpartners of strategic alliances for liner shippingrdquo InformationSciences vol 173 no 1ndash3 pp 197ndash225 2005

[14] L Li and Y Xi ldquo+e design and application study of theevaluation index system of research project collaborativeinnovation performancerdquo Science amp Technology Progress andPolicy vol 31 no 1 pp 123ndash129 2014

[15] S Maha and L Natalia ldquoSelecting an open innovationcommunity as an alliance partner looking for healthy com-munities and ecosystemsrdquo Research Policy vol 48 no 8Article ID 103766 2019

[16] J Jian M Wang L Li J Su and T Huang ldquoA partner se-lection model for collaborative product innovation from theviewpoint of knowledge collaborationrdquo Kybernetes vol 49no 6 pp 1623ndash1644 2019

[17] C Gu Y Wang and L Jia ldquoRanking fuzzy numbers based onfuzzy distancerdquo Journal of Henan Institute of Education(Natural Science Edition) vol 23 pp 17ndash21 2014

[18] Z Wang H Nie and H Zhao ldquoAn extended GEDM methodwith heterogeneous reference points of decision makers and anew hesitant fuzzy distance formulardquo Computers amp IndustrialEngineering vol 146 Article ID 106533 2020

[19] L Tran and L Duckstein ldquoComparison of fuzzy numbersusing a fuzzy distance measurerdquo Fuzzy Sets and Systemsvol 130 no 3 pp 331ndash341 2002

10 Discrete Dynamics in Nature and Society

performance of innovation resources so as to boost openinnovation

331 Barrier-Free Resource Inclusion and ExclusionMechanism In response to Haierrsquos technology require-ments a number of global resources have been included incollaborative innovation and they are evaluated under equalcompetition and access conditions On this basis a rea-sonable innovative solution may be proposed by anyqualified innovation source in accordance with modularrequirements Previously the majority of external resourcesof Haier conditioners were based in China Starting from2000 Haier has carried out the innovation strategy ofldquotaking the world as our RampD centerrdquo +is has driven theresource integration process of Haier toward globaliza-tion Under the right circumstances networking toolsallow resources in other countriesregions to participatein the fair bidding procedures of Haier and to interactwith the company easily On the other hand due to the

function of the innovation resource filtering mechanismthe innovation resources in the circles of collaborativeinnovation have to continuously enhance their capabil-ities and adjust their service strategies based on thedynamic needs of Haierrsquos T-AC In addition the ability tosecure qualified innovation resources has been includedin the assessment of Key Performance Indexes (KPIs) ofrelevant employees

332 Participation Constraint and Incentive CompatibilityMechanism In order to manage innovation resourceswithin a controllable scope their behaviors are wellregulated so as to avoid possible risks during collabo-rative innovation Specifically strict clauses relating toparticipation qualifications are defined in contracts be-tween Haier and innovation resources thus eliminatingthe possibility of a deliberate withdrawal from cooper-ation and misconduct +rough the use of incentivecompatibility measures innovation resources are able to

Module Module systemEvaporator module Evaporating module systemElectrical heating moduleMotor module (internal)

Air supplyingmodule system

Fan module (internal)Skeleton moduleAir induction moduleStepping motor moduleSelf-cleaning module ndashFan curtain module Casing module

system Display modulePanel moduleAnion generator module ndashElectrical control box (internal) Electrical control module

system Computer board (internal)PM25 scavenger module ndashformaldehyde scavenger module ndashExhaust module ndashFixed frequency control module Fixed frequency control

module system Electrical parts box moduleIntelligent sensing module ndashCondenser module ndashMotor module (external) Exhaustmodule systemFan module (external)rottling depressurization module ndashElectrical control box (external) Converter control module

system Computer board (external)Structural components module ndashFresh air module ndash

Mod

ular

des

ign

Figure 4 +e modular product architecture of T-AC

Air inductionmodule

Motor module

Stepping motormodule

Skeleton moduleFan module

Air supplyingmodule system

Figure 5 Composition of the air-supplying module system of T-AC

6 Discrete Dynamics in Nature and Society

Tabl

e1

Fuzzyevaluatio

nmatrixof

evaluatio

nindices

Index

P1P2

P3P4

P5P6

P7P8

P9

Expert

E1VH

(071

01

0)

H(050

71

0)

H(050

71

0)

M(020

50

8)

H(050

71

0)

VH

(071

01

0)

H(050

71

0)

L(00

30

5)

VL(00

03)

E2H

(050

71

0)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

M(020

50

8)

H(050

71

0)

M(020

50

8)

M(020

50

8)

L(00

30

5)

E3H

(050

71

0)

VH

(071

01

0)

VH

(071

01

0)

L(00

30

5)

L(00

30

5)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

VL(00

03)

Average

(057

08

10)

(063

09

10)

(047

0730

93)

(013

0430

7)

(023

05

077)

(063

09

10)

(030

57

087)

(013

0430

7)

(00

10

37)

Discrete Dynamics in Nature and Society 7

Table 2 Fuzzy evaluation matrix of module innovation resources

ResourcesIndices

P1 P2 P3 P4 P5 P6 P7 P8 P9M1 G VG G RP RP M VG GG VG M P RG RG M M RG VG RG G RG RG RG VG G VG G VGM2 RG M RG M M M G VG G VP VP P P P VP G RG RG M RP M RG VG VG G G GM3 VG VG G RP P RP VG VG G G G G VG RG VG M G M G VG VG RG M RG M M GM4 RG G M M RP RP M M G RG M RG G RG RG M M RG RG G VG RG G RG M M GM5 VP VP P RG G VG M M RG RP RP VP RG RG M P M M RG G G VG VG VG P P VPM6 P P VP G RG M RG G RG VP M VP M M RG P M P RG RG G G M RG P RP PM7 P VP P RG G M RG RG M VP P P RG M RG RP RP RP G G RG G RG RG P P RPM8 RP M P G RG M G G VG M M RG G G VG RG VG G RG G G G M RG M M PM9 M RG M RG M RG M G G P P P RG M M G RG VG P VP VP G RG M VG G RG

41117 38432

44135 39998

23915 28137 29611

36627 36723

30425 32815

27797 31736

47060 42674 41024

34875 34715

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 6 Visualization of the evaluation of module innovation resources at τ 0

4081937919

4391839654

2341427512

29224

36277 36149

30214 3255927414

31539

4726443057 41215

34757 34825

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 7 Visualization of the evaluation of module innovation resources at τ 05

Table 3 Evaluation of module innovation resources using the distance measurement method

τ Dmax Dmin M1 M2 M3 M4 M5 M6 M7 M8 M9

τ 0 Dmin 41117 38432 44135 39998 23915 28137 29611 36627 36723Dmax 30425 328148 27797 31736 47060 42674 41024 34875 34715

τ 12 Dmin 40819 37919 43918 39654 23414 27512 29224 36277 36149Dmax 30214 32559 27414 31539 47264 43057 41215 34757 34825

τ 1 Dmin 40635 37882 43791 39442 23092 27102 28974 36059 35907Dmax 30140 32745 27184 31430 47408 43319 41349 34696 34498

τ 2 Dmin 40425 37633 43654 39200 22705 26600 28670 32810 35529Dmax 30000 32757 26805 31281 47719 43866 41636 37629 34977

8 Discrete Dynamics in Nature and Society

tap their potential effectively for the purpose of profit-sharing For example a Chinese Southern manufacturerSunwill was originally only responsible for supplying thefan module of T-AC but later it became the designer andsupplier of the whole air supply module system thanks toHaierrsquos incentive compatibility mechanism A purchaseorder of more than 100000 units (module systems) perquarter was given to Sunwill together with a contractcommission for at least two years As a result Sunwill nowmanages other module suppliers of the air supply modulesystem including those offering motors and winddeflectors

333 Distributed Resource Management Mechanism +ecore modules (air supply module system evaporatormodule system etc) of the modular product architectureof T-AC were developed in the headquartersrsquo RampD centerof Haier At this center Haier utilizes its RampD capacitiesand interacts with innovation resources to strengthen itsinnovation competence With high-qualification re-quirements for standard modules among such innovationresources resource optimization frequency is low Haierseeks to establish a strategic cooperation relationship with

a number of excellent selected candidates Individualizedmodulessystems (formaldehyde elimination moduledouble-way fresh air module etc) are developed in areasclose to target markets +e benefits include more synergieswith innovation resources lower cost and higher serviceefficiency To cater for varying user needs the optimizationfrequency of the innovation resources in these areas isrelatively high

4 Conclusions

41 eoretical Contribution To fill the gaps in existingstudies the relationship between modularity and openinnovation is first analyzed Module innovation resourcesin an open environment are then grouped In order toselect qualified innovation resources for core modules ofmodular product architecture nine evaluation indices areconstructed in this study Next a fuzzy distance mea-surement method is presented to compare fuzzy numbersand decide which innovation resource is optimal By usinga case study of one of Haier Grouprsquos modular productsconsistent evaluation and selection results are achievedfor different decision-making attitudes proving that themethod proposed in this paper is robust

40635 37882

43791 39442

23092 27102 28974

36059

30140 32745

27184 31430

47408 43319 41349

34696

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

35907 34498

Figure 8 Visualization of the evaluation of module innovation resources at τ 1

40425 37633 43654

39200

22705 26600 28670

32810 35529

30000 32757 26805

31281

47719 43866 41636

37629 34977

0

1

2

3

4

5

6

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 9 Visualization of the evaluation of module innovation resources at τ 2

Discrete Dynamics in Nature and Society 9

42 Empirical Conclusions Based on the case study of theHaier Group modular product architecture plays a vitalrole in linking the enterprisersquos innovation requirementswith external innovation resources Meanwhile it isdiscovered that strategic suppliers not only collaboratewith Haier in terms of procurement but also actively getinvolved in core module system innovation +rough theimplementation of strategic supplier managementmechanisms such as contract binding dynamic optimi-zation and big resources in exchange for big resources astrategic supplier is able to improve its capabilities con-tinuously thus engaging in open innovation with thecompany in a win-win context

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that they have no conflicts of interest

Acknowledgments

+is research was supported by the Chinese NationalFunding of Social Sciences (19BGL045) Liaoning PublicWelfare Research Project (2020JH410100023) and theProject is sponsored by Liaoning BaiQianWan TalentsProgram (2020921084) +e authors herewith show ap-preciation for their support

References

[1] D Vrontis A +rassou G Santoro and A Papa ldquoAmbi-dexterity external knowledge and performance in knowledge-intensive firmsrdquo e Journal of Technology Transfer vol 42no 2 pp 374ndash388 2016

[2] M Candi D L Roberts T Marion and G Barczak ldquoSocialstrategy to gain knowledge for innovationrdquo British Journal ofManagement vol 29 no 4 pp 731ndash749 2018

[3] H Chesbrough Open Innovation e New Imperative forCreating and Profiting for Technology Harvard BusinessSchool Press Cambridge MA USA 2003

[4] R W Schmitt ldquoConflict or synergy University-industry re-search relationsrdquo Accountability in Research vol 5 no 4pp 251ndash254 2011

[5] A Ericsson and G Erixon Controlling Design VariantsModular Product Platforms American Society of MechanicalEngineers New York NY USA 1999

[6] R Sanchez and J T Mahoney ldquoModularity flexibility andknowledge management in product and organization de-signrdquo Strategic Management Journal vol 17 no S2pp 63ndash76 1996

[7] C Y Baldwin and K B Clark ldquo+e architecture of partici-pation Does code architecture mitigate free riding in the opensource development modelrdquo Management Science vol 52no 7 pp 1116ndash1127 2006

[8] H J Wang J Cheng and R S Zou ldquoStudy on the coordi-nating mechanism of knowledge transfer of IURU

collaborative innovation under modularity scenariordquo Studiesin Science of Science vol 36 no 7 pp 1274ndash1283 2018

[9] A Tiwana ldquoDoes interfirm modularity complement igno-rance A field study of software outsourcing alliancesrdquoStrategic Management Journal vol 29 no 11 pp 1241ndash12522008

[10] H J Wang and Y Zhang ldquoCorporate modularity-basedcollaborative innovation and innovation resources manage-ment analysis on an exploratory case of haierrdquo Science ampTechnology Progress and Policy vol 35 no 21 pp 97ndash1052018

[11] H Chesbrough S Kim and A Agogino ldquoChez PanisseBuilding an open innovation ecosystemrdquo California Man-agement Review vol 56 no 4 pp 144ndash171 2014

[12] J D Lewis ldquoMaking strategic alliances workrdquo Research-Technology Management vol 33 no 6 pp 12ndash15 1990

[13] J-F Ding and G-S Liang ldquoUsing fuzzy MCDM to selectpartners of strategic alliances for liner shippingrdquo InformationSciences vol 173 no 1ndash3 pp 197ndash225 2005

[14] L Li and Y Xi ldquo+e design and application study of theevaluation index system of research project collaborativeinnovation performancerdquo Science amp Technology Progress andPolicy vol 31 no 1 pp 123ndash129 2014

[15] S Maha and L Natalia ldquoSelecting an open innovationcommunity as an alliance partner looking for healthy com-munities and ecosystemsrdquo Research Policy vol 48 no 8Article ID 103766 2019

[16] J Jian M Wang L Li J Su and T Huang ldquoA partner se-lection model for collaborative product innovation from theviewpoint of knowledge collaborationrdquo Kybernetes vol 49no 6 pp 1623ndash1644 2019

[17] C Gu Y Wang and L Jia ldquoRanking fuzzy numbers based onfuzzy distancerdquo Journal of Henan Institute of Education(Natural Science Edition) vol 23 pp 17ndash21 2014

[18] Z Wang H Nie and H Zhao ldquoAn extended GEDM methodwith heterogeneous reference points of decision makers and anew hesitant fuzzy distance formulardquo Computers amp IndustrialEngineering vol 146 Article ID 106533 2020

[19] L Tran and L Duckstein ldquoComparison of fuzzy numbersusing a fuzzy distance measurerdquo Fuzzy Sets and Systemsvol 130 no 3 pp 331ndash341 2002

10 Discrete Dynamics in Nature and Society

Tabl

e1

Fuzzyevaluatio

nmatrixof

evaluatio

nindices

Index

P1P2

P3P4

P5P6

P7P8

P9

Expert

E1VH

(071

01

0)

H(050

71

0)

H(050

71

0)

M(020

50

8)

H(050

71

0)

VH

(071

01

0)

H(050

71

0)

L(00

30

5)

VL(00

03)

E2H

(050

71

0)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

M(020

50

8)

H(050

71

0)

M(020

50

8)

M(020

50

8)

L(00

30

5)

E3H

(050

71

0)

VH

(071

01

0)

VH

(071

01

0)

L(00

30

5)

L(00

30

5)

VH

(071

01

0)

M(020

50

8)

M(020

50

8)

VL(00

03)

Average

(057

08

10)

(063

09

10)

(047

0730

93)

(013

0430

7)

(023

05

077)

(063

09

10)

(030

57

087)

(013

0430

7)

(00

10

37)

Discrete Dynamics in Nature and Society 7

Table 2 Fuzzy evaluation matrix of module innovation resources

ResourcesIndices

P1 P2 P3 P4 P5 P6 P7 P8 P9M1 G VG G RP RP M VG GG VG M P RG RG M M RG VG RG G RG RG RG VG G VG G VGM2 RG M RG M M M G VG G VP VP P P P VP G RG RG M RP M RG VG VG G G GM3 VG VG G RP P RP VG VG G G G G VG RG VG M G M G VG VG RG M RG M M GM4 RG G M M RP RP M M G RG M RG G RG RG M M RG RG G VG RG G RG M M GM5 VP VP P RG G VG M M RG RP RP VP RG RG M P M M RG G G VG VG VG P P VPM6 P P VP G RG M RG G RG VP M VP M M RG P M P RG RG G G M RG P RP PM7 P VP P RG G M RG RG M VP P P RG M RG RP RP RP G G RG G RG RG P P RPM8 RP M P G RG M G G VG M M RG G G VG RG VG G RG G G G M RG M M PM9 M RG M RG M RG M G G P P P RG M M G RG VG P VP VP G RG M VG G RG

41117 38432

44135 39998

23915 28137 29611

36627 36723

30425 32815

27797 31736

47060 42674 41024

34875 34715

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 6 Visualization of the evaluation of module innovation resources at τ 0

4081937919

4391839654

2341427512

29224

36277 36149

30214 3255927414

31539

4726443057 41215

34757 34825

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 7 Visualization of the evaluation of module innovation resources at τ 05

Table 3 Evaluation of module innovation resources using the distance measurement method

τ Dmax Dmin M1 M2 M3 M4 M5 M6 M7 M8 M9

τ 0 Dmin 41117 38432 44135 39998 23915 28137 29611 36627 36723Dmax 30425 328148 27797 31736 47060 42674 41024 34875 34715

τ 12 Dmin 40819 37919 43918 39654 23414 27512 29224 36277 36149Dmax 30214 32559 27414 31539 47264 43057 41215 34757 34825

τ 1 Dmin 40635 37882 43791 39442 23092 27102 28974 36059 35907Dmax 30140 32745 27184 31430 47408 43319 41349 34696 34498

τ 2 Dmin 40425 37633 43654 39200 22705 26600 28670 32810 35529Dmax 30000 32757 26805 31281 47719 43866 41636 37629 34977

8 Discrete Dynamics in Nature and Society

tap their potential effectively for the purpose of profit-sharing For example a Chinese Southern manufacturerSunwill was originally only responsible for supplying thefan module of T-AC but later it became the designer andsupplier of the whole air supply module system thanks toHaierrsquos incentive compatibility mechanism A purchaseorder of more than 100000 units (module systems) perquarter was given to Sunwill together with a contractcommission for at least two years As a result Sunwill nowmanages other module suppliers of the air supply modulesystem including those offering motors and winddeflectors

333 Distributed Resource Management Mechanism +ecore modules (air supply module system evaporatormodule system etc) of the modular product architectureof T-AC were developed in the headquartersrsquo RampD centerof Haier At this center Haier utilizes its RampD capacitiesand interacts with innovation resources to strengthen itsinnovation competence With high-qualification re-quirements for standard modules among such innovationresources resource optimization frequency is low Haierseeks to establish a strategic cooperation relationship with

a number of excellent selected candidates Individualizedmodulessystems (formaldehyde elimination moduledouble-way fresh air module etc) are developed in areasclose to target markets +e benefits include more synergieswith innovation resources lower cost and higher serviceefficiency To cater for varying user needs the optimizationfrequency of the innovation resources in these areas isrelatively high

4 Conclusions

41 eoretical Contribution To fill the gaps in existingstudies the relationship between modularity and openinnovation is first analyzed Module innovation resourcesin an open environment are then grouped In order toselect qualified innovation resources for core modules ofmodular product architecture nine evaluation indices areconstructed in this study Next a fuzzy distance mea-surement method is presented to compare fuzzy numbersand decide which innovation resource is optimal By usinga case study of one of Haier Grouprsquos modular productsconsistent evaluation and selection results are achievedfor different decision-making attitudes proving that themethod proposed in this paper is robust

40635 37882

43791 39442

23092 27102 28974

36059

30140 32745

27184 31430

47408 43319 41349

34696

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

35907 34498

Figure 8 Visualization of the evaluation of module innovation resources at τ 1

40425 37633 43654

39200

22705 26600 28670

32810 35529

30000 32757 26805

31281

47719 43866 41636

37629 34977

0

1

2

3

4

5

6

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 9 Visualization of the evaluation of module innovation resources at τ 2

Discrete Dynamics in Nature and Society 9

42 Empirical Conclusions Based on the case study of theHaier Group modular product architecture plays a vitalrole in linking the enterprisersquos innovation requirementswith external innovation resources Meanwhile it isdiscovered that strategic suppliers not only collaboratewith Haier in terms of procurement but also actively getinvolved in core module system innovation +rough theimplementation of strategic supplier managementmechanisms such as contract binding dynamic optimi-zation and big resources in exchange for big resources astrategic supplier is able to improve its capabilities con-tinuously thus engaging in open innovation with thecompany in a win-win context

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that they have no conflicts of interest

Acknowledgments

+is research was supported by the Chinese NationalFunding of Social Sciences (19BGL045) Liaoning PublicWelfare Research Project (2020JH410100023) and theProject is sponsored by Liaoning BaiQianWan TalentsProgram (2020921084) +e authors herewith show ap-preciation for their support

References

[1] D Vrontis A +rassou G Santoro and A Papa ldquoAmbi-dexterity external knowledge and performance in knowledge-intensive firmsrdquo e Journal of Technology Transfer vol 42no 2 pp 374ndash388 2016

[2] M Candi D L Roberts T Marion and G Barczak ldquoSocialstrategy to gain knowledge for innovationrdquo British Journal ofManagement vol 29 no 4 pp 731ndash749 2018

[3] H Chesbrough Open Innovation e New Imperative forCreating and Profiting for Technology Harvard BusinessSchool Press Cambridge MA USA 2003

[4] R W Schmitt ldquoConflict or synergy University-industry re-search relationsrdquo Accountability in Research vol 5 no 4pp 251ndash254 2011

[5] A Ericsson and G Erixon Controlling Design VariantsModular Product Platforms American Society of MechanicalEngineers New York NY USA 1999

[6] R Sanchez and J T Mahoney ldquoModularity flexibility andknowledge management in product and organization de-signrdquo Strategic Management Journal vol 17 no S2pp 63ndash76 1996

[7] C Y Baldwin and K B Clark ldquo+e architecture of partici-pation Does code architecture mitigate free riding in the opensource development modelrdquo Management Science vol 52no 7 pp 1116ndash1127 2006

[8] H J Wang J Cheng and R S Zou ldquoStudy on the coordi-nating mechanism of knowledge transfer of IURU

collaborative innovation under modularity scenariordquo Studiesin Science of Science vol 36 no 7 pp 1274ndash1283 2018

[9] A Tiwana ldquoDoes interfirm modularity complement igno-rance A field study of software outsourcing alliancesrdquoStrategic Management Journal vol 29 no 11 pp 1241ndash12522008

[10] H J Wang and Y Zhang ldquoCorporate modularity-basedcollaborative innovation and innovation resources manage-ment analysis on an exploratory case of haierrdquo Science ampTechnology Progress and Policy vol 35 no 21 pp 97ndash1052018

[11] H Chesbrough S Kim and A Agogino ldquoChez PanisseBuilding an open innovation ecosystemrdquo California Man-agement Review vol 56 no 4 pp 144ndash171 2014

[12] J D Lewis ldquoMaking strategic alliances workrdquo Research-Technology Management vol 33 no 6 pp 12ndash15 1990

[13] J-F Ding and G-S Liang ldquoUsing fuzzy MCDM to selectpartners of strategic alliances for liner shippingrdquo InformationSciences vol 173 no 1ndash3 pp 197ndash225 2005

[14] L Li and Y Xi ldquo+e design and application study of theevaluation index system of research project collaborativeinnovation performancerdquo Science amp Technology Progress andPolicy vol 31 no 1 pp 123ndash129 2014

[15] S Maha and L Natalia ldquoSelecting an open innovationcommunity as an alliance partner looking for healthy com-munities and ecosystemsrdquo Research Policy vol 48 no 8Article ID 103766 2019

[16] J Jian M Wang L Li J Su and T Huang ldquoA partner se-lection model for collaborative product innovation from theviewpoint of knowledge collaborationrdquo Kybernetes vol 49no 6 pp 1623ndash1644 2019

[17] C Gu Y Wang and L Jia ldquoRanking fuzzy numbers based onfuzzy distancerdquo Journal of Henan Institute of Education(Natural Science Edition) vol 23 pp 17ndash21 2014

[18] Z Wang H Nie and H Zhao ldquoAn extended GEDM methodwith heterogeneous reference points of decision makers and anew hesitant fuzzy distance formulardquo Computers amp IndustrialEngineering vol 146 Article ID 106533 2020

[19] L Tran and L Duckstein ldquoComparison of fuzzy numbersusing a fuzzy distance measurerdquo Fuzzy Sets and Systemsvol 130 no 3 pp 331ndash341 2002

10 Discrete Dynamics in Nature and Society

Table 2 Fuzzy evaluation matrix of module innovation resources

ResourcesIndices

P1 P2 P3 P4 P5 P6 P7 P8 P9M1 G VG G RP RP M VG GG VG M P RG RG M M RG VG RG G RG RG RG VG G VG G VGM2 RG M RG M M M G VG G VP VP P P P VP G RG RG M RP M RG VG VG G G GM3 VG VG G RP P RP VG VG G G G G VG RG VG M G M G VG VG RG M RG M M GM4 RG G M M RP RP M M G RG M RG G RG RG M M RG RG G VG RG G RG M M GM5 VP VP P RG G VG M M RG RP RP VP RG RG M P M M RG G G VG VG VG P P VPM6 P P VP G RG M RG G RG VP M VP M M RG P M P RG RG G G M RG P RP PM7 P VP P RG G M RG RG M VP P P RG M RG RP RP RP G G RG G RG RG P P RPM8 RP M P G RG M G G VG M M RG G G VG RG VG G RG G G G M RG M M PM9 M RG M RG M RG M G G P P P RG M M G RG VG P VP VP G RG M VG G RG

41117 38432

44135 39998

23915 28137 29611

36627 36723

30425 32815

27797 31736

47060 42674 41024

34875 34715

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 6 Visualization of the evaluation of module innovation resources at τ 0

4081937919

4391839654

2341427512

29224

36277 36149

30214 3255927414

31539

4726443057 41215

34757 34825

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 7 Visualization of the evaluation of module innovation resources at τ 05

Table 3 Evaluation of module innovation resources using the distance measurement method

τ Dmax Dmin M1 M2 M3 M4 M5 M6 M7 M8 M9

τ 0 Dmin 41117 38432 44135 39998 23915 28137 29611 36627 36723Dmax 30425 328148 27797 31736 47060 42674 41024 34875 34715

τ 12 Dmin 40819 37919 43918 39654 23414 27512 29224 36277 36149Dmax 30214 32559 27414 31539 47264 43057 41215 34757 34825

τ 1 Dmin 40635 37882 43791 39442 23092 27102 28974 36059 35907Dmax 30140 32745 27184 31430 47408 43319 41349 34696 34498

τ 2 Dmin 40425 37633 43654 39200 22705 26600 28670 32810 35529Dmax 30000 32757 26805 31281 47719 43866 41636 37629 34977

8 Discrete Dynamics in Nature and Society

tap their potential effectively for the purpose of profit-sharing For example a Chinese Southern manufacturerSunwill was originally only responsible for supplying thefan module of T-AC but later it became the designer andsupplier of the whole air supply module system thanks toHaierrsquos incentive compatibility mechanism A purchaseorder of more than 100000 units (module systems) perquarter was given to Sunwill together with a contractcommission for at least two years As a result Sunwill nowmanages other module suppliers of the air supply modulesystem including those offering motors and winddeflectors

333 Distributed Resource Management Mechanism +ecore modules (air supply module system evaporatormodule system etc) of the modular product architectureof T-AC were developed in the headquartersrsquo RampD centerof Haier At this center Haier utilizes its RampD capacitiesand interacts with innovation resources to strengthen itsinnovation competence With high-qualification re-quirements for standard modules among such innovationresources resource optimization frequency is low Haierseeks to establish a strategic cooperation relationship with

a number of excellent selected candidates Individualizedmodulessystems (formaldehyde elimination moduledouble-way fresh air module etc) are developed in areasclose to target markets +e benefits include more synergieswith innovation resources lower cost and higher serviceefficiency To cater for varying user needs the optimizationfrequency of the innovation resources in these areas isrelatively high

4 Conclusions

41 eoretical Contribution To fill the gaps in existingstudies the relationship between modularity and openinnovation is first analyzed Module innovation resourcesin an open environment are then grouped In order toselect qualified innovation resources for core modules ofmodular product architecture nine evaluation indices areconstructed in this study Next a fuzzy distance mea-surement method is presented to compare fuzzy numbersand decide which innovation resource is optimal By usinga case study of one of Haier Grouprsquos modular productsconsistent evaluation and selection results are achievedfor different decision-making attitudes proving that themethod proposed in this paper is robust

40635 37882

43791 39442

23092 27102 28974

36059

30140 32745

27184 31430

47408 43319 41349

34696

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

35907 34498

Figure 8 Visualization of the evaluation of module innovation resources at τ 1

40425 37633 43654

39200

22705 26600 28670

32810 35529

30000 32757 26805

31281

47719 43866 41636

37629 34977

0

1

2

3

4

5

6

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 9 Visualization of the evaluation of module innovation resources at τ 2

Discrete Dynamics in Nature and Society 9

42 Empirical Conclusions Based on the case study of theHaier Group modular product architecture plays a vitalrole in linking the enterprisersquos innovation requirementswith external innovation resources Meanwhile it isdiscovered that strategic suppliers not only collaboratewith Haier in terms of procurement but also actively getinvolved in core module system innovation +rough theimplementation of strategic supplier managementmechanisms such as contract binding dynamic optimi-zation and big resources in exchange for big resources astrategic supplier is able to improve its capabilities con-tinuously thus engaging in open innovation with thecompany in a win-win context

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that they have no conflicts of interest

Acknowledgments

+is research was supported by the Chinese NationalFunding of Social Sciences (19BGL045) Liaoning PublicWelfare Research Project (2020JH410100023) and theProject is sponsored by Liaoning BaiQianWan TalentsProgram (2020921084) +e authors herewith show ap-preciation for their support

References

[1] D Vrontis A +rassou G Santoro and A Papa ldquoAmbi-dexterity external knowledge and performance in knowledge-intensive firmsrdquo e Journal of Technology Transfer vol 42no 2 pp 374ndash388 2016

[2] M Candi D L Roberts T Marion and G Barczak ldquoSocialstrategy to gain knowledge for innovationrdquo British Journal ofManagement vol 29 no 4 pp 731ndash749 2018

[3] H Chesbrough Open Innovation e New Imperative forCreating and Profiting for Technology Harvard BusinessSchool Press Cambridge MA USA 2003

[4] R W Schmitt ldquoConflict or synergy University-industry re-search relationsrdquo Accountability in Research vol 5 no 4pp 251ndash254 2011

[5] A Ericsson and G Erixon Controlling Design VariantsModular Product Platforms American Society of MechanicalEngineers New York NY USA 1999

[6] R Sanchez and J T Mahoney ldquoModularity flexibility andknowledge management in product and organization de-signrdquo Strategic Management Journal vol 17 no S2pp 63ndash76 1996

[7] C Y Baldwin and K B Clark ldquo+e architecture of partici-pation Does code architecture mitigate free riding in the opensource development modelrdquo Management Science vol 52no 7 pp 1116ndash1127 2006

[8] H J Wang J Cheng and R S Zou ldquoStudy on the coordi-nating mechanism of knowledge transfer of IURU

collaborative innovation under modularity scenariordquo Studiesin Science of Science vol 36 no 7 pp 1274ndash1283 2018

[9] A Tiwana ldquoDoes interfirm modularity complement igno-rance A field study of software outsourcing alliancesrdquoStrategic Management Journal vol 29 no 11 pp 1241ndash12522008

[10] H J Wang and Y Zhang ldquoCorporate modularity-basedcollaborative innovation and innovation resources manage-ment analysis on an exploratory case of haierrdquo Science ampTechnology Progress and Policy vol 35 no 21 pp 97ndash1052018

[11] H Chesbrough S Kim and A Agogino ldquoChez PanisseBuilding an open innovation ecosystemrdquo California Man-agement Review vol 56 no 4 pp 144ndash171 2014

[12] J D Lewis ldquoMaking strategic alliances workrdquo Research-Technology Management vol 33 no 6 pp 12ndash15 1990

[13] J-F Ding and G-S Liang ldquoUsing fuzzy MCDM to selectpartners of strategic alliances for liner shippingrdquo InformationSciences vol 173 no 1ndash3 pp 197ndash225 2005

[14] L Li and Y Xi ldquo+e design and application study of theevaluation index system of research project collaborativeinnovation performancerdquo Science amp Technology Progress andPolicy vol 31 no 1 pp 123ndash129 2014

[15] S Maha and L Natalia ldquoSelecting an open innovationcommunity as an alliance partner looking for healthy com-munities and ecosystemsrdquo Research Policy vol 48 no 8Article ID 103766 2019

[16] J Jian M Wang L Li J Su and T Huang ldquoA partner se-lection model for collaborative product innovation from theviewpoint of knowledge collaborationrdquo Kybernetes vol 49no 6 pp 1623ndash1644 2019

[17] C Gu Y Wang and L Jia ldquoRanking fuzzy numbers based onfuzzy distancerdquo Journal of Henan Institute of Education(Natural Science Edition) vol 23 pp 17ndash21 2014

[18] Z Wang H Nie and H Zhao ldquoAn extended GEDM methodwith heterogeneous reference points of decision makers and anew hesitant fuzzy distance formulardquo Computers amp IndustrialEngineering vol 146 Article ID 106533 2020

[19] L Tran and L Duckstein ldquoComparison of fuzzy numbersusing a fuzzy distance measurerdquo Fuzzy Sets and Systemsvol 130 no 3 pp 331ndash341 2002

10 Discrete Dynamics in Nature and Society

tap their potential effectively for the purpose of profit-sharing For example a Chinese Southern manufacturerSunwill was originally only responsible for supplying thefan module of T-AC but later it became the designer andsupplier of the whole air supply module system thanks toHaierrsquos incentive compatibility mechanism A purchaseorder of more than 100000 units (module systems) perquarter was given to Sunwill together with a contractcommission for at least two years As a result Sunwill nowmanages other module suppliers of the air supply modulesystem including those offering motors and winddeflectors

333 Distributed Resource Management Mechanism +ecore modules (air supply module system evaporatormodule system etc) of the modular product architectureof T-AC were developed in the headquartersrsquo RampD centerof Haier At this center Haier utilizes its RampD capacitiesand interacts with innovation resources to strengthen itsinnovation competence With high-qualification re-quirements for standard modules among such innovationresources resource optimization frequency is low Haierseeks to establish a strategic cooperation relationship with

a number of excellent selected candidates Individualizedmodulessystems (formaldehyde elimination moduledouble-way fresh air module etc) are developed in areasclose to target markets +e benefits include more synergieswith innovation resources lower cost and higher serviceefficiency To cater for varying user needs the optimizationfrequency of the innovation resources in these areas isrelatively high

4 Conclusions

41 eoretical Contribution To fill the gaps in existingstudies the relationship between modularity and openinnovation is first analyzed Module innovation resourcesin an open environment are then grouped In order toselect qualified innovation resources for core modules ofmodular product architecture nine evaluation indices areconstructed in this study Next a fuzzy distance mea-surement method is presented to compare fuzzy numbersand decide which innovation resource is optimal By usinga case study of one of Haier Grouprsquos modular productsconsistent evaluation and selection results are achievedfor different decision-making attitudes proving that themethod proposed in this paper is robust

40635 37882

43791 39442

23092 27102 28974

36059

30140 32745

27184 31430

47408 43319 41349

34696

005

115

225

335

445

5

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

35907 34498

Figure 8 Visualization of the evaluation of module innovation resources at τ 1

40425 37633 43654

39200

22705 26600 28670

32810 35529

30000 32757 26805

31281

47719 43866 41636

37629 34977

0

1

2

3

4

5

6

M1 M2 M3 M4 M5 M6 M7 M8 M9

DminDmax

Figure 9 Visualization of the evaluation of module innovation resources at τ 2

Discrete Dynamics in Nature and Society 9

42 Empirical Conclusions Based on the case study of theHaier Group modular product architecture plays a vitalrole in linking the enterprisersquos innovation requirementswith external innovation resources Meanwhile it isdiscovered that strategic suppliers not only collaboratewith Haier in terms of procurement but also actively getinvolved in core module system innovation +rough theimplementation of strategic supplier managementmechanisms such as contract binding dynamic optimi-zation and big resources in exchange for big resources astrategic supplier is able to improve its capabilities con-tinuously thus engaging in open innovation with thecompany in a win-win context

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that they have no conflicts of interest

Acknowledgments

+is research was supported by the Chinese NationalFunding of Social Sciences (19BGL045) Liaoning PublicWelfare Research Project (2020JH410100023) and theProject is sponsored by Liaoning BaiQianWan TalentsProgram (2020921084) +e authors herewith show ap-preciation for their support

References

[1] D Vrontis A +rassou G Santoro and A Papa ldquoAmbi-dexterity external knowledge and performance in knowledge-intensive firmsrdquo e Journal of Technology Transfer vol 42no 2 pp 374ndash388 2016

[2] M Candi D L Roberts T Marion and G Barczak ldquoSocialstrategy to gain knowledge for innovationrdquo British Journal ofManagement vol 29 no 4 pp 731ndash749 2018

[3] H Chesbrough Open Innovation e New Imperative forCreating and Profiting for Technology Harvard BusinessSchool Press Cambridge MA USA 2003

[4] R W Schmitt ldquoConflict or synergy University-industry re-search relationsrdquo Accountability in Research vol 5 no 4pp 251ndash254 2011

[5] A Ericsson and G Erixon Controlling Design VariantsModular Product Platforms American Society of MechanicalEngineers New York NY USA 1999

[6] R Sanchez and J T Mahoney ldquoModularity flexibility andknowledge management in product and organization de-signrdquo Strategic Management Journal vol 17 no S2pp 63ndash76 1996

[7] C Y Baldwin and K B Clark ldquo+e architecture of partici-pation Does code architecture mitigate free riding in the opensource development modelrdquo Management Science vol 52no 7 pp 1116ndash1127 2006

[8] H J Wang J Cheng and R S Zou ldquoStudy on the coordi-nating mechanism of knowledge transfer of IURU

collaborative innovation under modularity scenariordquo Studiesin Science of Science vol 36 no 7 pp 1274ndash1283 2018

[9] A Tiwana ldquoDoes interfirm modularity complement igno-rance A field study of software outsourcing alliancesrdquoStrategic Management Journal vol 29 no 11 pp 1241ndash12522008

[10] H J Wang and Y Zhang ldquoCorporate modularity-basedcollaborative innovation and innovation resources manage-ment analysis on an exploratory case of haierrdquo Science ampTechnology Progress and Policy vol 35 no 21 pp 97ndash1052018

[11] H Chesbrough S Kim and A Agogino ldquoChez PanisseBuilding an open innovation ecosystemrdquo California Man-agement Review vol 56 no 4 pp 144ndash171 2014

[12] J D Lewis ldquoMaking strategic alliances workrdquo Research-Technology Management vol 33 no 6 pp 12ndash15 1990

[13] J-F Ding and G-S Liang ldquoUsing fuzzy MCDM to selectpartners of strategic alliances for liner shippingrdquo InformationSciences vol 173 no 1ndash3 pp 197ndash225 2005

[14] L Li and Y Xi ldquo+e design and application study of theevaluation index system of research project collaborativeinnovation performancerdquo Science amp Technology Progress andPolicy vol 31 no 1 pp 123ndash129 2014

[15] S Maha and L Natalia ldquoSelecting an open innovationcommunity as an alliance partner looking for healthy com-munities and ecosystemsrdquo Research Policy vol 48 no 8Article ID 103766 2019

[16] J Jian M Wang L Li J Su and T Huang ldquoA partner se-lection model for collaborative product innovation from theviewpoint of knowledge collaborationrdquo Kybernetes vol 49no 6 pp 1623ndash1644 2019

[17] C Gu Y Wang and L Jia ldquoRanking fuzzy numbers based onfuzzy distancerdquo Journal of Henan Institute of Education(Natural Science Edition) vol 23 pp 17ndash21 2014

[18] Z Wang H Nie and H Zhao ldquoAn extended GEDM methodwith heterogeneous reference points of decision makers and anew hesitant fuzzy distance formulardquo Computers amp IndustrialEngineering vol 146 Article ID 106533 2020

[19] L Tran and L Duckstein ldquoComparison of fuzzy numbersusing a fuzzy distance measurerdquo Fuzzy Sets and Systemsvol 130 no 3 pp 331ndash341 2002

10 Discrete Dynamics in Nature and Society

42 Empirical Conclusions Based on the case study of theHaier Group modular product architecture plays a vitalrole in linking the enterprisersquos innovation requirementswith external innovation resources Meanwhile it isdiscovered that strategic suppliers not only collaboratewith Haier in terms of procurement but also actively getinvolved in core module system innovation +rough theimplementation of strategic supplier managementmechanisms such as contract binding dynamic optimi-zation and big resources in exchange for big resources astrategic supplier is able to improve its capabilities con-tinuously thus engaging in open innovation with thecompany in a win-win context

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that they have no conflicts of interest

Acknowledgments

+is research was supported by the Chinese NationalFunding of Social Sciences (19BGL045) Liaoning PublicWelfare Research Project (2020JH410100023) and theProject is sponsored by Liaoning BaiQianWan TalentsProgram (2020921084) +e authors herewith show ap-preciation for their support

References

[1] D Vrontis A +rassou G Santoro and A Papa ldquoAmbi-dexterity external knowledge and performance in knowledge-intensive firmsrdquo e Journal of Technology Transfer vol 42no 2 pp 374ndash388 2016

[2] M Candi D L Roberts T Marion and G Barczak ldquoSocialstrategy to gain knowledge for innovationrdquo British Journal ofManagement vol 29 no 4 pp 731ndash749 2018

[3] H Chesbrough Open Innovation e New Imperative forCreating and Profiting for Technology Harvard BusinessSchool Press Cambridge MA USA 2003

[4] R W Schmitt ldquoConflict or synergy University-industry re-search relationsrdquo Accountability in Research vol 5 no 4pp 251ndash254 2011

[5] A Ericsson and G Erixon Controlling Design VariantsModular Product Platforms American Society of MechanicalEngineers New York NY USA 1999

[6] R Sanchez and J T Mahoney ldquoModularity flexibility andknowledge management in product and organization de-signrdquo Strategic Management Journal vol 17 no S2pp 63ndash76 1996

[7] C Y Baldwin and K B Clark ldquo+e architecture of partici-pation Does code architecture mitigate free riding in the opensource development modelrdquo Management Science vol 52no 7 pp 1116ndash1127 2006

[8] H J Wang J Cheng and R S Zou ldquoStudy on the coordi-nating mechanism of knowledge transfer of IURU

collaborative innovation under modularity scenariordquo Studiesin Science of Science vol 36 no 7 pp 1274ndash1283 2018

[9] A Tiwana ldquoDoes interfirm modularity complement igno-rance A field study of software outsourcing alliancesrdquoStrategic Management Journal vol 29 no 11 pp 1241ndash12522008

[10] H J Wang and Y Zhang ldquoCorporate modularity-basedcollaborative innovation and innovation resources manage-ment analysis on an exploratory case of haierrdquo Science ampTechnology Progress and Policy vol 35 no 21 pp 97ndash1052018

[11] H Chesbrough S Kim and A Agogino ldquoChez PanisseBuilding an open innovation ecosystemrdquo California Man-agement Review vol 56 no 4 pp 144ndash171 2014

[12] J D Lewis ldquoMaking strategic alliances workrdquo Research-Technology Management vol 33 no 6 pp 12ndash15 1990

[13] J-F Ding and G-S Liang ldquoUsing fuzzy MCDM to selectpartners of strategic alliances for liner shippingrdquo InformationSciences vol 173 no 1ndash3 pp 197ndash225 2005

[14] L Li and Y Xi ldquo+e design and application study of theevaluation index system of research project collaborativeinnovation performancerdquo Science amp Technology Progress andPolicy vol 31 no 1 pp 123ndash129 2014

[15] S Maha and L Natalia ldquoSelecting an open innovationcommunity as an alliance partner looking for healthy com-munities and ecosystemsrdquo Research Policy vol 48 no 8Article ID 103766 2019

[16] J Jian M Wang L Li J Su and T Huang ldquoA partner se-lection model for collaborative product innovation from theviewpoint of knowledge collaborationrdquo Kybernetes vol 49no 6 pp 1623ndash1644 2019

[17] C Gu Y Wang and L Jia ldquoRanking fuzzy numbers based onfuzzy distancerdquo Journal of Henan Institute of Education(Natural Science Edition) vol 23 pp 17ndash21 2014

[18] Z Wang H Nie and H Zhao ldquoAn extended GEDM methodwith heterogeneous reference points of decision makers and anew hesitant fuzzy distance formulardquo Computers amp IndustrialEngineering vol 146 Article ID 106533 2020

[19] L Tran and L Duckstein ldquoComparison of fuzzy numbersusing a fuzzy distance measurerdquo Fuzzy Sets and Systemsvol 130 no 3 pp 331ndash341 2002

10 Discrete Dynamics in Nature and Society