Combining Bayesian Networks and Total Cost of Ownership method for supplier selection analysis☆
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)
- et al.
Decision support for real-time telemarketing operations through Bayesian network learning
Decision Support Systems
(1997) - et al.
A closer look at the use of DEA for technology selection
Computers and Industrial Engineering
(1997) - et al.
A fuzzy-QFD approach to supplier selection
Journal of Purchasing and Supply Management
(2006) - et al.
An adapted multi-criteria approach to suppliers and products selection – An application oriented to lead-time reduction
International Journal of Production Economics
(2008) - et al.
Global supplier development considering risk factors using fuzzy extended AHP-based approach
Omega – International Journal of Management Science
(2007) - et al.
A fuzzy approach for supplier evaluation and selection in supply chain management
International Journal of Production Economics
(2006) - et al.
Development of a case based intelligent customer – Supplier relationship management system
Expert Systems with Applications
(2002) - et al.
A knowledge-based supplier intelligence retrieval system for outsource manufacturing
Knowledge-Based Systems
(2005) - et al.
Total Cost of Ownership purchasing of a service: The case of airline selection at Alcatel Bell
European Journal of Operational Research
(2004) - et al.
A bayesian network approach to root cause diagnosis of process variations
International Journal of Machine Tools and Manufacture
(2005)
BBN-based software project risk management
Journal of Systems and Software
Analytic network process in supplier selection: A case study in an electronic firm
Applied Mathematical Modelling
Multi-criteria decision making approaches for supplier evaluation and selection: A literature review
European J Operations Research
Using case-based reasoning to evaluate supplier environmental management performance
Expert Systems with Applications
Integrating environmental criteria into the supplier selection process
Journal of Materials Processing Technology
Integrating Bayesian networks and decision trees in a sequential rule-based transportation model
European Journal of Operational Research
Supply chain diagnostics with dynamic Bayesian networks
Computers and Industrial Engineering
A fuzzy goal programming approach for vendor selection problem in a supply chain
Computers and Industrial Engineering
The EM algorithm for graphical association models with missing data
Computational Statistics and Data Analysis
The voting analytic hierarchy process method for selecting supplier
International Journal of Production Economics
Fuzzy analytical approach of partnership selection in formation of virtual enterprises
Omega
Vendor selection and evaluation: An activity based costing approach
European Journal of Operational Research
A decision model for selecting technology suppliers in the presence of nondiscretionary factors
Applied Mathematics and Computation
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
Evaluation of supplier capability and performance: A method for supply base reduction
Journal of Purchasing and Supply Management
A multi-phase mathematical programming approach for effective supply chain design
European Journal of Operational Research
A methodology for strategic sourcing
European Journal of Operational Research
An application of the AHP in vendor selection of a telecommunications system
Omega
The adoption of total cost of ownership for sourcing decisions––A structural equations analysis
Accounting, Organizations and Society
A soft computing method for multi-criteria decision making with dependence and feedback
Applied Mathematics and Computation
Bayesian learning in negotiation
International Journal of Human–Computers Studies
Web based casting supplier evaluation using analytical hierarchy process
Journal of Operational Research Society
A weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply chain
International Journal of Production Economics
An application of the analytic hierarchy process to the supplier selection problem
Production Inventory Management
Use of analytic network process in vendor selection decisions
Benchmarking: An International Journal
A Bayesian network model of the consumer complaint process
Journal of Service Research
A review of methods supporting supplier selection
European Journal of Purchasing and Supply Management
A quality assurance-oriented methodology for handling trade-offs in supplier selection
International Journal of Physical Distribution and Logistics Management
Interactive selection model for supplier selection process: An analytical hierarchy process approach
International Journal of Production Research
Cited by (62)
Supplier selection for aerospace & defense industry through MCDM methods
2023, Cleaner Engineering and TechnologySustainable supplier selection under must-be criteria through Fuzzy inference system
2020, Journal of Cleaner ProductionBuilding Bayesian networks based on DEMATEL for multiple criteria decision problems: A supplier selection case study
2019, Expert Systems with ApplicationsCitation Excerpt :Delivery Performance includes factors such as delivery duration, packaging and transportation conditions, discrepancies between the ordered and delivered quantity, and satisfactory documentation regarding the delivery (Badurdeen et al., 2014; Dogan & Aydin, 2011; Lockamy & McCormack, 2012). Quality System Certifications such as ISO 9001 and ISO/TS16949 are taken into account when selectiong suppliers (Dogan & Aydin, 2011). Flexibility represents the supplier's ability to adapt to changes and needs of customers, and it is considered to a crucial factor for supplier selection (Oly Ndubisi, Jantan, Cha Hing, & Salleh Ayub, 2005).
Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights
2019, Omega (United Kingdom)Citation Excerpt :Traditionally based on an activity-based costing approach, TCO considers both the direct cost and indirect cost of the operations that are needed for business relationships with suppliers [6]. Therefore, TCO is a powerful tool for comprehensively evaluating supplier performance and guiding sourcing decisions [7–9]. TCO is not commonly applied because it requires a significant amount of time and effort to attribute costs to different activities, which is an essential initial step of the activity-based costing procedure [10].
Supplier portfolio of key outsourcing parts selection using a two-stage decision making framework for Chinese domestic auto-maker
2019, Computers and Industrial EngineeringCitation Excerpt :Therefore, the TCO-based (total cost of ownership) and TLCC-based (total lifecycle cost) programming models are employed and addressed to fill the gap by OEMs (Smytka & Clemens, 1993). This philosophy is employed in recent studies reflecting the long run economics of the product (Aissaoui, Haouari, & Hassini, 2007; Degraeve, Labro, & Roodhooft, 2000; Demirtas & Üstün, 2008; Dogan & Aydin, 2011; Kanagaraj, Ponnambalam, & Jawahar, 2014; Saccani, Perona, & Bacchetti, 2017). Kanagaraj et al. (2014) proposed a nonlinear integer programming model targeting TCO minimization, which addresses the indirect cost associated with quality, the maintenance and service process, and the hybrid GA-based algorithm with cuckoo search (CS) is designed to study the outsourcing supplier selection problem.
- ☆
This manuscript was processed by Area Editor Joseph Geunes.