Theory and Methodology
An evaluation of vendor selection models from a total cost of ownership perspective

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Abstract

Many different vendor selection models have been published in the purchasing literature. However there has been no systematic approach to compare the relative efficiency of the systems. In this paper we propose to use the concept of Total Cost of Ownership as a basis for comparing vendor selection models. We illustrate the comparison with a real life data set of the purchasing problem of ball bearings at Cockerill Sambre, a Belgian multinational company in the steel industry. From a Total Cost of Ownership perspective mathematical programming models outperform rating models and multiple item models generate better results than single item models for this specific case study.

Introduction

In the literature Dickson, 1966, Weber et al., 1991 several dimensions are mentioned that are important for the multiple objective vendor selection decision. These include net price, quality, delivery, performance history, capacity, communication system, service, geographical location, etc. The problem is how to select suppliers that perform optimally on the desired dimensions. The published vendor selection decision models formulate answers to this multiple objective problem. Some authors propose linear weighting models in which suppliers are rated on several criteria and in which these ratings are combined into a single score. Others propose mathematical programming formulations in which quantifiable criteria are taken into account. Some approach the problem on an item-by-item basis, others consider it a multiple item decision. Only a few authors incorporate the multi-period inventory management issue into the supplier selection decision Bender et al., 1985, Degraeve and Roodhooft, 1999b, Ronen and Trietsch, 1988.

No research has been done on how to compare these different approaches to vendor selection and to find out the “best” way to handle the decision. The problem is to find a basis for comparison that is theoretically sound. In this paper we propose the concept of Total Cost of Ownership (TCO) to compare the relative efficiency of different vendor selection decision models. The Total Cost of Ownership quantifies all costs associated with the purchasing process throughout the entire value chain of the firm. We apply each of the vendor selection models proposed in the literature to a real life data set describing the purchasing problem of ball bearings at Cockerill Sambre. Subsequently, we calculate the TCO of the resulting solutions, i.e. choices of what to buy from whom and when. In this way, we are able to evaluate and compare the existing vendor selection models from a TCO perspective.

The contribution of this paper is fourfold. First, we present a literature review and classification of published vendor selection models. Second, these models are evaluated from a TCO perspective. Third, the application and evaluation of the different vendor selection models is done using a real life data set describing the purchasing problem of ball bearings at Cockerill Sambre. The data consist of information on various criteria other than the traditional quality, time and quantity discount parameters. Fourth, several conclusions will be drawn regarding the efficiency of the different kinds of vendor selection models.

The rest of the paper is organised as follows. In Section 2, we explain the TCO perspective from which the evaluation will be made and all parameters associated with it. We discuss the classification of the vendor selection models in Section 3. In Section 4 we describe the real life case used to evaluate the published vendor selection models. In Section 5 the results of the comparison will be discussed. Finally, we will draw conclusions and make suggestions for future research in Section 6.

Section snippets

The total cost of ownership approach

The TCO quantifies all costs associated with the purchasing process throughout the entire value chain of the firm. The cost of the acquisition and subsequent use of an item or service that is to be purchased is determined. The approach goes beyond price to consider all costs over the items’ entire life such as those related to service, quality, delivery, administration, communication, failure, maintenance, etc. Ellram, 1994, Ellram, 1995b, Ellram, 1995a. The analysis of costs throughout the

Classification of vendor selection decision models

As reported in Table 1, a distinction can be made between single item (Timmerman, 1986, Gregory, 1986, Nydick and Hill, 1992, Barbarosoglu and Yazgaç, 1997, Willis et al., 1993, Li et al., 1997, Soukoup, 1987, Thompson, 1990, Monczka and Trecha, 1988, Smytka and Clemens, 1993, Chaudhry et al., 1993, Weber and Current, 1993, Pan, 1989) and multiple item models Grando and Sianesi, 1996, Turner, 1988, Current and Weber, 1994, Akinc, 1993, Sadrian and Yoon, 1994, Rosenthal et al., 1995, Benton, 1991

The problem of purchasing ball bearings at Cockerill Sambre

We study the procurement of ball bearings at Cockerill Sambre S.A., a Belgian multinational company in the steel industry with external purchases approaching £0.6 billion annually accounting for more than 70% of total costs. Management wants to improve the efficiency of the purchasing process and to reconsider the sourcing strategies for different product groups. Our case study refers to the ball bearings, a product selected for study by the purchasing managers of the firm and a business of

Results of comparison from a total cost of ownership perspective

In Table 2, we summarise the resulting TCO for the solutions of the different vendor selection models. The second and third column, respectively, state the inventory management and order splitting assumption, if applicable. The fourth column gives the TCO in percentages of the results of the TCO minimising Degraeve and Roodhooft (1999a) model. The fifth, sixth and seventh column give the three components of the TCO, namely supplier level cost, order level cost and unit level cost in

Conclusions and suggestions for future research

Given the TCO perspective, several conclusions can be drawn from Table 2. First, multiple item mathematical programming models are always performing better than single item rating models. Single item models fail to take into account the interdependencies that could exist among the different products. A supplier can be offering a larger discount based on total sales volume, irrespective of the product mix. Order level costs could be minimised by combining orders for several products into one

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