A multiple-vendor single-buyer integrated inventory model with a variable number of vendors

Supply chain management is concerned with the coordination of material and information flows in multi-stage production systems. A closer look at the literature reveals that previous research on the coordination of multi-stage production systems has predominantly focused on the sales side of the supply chain, whereas problems that arise on the supply side have often been neglected. This article closes this gap by studying the coordination of a supplier network in an integrated inventory model. Specifically, we consider a buyer sourcing a product from heterogeneous suppliers and tackle both the supplier selection and lot size decision with the objective to minimise total system costs. First, we provide mathematical formulations for the problem under study, and then suggest a two-stage solution procedure to derive a solution. Numerical studies indicate that our solution procedure reduces the total number of supplier combinations that have to be tested for optimality, and that it may support initiatives which aim on increasing the efficiency of the supply chain as a heuristic planning tool.     

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

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.     

A fuzzy supplier selection model with the consideration of benefits,opportunities, costs and risks

Even though the research on supplier selection is abundant, the works usually only consider the critical success factors in the buyer–supplier relationship. However, the negative aspects of the buyer–supplier relationship must also be considered simultaneously. The main objective in this study is to propose an analytical approach to select suppliers under a fuzzy environment. A fuzzy analytic hierarchy process (FAHP) model, which incorporates the benefits, opportunities, costs and risks (BOCR) concept, is constructed to evaluate various aspects of suppliers. Multiple factors that are positively or negatively affecting the success of the relationship are analyzed by taking into account experts’ opinion on their importance, and a performance ranking of the suppliers is obtained. TFT-LCD manufacturers in Taiwan, which is the largest TFT-LCD producer country in the world, are facing increasing competition nowadays, and the selection of the most appropriate suppliers for cooperation is essential for firms to achieve competitive advantage. A case study of backlight unit supplier selection for a TFT-LCD manufacturer is presented, and the proposed model is applied to facilitate the decision process. The model is a general form that can be tailored and applied by firms that are making decisions on supplier selection.     

Production planning and backup sourcing strategy of a buyer-dominant supply chain with random yield and demand

This paper studies the backup sourcing strategy of the buyer and the production planning of the supplier in presence of both random yield and random demand. Since the production is susceptible to the randomness of yield beyond the control of the supplier, the buyer may access to a backup sourcing option for the finished items. We analyse the value of backup sourcing for both the decentralised and centralised channels. Backup sourcing strategy of the buyer may lower the supply chain's performance. We show that the order quantity of the buyer does not change the stocking factor of the supplier's input. Meanwhile, compared with the centralised operation, the decentralised operation is more dependent on the backup sourcing to reduce supply shortage of the contracting supplier. From the channel's perspective, an incentive scheme is developed to facilitate the coordination of both the buyer and the contracting supplier, we show that the proposed option contract can allow the supply chain members to share the respective risks involved in the production and selling processes. Finally, we also provide qualitative insights based on numerical examples of the centralised and decentralised solutions.     

A goal programming model for joint decision making of inventory lot-size,supplier selection and carrier selection

In this paper, we address a problem in which a storage space constrained buyer procures a single product in multiple periods from multiple suppliers. The production capacity constrained suppliers offer all-unit quantity discounts. The late deliveries and rejections are also incorporated in sourcing. In addition, we consider transportation cost explicitly in decision making which may vary because of freight quantity and distance of shipment between the buyer and a supplier. We propose a multi-objective integer linear programming model for joint decision making of inventory lot-sizing, supplier selection and carrier selection problem. In the multi-objective formulation, net rejected items, net costs and net late delivered items are considered as three objectives that have to be minimized simultaneously over the decision horizon. The intent of the model is to determine the timings, lot-size to be procured, and supplier and carrier to be chosen in each replenishment period. We solve the multi-objective optimization problem using three variants of goal programming (GP) approaches: preemptive GP, non-preemptive GP and weighted max–min fuzzy GP. The solution of these models is compared at different service-level requirements using value path approach.     

Optimal apparel supplier selection with forecast updates under carbon emission taxation scheme

Carbon emission tax is an important measure for sustainable supply chain management. This paper studies an optimal supplier selection problem in the fashion apparel supply chain in the presence of carbon emission tax. We consider the scenario in which there are multiple suppliers in the market. In the basic model, each supplier offers a supply lead time and a wholesale pricing contract to the fashion retail buyer. For the fashion retail buyer, the supplier which offers a shorter lead time allows it to postpone the ordering decision with updated and better forecast, and also a smaller carbon tax. However, the wholesale price is usually larger. We propose a two-phase optimal supplier selection scheme in which phase one filters the inferior suppliers and phase two helps to select the best supplier among the set of non-inferior suppliers by multi-stage stochastic dynamic programming. The impacts brought by different formats of carbon emission tax are explored. Finally, we examine an extended model in which there is a local supplier who offers a buyback contract and accepts product returns. Insights from the analysis are discussed.     

Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing

The coordinated supplier selection and customer order scheduling in the presence of supply chain disruption risks is studied for single and multiple sourcing strategies. Given a set of customer orders for products, the decision maker needs to select a single supplier or a subset of suppliers for purchasing parts required to complete the customer orders, and schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The suppliers are located in different geographic regions and the supplies are subject to different types of disruptions: to random local disruptions of each supplier individually, to random regional disruptions of all suppliers in the same region simultaneously and to random global disruptions of all suppliers simultaneously. For any combination of suppliers hit by different types of disruptions, a formula for calculating the corresponding disruption probability is developed. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional value-at-risk as a risk measure. The problem objective is either to minimize expected worst-case cost or to maximize expected worst-case customer service level, i.e., the expected worst-case fraction of customer orders filled on or before their due dates. The risk-averse solutions that optimize worst-case performance of a supply chain under disruptions risks are compared for the two sourcing strategies and the two objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.     

Sourcing with random yields and stochastic demand: A newsvendor approach

We studied a supplier selection problem, where a buyer, while facing random demand, is to decide ordering quantities from a set of suppliers with different yields and prices. We provided the mathematical formulation for the buyer's profit maximization problem and proposed a solution method based on a combination of the active set method and the Newton search procedure. Our computational study shows that the proposed method can solve the problem efficiently, and is able to generate interesting and insightful results that lead us to various managerial implications.

Scope and purpose

In today's globally competitive environment, decision makers in supply chains face numerous challenges particularly regarding the selection of suppliers or outsourcing partners. To assist in this endeavor, we examined a double-layered supply chain where a buyer facing the end users has the option of selecting among a cohort of suppliers. The available suppliers may have different yield rates and unit costs. The buyer has to decide, given the stochastic nature of the problem's governing parameters, whether or not to order from each supplier, and if so how much. We developed a ‘newsvendor-style’ model for the problem, and proposed a solution algorithm for it. Numerical studies were performed to provide some insights for supplier selection and order quantity decisions.     

A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method

Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain. On the other hand, it is a hard problem since supplier selection is typically a multi criteria group decision-making problem involving several conflicting criteria on which decision maker’s knowledge is usually vague and imprecise. In this study, TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate supplier in group decision making environment. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers for rating the importance of criteria and alternatives. Finally, a numerical example for supplier selection is given to illustrate application of intuitionistic fuzzy TOPSIS method.     

Supplier selection and procurement decisions with uncertain demand,fixed selection costs and quantity discounts

In this paper, we study the supplier selection and procurement decision problem with uncertain demand, quantity discounts and fixed selection costs. In addition, a holding cost is incurred for the excess inventory if the buyer orders more than the realized demand and the shortage must be satisfied by an emergent purchase at a higher price otherwise. The objective is to select the suppliers and to allocate the ordering quantity among them to minimize the total cost (including selecting, procurement, holding and shortage costs, etc.). The problem is modeled as a Mixed Integer Programming (MIP) and is shown to be NP-hard. Some properties of the optimal policy are provided and an optimal algorithm is proposed based on the generalized Bender's decomposition. Numerical experiments are conducted to show the efficiency of the algorithm and to obtain some managerial insights.     

Supplier selection and supply quantity allocation of common and non-common parts with multiple criteria under multiple products

With limited capacity of suppliers, how to reduce the total operating cost of the enterprise by determining the most suitable production capacity allocation has become the major issue faced by various enterprises in producing multiple types of products. In addition, when manufacturing multiple types of products, due to the high demand of common and non-common parts, which is applicable to various products, enterprises will place special emphasis on the procurement of common and non-common parts, to select most suitable suppliers of parts with the highest quality and minimum time and costs, in order to cut down on operating costs of enterprises. This research first lists parts of various products through bill of material (BOM), and constructs an optimal mathematical model suitable for multi-phase products’ parts, in order to assess the assembling relationship of various parts; it makes use of the linkage among those to select the supplier of common and non-common parts when assessing multiple products. Then considering the limited production capacity of suppliers, it selects the best combination of suppliers of special common and non-common parts. To solve the optimal mathematical model, a genetic algorithm (GA) is proposed to find the acceptable results of the supply selection and quantity allocation problem. It then provides a benchmark for enterprise in current diversified market to purchase and assess common and non-common parts, and makes such benchmark a normal standard for selection of suppliers in the future.     

Supplier selection model with contingency planning for supplier failures

We consider the optimal allocation of demand across a set of suppliers given the risk of supplier failures. We assume items sourced are used in multiple facilities and can be purchased from multiple suppliers with different cost and reliability characteristics. Suppliers have production flexibility that allows them to deliver a contingency quantity in case other suppliers fail. Costs considered include supplier fixed costs and variable costs per unit, while failure to deliver to a demand point results in a particular financial loss. The model utilizes the decision tree approach to consider all the possible states of nature when one or more suppliers fail, as well as expand the traditional transportation problem. Unlike other supplier selection models, this model considers contingency planning in the decision process, minimizing the total network costs. This results in a base allocation to one or more of the available suppliers and a state of nature specific delivery contingency plan from the suppliers to each demand point. A numerical example, as well as sensitivity analysis, is presented to illustrate the model and provide insights.     

A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection

Supplier selection has become a very critical activity to the performance of organizations and supply chains. Studies presented in the literature propose the use of the methods Fuzzy TOPSIS (Fuzzy Technique for Order of Preference by Similarity to Ideal Solution) and Fuzzy AHP (Fuzzy Analytic Hierarchy Process) to aid the supplier selection decision process. However, there are no comparative studies of these two methods when applied to the problem of supplier selection. Thus, this paper presents a comparative analysis of these two methods in the context of supplier selection decision making. The comparison was made based on the factors: adequacy to changes of alternatives or criteria; agility in the decision process; computational complexity; adequacy to support group decision making; the number of alternative suppliers and criteria; and modeling of uncertainty. As an illustrative example, both methods were applied to the selection of suppliers of a company in the automotive production chain. In addition, computational tests were performed considering several scenarios of supplier selection. The results have shown that both methods are suitable for the problem of supplier selection, particularly to supporting group decision making and modeling of uncertainty. However, the comparative analysis has shown that the Fuzzy TOPSIS method is better suited to the problem of supplier selection in regard to changes of alternatives and criteria, agility and number of criteria and alternative suppliers. Thus, this comparative study contributes to helping researchers and practitioners to choose more effective approaches for supplier selection. Suggestions of further work are also proposed so as to make these methods more adequate to the problem of supplier selection.     

A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context

Sustainable supply chain management (SSCM) has received much consideration from corporate and academic over the past decade. Sustainable supplier performance evaluation and selection plays a significant role in establishing an effective SSCM. One of the techniques that can be used for sustainable supplier performance evaluation and selection is data envelopment analysis (DEA). In real world problems, the inputs and outputs might be imprecise. This paper develops an integrated DEA enhanced Russell measure (ERM) model in fuzzy context to select the best sustainable suppliers. A case study is presented to exhibit the efficacy of the proposed method for sustainable supplier selection problem in a resin production company. The case study demonstrates that the proposed model can measure effectiveness, efficiency, and productivity in uncertain environment with different α levels. Also, it shows that the proposed model aids decision makers to deal with economic, social, and environmental factors when selecting sustainable suppliers.     

A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules

Fuzzy set theory has been used as an approach to deal with uncertainty in the supplier selection decision process. However, most studies limit applications of fuzzy set theory to outranking potential suppliers, not including a qualification stage in the decision process, in which non-compensatory types of decision rules can be used to reduce the set of potential suppliers. This paper presents a supplier selection decision method based on fuzzy inference that integrates both types of approaches: a non-compensatory rule for sorting in qualification stages and a compensatory rule for ranking in the final selection. Fuzzy inference rules model human reasoning and are embedded in the system, which is an advantage when compared to approaches that combine fuzzy set theory with multicriteria decision making methods. Fuzzy inference combined with a fuzzy rule-based classification method is used to categorize suppliers in qualification stages. Classes of supplier performance can be represented by linguistic terms, which allow decision makers to deal with subjectivity and to express qualification requirements in linguistic formats. Implementation of the proposed method and techniques were analyzed and discussed using an illustrative case. Three defuzzification operators were used in the final selection, yielding the same ranking. Factorial design was applied to test consistency and sensitivity of the inference rules. The findings reinforce the argument that including stages of qualification based on fuzzy inference and categorization makes this method especially useful for selecting from a large set of potential suppliers and also for first time purchase.     

A fuzzy solution approach for multi objective supplier selection

In today’s severe competitive environment the selection of appropriate suppliers is a significantly important decision for effective supply chain management. Appropriate suppliers reduce purchasing costs, decrease production lead time, increase customer satisfaction and strengthen corporate competitiveness. In this study a multiple sourcing supplier selection problem is considered as a multi objective linear programming problem. Three objective functions are minimization of costs, maximization of quality and maximization of on-time delivery respectively. In order to solve the problem, a fuzzy mathematical model and a novel solution approach are proposed to satisfy the decision maker’s aspirations for fuzzy goals. The proposed approach can be efficiently used to obtain non-dominated solutions. A numerical example is given to illustrate how the approach is utilized.     

Sole versus dual sourcing under order dependent lead times and prices

In this paper, we consider a dual-sourcing model with constant demand and stochastic lead times. Two suppliers may be different in terms of purchasing prices and lead-time parameters. The ordering takes place when the inventory level depletes to a reorder level, and the order is split among two suppliers. Unlike previous works in the order splitting literature, the supply lead time between vendor and buyer as well as unit purchasing prices is considered to be order quantity dependent. The proposed model finds out the optimal reorder point, order quantity and splitting proportion, using a solution procedure. Numerical results show that neglecting the relationship between ordering batch size and lead times is a shortcoming that hides one of order splitting advantages. Moreover, connecting unit prices to order quantity can decrease the percentage saving from dual sourcing compared to sole sourcing. Furthermore, sensitivity analysis shows some managerial insights.     

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

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.     

Supplier behavior modeling and winner determination using parallel MDP

Behavioral uncertainty of a supplier is a major challenge to a buyer operating in e-procurement setting. Modeling suppliers’ behavior from past transactions, estimation of possible future performance and integrating this knowledge with the winner determination process can bring a new dimension to procurement process automation. We propose a states-space model to capture the uncertainty involved in long-term supplier behavior. The states represent the performance level of a supplier. This behavioral aspect is then integrated with the winner determination process of a multi-attribute reverse auction for efficient supplier selection using parallel MDP. We also propose an implementation framework to collect the feedback on supplier, generate an aggregate performance score and integrate it with the winner determination process. The performance aggregation and winner determination with help of Markov decision process effectively uses the past performance information. In addition, it updates performance information in regular invervals and allevates the problem of maintaining a long history. We compare the MDP-based selection with that of performance score-based selection through a simulation experiment. It is observed that our scheme gives better buyer utility, selects best suppliers and fetches better quality product. The benefits realized through these attributes to the buyer increases the efficiency of the MDP-based selection process.     

To be or not to B2B: Evaluating managerial choices for e-procurement channel adoption

E-procurement systems are computer systems and communication networks through which firms buy and sell products. We identify two types of e-procurement systems: extranets and e-markets. Extranets connect the buyer and its suppliers with a closed network, while e-markets create open networks for buyer and supplier interactions. The differences between them lie in system implementation costs, marketplace benefits, and the extent of supplier competitive advantage that develops due to information sharing. In this article, we develop a new theoretical model to analyze the adoption of e-procurement systems from the buyer’s perspective, to explore the set of conditions under which the buyer will prefer to procure via an electronic market instead of using proprietary extranet connections. The primary finding is that a buyer will adopt an e-market approach when the supplier’s competitive advantage derived from access to strategic information is modest compared with the marketplace benefits less the channel costs. In addition, we find that the buyer is likely to have a bigger trading network with an e-market than with an extranet in order to capture the greatest available benefits. Overall, this study offers guidelines for managers to design and select e-procurement channels to fit different procurement needs.