A dynamic model of supplier switching

While a broad branch of literature deals with the development of buyer–supplier relationships, limited research exists under which circumstances a buyer should terminate such a relationship and switch to a new supplier. Recently, Wagner and Friedl (2007) have developed a framework to analyze a static one-shot supplier switching decision when the buyer has asymmetric information about the supplier’s production costs. We extend their basic framework to a dynamic one, assuming that the supplier learns the production costs over time when he sets up the production process. Since the supplier’s cost information at the individual stages crucially determines the setup and the switching decision, it becomes essential for supply chain management to provide proper incentives so that the supplier reveals his cost information truthfully over time. We characterize the optimal setup and switching strategy as well as the optimal supply chain contract. We also compare our findings with those of the static setting to provide further insights.     

Controlling for supplier switching in the presence of real options and asymmetric information

Applying a real option approach, this paper examines how asymmetric information alters key variables of a firm’s supplier switching process, such as the timing of contracting (hurried versus delayed contracting), transfer payments, set-up, switching, and abandonment decisions. In a symmetric information setting, delayed contracting is unambiguously beneficial. Abandoning the once established relation with the entrant supplier is never an issue. In contrast, under asymmetric information hurried contracting with potentially abandoning the relation can be beneficial. Consistent with adverse selection models, we find that under delayed contracting, in equilibrium, the firm switches less frequently to the entrant supplier (switching inertia). Surprisingly, we also find that under hurried contracting the firm switches more frequently to the entrant supplier (switching acceleration) and may abandon the relation. Finally, we study how these key variables of the supplier switching process change when also the incumbent supplier has private information (two-sided asymmetric information case).     

An efficient and simple model for multiple criteria supplier selection problem

Simply looking for vendors offering the lowest prices is not “efficient sourcing” any more. Selection of suppliers is a multiple criteria decision. We propose a weighted linear program for the multi-criteria supplier selection problem. In addition to mathematical formulation, this paper studies a transformation technique which enables our proposed model to be solved without an optimizer. The model for multi-criteria supplier selection problem can be easily implemented with a spreadsheet package. The model can be widely applied to practical situations and does not require the user with any optimization background.     

The effect of deception on optimal decisions

The buyer in a seller-first offer bargaining situation use deception to cause the seller to arrive at an inaccurate subjective probability distribution of the buyer's position. The effects on the optimal behavior of the seller and on the payoffs to the two parties are examined.     

Supplier default dependencies: Empirical evidence from the automotive industry

Common ways to mitigate the detrimental consequences of supplier bankruptcies are to install redundancy and to pursue a multiple-sourcing strategy. This is based on the assumption that the adverse event of one supplier going out of business is largely independent from the default of other suppliers. However, this implicit assumption does not hold in all cases. This study – based on empirical data from automotive suppliers – reveals that default dependencies among suppliers do often exist and can have significant consequences. We use copula functions, a method of representing joint distribution functions with particular marginals, to capture the default dependence between automotive suppliers and to simulate various default dependence scenarios. We also conduct a comparative static analysis illustrating the significant impact of default correlation in a supplier portfolio. Our findings should spur managers to analyze their supplier portfolios with respect to default dependencies, and to take this phenomenon into consideration when making sourcing decisions.     

Supplier selection for one-sided processes with unequal sample sizes

In this paper, we consider the supplier selection problem which deals with comparing two one-sided processes and selecting the one that has a significantly higher capability value. We present an exact approach to tackle the supplier selection problem. Testing hypotheses with two phases for comparing two processes are considered. Critical values of the tests are calculated to determine the selection decisions. Sample size required for a designated selection power and confidence level is also investigated. The results provide useful information to practitioners. The proposed approach generalizes the existing method by not requiring equal sample sizes for the two processes. An application example on comparing two wavelength division multiplexer (WDM) manufacturing processes is presented to illustrate the practicality of our approach to actual data collected from the factories.     

Restricting weights in supplier selection decisions in the presence of dual-role factors

One of the uses of data envelopment analysis (DEA) is supplier selection. Weight restrictions allow for the integration of managerial preferences in terms of relative importance levels of various inputs and outputs. As well, in some situations there is a strong argument for permitting certain factors to simultaneously play the role of both inputs and outputs. The objective of this paper is to propose a method for selecting the best suppliers in the presence of weight restrictions and dual-role factors. This paper depicts the supplier selection process through a DEA model, while allowing for the incorporation of decision maker’s preferences and considers multiple factors which simultaneously play both input and output roles. The proposed model does not demand exact weights from the decision maker. This paper presents a robust model to solve the multiple-criteria problem. A numerical example demonstrates the application of the proposed method.     

Supplier quality improvement: The value of information under uncertainty

We consider supplier development decisions for prime manufacturers with extensive supply bases producing complex, highly engineered products. We propose a novel modelling approach to support supply chain managers decide the optimal level of investment to improve quality performance under uncertainty. We develop a Poisson–Gamma model within a Bayesian framework, representing both the epistemic and aleatory uncertainties in non-conformance rates. Estimates are obtained to value a supplier quality improvement activity and assess if it is worth gaining more information to reduce epistemic uncertainty. The theoretical properties of our model provide new insights about the relationship between the degree of epistemic uncertainty, the effectiveness of development programmes, and the levels of investment. We find that the optimal level of investment does not have a monotonic relationship with the rate of effectiveness. If investment is deferred until epistemic uncertainty is removed then the expected optimal investment monotonically decreases as prior variance increases but only if the prior mean is above a critical threshold. We develop methods to facilitate practical application of the model to industrial decisions by a) enabling use of the model with typical data available to major companies and b) developing computationally efficient approximations that can be implemented easily. Application to a real industry context illustrates the use of the model to support practical planning decisions to learn more about supplier quality and to invest in improving supplier capability.     

Delivery performance in vendor selection decisions

Increased reliance upon outsourcing has made the issue of vendor selection even more critical to the success of the modern manufacturing organization. The usual performance measure on which selection is based has been the distribution of the vendor’s delivery lead-time (LT), often as characterized by the mean and variance. In this paper, we show that the distribution of demand per unit time (DPUT) must also be considered if an optimal decision is to be made.     

Selection of contract suppliers under price and demand uncertainty in a dynamic market

In this paper, we consider a supply contracting problem in which the buyer firm faces non-stationary stochastic price and demand. First, we derive analytical results to compare two pure strategies: (i) periodically purchasing from the spot market; and (ii) signing a long-term contract with a single supplier. The results from the pure strategies show that the selection of suppliers can be complicated by many parameters, and is particularly affected by price uncertainty. We then develop a stochastic dynamic programming model to incorporate mixed strategies, purchasing commitments and contract cancellations. Computational results show that increases in price (demand) uncertainty favor long-term (short-term) suppliers. By examining the two-way interactions of contract factors (price, demand, purchasing bounds, learning and technology effect, salvage values and contract cancellation), both intuitive and non-intuitive managerial insights in outsourcing strategies are derived.     

Capacity switching options under rivalry and uncertainty

Deregulated infrastructure industries exhibit stiff competition for market share. Firms may be able to limit the effects of competition by launching new projects in stages. Using a two-stage real options model, we explore the value of such flexibility. We first demonstrate that the value of investing in a sequential manner for a monopolist is positive but decreases with uncertainty. Next, we find that a typical duopoly firm’s value relative to a monopolist’s decreases with uncertainty as long as the loss in market share is high. Intriguingly, this result is reversed for a low loss in market share. We finally show that this loss in value is reduced if a firm invests in a sequential manner and specify the conditions under which sequential capacity expansion is more valuable for a duopolist firm than for a monopolist.     

Using a DEA-cross efficiency approach in public procurement tenders

The paper addresses the topic of supplier selection in public procurement. According to European directives, when tenders are awarded through the “Most Economically Advantageous Tender” (MEAT) criterion, the awarding committee has to decide the tender evaluation criteria of the presented bids in advance. The authors propose a decision making tool that is aimed at helping the awarding committee in this difficult task and, at the same time, maintaining a transparent procedure in accordance with governmental procurement regulations and requirements as well as guaranteeing fair and equal evaluation of all bids. In this regard, the decision problem of supplier selection is addressed by applying an extension of the DEA (data envelopment analysis) methodology. The cross-efficiency evaluation is used for selecting the best supplier among the eligible candidates. The proposed technique allows the evaluation of quantitative data related to vendor selection and keeps the transparency features requested by public procurement. In addition, all bids are equally assessed according to the same objectively defined weights without any subjective setting by the public officers. The effectiveness and efficiency of the approach is supported by a case study that pertains to the tender of an Italian public agency.     

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

Supplier selection, which is the first step of the activities in the product realization process starting from the purchasing of material till to the end of delivering the products, is evaluated as a critical factor for the companies desiring to be successful in nowadays competition conditions. With the scope of this paper, supplier selection was considered as a multi criteria decision problem. A model aiming the usage of analytic network process (ANP) in supplier selection is developed owning to the evaluation of the relations between supplier selection criterias in a feedback systematic. The proposed model is implemented in a company of electronic.     

基于模糊需求的多产品供应商选择的多目标规划模型

由决策于环境的不确定性,供应商选择问题存在大量的模糊信息,传统的确定性规划模型已经不能够很好地处理此类问题。本文基于模糊需求量信息,对于多产品供应商问题建立了模糊多目标规划模型。同时考虑到各目标及约束的重要性程度不同的影响,通过引进适当的权重对多目标规划模型进行求解。文中结合实际算例验证模型的可行性和有效性。     

Some considerations concerning industrial location decisions

The approach most often followed by operations researchers when dealing with the problem of industrial location is based on the notion that very complex location decisions can be reduced into an algorithmic form involving few cost factors. In this they follow closely the orthodox location theory. Recently, there has been a growing dissatisfaction with the classical location theory. Empirical studies carried out during the past two decades tend to analyze a staggering and ever growing number of specific locational factors. The purpose of the study briefly reported on below was limited to an attempt to classify the numerous factors affecting location decisions and to reduce their numbers by grouping and deriving indexes. The validity of the indexes was tested by confronting them with the actual distribution of industries in the U.S.     

An exact algorithm for the Capacitated Total Quantity Discount Problem

In this paper we analyze the procurement problem of a company that needs to purchase a number of products from a set of suppliers to satisfy demand. The suppliers offer total quantity discounts and the company aims at selecting a set of suppliers so to satisfy product demand at minimum purchasing cost. The problem, known as Total Quantity Discount Problem (TQDP), is strongly NP-hard. We study different families of valid inequalities and provide a branch-and-cut approach to solve the capacitated variant of the problem (Capacitated TQDP) where the quantity available for a product from a supplier is limited. A hybrid algorithm, called HELP (Heuristic Enhancement from LP), is used to provide an initial feasible solution to the exact approach. HELP exploits information provided by the continuous relaxation problem to construct neighborhoods optimally searched through the solution of mixed integer subproblems. A streamlined version of the proposed exact method can optimally solve in a reasonable amount of time instances with up to 100 suppliers and 500 products, and largely outperforms an existing approach available in the literature and CPLEX 12.2 that frequently runs out of memory before completing the search.     

Supplier selection and order allocation problem using a two-phase fuzzy multi-objective linear programming

The aim of this paper is to solve a supplier selection problem under multi-price level and multi-product using interactive two-phase fuzzy multi-objective linear programming (FMOLP) model. The proposed model attempts to simultaneously minimize total purchasing and ordering costs, a number of defective units, and late delivered units ordered from suppliers. The piecewise linear membership functions are applied to represent the decision maker’s fuzzy goals for the supplier selection and order allocation problem, and can be resulted in more flexibility via an interactive decision-making process. To demonstrate effectiveness of the proposed model, results of applying the proposed model are shown by a numerical example. The analytical results show that the proposed approach is effective in uncertain environments and provide a reliable decision tool for integrated multi-objective supplier selection problems.     

A Dynamic Supplier Selection and Inventory Management Model for a Serial Supply Chain with a Novel Supplier Price Break Scheme and Flexible Time Periods

A new supplier price break and discount scheme taking into account order frequency and lead time is introduced and incorporated into an integrated inventory planning model for a serial supply chain that minimizes the overall incurred cost including procurement, inventory holding, production, and transportation. A mixed-integer linear programming (MILP) formulation is presented addressing this multi-period, multi-supplier, and multi-stage problem with predetermined time-varying demand for the case of a single product. Then, the length of the time period is considered as a variable. A new MILP formulation is derived when each period of the model is split into multiple sub-periods, and under certain conditions, it is proved that the optimal solution and objective value of the original model form a feasible solution and an upper bound for the derived model. In a numerical example, three scenarios of the derived model are solved where the number of sub-period is set to 2, 3, and 4. The results further show the decrease of the optimal objective value as the length of the time period is shortened. Sufficient evidence demonstrates that the length of the time period has a significant influence on supplier selection, lot sizing allocation, and inventory planning decisions. This poses the necessity of the selection of appropriate length of a time period, considering the trade-off between model complexity and cost savings.     

First-passage times of regime switching models

The probability of a stochastic process to first breach an upper and/or a lower level is an important quantity for optimal control and risk management. We present those probabilities for regime switching Brownian motion. In the 2- and 3-state model, the Laplace transform of the (single and double barrier) first-passage times is–up to the roots of a polynomial of degree 4 (respectively 6)–derived in closed-form by solving the matrix Wiener–Hopf factorization.¹ This extends single barrier results in the 2-state model by Guo (2001b). If the quotient of drift and variance is constant over all states, we show that the Laplace transform can even be inverted analytically.     

Optimal stopping of switching diffusions with state dependent switching rates

This paper is concerned with a continuous-time and infinite-horizon optimal stopping problem in switching diffusion models. In contrast to the assumption commonly made in the literature that the regime-switching is modeled by an independent Markov chain, we consider in this paper the case of state-dependent regime-switching. The Hamilton–Jacobi–Bellman (HJB) equation associated with the optimal stopping problem is given by a system of coupled variational inequalities. By means of the dynamic programming (DP) principle, we prove that the value function is the unique viscosity solution of the HJB system. As an interesting application in mathematical finance, we examine the problem of pricing perpetual American put options with state-dependent regime-switching. A numerical procedure is developed based on the DP approach and an efficient discrete tree approximation of the continuous asset price process. Numerical results are reported.