key approaches to supplier selection

Key approaches to supplier selection

6 May 2013

Dennis Bours, [email protected]

BSM520 Strategic Purchasing (A), RGU

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Executive summary

Direct and indirect consequences resulting from poor decision making in supplier selection are becoming

more severe with the role of the purchasing function becoming more strategic within the organization.

Factors both internal and external to the purchasing function, like faster changing customer preferences,

shorter product life cycles, integrated supply chain models, total quality management, globalization,

increased outsourcing, e-procurement and the internet, environmental and sustainability concerns and

government trade regulations increase the number of decisions made and the importance of objective

supplier selection decisions.

Supplier selection decision models should be seen as instruments that guide and correct a persons

subjective preferences and uncertainties, rather than a rigid format replacing supplier management from

a relational perspective.

This report takes into account the diversity in purchasing situations and their complexity, classifies

supplier selection decisions into purchasing portfolio matrix groups and follows the supplier selection

phases as laid out below. The two main supplier selection decision making moments are the supplier

pre-qualification (Phase 3) and the final choice-phase supplier selection (Phase 4).

Preferred supplier pre-qualification (Phase 3) methods per purchasing situation are identified as follows:

1. New supplier pre-qualification task: k-means cluster analysis (CA)

2. Modified rebuy (leverage items): Case based reasoning (CBR)

3. Straight rebuy (routine items): Jointing-tree cluster analysis (CA)

4. Straight or modified rebuy of bottleneck items: N/A. Focus on supplier relationship management.

If needed, use k-means CA with distinguishing CA factors focusing on supplier reliability and

historical delivery performance data.

5. Straight or modified rebuy of strategic items: N/A. Focus on supplier relationship management. If

needed, use k-means CA with distinguishing factors focusing on price, total cost and product value.

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Preferred final choice-phase supplier selection (Phase 4) methods per purchasing situation are:

1. New supplier selection task: Compensatory weighing model

2. Modified rebuy (leverage items): Analytical network process (ANP), or Total cost ownership

approach if TCO is already being implemented for other supplier decisions support

3. Straight rebuy (routine items): Compensatory weighing model

4. Straight or modified rebuy of bottleneck and strategic items: Total cost ownership (TCO).

The choice for the supplier (pre-)selection methods identified above is further explained in Chapter 4.

The current models used in practice and informed choices made in this report towards preferred supplier

selection methods point in most cases to methods and techniques that either follow a simple

classification method, or have a cost-focus. Most of these methods have been in use for decades and it is

surprising that newer mathematical programming (MP) or artificial intelligence (AI)-based models have

not developed faster with the growth of calculating power and software modelling capacity of standard

computer systems over the past 10 to 15 years.

Based on the amount of research on these models, and their use in financial forecasting and decision

making in the financial sector, it is clear that hybrid models with fuzzy logic or AI-elements for final

choice-phase supplier selection have the future. In 5 to 10 years the discussion on supplier selection

methods will possibly have become one between software programmers and mathematicians instead of

buyers.

Another point which can be derived from all research papers reviewed for this report is that the

multitude of research, interest and innovation in the field of supplier selection modelling takes place in

Asia, with most work being doing in China and Taiwan. In the long run this might contribute to the

decline of global competitiveness of the US and Europe vis--vis Asian economies and companies.

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Table of Contents

Executive summary .................................................................................................................................. 2

Table of Contents..................................................................................................................................... 4

List of abbreviations................................................................................................................................. 6

List of figures ........................................................................................................................................... 7

List of tables ............................................................................................................................................ 7

1. Introduction ..................................................................................................................................... 8

1.1 Phases in the supplier selection process ......................................................................................... 8

1.2 Classification of purchasing situations ............................................................................................ 9

1.3 Purchasing portfolio matrix .......................................................................................................... 10

1.4 De Boers modified supplier selection framework......................................................................... 10

1.5 Report outline .............................................................................................................................. 12

2. Supplier pre-qualification approaches ............................................................................................ 13

2.1 Categorical methods .................................................................................................................... 13

2.2 Linear weighted average method ................................................................................................. 14

2.3 Data envelopment analysis........................................................................................................... 14

2.4 Cluster analysis ............................................................................................................................ 14

2.5 Case-based reasoning method ..................................................................................................... 15

3. Approaches in final choice-phase supplier selection ....................................................................... 16

3.1 Linear weighing models ................................................................................................................ 16

3.1.1 (Non-)compensatory weighing models .................................................................................. 16

3.1.2 Analytical hierarchy process .................................................................................................. 16

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3.1.3 Analytical network process .................................................................................................... 18

3.1.4 Fuzzy sets theory, and combined approaches ........................................................................ 18

3.2 Total cost approach ...................................................................................................................... 18

3.3 Total cost ownership .................................................................................................................... 19

3.4 Mathematical programming models............................................................................................. 19

3.4.1 Data envelopment analysis .................................................................................................... 19

3.4.2 Linear programming .............................................................................................................. 20

3.4.3 Multi-objective programming ................................................................................................ 20

3.4.4 AHP/ANP-MP combinations ................................................................................................... 20

3.4.5 Multi attribute utility theory .................................................................................................. 20

3.4.6 Simple multi-attribute rating technique ................................................................................. 20

3.5 Statistical models ......................................................................................................................... 21

3.6 Artificial Intelligence-based models .............................................................................................. 21

3.6.1 Genetic Algorithm-based models ........................................................................................... 21

3.6.2 Neural Networks ................................................................................................................... 21

3.6.3 Rough set theory ................................................................................................................... 21

3.6.4 Case-based reasoning ............................................................................................................ 22

4. Preferred supplier selection approaches ........................................................................................ 23

4.1 Pre-qualification methods ............................................................................................................ 23

4.2 Final choice-phase supplier selection methods ............................................................................. 25

5. Conclusions .................................................................................................................................... 28

References ............................................................................................................................................. 29

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List of abbreviations

AHP Analytical hierarchy process

AI Artificial intelligence

ANP Analytical network process

BPA Blanket purchasing agreement

CA Cluster analysis

CBR Case-based reasoning

CRP Capacity requirements planning

DEA Data envelopment analysis

EFHNN Evolutionary fuzzy hybrid neural networks

ERP Enterprise resource planning

FEAHP Fuzzy extended analytic hierarchy process

FNN Fuzzy neural network

FST Fuzzy sets theory

GA Genetic algorithm

GP Goal programming

JIT Just in time

LP Linear programming

MAUT Multi attribute utility theory

MCDM Multi-criteria decision making

MOP Multi-objective programming

MOLP Multi-objective linear programming

MP Mathematical programming

MRP Materials requirements planning

MRP II Manufacturing resource planning

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NN Neural networks

RST Rough set theory

SMART Simple multi-attribute rating technique

TCA Total cost approach

TCO Total cost ownership

TOPSIS Technique for order performance by similarity to ideal solution

TQM Total quality management

List of figures

Figure 1: Phases in the supplier selection process .................................................................................... 8

Figure 2: Purchasing portfolio matrix ..................................................................................................... 10

Figure 3: De Boers modified supplier selection framework .................................................................... 11

Figure 4: Overview of supplier pre-qualification approaches .................................................................. 13

Figure 5: Overview of final choice-phase supplier selection methods ..................................................... 17

Figure 6: Preferred pre-qualification methods vis--vis level of complexity ............................................ 23

Figure 7: Preferred final choice-phase supplier selection methods vis--vis level of complexity .............. 26

List of tables

Table 1: Classification of purchasing situations......................................................................................... 9

Table 2: Most used techniques in supplier selection literature ............................................................... 25

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1. Introduction

Over the past 40 years the purchasing function has developed from a separate function aimed merely at

the successful procurement of goods and services for the successful operation of an organization

towards a more strategic role as one of the organizations boundary-spanning functions. As purchasing

becomes a more strategic element within the organization, the direct and indirect consequences

resulting from poor decision making in supplier selection will also become more severe. Faster changing

customer preferences, shorter product life cycles, integrated supply chain models, total quality

management, globalization, increased outsourcing, e-procurement and the internet, environmental and

sustainability concerns and government trade regulations increase the number of decisions made and

the importance of objective supplier selection decisions. (Chen, Paulraj and Lado 2004; Long 2004;

Paulraj, Chen and Flynn 2006; Quayle 2006; Ting and Cho 2008; Aksoy and Oztrk 2011; Omurca 2013)

Towards the late 90s more research emerged on the role of supplier relationship management as part of

strategic procurement and supplier management (Cox 1996; Goffin, Szwejczewski and New 1997; Chen,

Paulraj and Lado 2004; Nollet and Beaulieu 2005; Ramsay 2005; Svahn and Westerlund 2009). One might

wonder whether hard and cold mathematical supplier selection decision models capture the intricacies

of supplier relationship management. Supplier selection decision models should be seen as instruments

that guide and correct a persons subjective preferences and uncertainties, rather than a rigid format

replacing supplier management from a relational perspective.

1.1 Phases in the supplier selection process

Supplier selection does not only relate to the final decision for a specific supplier but should recognize

various decision making steps as part of the entire decision making process, including the development

of requirements, decision making and evaluation criteria, pre-qualification of potential suppliers, final

choice-phase decision making and continuous performance and relationship management as part as

feedback loop towards the need for new suppliers.

Figure 1: Phases in the supplier selection process

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Finally, there should be a consideration for the diversity of purchasing situations. This could come down

to recognizing the differences between first time buys, straight rebuys, modified rebuys, routine items,

leverage items, strategic items and bottleneck items (Robinson, Faris and Wind 1967; Kraljic 1987;

Weber, Current and Benton 1991). The purchasing environment could also be part of the buyer situation,

where you could distinguish between the type of business or production strategy and related systems,

for example JIT, MRP, MRP II, CRP, ERP, etc. (Weber, Current and Benton 1991; Degraeve, Labro and

Roodhooft 2000; De Boer, Labro and Morlacchi 2001)

Without challenging the importance of distinguishing between specific purchasing environments, I argue

in line with De Boer, Labro and Morlacchi (2001) and Bhutta and Huq (2002) that the situation and

the choice for specific supplier selection decision making methods are best captured by:

1. the number of suppliers available,

2. the importance of the purchase and

3. the level of uncertainty related to the decision.

1.2 Classification of purchasing situations

The classification of purchasing situations by Robinson, Faris and Wind (1967), as presented in Table 1,

gives guidance towards the level of uncertainty and some direction towards the number of suppliers

available in supplier selection decisions given specific types of purchasing situations. (Van Weele 2010)

Table 1: Classification of purchasing situations

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1.3 Purchasing portfolio matrix

Kraljics (1983) purchasing portfolio matrix gives guidance towards the number of suppliers available,

and towards the perceived importance and complexity of a purchasing situation, identified by the factors

profit impact and supply risk. Purchases, and thus purchase and supplier selection decisions, can be

grouped into 4 categories, being: routine, bottleneck, leverage and strategic purchases, as presented in

Figure 2. (Kraljic 1983; De Boer, Labro and Morlacchi 2001; Van Weele 2010)

Figure 2: Purchasing portfolio matrix

1.4 De Boers modified supplier selection framework

Based on the criteria capturing the supplier selection process and the elements part of the classification

of purchasing situations and the purchasing portfolio matrix, a modified version of De Boers framework

(De Boer, Labro and Morlacchi 2001, p. 77) has been constructed in Figure 3 on the following page,

including the phases of the supplier selection process as identified earlier in Figure 1.

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Figure 3: De Boers modified supplier selection framework

A first distinction is between new supplier selection tasks and repeated supplier selections. It can be

noticed that new supplier selection tasks have not been rated according to the importance of items

purchased, given that the steps to be taken for the selection of suppliers should not depend on the

importance of the items in the product portfolio, but should follow the basic phases to be followed in the

supplier selection process.

The supplier selection process in rebuy situations is different and linked to the importance of the item.

For routine items there will be a wide variety of suppliers, but given the low value and low importance of

the item, to justify a frequent change and choice for a new supplier.

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Leverage items generally result in modified rebuys. With many suppliers available and a high savings

potential due to the high profit impact of the item, there might be a frequent change and selection of

suppliers. This does not necessarily need to follow all supplier selection phases every time; ie. phases 1

to 3 (Figure 1) could result in a vendor list, which would then be used for final supplier selections.

In the case of bottleneck and strategic items the supplier choice is often limited, or even non-existent,

and the focus is on supplier performance and relationship management. The high level of supply risk

either comes down to very unique specifications resulting in only a few suppliers being able to deliver, or

because of the scarcity of the resource needed also resulting in a very limited number of suppliers to

choose from. The distinction between a modified and a straight rebuy for bottleneck and strategic items

lays in the negotiation on changed requirements, which could result in a change and thus selection of a

new supplier if the current one would not be able to deliver in line with changed requirements.

1.5 Report outline

Chapter 2 focuses on supplier pre-qualification methods, phase 3 in the supplier selection process, while

Chapter 3 takes a closer look at the final choice-phase of supplier selection (Phase 4 in the supplier

selection process). Classifications used to categorize pre-qualification and final choice supplier selection

methods are based on the classification used by Mendoza (2007), but has been further extended with

specific methods not mentioned in his work.

Preferred supplier selection methods for both the pre-qualification as well as the final choice-phase are

discussed in Chapter 4. The report ends in Chapter 5 with conclusions towards the supplier qualification

methods out there and some observations made during the development of this report.

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2. Supplier pre-qualification approaches

Phase 3 of the supplier selection process (Figure 1), supplier pre-qualification, can range from a simple

initial evaluation of suppliers on the basis of factors such as experience, financial ability, managerial

ability, reputation and work history, up to a full supplier analysis, including on-site inspection of pre-

qualified suppliers to develop a list of potential key suppliers. I will use the definition and process

description of pre-qualification as used by De Boer, Labro and Morlacchi (2001):

Pre-qualification is defined as the process of reducing the set of all suppliers to a smaller

set of acceptable suppliers. This process may be carried out in more than one step. However,

the first step always consists of defining and determining the set of acceptable suppliers

while possible subsequent steps serve to reduce the number of suppliers to consider.

Basically therefore, pre-qualification is sorting process rather than a ranking process.

Pre-qualification is used to limit the number of suppliers for the final selection in order to make use of a

more comprehensive and in-depth analysis on a smaller supplier-base at that point. The following

supplier pre-qualification approaches will be discussed in the following paragraphs:

Figure 4: Overview of supplier pre-qualification approaches

2.1 Categorical methods

Categorical methods are qualitative in nature in the sense that they help decision makers pre-qualify

suppliers based on performance categories using historical data and buyers experience. Performance

categories can focus on price, customer focus, delivery requirements, communication, innovation,

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problem solving capacity, etc. on a scale (eg. positive, neutral and negative) and an overall rating

following the same scale. Categorical methods are easy to use, are inexpensive and have limited data

need, but highly rely on the evaluators judgment and all criteria are assumed equally important.

(Timmerman 1986; De Boer, Labro and Morlacchi 2001; Mendoza 2007; Ordoobadi and Wang 2011)

2.2 Linear weighted average method

Opposed to categorical methods, the linear weighted average method assigns weights of relative

importance to each criterion. Suppliers performance on each criterion is multiplied by the relative

criterion importance, resulting in weighted scores. The sum of all weighted scores results in a ranking.

Criteria dont have equal importance anymore, but the importance of each criterion is still based on the

evaluators judgment. (Timmerman 1986; Ordoobadi and Wang 2011).

2.3 Data envelopment analysis

Data Envelopment Analysis (DEA) is a data oriented approach to performance evaluation and in the case

of pre-qualification of suppliers it classifies suppliers between efficient and inefficient. Supplier

performance is analyzed by looking at the benefits (output) and costs (input), and a suppliers efficiency

is calculated as the ratio of the weighted sum of its outputs (being the suppliers performance) to the

weighted sum of its inputs (being the cost of making use of the supplier). (Liu, Ding and Lall 2000; De

Boer, Labro and Morlacchi 2001; Mendoza 2007; Cooper, Seiford and Zhu 2011; Chai, Liu and Ngai 2013)

2.4 Cluster analysis

Cluster Analysis (CA) is a statistical method that focuses on grouping suppliers into clusters of suppliers

with similar characteristics, often by means of a classification algorithm. The selection of suppliers within

a cluster can be based on either specific characteristics of suppliers or scores on performance criteria of

these suppliers, which would result in clusters of suppliers scoring the same level of performance on

specific evaluation criteria. As such, the method also identifies the most significant discriminators.

Jointing-tree clustering is a type of cluster analysis that establishes the most significant number of

clusters within a given dataset. The output of this type of clustering would be a tree diagram, called a

dendrogram, in which similarity is represented in the tree as distinct branches.

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k-means clustering is a CA method that starts with k random clusters and then moves suppliers

between the clusters to minimize differences within each cluster and maximize differences between

clusters. (Holt 1998; De Boer, Labro and Morlacchi 2001; Mendoza 2007)

2.5 Case-based reasoning method

The Case-based reasoning (CBR) method is part of the artificial intelligence (AI) approach and a software-

driven decision making support tool. It makes use of previous, similar decision situations and with

models on how people reason it uses that knowledge and information to find solutions towards decision

making. Most AI approaches make use of general knowledge surrounding a certain problem, whereas

CBR makes use of specific knowledge on past experiences and decision making, called the case-base.

Another difference is that it gains case and not general knowledge each time it solves a problem. Choy,

Fan and Lo are the first to implement CBR in a supplier selection and supplier relationship management

software environment (Ng and Skitmore 1995; De Boer, Labro and Morlacchi 2001; Choy, Fan and Lo

2003).

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3. Approaches in final choice-phase supplier selection

Phase 4 of the supplier selection process, focusing on the final supplier selection, can roughly be

categorized in two types of methods, being single-deal and multiple-deal models. Single-deal models

look at a product or a group of products and the selection of a supplier, while multiple-deal models take

into account the interlinkages between products or product groups. About 70% of all models are single-

deal, but these models can take into account a multitude of decision criteria.

Another method to categorize supplier selection models is based on the specific technique used by the

model or process in coming to a supplier selection decision. The paragraphs below discuss supplier

selection methods by the specific technique used, though in some cases the categorization is arbitrary

and methods could have been categorized differently. In such instances the choice for the categorization

is made on what inherently characterizes the model the most and how it originated as model or theory,

eg. what is of most importance; the fact that it is linear, or that it uses mathematical processes?

A graphical representation of all approaches discussed is presented in Figure 5 on the following page.

3.1 Linear weighing models

Linear weighing models, like the earlier discussed linear weighted average method in paragraph 2.2, are

based on weights (subjectively) given to selection criteria by the buyers or evaluators. The final value for

each supplier would be the sum of all the ratings given on criteria multiplied by the criterias weights,

and the supplier with the highest value should be selected.

3.1.1 (Non-)compensatory weighing models

Compensatory weighing models give the opportunity to compensate a low rate on one criterion with a

high rate on another criterion, whereas in non-compensatory weighing models the suppliers need to

reach specific minimum levels on each criterion in order to be included in the final supplier choice.

3.1.2 Analytical hierarchy process

The analytic hierarchy process (AHP) is a process (and often a mathematical programming process)

taking into account multiple decision criteria and capturing both quantitative as well as qualitative data

in a hierarchical structure of criteria, sub-criteria and alternatives. It determines scores and weights for

multiple criteria in a structured way, for them to be compared in decision making towards supplier

selection. Disadvantages of AHP are that it does not recognize correlation between criteria and assumes

them independent of one another, concealing the diverse nature of criteria and their relations. (Saaty

1990; Bhutta and Huq 2002; Mendoza 2007; Tahriri et. al. 2008; Ordoobadi and Wang 2011)

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Figure 5: Overview of final choice-phase supplier selection methods

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3.1.3 Analytical network process

The analytical network process (ANP) approach is a more sophisticated version of AHP, differing in its

ability to consider interrelationships between decision criteria. This makes it more accurate, but also

increases its complexity with increasing criteria. The ANP approach also gives more guidance in a

multiple-supplier selection situation. (Saaty 1999; Sarkis and Talluri 2002; Gencer and Gurpinar 2007; Lin,

et. al. 2010; Ordoobadi and Wang 2011)

3.1.4 Fuzzy sets theory, and combined approaches

Fuzzy sets theory (FST) can be used to model uncertainty and vague preferences in the supplier selection

process, and though linear in nature this method can also be seen as a mathematical programming (MP)

model (paragraph 3.4), or artificial intelligence (AI) model (paragraph 3.6) in the case of fuzzy neural

network (FNN) methods analyzing the data. (Li et. al. 2012; Xiao, Chen and Li 2012; Omurca 2013)

FST has been combined with AHP in a number of instances to improve the quality of AHP by means of

fuzzy sets aiming to resemble human reasoning and adding more extensive multi-criteria decision

making (MCDM) capabilities to AHP. The fuzzy extended analytic hierarchy process (FEAHP) by Chan and

Kumar (2007) is one example. (Tahriri et. al. 2008; Van der Rhee, Verma and Plaschka 2009)

Another example is fuzzy TOPSIS (technique for order performance by similarity to ideal situation),

simultaneously considering ideal situations and their best alternatives. Fuzzy TOPSIS also adds more

extensive MCDM to the analysis method and takes into account trade-offs between different criteria

considered. It has to be pointed out that though these techniques are strongly discussed, debated and

tested in the academic sphere, practical application of FST approaches is still in the development phase.

(Boran et. al. 2009; Deng and Chan 2011; Kara 2011; Ordoobadi and Wang 2011; Chai, Liu and Ngai 2013)

3.2 Total cost approach

In the total cost approach all important non-monetary evaluation criteria considered are being replaced

by a cost factor, which is either added (in case of negative performance on a criterion) or subtracted

from the quoted price. The suppliers are in the end rated by their lowest unit total cost. A disadvantage

of this approach is that valuing non-monetary criteria, such as lead time, quality performance or after-

sales service, can be hard and subjective to value. (Porter 1993; Bhutta and Huq 2002)

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3.3 Total cost ownership

Total cost ownership (TCO) is a methodology comparable to the total cost approach with the difference

that it puts value on actual costs and not on non-monetary criteria, which means organizations need to

have a good look at their cost and include costs like order placement costs, transportation costs,

receiving, inspecting, warehousing costs etc.

There are evaluation approaches similar to TCO, like life-cycle costing and cost-ratio methods, but it is

TCO that receives most support given it is not the most complex of all the costing-based approaches and

it can be used both for supplier selection and for ongoing supplier performance evaluation.

Disadvantages are that there are various models to choose from, TCO is situation-specific, it can be

expensive to implement and using TCO might require a cultural change in the organization from price

towards cost orientation. (Timmerman 1986; Degraeve, Labro and Roodhooft 2000; De Boer, Labro and

Morlacchi 2001; Mendoza 2007; Tahriri et. al. 2008)

3.4 Mathematical programming models

A difference between mathematical programming (MP) models and other types of rating models is that

MP-models are seen as being more objective given they force decision-makers to explicitly state the

mathematical objective. It is this mathematical objective that either needs to be maximized (for example

maximizing profits or cost-savings), or minimized (for example minimizing lead time, inventory levels,

transport cost, ec.) by comparing values of the variables in the objective function. On the other hand,

one has to wonder if it is realistic to reduce all real judgements of decision makers on specific objectives

to a certain numerical value. (Liu, Ding and Lall 2000; De Boer, Labro and Morlacchi 2001; Mendoza

2007; Cooper, Seiford and Zhu 2011; Tahriri et. al. 2011; Chai, Liu and Ngai 2013; Omurca 2013).

3.4.1 Data envelopment analysis

Data Envelopment Analysis (DEA) is a data oriented approach to performance evaluation. In the case of

pre-qualification it classifies suppliers between efficient and inefficient. In the case of final choice-

phase supplier selection DEA focuses on relative efficiency and is among the most used techniques for

supplier selection, but often seen as an element of a decision approach and not as an approach on its

own (Ramanathan 2007; Wu 2009; Chai, Liu and Ngai 2013). When used as an element of a hybrid

approach it is mostly used as a pre-qualification technique opposed to a final choice-phase method (Liu,

Ding and Lall 2000; De Boer, Labro and Morlacchi 2001; Mendoza 2007; Cooper, Seiford and Zhu 2011;

Kontis and Vrysagotis 2011; Ordoobadi and Wang 2011).

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3.4.2 Linear programming

The linear programming (LP) approach seeks to develop a mathematical model in which requirements to

base decisions on are represented in linear relationships. One important class of LP approaches are

called MOLPs (multi-objective linear programming), where MP and LP approaches are combined with

AHP or ANP, as discussed in sub-paragraph 3.4.4. (Chai, Liu and Ngai 2013)

3.4.3 Multi-objective programming

The multi-objective programming (MOP) approach is generally used in the JIT scenarios, given it focuses

on multiple and conflicting mathematical objectives, allows a varying number of suppliers into the

solution and provides suggested volume allocation by supplier. However, the process is complex and in

many cases impractical to implement (Weber and Ellram, 1993; Bhutta and Huq 2002; Tahriri et. al.

2008).

3.4.4 AHP/ANP-MP combinations

An extension of the MOP approach is called goal programming (GP), where conflicting objectives are

given a goal value that needs to be achieved (Chai, Liu and Ngai 2013). Given that GP as decision tool is

often combined with the AHP or ANP process tools, it is seen as part of AHP/ANP-MP group of

approaches. AHP/ANP-MP/LP combinations are often seen as MOLP (multi-objective linear

programming) approaches. The AHP/ANP part is used to identify a set of candidate suppliers, ie. a pre-

qualification, while the mathematical programming model optimizes the selection around a set of

optimization objectives (eg. delivery reliability) and a set of constraints (eg. purchasing budget). (De

Boer, Labro and Morlacchi 2001; Tahriri et. al. 2008; Ting and Cho 2008)

3.4.5 Multi attribute utility theory

Multiple attribute utility theory (MAUT) can handle multiple conflicting attributes being part of the

selection criteria at the same time, identifying relative importance of all attributes. It also enables the

purchasing manager to evaluate "what if" scenarios by giving the possibility to se how well alternative

supplier selection decisions would rate. (Bard 1992; Von and Weber 1993; Bhutta and Huq 2002)

3.4.6 Simple multi-attribute rating technique

The simple multi-attribute rating technique (SMART) is a simplified, more practically applicable form of

MAUT, which is at times perhaps more realistic in valuing trade-offs (Huang and Keska, 2007; Ho, Xu and

Dey 2010). Barla (2003) used SMART in a supplier selection procedure in a glass manufacturing company.

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3.5 Statistical models

Statistical models mainly deal with the stochastic uncertainty element related to supplier selection.

There are currently no statistical models able to take into account uncertainty of multiple criteria at the

same time, making the application of statistical models in supplier selection limited to situations with

only one uncertain criterion. (De Boer, Labro and Morlacchi 2001)

3.6 Artificial Intelligence-based models

The main AI techniques used in supplier selection models are genetic algorithm (GA), neural network

(NN), rough set theory (RST) and case-based reasoning (CBR). A general characteristic of AI models is that

these are self-learning by taking into account and analyzing results of previous decisions. (De Boer, Labro

and Morlacchi 2001; Choy, Fan and Lo 2003; Ordoobadi and Wang 2011; Chai, Liu and Ngai 2013)

3.6.1 Genetic Algorithm-based models

GA is a method for finding solutions for complex optimization problems, following steps and processes as

seen in biological process evolution. Given that GA is seen as a heuristic method, it does not necessarily

guarantee a final choice that will be the most optimal solution. (Chai, Liu and Ngai 2013).

3.6.2 Neural Networks

Neural network (NN) methodology has been applied to supplier selection in the construction industry

(Albino and Garavelli 1998; De Boer, Labro and Morlacchi 2001), but with neural networks needing to be

trained on the decision makers behaviour you have to wonder if the subjectivity of decision making is

now replaced by subjectivity in the training of your neural networks. Moreover, the NN methodology

also requires the system to be tested as being properly trained, which also holds an element of

subjectivity.

Latest research focuses on combining the NN methodology with DEA (Wu 2009), with AHP (Tang et. al.

2013) and with GA-based models, the latter being referred to as evolutionary fuzzy hybrid neural

networks (EFHNN) (Aksoy and Ozturk 2011; Cheng, Tsai and Sudjono 2011).

3.6.3 Rough set theory

Pawlakss (1982) rough set theory (RST) is a data analysis method to be used with imperfect knowledge

and datasets. The fundamental concept behind RST is based on the concept that anything in the universe

is defined by information and objects characterized by the same information are similar in the view of

the information that defines them. This relationship of similarity forms the basis of RST.

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RST has been used in decision support systems in financial and market analysis, the banking industry and

business intelligence, with its main advantage being that it does not require preliminary information

about data on for example probability. A disadvantage would be that although receiving quite the

interest from researchers, it has not yet been tested as a practical tool in supplier selection decisions.

(Pawlak 2002; Chai, Liu and Ngai 2013; Omurca 2013)

3.6.4 Case-based reasoning

The Case-based reasoning (CBR) method was already mentioned as supplier pre-qualification approach.

Choy, Fan and Lo (2003) are the first to implement CBR in a supplier selection and supplier relationship

management software environment. Zhao and Yo (2011) improved supplier selection procedures by

using CBR in a case study of Chinese petroleum enterprises. (Ng and Skitmore 1995; De Boer, Labro and

Morlacchi 2001; Chai, Liu and Ngai 2013)

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4. Preferred supplier selection approaches

The comparison of supplier selection approaches will focus on Phase 3 and Phase 4 of the supplier

selection process as presented in Figure 1 and will select preferred approaches for each phase based on

their suitability, taking into account the various levels of complexity of the supplier selection decision

making process, as adopted in the De Boers modified supplier selection framework in Figure 3.

4.1 Pre-qualification methods

The choice for specific pre-qualification methods depends on the level of complexity of the supplier pre-

qualification decision making process, which depends on the purchasing situation (Table 1), type of

product as shown the product portfolio matrix (Figure 2), the size of the initial set of suppliers and the

availability of sufficient historical data to support pre-qualification decisions. An overview of preferred

methods vis--vis level of decision complexity is presented below and explained on the following page.

Figure 6: Preferred pre-qualification methods vis--vis level of complexity

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1. New supplier pre-qualification task: k-means cluster analysis. New supplier pre-qualification tasks

are at the highest level of supplier selection decision making complexity, given there are no historical

records on this specific set of suppliers, nor is it unlikely a similar decision has been made in the past. The

preferred pre-qualification methods in this case is k-means cluster analysis, because depending on the

number of suppliers you can change the number of factors (clusters) that distinguish suppliers. CA does

not need historical data, though it can make use of historical data if available. It also sorts rather than

ranks the suppliers, which is preferred in this situation.

2. Modified rebuy (leverage items): Case based reasoning. A modified rebuy of leverage items is

characterized by a large set of suppliers, a high savings potential, and historical data on these suppliers is

available, perhaps because all are part of an extensive vendor list. Case based reasoning is preferred,

because of the profit impact and thus savings potential for this type of item. CBR also takes into account

historical data and combines sorting and ranking.

3. Straight rebuy (routine items): Jointing-tree cluster analysis. This type of decision does not justify the

use of CBR, given the low savings potential and low risk. Jointing-tree CA is the preferred pre-

qualification method, because it identifies the number of factors (clusters) that distinguish suppliers

from one another. K-means CA is not preferred, because of the large set of suppliers makes it harder to

identify the number of distinguishing factors one wants to take into account with the decision.

4. Straight or modified rebuy of bottleneck items: N/A. It is questionable whether pre-selection is

needed with a very small set of suppliers to choose from and it being more about supplier relationship

management than supplier selection with bottleneck items. If a pre-qualification selection would need to

be made, k-means cluster analysis would be preferred given the very small set of suppliers and possibly

limited data on previous supplier selection decision. Distinguishing CA factors should focus on supplier

reliability and historical delivery performance data.

5. Straight or modified rebuy of strategic items: N/A. What applies to the straight or modified rebuy of

bottleneck items also applies to strategic items. The focus should be on relationship management, but if

a pre-qualification selection would need to be made, k-means CA is preferred with CA factors that should

focus on price, total cost and product value.

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4.2 Final choice-phase supplier selection methods

The choice for final choice-phase methods also depends on the level of complexity of the supplier pre-

qualification decision making process as explained in paragraph 4.1 and dependent on the purchasing

situation (Table 1), type of product (Figure 2), the size of the initial set of suppliers and the availability of

sufficient historical data to support final choice-phase supplier selection decisions.

Given the wide variety of methods available the focus will be on methods that have developed beyond

academic modelling and conceptual discussions and where practical applicability has been proven. Based

on a literature review Chai, Liu and Ngai (2013) come to the following techniques for supplier selection

being used the most in literature cases:

Table 2: Most used techniques in supplier selection literature

Two other approaches, being Compensatory weighing model and Total cost ownership (TCO), will be

taken into account as well, because they were not part of Chai, Liu and Ngais (2013) review and used

quite often in practice. An overview of preferred methods vis--vis level of decision complexity is

presented on the next page in Figure 7 and further explained below.

1. New supplier selection task: Compensatory weighing model. There are no historical records on the

specific set of suppliers and it is unlikely a similar decision has been made in the past. The preferred

supplier selection method is the use of compensatory weighing models, given its easy to use and

focuses on ranking. The subjectivity in ranking by the decision maker will be lower than in rebuy

situations, given the lack of historical data on the suppliers and decision criteria.

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Figure 7: Preferred final choice-phase supplier selection methods vis--vis level of complexity

2. Modified rebuy (leverage items): Analytical network process (ANP). Given the high savings potential,

initial problem definition being more, fewer or other suppliers (Figure 3), multiple-supplier selection

might be giving the highest cost savings potential. ANP has been chosen as preferred method, based on

the guidance it gives towards multiple-supplier selection decisions.

If TCO is implemented by the organization for other decisions support, this would be a good second

choice as supplier election method. TCO focuses on cost and supplier selection decisions concerning

leverage items are cost-focused. Also, TCO

3. Straight rebuy (routine items): Compensatory weighing model. Compensatory weighing is the

preferred selection method for straight rebuys of routine items, given its simplicity as a method and

because we dont want to spend too much time on routine items. The ranking subjectivity element can

be circumvented by making use of archived historical supplier performance data.

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4. Straight or modified rebuy of bottleneck and strategic items: Total cost ownership (TCO).

Implementing the TCO method can take some effort, given it will possibly result in changing the way the

organization manages costs and accounts. It is nevertheless worth it, because the technique can also be

used for decisions in other purchasing situations, and as supplier performance evaluation and related

relationship management approach.

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5. Conclusions

The current models used in practice and informed choices made towards preferred supplier selection

methods point in most cases to methods and techniques that either follow a simple classification

method, or have a cost-focus. Most of these methods have been in use for decades and it is surprising

that newer mathematical programming or artificial intelligence-based models have not developed faster

with the growth of calculating power of standard computer systems over the past 10 to 15 years.

Though based on the amount of research on these models, and their use in financial forecasting and

decision making, it is clear that hybrid models with fuzzy logic or AI-elements for final choice-phase

supplier selection have the future. In 5 to 10 years the discussion on supplier selection methods will

possibly have become one between software programmers and mathematicians instead of buyers.

Another point which can be derived from all research papers reviewed for this report is that the

multitude of research, interest and innovation in the field of supplier selection modelling takes place in

Asia, with most work being doing in China and Taiwan. In the long run this might contribute to the

decline of global competitiveness of the US and Europe vis--vis Asian economies and companies.

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Executive summaryTable of ContentsList of abbreviationsList of figuresList of tables1. Introduction1.1 Phases in the supplier selection process1.2 Classification of purchasing situations1.3 Purchasing portfolio matrix1.4 De Boers modified supplier selection framework1.5 Report outline

2. Supplier pre-qualification approaches2.1 Categorical methods2.2 Linear weighted average method2.3 Data envelopment analysis2.4 Cluster analysis2.5 Case-based reasoning method

3. Approaches in final choice-phase supplier selection3.1 Linear weighing models3.1.1 (Non-)compensatory weighing models3.1.2 Analytical hierarchy process3.1.3 Analytical network process3.1.4 Fuzzy sets theory, and combined approaches

3.2 Total cost approach3.3 Total cost ownership3.4 Mathematical programming models3.4.1 Data envelopment analysis3.4.2 Linear programming3.4.3 Multi-objective programming3.4.4 AHP/ANP-MP combinations3.4.5 Multi attribute utility theory3.4.6 Simple multi-attribute rating technique

3.5 Statistical models3.6 Artificial Intelligence-based models3.6.1 Genetic Algorithm-based models3.6.2 Neural Networks3.6.3 Rough set theory3.6.4 Case-based reasoning

4. Preferred supplier selection approaches4.1 Pre-qualification methods4.2 Final choice-phase supplier selection methods

5. ConclusionsReferences

key approaches to supplier selection

Documents

supplier management

supplier selection phases

supplier reliability

supplier preselection

new supplier selection

supplier decisions support

modified rebuy leverage

straight rebuy routine