phd thesis igor barahona july 26th of 2013

The level of adoption of analytical tools.

Igor BARAHONA

Barcelona, Spain. July 26th of 2013

1. Introduction.

2. The level of adoption of analytical tools. A theoretical perspective.

3. The analytical tools in different areas of the company.

4. The questionnaire design.

5. The cases of study.

• Statistical Engineering.

• Evidential Reasoning.

• The laddering technique.

6. Conclusions and guidelines to stakeholders.

7. Further lines of research.

Contents.

2

1. Introduction.










3

Processor Dhrystone MIPS Cost Year

UNIVAC I 0.002 MIPS at 2.25 MHz

$11.500.000. USD 1951UNIVAC I

Computers performance.

1951

IBM System/370 mod

el 158-3

IBM System/370 model

158-3 1 MIPS at 8.69 MHz

$2,248,550. USD 1971 1971

INTEL 286 Intel 286 2.66 MIPS at 12.5 MHz $3,500. USD 1982 1982

Intel Pentium III Intel Pentium III 2,054 MIPS at

600 MHz $2,000. USD 1999 1999

Intel Core i7 2600K

128,300 MIPS at 3.4 GHz $1,000. USD 2011 2011Intel

Core i7 2600K

http://en.wikipedia.org/wiki/IBM_System/370



If computers are more powerful...........

Lots of data but not information

Powerful computers but unstructured problems

Difficulties of getting fast and accurate information.

Make sense of “data tsunami” that is hitting modern industries

?5

What does it imply?

Burby & Atchison (2007)

Kaushilk (2011)

The business environment...................

More complexity requires analyzing real-time-data and for making better decisions.

Reduction on differentiation points due to the globalization of markets.

Customers better informed with more alternatives.

Data has to be converted into “Information” that triggers managerial action.

6

How is it changing?

Stubbs (2011)

McDonough (2009)

Extensive utilization of data, information and quantitative models.

Understand past / present performance

Reduce uncertainty

Predict future results

Making better decisions based on quantitative evidence

OUTPUTS

ADDED VALUE

INPUTS

7

It can be defined in terms of inputs and outputs

What is an analytical tool?

Davenport & Harris (2007)

• Using analytics– Finding the best customers, and charging

them the right price– Minimizing inventory in supply chains– Allocating costs accurately and

understanding how financial performance is driven.

Using analytical tools is good.......

8

But It is better competing with them...

• Competing with analytics.– Making analytics and fact-

based decisions a key element of strategy and competition

Davenport, Harris & Morrison (2010)

Thesis Objectives

1. Propose a theoretical scale to measure the level of adoption of analytical tools in companies.

2. Design a reliable and valid instrument to collect data from a sample of companies located in Barcelona, Spain.

3. Analyze data collected from the surveyed companies, in order to draw conclusions about the level of adoption of analytical tools in Barcelona by applying the Statistical Engineering approach.

4. Rank the sampled companies in the scale by applying the Evidential Reasoning approach.

5. Conduct in-depth interviews with managers, consultants and academics with the purpose of finding out soft and unstructured aspects about the level of adoption of analytical tools in Barcelona by applying the Laddering Methodology.

6. Based on results generated, provide practical guidelines to stakeholders who are interested in expanding the use of analytical tools in companies and creating competitive advantages.

The level of adoption of analytical

tools.

9

1. Introduction.










10

Systemic thinking

Management support

Removing obstacles

Human, technical and financial resources.

.

Encouraging staff involved in the project

Emergency

Hierarchy

Communication

Control

1

4 key-drivers for LAAT expansion.

2

Deming, (2000)

Deming, (2000)

Hahn et al (2000)

Yeo (1993)

Tort-Martorell et al (2011)

Hoerl & Snee (2010)

Checkland (1999)

11

DB. Competitive advantage

Communication outside the company.

Lower price / cost

Market niche

Differentiation

Privileged location.

Customer Relationship Managers (CRM)

Trust

Long term relationships

3

4Supply chain Managers (SCM)

4 key-drivers for LAAT expansion.

Langfield-Smith & Greenwood (1998)

Davenport, Harris & Morrison (2010)

Porter (1990)

Poon &Wagner (2001)

Blanchard (2010)12

13

Theoretical model and 4 key drivers.

LAAT

MANAGEMENT SUPPORT

COMMUNICATION OUTSIDE

SYSTEMIC THINKING

DB. COMPETITIVE ADVANTAJE

13

1. We proposed a 5 level scale

2. At level 1 we find companies that do not use any analytical tool.

3. At level 5 we find companies that use analytical tools as a strategic support for their competitive advantage.

4. At levels 2, 3 and 4 we find companies that are improving on using analytical tools

Five level scale

14

1. Introduction.










15

Analytical tools on finance.

S – Strength W – Weaknesses O – OpportunitiesT – Threats.

Score Cards

Financial benchmarking

Predictive analytics applied to Ratios analysis, Balance sheets and income statements .

Janis (2008) Xu DL (2012) Morris et al. (2002)

16

Analytical tools on manufacturing

Design of experiments (DOE)

Six Sigma.

Statistical Process Control. (SPC)

The seven management tools

Surface response

Hoerl et al (1993) Ishikawa (1988)

Futami (1986) Deming (2000)

17

Analytical tools on R&D

Clinical trials

Control groups

Survival analysis

Stochastic process

Multivariate analysis

Davenport, Harris & Morison (2010)

Liu et al (2008)18

Analytical tools on Human Resources

Multivariate regression

Assess intangible assets

MCDA methods

Correspondence analysis

Decisions trees

Harris, Craig & Egan (2009)

Lewis (2003)

Armstrong (2012)19

Analytical tools on Marketing

Time series

General linear models

MCDA methods


Survey research methods

Customer relationships management

Armstrong (2012)

Deming (2002)

Burby & Atchison (2007)

20

Analytical tools with suppliers

Decisions trees

MCDA methods


Supply chain management

Petroni & Braglia (2000)

Verma & Pullman (1998)

Nydick & Hill (1992)

21

1. Introduction.










22

Questionnaire design.

• A 7-step methodology was adapted to design and validate the scale.

• In the first part the issue is to provide valid and reliable items

• The second part is focused on the validity and reliability of the scales

23

Menor & Roth (2007)

Theoretical domain (1/7)

• Four theoretical constructs were investigated

• A total of 17 items were derived from the theoretical constructs

• Each item was associated to a five level Likert scale.

24

Bryman (2012)

Menor & Roth (2007)

Michie et al (2005)

Item generation (2/7)

The “degree of understanding” was calculated in order to ensure each item is understandable and easy to read. (Kappa index for multiple raters)

ITEM Judge1 Judge2 Judge3 Judge4 Judge5 Judge6 Judge7 Judge8DB-CA1 4 4 4 4 4 4 4 4DB-CA2 4 4 4 4 4 4 4 4DB-CA3 4 5 4 5 4 5 4 5DB-CA4 5 5 4 4 4 4 4 4DB-CA5 4 4 4 4 4 4 4 4MS-DA1 4 4 4 4 4 4 4 4MS-DA2 4 4 4 4 4 4 4 4MS-DA3 4 4 4 5 4 5 4 4MS-DA4 4 4 4 4 4 4 4 4MS-DA5 4 4 4 4 4 4 4 4MS-DA6 5 5 5 5 5 5 5 5

SYS1 5 5 5 5 5 5 5 5SYS2 5 5 5 5 5 5 5 5SYS3 4 5 5 5 5 5 4 5SYS4 5 5 5 5 5 5 5 5SYS5 5 5 5 5 5 5 5 5

COMOUT 5 5 5 5 5 5 5 5

A total of four constructs were operationalized in 17 items. All the questions were designed in a Likert scale from 1 to 5

Grade Kappa Standard Error z Prob>Z

4 0.77980 0.045835 170132 <.0001

5 0.77980 0.045835 170132 <.0001

Overall 0.77980 0.045835 170132 <.0001

Item refining (3/7)

25

Good level of understanding.

Fleiss (1971) Cohen (1960)

Questionnaire development (4/7)Section Number

of items Categorical questions 3 Data Based Competitive Advantage 5 Management Support Data Analysis 6 Systemic Thinking 5 Communication outside the company 1

Total 20

Structure of the first draft of questionnaire

1Two steps on the pilot test

It was shared in social networks

2 It was sent to 300 companies members of the UPC-Alumni

31 responses were obtained

Improving the order of questions

Reviewing features of the cover letter

Final writing of questions

26

The questionnaire (Fragment)

27

Survey data collection (5/7)

602.161

41.15286.094,00

474.915

Total Indústria Construcció Serveis

6,064 companies were invited by sending it electronically

255 responses were obtained.

Analytics diagnostic free of charge. Open to share results.

28 IDESCAT (2013)

Dataset (3/5)

29

Reliability (6/7)

Cronbach AlphaITEM Response

We apply analytical tools in all decisions we make strongly agree

completely agreeWe exploited and analyzed plenty of data during the last year

The use of statistics is useless to build competitive advantages in our company completely agree?

Alphas are helpful to identify these type of incoherence

Formulation and outputs

K= Items of the section

Si= standard deviation of the item

St= standard deviation of the section

Subsection AlphaData Based Competitive Advantage. (DB-CA)

0.8884

Management Support in Data Analysis. (MS-DA)

0.8025

Systemic Thinking (SYS) 0.7761Communication Outside the Company (COM-OUT)

1.0000

30Cronbach (1951) Streiner (2003)

Reliability (6/7)Interclass correlation coefficient

row /company-effect

column/ item-effect

Source of variation Sum of Sq D.F Mean of

SqF-

Value Pr(>F)

Between Companies (row-effect) 1734.138 153 11.334

Within Companies

(item-effect)

Within Companies 817.168 15 54.478 55.768 .000

Residuals 2241.894 2295 .977

Total 3059.063 2310 1.324

Formulation and outputs

Intra- class Correlation Coefficient (ICC)

Two-way Random Effect

ModelICC

95.00% C.I

Lower Upper

Average Measure

(Within effect) .887 .851 .915

Shrout & Fleiss (1979) Tian (2005)31

Item and scale refinement (7/7)

Explanatory factor analysis (EFA)

Que ti onnaire ITEM Factor1 Factor2 Factor3 Factor4

Unde rstandin g be ne ti fs DB_CA 1 0.757P roduct Im prove m e n t DB_CA 2 0.756Stati sti cs Sup port DB_CA 3 0.831Stati sti cs Im portance DB_CA 4 0.806Stati sti cs Encourage m e n t DB_CA 5 0.659Stati csti cs Train ing MS_DA 1 0.826N e w know le dge im ple m e ntati on MS_DA 2 0.723Data co l le cti on proce ss MS_DA 3 0.527Budge t for proje cts MS_DA 4 0.837Te chnological re source s MS_DA 5 0.622Com pe ti tor's Inve sti gati on MS_DA 6 0.561Eff orts re cogniti on SYS1 0.595Mission unde rstanding SYS2 0.693Com m un icati on ope nne ss SYS3 0.571Te am w ork culture SYS4 0.764Re inforce m e nt on data usage SYS5 0.534Com m un icati on supp lie rs/custom e rs CO M_O UT 0.852

DB-CA. Data-Based Competitive Advantage

MS-DA. Management Support on Data Analysis

SYS. Systemic Vision of the business

COM-OUT. Communication Outside company. (clients and suppliers)

In order to validate our questionnaire, the 17 items were clustered on the first 4 factors using the loadings as classification criteria

32 Krzanowski (2000) Long (1983) Kaiser (1958)

1. Introduction.










33

Statistical Engineering case of study

Don't focus on the introduction of new theories but rather how they might be best utilized for practical benefit

Strategic: Statistical Thinking.

Tactical: Statistical

Engineering.

Operational: Statistical Methods

and Tools.

Statistical Theory.

Statistical Practice.

How to utilize the principles and techniques of statistical science for benefit of humankind.

How to best utilize statistical concepts, methods, and tools and integrate them with information technology and other relevant sciences to generate improved results.

Systemic thinkingVariance reductionHolistic approach

Statistical MethodsSpecialized softwareSkilled staff

34Hoerl & Snee (2010) Anderson-Cook et al (2012) Hoerl & Snee (2012)

Statistical Engineering case of study

Data collection2

Confirmatory analysis3

Relationship between companies

4

Relationship between key drivers

5

Conclusions

Understanding project’s scope

1Flowchart

QuestionnaireSurveyDatasheet

Correspondence analysis (CA)

Factor Analysis

Logistic Regression (LR)Correlation Matrix (CM)

illustrates the relation between statistical thinking and statistical methods.

35Seven statistical tools were wisely integrated in a five step process to accomplish a unique objective.

Data collection (2/5)

6,064 companies were invited by sent it electronically

255 responses were obtained.

Confirmatory analysis (3/5)

Que ti onnaire ITEM Factor1 Factor2 Factor3 Factor4

Unde rstanding be ne ti fs DB_CA 1 0.757Product Im prove m e nt DB_CA 2 0.756Stati sti cs Support DB_CA 3 0.831Stati sti cs Im portance DB_CA 4 0.806Stati sti cs Encourage m e nt DB_CA 5 0.659Stati csti cs Train ing MS_DA 1 0.826N e w know le dge im ple m e ntati on MS_DA 2 0.723Data col le cti on proce ss MS_DA3 0.527Budge t for proje cts MS_DA 4 0.837Te chnological re source s MS_DA 5 0.622Com pe ti tor's Inve sti gati on MS_DA 6 0.561Eff orts re cogniti on SYS1 0.595Mission unde rstand ing SYS2 0.693Com m unicati on ope nne ss SYS3 0.571Te am w ork culture SYS4 0.764Re inforce m e nt on data usage SYS5 0.534Com m unicati on supplie rs/custom e rs COM_O UT 0.852

ITEMS

36

37

The 255 responses were discomposed and represented at the 2 biggest factors

Correspondence analysis (4/5)

39

Level 1 is close from Micro Size.

Level 4 is close from Middle Size

40

Services Companies are more suitable to be analytical oriented

Products Companies are more related with level 1 and Micro size

41

Middle size companies are closer to “better and different” strategies.

There is a group for Micro-size, Products, Level 1 and No Competitive Advantage

41

COMMUNICATION OUTSIDE

COMPANY

DB. COMPETITIVE ADVANTAGE

SYSTEMATIC THINKING

MANAGEMENT SUPPORT. DA

C.M allows us to understand and quantify relationships between the AVERAGES of the Key Drivers

0.702

0.648

0.300

Pearson Correlation Coefficients

DBCA MSDA SYS COMOUT

DBCA. Data Based Competitive Advantage 1.000 0.70243 0.69484 0.05246

MSDA. Management support data analysis 1.000 0.64852 -0.03397

SYS. Systematic Thinking 1.000 0.30036

COMOUT. Communication Outside Company 1.000

0.695 These correlations were calculates with the AVERAGES of ITEMS.

Correlation Matrix (4/5)

42

Hair, et al (2006)

Krzanowski (2000)

To predict if on a set of 255 Spanish companies, either a company has analytics aspirations or not. (Level=>4)

Level 4 is the starting point of the use of analytical tools as a distinctive competence in the industry

RESPONSE VARIABLE:

0Y

1Y

If the company does not has analytical aspirations. (Level<4)

If the company has analytical aspirations. (Level>=4)

NO ANALYTICAL ASPIRATIONS. (LEVEL

1 , 2AND 3)

ANALYTICAL ASPIRATIONS

(LEVEL 4 AND 5)TOTAL

186 69 25573% 27% 100%

PREDICTORS

G1 Understanding the benefits of Statistics

G2 Statistics builds the Comp. Adv

G3 There is one mission and vision

G4 Communication with clients and suppliersThe predictors were

taken from the questionnaire ITEMS

Logistic regression (5/5)

43 Philip and Teachman (1998)

)(432101 ijkllkji GGGGP

PLn

THE MODEL

have p-values less than 0.05, indicating that there is sufficient evidence that the coefficients are not zero using an alfa level of 95%

The goodness-of-fit tests, with p-value equal to 1.000. Indicate that there is insufficient evidence to claim that the model does not fit the data adequately.

1. UNDERSTANDING THE BENEFITS OF APPLIED STATISTICS BUSINESS.

2. BUILDING A COMPETITIVE ADVANTAGES BY DATA ANALYSIS.

3. ESTABLISHING A MISSION AND VISION STATEMENTS ON THE COMPANY

4. STIMULATING COMMUNICATION OUTSIDE COMPANY.

Coefficients for these variables are not cero.

Logistic Regression Table Odds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant -17.8045 3.13596 -5.68 0.000DB_CA1 1.65439 0.313537 5.28 0.000 5.23 2.83 9.67DB_CA3 0.723906 0.271505 2.67 0.008 2.06 1.21 3.51SYS2 1.12321 0.273354 4.11 0.000 3.07 1.80 5.25COM_OUT 1.54055 0.382019 4.03 0.000 4.67 2.21 9.87

Goodness-of-Fit TestsMethod Chi-Square DF PPearson 105.652 111 0.625Deviance 72.350 111 0.998Hosmer-Lemeshow 4.405 8 0.819

44

1. Introduction.










45

Evidential Reasoning case of studyA generic evidence-based multi-criteria decision analysis (MCDA) approach for dealing with problems having both quantitative and qualitative criteria under various uncertainties including ignorance and randomness.

Main documented applications:

• Environmental impact assessment,

• Organizational self-assessment

• Portfolio investments

• Prioritizing voices of customers.

The ER approach is implemented in a software called Intelligent Decision System.

The Belief Decision Matrix allows us more realistic assessments than traditional Decisions Matrix.

Accepts data of different formats with various types of uncertainties as inputs, such as single numerical values, probability distribution, and subjective judgments with belief degrees.

Main features in the ER approach

Xu & Yang (2001) Yang & Singh (1994)46

http://www.e-ids.co.uk/

Main ER assumptions

Main assumptions.

Exclusiveness of grades: A grade Hn is assumed to be mutually exclusive of another Hn+1

Completeness of criteria: Suppose an overall criterion is assessed through m sub-criteria. These m sub-criteria are said to be collectively exhaustive or complete.

Weight as degree of importance: The weight of a sub-criterion yi, denoted by wi, is a degree of importance of yi in the assessment of the overall criterion.

Distribution assessment.

Generation of the overall belief Linear algorithm Non -linear algorithm

Xu & Yang (2002) Yang (2001)

Yang & Singh (1994)

47

Model definition2

Relate father and bottom attributes.3

Assigning weights.4

Assigning belief degree.5

Calculate assessments.6

Data collection1

Implement the set of rules

Pair-wise comparison

Define uncertainty

Sensitivity test

Overall performanceCompare alternatives

Transform means-values to degree of belief

Conclusions

Evidential reasoning case of study

A six-step methodology was adapted for this case of study48

Apply ER algorithm to extract relevant conclusions about which attributes clearly contribute to the expansion of LAAT and therefore to reach competitive advantages.

Model definition (2/6)

Model summary GradesNumer of parent attributes: 4 u(H1):= u (Analytic ignorance) =0.00

u(H2):= u (Local applications) = 0.25Numer of bottom atributes: 17 u(H3):= u (Analytical aspirations) =0.50Selected method for relating parent and bottom attributes: RULE-BASED APPROACH (Yang 2001)

u(H4):= u (Analytics as a systems) = 0.75u(H5):= u(Analytics as competitive advantage) = 1.00

49

Relate father and bottom attributes. (3/6)

Relating attributes is defined as how the assigned grades are converted to the ones of their parents

If MSDA is worst =0.00 Then Overall Performance is Analytical Ignorance=100%

If MSDA is poor=0.25 Then Overall Performance is Local Focus=100%

If MSDA is average=0.50 Then Overall Performance is Analytical aspirations=100%

If MSDA is good=0.75 Then Overall Performance is Analytics as System=100%

If MSDA is excellent=1.00 Then Overall Performance is Analytics as Comp. Advantage=100%

In similar way, the attributes • SYS• COM-OUT• DBCA

were related to their parent attributes50

Yang (2001)

Assigning weights . (4/6)

0.220.18

0.21 0.210.17 0.14 0.15 0.16

0.12

0.16

0.28

0.160.20 0.18

0.23 0.22

COM_OUT

1.00

Data-Based competitive advantage

Management support on data analysis

Systemic Thinking Communication outside the company

MS-DA6 was the most important. It refers to whether the top management promotes the use of data to evaluate how the competitors are evolving

SYS4 refers to whether there is a teamwork culture in the company.

51

Assigning belief degree. (5/6)

The degree of belief represents the extent to which an answer is believed to be true.

The following expression was utilized for assigning belief degrees

For SYS4 µ= 3.80 The belief structure is

{(“Worst” with β=0.00), (“Poor” with β =0.00), (“Average” with β =0.20), (“Good” with β =0.80), (“Best” with β =0.00)}.

In this way the mean-value was transformed into 5 values, which describe the scenario more accurately

The transformation was applied to the 17 attributes of the model

52

Calculate assessments. (6/6)

The overall performance

Middle companies are slightly more analytical oriented than big

This result is coherent with CA of the slide 42.

In the CA, middle companies are closest to the Level 5.

Distance among Big and Middles is also small.

53


Distributed assessments

Bel

ief d

egre

e

Small CompanySmall Company

0,27%

24,22%

67,79%

7,39%0,33%

Bel

ief d

egre

e

Middle company Middle company

Evaluation grades

0,24%8,34%

76,41%

14,05%

0,96%

Bel

ief d

egre

eBig CompanyBig Company

0,00%

12,97%

69,36%

17,67%

0,00%

L1 L2 L3 L4 L5 L1 L2 L3 L4 L5

L1 L2 L3 L4 L5 L1 L2 L3 L4 L5 54

Sensitivity test


Micro Company Small CompanyMiddle company Big Company

0%10%20%30%40%50%60%70%80%90%

100%

Ave

rage

sco

re

Average scores for the Overall PerformanceAverage scores for the Overall Performance

Weight of Systematic Thinking

Giv

en w

eigh

t

Micro Company Small CompanyMiddle company Big Company

0%

20%

40%

60%

80%

100%

Ave

rage

sco

re

Average scores for the Overall PerformanceAverage scores for the Overall Performance

Weight of Communication Outside the company

Giv

en w

eigh

t

Sensitivity to changes in the weight of SYS

For lower weights on SYS sensitivity increasesFor higher weights, the sensitivity decreases

Sensitivity to changes in the weight of COM-OUT

For higher weights, the sensitivity increases

55

For lower weights on COM-OUT, the sensitivity decreases

1. Introduction.










56

57

How customers translate the attributes of products into meaningful associations with respect to self, following Means-End Theory

Understand how customers underlying personal motivations with respect to any given product or service

Higher level distinctions provides a perspective of how the attributes are processed from a motivational perspective

Investigate about the connections between the attributes and personal motivations.

Reynolds & Gutman 1988, Gutman 1982

The Laddering technique

57

How ladders are built?

ATTRIBUTES

CONSEQUENCES

VALUES

Reynolds & Gutman 1988; Gutman 1982


Full-bodied taste/less alcohol

Avoid getting drunk(wasted) / Socialize

Sense of belonging / Responsibility to family

Respondent Industry Length

1 Project Manager Consultancy Services 50 min

2 CEO Entertainment Services 55 min

3 Head of department Government 1:15 hr

4 Owner / Founder Marketing Research 56 min

5 CEO Plastic packages manufacturer 45 min

6 Professor / Researcher Academy 36 min


8 Business consultant Consultancy Services 50 min

9 Analytics consultant Consultancy Services 48 min


The persons who responded the interview.

The designed script was used in all interviews

The interviews took place on respondent ’s office

There are digital records for each one interview

The script’s structure follows the 5 key drivers


59


60

One example of ladder

ATTRIBUTES

CONSEQUENCES

VALUES

A Improve the knowledge of dataA Goal setting

Ladder taken from the interview with a CEO of Packaging Manufacturer

C Lower costC Continuous learning

60

V Serving the societyV Add value to stakeholders

Implication Matrix

Data is accessible and supports decisions (1)

Improve D. Analysis (21) 17 times

Improve D. Analysis (21) add value to stakeholders (29)

18 times

Hierarchy Value Map (HMV)

HVM is a way to graphically represent the most dominant connections. It is a representation of the linkages across levels of abstraction, starting with attributes and finishing with values

It should include ladders with 4 or more direct relations. (A total of 84 in this case)

The main purpose is to highlight meaningful connections between (A)-(C)-(V)

Obtained by the cumulative frequency of direct relations.

Reynolds & Gutman 1988; Gutman 198262

(1)Data is

accessible and supports

decisions

(2)Data

online

(5)High

skilled staff

(6)Enough support

(7)High tech

(4)standar

dized proced

ures

(12)the most efficient structure

(15)innovate products

and services

(8)Commun

ication with C&S

(10)informati

on outside

(11)Market

research

(9)Creativity to new

ideas

(14)Respond

more quickly

(13)Flexibility

(3)Goal

Setting

17 7 13115 6 3 58 7 46 563

(21)Improve

data analysis

(28)staff

efficiency and

motivation(16)

Analyze data from

market

(24)Knowledge

of data

(19)Exceeding customer

exp

(20) Good

image of the

company

10

8

(29)add value to stake holders

(30)Being a leader

(31)Communication and

trust

(33)Passion,

Quality and Excellence

(25)Long term

relationships7

8

11

1412

(22)improving process

(23)Improving results

4 7

(27)More

money (17)Continuous

learning

(18)Distinctive

competence

(26)Lower cost 4

65

14

11

4

4

6

1312

(34)serving

the society

(32)honesty

and credibility

6 7 9 6

14

10

18

8

Hierarchy Value Map (HMV)

63

Summary table

The 10 attributes which have the biggest number of relations.

They concentrate the 80% of the total relations .

This table allow us to identify the attributes which have the biggest impact on values.

64

1. Introduction.










65

LEVEL 1

A small interest on using analytical tools shows up.

LEVEL 2

Small and local success brings the attention of the Senior

Management.

Final point. Analytical initiatives did not reach

expectations from the senior management.

Having the leadership in the market though the use of

analytical tools.

“Prove-it” Strategy

LEVEL 3

LEVEL 4

LEVEL 5

The first attempt to broad an analytical project. Define an

analytical vision “Plan your work” .

Embedding the strategic and critical process with the Analytical

Vision. “Work your plan”

Analytical Ignorance

Analytical Focus

Analytical Aspirations

Analytical Engineering

Analytics as competitive advantage

Road Map for upgrading the scale

Keep working on:

Diagnostic Actions Diagnostic.

Until the highest level in the scale is reached.

66

Strategic: Statistical Thinking.

Tactical: Statistical

Engineering.

Operational: Methods and Tools.

Statistical Theory.

Statistical Practice.

Goal setting.

Creativity. Information outside.

Serving the society

LeadershipHonestyValues

Attributes

Impact of attributes and values on the LAAT

Companies in levels 1, 2 and 3 focus on improving attributes.

Companies in levels 4 and 5 focus on improving values.67

1. Introduction.










68

Scale aggregation

Overall assessment. Level of Adoption of Analytical Tools.

(LAAT)

Questionnaire

Data-Based competitive advantages

In-depth interviews

Operative attributes

Systematic thinking

Management support

Communication outside

Tactical features

Organizational values

17 attributes on a five-level scale 33 concepts on a three-level scale

How aggregate them while losing or

distorting information is prevented?

69

Scale aggregation

The research presented on:

Yang, J. B., Xu, D. L., Xie, X., & Maddulapalli, A. K. (2011). Multicriteria evidential reasoning decision modelling and analysis-prioritizing voices of customer.Journal of the Operational Research Society, 62(9), 1638-1654.

Will be adapted to investigate this aggregation and accomplish the following objectives:

• Investigate the scales from questionnaires and in-depth interviews in order to aggregate them into a unique framework. • Apply the evidential reasoning approach for calculating the overall performance of the level of adoption of analytical tools.• Offer relevant guidelines to organizations that are interesting in improving their analytical capabilities.

70

Publications

Conference papers

Submitted for publication

Barahona, I., & Riba, A. (2012). Applied Statistics on Business at Spain: A Case of Statistical Engineering. In ASA (Ed.). In 2012 Joint Statistical Meetings. Vol. Book of abstracts, pp. p 246). San Diego CA:(ASA).

Barahona Igor, & Alex, R. (2013). The level of adoption of analytical tools in Barcelona, Spain. In JIPI (Ed.). In Jornada d'Investigadors Predoctorals Interdisciplinària[February 7th of 2013]. Vol. Book of abstracts, pp. Page 7.). Barcelona, Spain:(Universitat de Barcelona).

Igor, B., & Alex, R. (2011a). Applied statistics as competitive advantage. In ENBIS (Ed.). In11th Annual ENBIS Conference. Vol. Book of abstracts, pp. P. 67). Coimbra, Portugal:(ENBIS).

Igor, B., & Alex, R. (2011b). La estadística aplicada a la gestión como una ventaja competitiva. In S. d. e. aplicada (Ed.). In I Jornades de Consultoria Estadística i Software. pp. p. 16-17). Barcelona, Spain:(Servei d'estadística aplicada).

Barahona, Igor., Riba Alex & Yang, Jian-Bo. “The level of adoption of analytical tools inSpain. An empirical study based on the evidential reasoning approach”. Decisions Support Systems. Ref. No: DECSUP-D-13-00247

Barahona, Igor & Riba Alex. “The level of use of statistical tools. A case of statisticalEngineering”. Quality Engineering. Ref. No: LQEN-2013-0088.

THE END OF PRESENTATION

phd thesis igor barahona july 26th of 2013

Education