Jafar Rezaei

Multi-criteria supplier segmentation using a fuzzy preference relations based AHP

One of the strategic activities of a firm is supplier segmentation, whereby a firm creates groups of suppliers to handle them differently. Existing literature provides several typologies of suppliers, each of which uses different dimensions/variables. In this paper, different typologies are combined by distinguishing two overarching dimensions, the capabilities and the willingness of suppliers to cooperate with a particular firm. These dimensions cover almost all the existing supplier segmentation criteria mentioned in existing literature. For each particular situation, these dimensions can be specified using a multi-criteria decision-making method. A methodology is proposed that includes a fuzzy Analytic Hierarchy Process (AHP) which uses fuzzy preference relations to incorporate the ambiguities and uncertainties that usually exist in human judgment. The proposed methodology is used to segment the suppliers of a broiler company. The result is a segmentation of suppliers based on two aggregated dimensions. Finally some strategies to handle different segments are discussed and concluding remarks and suggestions for future research are provided.

Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP

Abstract Since a company can only perform as well as it is allowed to by its suppliers, the importance of supplier selection in supply chain management has been increasingly recognized. Supplier selection can best be described as a highly complex process, due to the involvement of many, sometimes conflicting, qualitative and quantitative criteria. The objective is to select the most suitable supplier(s) that meet a company’s specific needs. This paper investigates supplier selection in the airline retail industry. We discuss a number of issues that make airline retail complex and distinguish it from conventional retail. The supplier selection problem is solved by means of a two-phased methodology. In the first phase, a conjunctive screening method is used, which aims to reduce the initial set of potential suppliers prior to the comprehensive final choice phase. In the second phase, a fuzzy analytic hierarchy process (AHP) is used, in which suppliers are evaluated against the main criteria and sub-criteria. By combining the decision-maker’s preferences, using the developed methodology will eventually result in a ranking of suppliers that makes it possible to select the most suitable supplier(s). The proposed methodology is applied to one of the largest airlines in Europe, the Royal Dutch Airlines (KLM), and the results are discussed extensively in this paper. We conclude by proposing avenues for future research regarding the general applicability and further extensions.

A multi-variable approach to supplier segmentation

The aim of this paper is to develop a new approach to supplier segmentation that considers the various variables used in existing literature to segment suppliers. A literature review reveals a serious problem from a management perspective. The problem is that many different supplier segmentation methods have been proposed in the last three decades, each of which uses different segmentation variables and hence results in different segments. An overarching supplier segmentation method, considering various segmentation variables, is lacking. Based on an extensive literature review, we have analysed the current methods and we conclude that the literature on supplier segmentation can be divided into three different schools of thoughts. By clearly identifying the deficiencies of the existing theory on supplier segmentation, we developed a new approach. As the basis for this new approach, we developed three requirements to make an overarching approach to supplier segmentation. Firstly, supplier segmentation should be based on their long-term potential, which we propose to assess in terms of supplier capabilities and willingness to cooperate. Secondly, other functional areas beyond purchasing have to be considered when segmenting suppliers. Thirdly, supplier segmentation should be viewed as a step in a longitudinal process that includes selecting suppliers, segmenting them, managing the relationship with them and actively developing their role over time. We illustrate the proposed approach by segmenting the suppliers of a company in the food industry.

A rule-based multi-criteria approach to inventory classification

The traditional inventory classification method classifies stock keeping units (SKUs) to three classes based on their annual dollar usage, while in real world problems, other criteria are important as well. In this paper, considering multi-criteria situations, a simple, effective and practical rule-based method is designed and implemented in a real world case, using MATLAB software. The most important characteristic of the proposed method is taking into account the inherent ambiguities that exist in the reasoning process of the system of classification. The methodology and the method proposed here may be easily implemented by inventory managers. The results obtained from the case study in this paper are compared with the analytic hierarchy process (AHP) method. Finally concluding remarks and suggestions for future work are provided.

A joint pricing, lot-sizing, and supplier selection model

In this paper, we integrate the three strategies that are important to most firms, namely pricing, lot-sizing and supplier selection. Combining the three objectives of total profit, inconsistency, and deficiency with a set of constraints, we formulate this integrated problem as a multi-objective nonlinear programming model, proposing a genetic algorithm (NSGA-II) that provides decision-makers with a number of Pareto-optimal solutions, one of which can be selected on the basis of the higher-level information. We analyse the trade-off between the different Pareto-optimal solutions and discuss the results of that analysis. We then evaluate the performance of NSGA-II compared with SPEA2 in solving the model, which shows NSGA-II performs better. Finally, concluding remarks and suggestions for future research are provided.

An improved fuzzy preference programming to evaluate entrepreneurship orientation

This paper describes an approach to measuring the entrepreneurship orientation (EO) of firms. EO is a widely accepted way to measure the degree in which a firm is entrepreneurial. The scale has three dimensions - innovativeness, risk-taking and proactiveness - each of which is assessed using multiple items. Measuring EO is important for entrepreneurial firms and for organizations like venture capitalists, business angels, investment banks and governments investing in these firms. Both the traditional statistical and the simple approach of assessing the overall level of EO (adding item scores) have their disadvantages. The aim of this article is to discuss these disadvantages and describe how some of them can be removed by using fuzzy analytic hierarchy process (AHP), which is a multi-criteria decision-making (MCDM) method that is particularly suited to tackle multi-dimensional, fuzzy, and perception-based constructs such as EO. We first improve a fuzzy AHP and then apply it using the pairwise comparisons of three experts to evaluate the EO of 59 small to medium-sized enterprises (SMEs) and rank the firms based on their EO score. The results indicate that proactiveness is by far the most important dimension, followed by innovativeness. Furthermore, there are considerable differences when it comes to the weights of the items.

Measuring efficiency of university-industry Ph.D. projects using best worst method

A collaborative Ph.D. project, carried out by a doctoral candidate, is a type of collaboration between university and industry. Due to the importance of such projects, researchers have considered different ways to evaluate the success, with a focus on the outputs of these projects. However, what has been neglected is the other side of the coin—the inputs. The main aim of this study is to incorporate both the inputs and outputs of these projects into a more meaningful measure called efficiency. A ratio of the weighted sum of outputs over the weighted sum of inputs identifies the efficiency of a Ph.D. project. The weights of the inputs and outputs can be identified using a multi-criteria decision-making (MCDM) method. Data on inputs and outputs are collected from 51 Ph.D. candidates who graduated from Eindhoven University of Technology. The weights are identified using a new MCDM method called Best Worst Method (BWM). Because there may be differences in the opinion of Ph.D. candidates and supervisors on weighing the inputs and outputs, data for BWM are collected from both groups. It is interesting to see that there are differences in the level of efficiency from the two perspectives, because of the weight differences. Moreover, a comparison between the efficiency scores of these projects and their success scores reveals differences that may have significant implications. A sensitivity analysis divulges the most contributing inputs and outputs.

Economic order quantity model with backorder for imperfect quality items

In the classical inventory models, the issue of quality is ignored. However, in a real production environment, it can be observed that there are defective items being produced. These defective items must be rejected, repaired, reworked, or, if they have reached the customer, refunded, and hence, extra costs are incurred. Recently some of researchers explicitly elaborate on the significant relationship between quality imperfection and lot size. In the recent models although quality entered into the models but none of them have considered shortage problem. This paper extends the traditional EPQ/EOQ model by accounting for imperfect quality items when using the backorder EPQ/EOQ formulae. Finally, numerical example is provided to illustrate the solution procedure and concluding remarks are given.

Supporting Decision Making in Technology Standards Battles Based on a Fuzzy Analytic Hierarchy Process

In many markets, battles are fought over technology standards. Often, these battles result in a single standard that achieves dominance. Decision making in standards battles is complex due to the lack of insights about the factors that influence the outcome of such battles. These include the characteristics of the standard, the stakeholders, the standard supporters, and the standard support strategies. The importance of these factors determines the dominance of a technology standard. This study investigates the usability of a multiattribute utility approach named fuzzy analytic hierarchy process in decision making in technology standards battles. Three technology standards battles are analyzed using a fuzzy analytic hierarchy process approach. The empirical results show that the outcome of these standards battles is not fully characterized by path dependency, but that factors for standard dominance can be used to explain the outcome of these battles. We show that it is possible to model the process of standard selection. The fuzzy analytic hierarchy process decision support tool is useful to determine the relative weight of factors for standard dominance, and can be successfully used in decision-making problems relating to standardization.

Measuring entrepreneurship: Expert-based vs. data-based methodologies

The concept of entrepreneurial orientation (EO) has become essential in research into the degree of entrepreneurial behavior at firm level. It is relevant to managers to be able to assess explicitly the level of entrepreneurship of a firm. Incubators, venture capitalists, corporate venturing units, angel investors, investment banks and governments need solid measures that go beyond expert intuition to assess the entrepreneurial nature of firms before they invest in them. Researchers have examined EO and consider innovativeness, risk taking, and proactiveness are important dimensions of this concept. Although the concept is seen as a multidimensional construct, there has been a great deal of debate among scholars on how to analyse it. The traditional statistical methodology has a number of drawbacks. In this article, we extend the debate and assess the construct of EO using four different methodologies: the traditional statistical methodology, a fuzzy-logic methodology, a DEA-like methodology and a naive methodology. As an expert-based methodology, fuzzy logic compensates some of the limitations of the statistical methodology. Drawing on a sample of 59 start-ups in a self-administered questionnaire, we measure innovativeness, risk taking and proactiveness and subsequently compare the resulting EO scores using the four methodologies. We found several differences, the most prominent of which are discussed in greater detail. The EO score from a naive methodology yields a value that lies between the other results, while the entrepreneurial score from a fuzzy logic methodology is most different from the other results.