Combining Bayesian Networks and Total Cost of Ownership method for supplier selection analysis

https://doi.org/10.1016/j.cie.2011.06.021Get rights and content

Abstract

In this study, we analyze the supplier selection process by combining Bayesian Networks (BN) and Total Cost of Ownership (TCO) methods. The proposed approach aims to efficiently incorporate and exploit the buyer’s domain-specific information when the buyer has both limited and uncertain information regarding the supplier. This study examines uncertainty from a total cost perspective, with regards to causes of supplier performance and capability on buyer’s organization. The proposed approach is assessed and tested in automotive industry for tier-1 supplier for selecting its own suppliers. To efficiently facilitate expert opinions, we form factors to represent and explain various supplier selection criteria and to reduce complexity. The case study in automotive industry shows several advantages of the proposed method. A BN approach facilitates a more insightful evaluation and selection of alternatives given its semantics for decision making. The buyer can also make an accurate cost estimation that are specifically linked with suppliers’ performance. Both buyer and supplier have clear vision to reduce costs and to improve the relations.

Highlights

► We analyze the supplier selection process by combining BN and TCO methods. ► Uncertainties due to causes of supplier performance on buyer are examined. ► The model allows a system of reasoning under the absence of complete information. ► Relations and intensities among supplier selection criteria and costs are identified. ► Flexibility, delivery, and price are among the most critical factors.

Introduction

Effective operations for companies are vital for success in the marketplace. This can only be achieved by integrating suppliers who provide high quality products, flexible operations, and systems; who maintain close relations, and who contribute to the product design operations (Stevenson, 2009). Therefore, selecting the right suppliers has become one of the most important purchasing functions in supply chain management (Boer et al., 2001, Chen et al., 2006). With the increasingly important role of suppliers in supply chain management, the selection process strategy has changed; other than scanning a series of pricelists, a wide range of qualitative, quantitative and environmental criteria has now been folded into the process (Ho et al., 2010, Humphreys et al., 2003).

Researchers have proposed a number of methods to measure the suppliers’ performance and select them according to the determined criteria (Degraeve and Roodhooft, 1999, Roodhooft and Konings, 1997). Although each approach has advantages in terms of selecting and evaluating the supplier, ultimately they also have some limitations. First, none explicitly consider the uncertain nature of the problem context. Uncertainty in supplier selection primarily arises in two different ways: uncertainty of supplier performance on a specific criterion such as uncertainty in delivery reliability of the supplier, and uncertainty of the resulting poor performance effects of a supplier on the purchasing company, such as uncertainty in costs at the buyer due to the delivery performance of the supplier. Second, the buyer sometimes needs to make a decision about a supplier with only limited experience or information regarding the supplier. However the buyer might have some domain-specific knowledge that makes a difference and needs to be accounted for in the selection process. Current selection models do not explicitly account for this type of variation in the process. Furthermore, supplier selection criteria have specific causal relations and consequences with relationship to the buyer, and many models have shortcomings in terms of formalizing these relations. For example, if the supplier shows poor performance on a delivery capability, this drawback can easily increase multiple cost items: downtime costs, operation costs, logistics costs, etc. Modeling and exploring the interdependencies among variables, the supplier and buyer recognize accurate effects of supplier performance. This results in improved operations and relations between supplier and buyer.

In this study, we propose an integrated approach combining Bayesian Networks (BN) and Total Cost of Ownership (TCO) to overcome the aforementioned limitations of current approaches. Our goal is to more clearly identify uncertainty issues, and integrate and utilize the buyer’s domain-specific knowledge. Even if the buyer has incomplete information, he can still evaluate and select alternatives with the proposed approach. The model will also explore the interdependent relations between supplier selection and different cost items in the selection process. The application process is presented for a tier-1 supplier in automotive industry.

Bayesian Networks (BN) are very powerful for making inferences and drawing conclusions based on available information (Jensen, 1996). They are effective for modeling uncertainty by accepting probability distributions. BN can combine expert and domain knowledge that allows flexible inference even with partial and limited information (Lauritzen, 1995). The domain knowledge of a buyer normally encodes in the form of conditional statements. BN allow modeling of probabilistic causal relations among variables (Bishop, 2006). Therefore, BN can facilitate a more insightful evaluation and selection of alternatives given the semantics used for decision making.

On the other hand, TCO provides a better inspection opportunity for determining the total cost caused by supplier activities on a buyer’s organization. The TCO approach is a structured methodology for determining the true cost of acquisition of a product, considering all the costs related to purchasing and using the product. TCO considers the buyer’s entire value chain and mainly evaluates the supplier performance by taking into account all the costs caused by a supplier (Degraeve, Labro, & Roodhooft, 2000). These costs are not limited to the purchasing price but also include cost elements such as: quality, transportation, maintenance, and administration (Degraeve et al., 2000, Ellram, 1995). As opposed to an initial-price perspective that mainly accepts short term approach, TCO allows for a long-term perspective selecting different buying situations (Ferrin & Plank, 2002).

The remainder of this paper is structured as follows. In Section 2, we present the relevant research concerning the supplier selection. In Section 3, we provide a brief overview of Bayesian Networks, including inference. In Section 4, we explain the details of our model. In Section 5, we test our proposed framework using an illustrative example, and present the results and detailed sensitivity analyses to identify the critical factors in the supplier selection process. The value of information is discussed for both mean and variance points of views. The last section is allocated for a summary and conclusion of the proposed method.

Section snippets

Literature review

Due to its key importance to manufacturers’ cost management strategies, the supplier selection problem has received significant interest in both academia and industry. Several studies have been presented to firms to gain competitive advantage and to decrease system wide costs in supply chains while forming group of healthy suppliers. Multi-Criteria Decision Making (MCDM) methods such as Analytical Hierarchical Process (AHP), Multi-Attribute Utility Theory (MAUT), Analytical Network Process

Bayesian Networks

A Bayesian network, also known as belief network, is a directed graphical model that represents conditional probabilities among variables of interest. Formally, a Bayesian network for a set of random variables U = {X1,  , Xn} is a pair, B = G, Θ〉 where G represents its directed acyclic graph (DAG) structure, and Θ represents the parameters that quantifies the network. The random variables are represented as vertices, and parental relationships between these random variables are represented as edges.

The supplier selection model

There are always risks associated with supplier selection in automotive industry. Although there is sometimes available data regarding the performance of a supplier, the buyer will not be sure about how the supplier will perform. The selection and evaluation process are conducted with imperfect information which contains noise or limited information. Unawareness of the risks associated with supplier selection may cause irreparable damage. Since the automotive supply chains are mostly deep as

Illustrative case

The proposed methodology is applied to a firm that needs to select the best supplier out of three alternatives (see list in Table 3). Supplier information includes the knowledge of the buyer regarding each supplier as well as financial data such as unit price of each supplier. Unit price is known explicitly; the first supplier offers the lowest price whereas the third supplier offers the highest price. Since the buyer has limited information, some of the suppliers’ criterion states are unknown

Conclusion

The proposed method combining Total Cost of Operations (TCO) and Bayesian Networks (BN) provides several advantages in analyzing the supplier selection problem. The proposed framework accounts for and handles uncertainty. The probabilistic nature of the supplier selection problem due to supplier performance and its result on buyer organization makes BN a powerful candidate comparing to the other methods. We explicitly model the uncertainty of the total cost and compared supplier performance by

References (81)

  • C.-F. Fan et al.

    BBN-based software project risk management

    Journal of Systems and Software

    (2004)
  • C. Gencer et al.

    Analytic network process in supplier selection: A case study in an electronic firm

    Applied Mathematical Modelling

    (2007)
  • W. Ho et al.

    Multi-criteria decision making approaches for supplier evaluation and selection: A literature review

    European J Operations Research

    (2010)
  • P.K. Humphreys et al.

    Using case-based reasoning to evaluate supplier environmental management performance

    Expert Systems with Applications

    (2003)
  • P.K. Humphreys et al.

    Integrating environmental criteria into the supplier selection process

    Journal of Materials Processing Technology

    (2003)
  • D. Janssens et al.

    Integrating Bayesian networks and decision trees in a sequential rule-based transportation model

    European Journal of Operational Research

    (2006)
  • H.Y. Kao et al.

    Supply chain diagnostics with dynamic Bayesian networks

    Computers and Industrial Engineering

    (2005)
  • M. Kumar et al.

    A fuzzy goal programming approach for vendor selection problem in a supply chain

    Computers and Industrial Engineering

    (2004)
  • S.T. Lauritzen

    The EM algorithm for graphical association models with missing data

    Computational Statistics and Data Analysis

    (1995)
  • F.H.F. Liu et al.

    The voting analytic hierarchy process method for selecting supplier

    International Journal of Production Economics

    (2005)
  • L. Mikhailov

    Fuzzy analytical approach of partnership selection in formation of virtual enterprises

    Omega

    (2002)
  • F. Roodhooft et al.

    Vendor selection and evaluation: An activity based costing approach

    European Journal of Operational Research

    (1997)
  • R.F. Saen

    A decision model for selecting technology suppliers in the presence of nondiscretionary factors

    Applied Mathematics and Computation

    (2006)
  • A. Sanayei et al.

    An integrated group decision-making process for supplier selection and older allocation using multi-attribute utility theory and linear programming

    Journal of the Franklin Institute

    (2008)
  • A. Sarkar et al.

    Evaluation of supplier capability and performance: A method for supply base reduction

    Journal of Purchasing and Supply Management

    (2006)
  • S. Talluri et al.

    A multi-phase mathematical programming approach for effective supply chain design

    European Journal of Operational Research

    (2002)
  • S. Talluri et al.

    A methodology for strategic sourcing

    European Journal of Operational Research

    (2004)
  • M. Tam et al.

    An application of the AHP in vendor selection of a telecommunications system

    Omega

    (2001)
  • M. Wouters et al.

    The adoption of total cost of ownership for sourcing decisions––A structural equations analysis

    Accounting, Organizations and Society

    (2005)
  • R. Yu et al.

    A soft computing method for multi-criteria decision making with dependence and feedback

    Applied Mathematics and Computation

    (2006)
  • D. Zeng et al.

    Bayesian learning in negotiation

    International Journal of Human–Computers Studies

    (1998)
  • M.M. Akarte et al.

    Web based casting supplier evaluation using analytical hierarchy process

    Journal of Operational Research Society

    (2001)
  • A. Amid et al.

    A weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply chain

    International Journal of Production Economics

    (2007)
  • G. Barbarosoglu et al.

    An application of the analytic hierarchy process to the supplier selection problem

    Production Inventory Management

    (1997)
  • O. Bayazit

    Use of analytic network process in vendor selection decisions

    Benchmarking: An International Journal

    (2006)
  • Bishop, C. M. (2006). Pattern recognition and machine learning....
  • J. Blodgett et al.

    A Bayesian network model of the consumer complaint process

    Journal of Service Research

    (2000)
  • L.D. Boer et al.

    A review of methods supporting supplier selection

    European Journal of Purchasing and Supply Management

    (2001)
  • M. Braglia et al.

    A quality assurance-oriented methodology for handling trade-offs in supplier selection

    International Journal of Physical Distribution and Logistics Management

    (1997)
  • F.T.S. Chan

    Interactive selection model for supplier selection process: An analytical hierarchy process approach

    International Journal of Production Research

    (2003)
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