Multiple Sourcing Research Articles

Un enfoque multicriterio para el diseño de una red para el transporte de embarques internacionales

One of the important challenges for manufacturers and distributors of autoparts is the coordination of multiple sourcing areas where their international suppliers are located. The objective of this research is the design of a service transportation network that takes into account the time shipments are held at customs and transshipment terminals, as well as the possible delays due to the use of different transportation modes. To assure the best performance of the whole transportation network, a bi-criteria optimization model was formulated, the two criteria considered were cost and time. The solution to the model was the structure of the transportation network to be used by a manufacturer of auto parts located in the Toluca industrial zone; this firm imports auto parts produced by suppliers located in the United States (E.E.U.U.) and commercializes them among multiple automakers. The structure of the optimization problem was analyzed to obtain a convenient separable arrangement and then the problem was solved by using Lagrangian relaxation. A set of possible efficient solutions for the structure of the transportation network was generated by using the weighting method and the ε-constraint method; the resulting solutions represent the set of best alternatives from which the logistics managers may choose the service network that best attends the needs and logistics performance goals of the firm.

Approximate dynamic programming for stochastic linear control problems on compact state spaces

This paper addresses Markov Decision Processes over compact state and action spaces. We investigate the special case of linear dynamics and piecewise-linear and convex immediate costs for the average cost criterion. This model is very general and covers many interesting examples, for instance in inventory management. Due to the curse of dimensionality, the problem is intractable and optimal policies usually cannot be computed, not even for instances of moderate size.We show the existence of optimal policies and of convex and bounded relative value functions that solve the average cost optimality equation under reasonable and easy-to-check assumptions. Based on these insights, we propose an approximate relative value iteration algorithm based on piecewise-linear convex relative value function approximations. Besides computing good policies, the algorithm also provides lower bounds to the optimal average cost, which allow us to bound the optimality gap of any given policy for a given instance.The algorithm is applied to the well-studied Multiple Sourcing Problem as known from inventory management. Multiple sourcing is known to be a hard problem and usually tackled by parametric heuristics. We analyze several MSP instances with two and more suppliers and compare our results to state-of-the-art heuristics. For the considered scenarios, our policies are always at least as good as the best known heuristic, and strictly better in most cases. Moreover, by using the computed lower bounds we show for all instances that the optimality gap has never exceeded 5%, and that it has been much smaller for most of them.

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.

Choosing multiple offshoring strategies: Determinants and complementarity

In contrast to the existing offshoring literature that typically views foreign outsourcing and integration as alternative strategies, the simultaneous use of these two offshoring strategies by multinational firms becomes common practice in many countries. In this paper, we employ bivariate choice models to investigate firm-level decision-making on foreign outsourcing and insourcing strategies. Analyzing Korean firm-level data, we find a possible complementarity between the two strategies and that a firm’s choice of multiple sourcing strategies is related to its expected gains from increased bargaining power, reduced price risk in purchasing inputs, and costs saved in the production process. Furthermore, our empirical results reveal distinctive roles of various firm-specific characteristics in determining the two foreign sourcing strategies adopted by Korean manufacturing firms.

Failure Risk and Quality Cost Management in Single versus Multiple Sourcing Decision

ABSTRACTThe advantage of multiple sourcing to protect against supplier failures arising from undependable products due to latent defects is examined using a model with nonlinear external failure costs. Prior research has focused only on supplier failures arising from unreliable supply, such as late, insufficient, or no delivery. I derive a closed‐form characterization of the optimal production quota allocation for the LUX (Latent defect‐Undependable product‐eXternal failure) setting. The allocation determines the optimal supply base, with intuitive properties that hold under a mild requirement. The requirement includes the special case of equal procurement costs charged by suppliers but also allows unequal costs without any particular order. The key result of the article is a necessary and sufficient condition determining whether single or multiple sourcing is optimal. Another condition is obtained to determine the exact size of the optimal supply base, provided the mild requirement holds. With minor modifications, the results also hold when a buyer‐initiated procurement contract can be used to elicit private information on the suppliers’ unit variable production costs.

Integrated selection of suppliers and scheduling of customer orders in the presence of supply chain disruption risks

This paper presents a new stochastic mixed integer programming approach to integrated supplier selection, order quantity allocation and customer order scheduling in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to decide from which supplier to purchase parts required to complete the orders, how to allocate the demand for parts among the selected suppliers, and how to schedule the customer orders over the planning horizon to minimize total cost of ordering and purchasing of parts plus penalty cost of delayed and unfulfilled customer orders and to mitigate the impact of disruption risks. The risk-neutral and risk-averse solutions that optimize, respectively average and worst-case performance of a supply chain are compared for both single and multiple sourcing strategy. Numerical examples are presented and computational results are reported.

Integrated Fuzzy ANP, Fuzzy VIKOR and goal programming for sourcing in a supply chain: A case study from cable industry

Article history: Received January 18, 2013 Received in revised format 10 May 2013 Accepted May 15 2013 Available online May 16 2013 In today’s competitive business environment, companies strive to increase their market shares. All companies clearly understand that they have to reach this goal by implementing cost effective methods and increase profits as much as possible. The cost of purchasing raw materials and component parts are significant portion of products in most manufacturing firms. Supplier selection and evaluation have been widely recognized to be one of the most substantial issues on material purchasing. In order to choose reliable suppliers it is necessary to have a trade-off between some tangible and intangible factors where some of them are in serious conflict. In this paper, an integrated technique of analytical network process improved by VIKOR and fuzzy sets theory and multi-objective mixed integer nonlinear programming is proposed to determine the appropriate suppliers. The proposed model of this paper also determines the order quantity allocated to each supplier in the case of multiple sourcing, multiple products and multi-period time horizon for an Iranian cable company. © 2013 Growing Science Ltd. All rights reserved.

A new mixed fuzzy-LP method for selecting the best supplier using fuzzy group decision making

Even though there have been so many articles and studies that focused on the supplier selection problem, a few of them considered the major decisions related to supplier selection. In this paper, the supplier selection problem is studied under a novel fuzzy approach in multiple sourcing conditions to select appropriate supplier in group decision-making environment regarding multiple sources. The use of the model is suitable in evaluating situations that involve subjectivity, vagueness and imprecise information. The group data have been used in multiple criteria decision making with multiple objective supplier selection. Trapezoidal fuzzy numbers are applied to evaluate the weights of supplier selection criteria under uncertainty conditions. The use of the model is suitable in evaluating situations that involve subjectivity, vagueness and imprecise information. The proposed method can use imperfect or insufficient knowledge of data to deal with decision-making problems. Additionally, fuzzy linear programming concept developed to enhance the optimum amount of the purchase under surrounding circumstance. This model allows the decision maker to meet the challenges with different weight and criteria for each criterion with respect to the required strategy. The considered fuzzy method leads to more consistence results compared to the existing methods and shows its preference in a numerical example.

Single Sourcing and Multiple Sourcing in an Analysis of the Purchasing Strategy of a Selected Enterprise

Tematem artykułu jest jeden z obszarów decyzyjnych wyróżnianych w ramach strategii zakupów - struktura bazy dostawców. Rozpatrywany jest on wg kryterium liczebności dostawców, zgodnie z którym wyróżnia się dwie główne strategie: single sourcing i multiple sourcing. Artykuł rozpoczyna się wprowadzeniem teoretycznym dotyczącym strategii i strategii zakupów. Następnie przeanalizowana zostaje struktura bazy dostawców wybranego przedsiębiorstwa pod kątem stosowanej strategii. Analiza opiera się na czterech kategoriach dóbr wyróżnionych w macierzy Kraljica: strategicznych, o charakterze dźwigni, wąskich gardeł i zwykłych oraz prawidłowym doborze odpowiadających im strategii single i multiple sourcing. (abstrakt oryginalny)

An interactive solution approach for multiple objective supplier selection problem with fuzzy parameters

Increased competition and the globalization of markets have made the purchasing function an increasingly vital activity in supply chain management. The most crucial decision in purchasing is the selection of appropriate suppliers which reduce purchasing costs, decrease production lead time, increase customer satisfaction and strengthen corporate competitiveness. This decision is complicated when buyers face multiple suppliers, multiple conflicting criteria and imprecise parameters. In this study a multiple sourcing supplier selection problem is considered as a multiple objective linear programming problem with fuzzy demand level and/or fuzzy aspiration levels of objectives. Three objective functions are minimizing the total monetary costs, maximizing the total quality and maximizing the service level of purchased items respectively. In order to solve the problem, a novel interactive solution procedure which integrates three well-known fuzzy mathematical models is presented. The proposed approach handles three different scenarios related with mainly the sources of fuzziness and can be efficiently used to obtain non-dominated solutions. Interactivity gives the Decision Maker opportunity to incorporate his/her preferences during the iterations of the optimization process. A numerical example is given to illustrate how the approach is utilized.

A multiple sourcing inventory model under disruption risk

Interruptions in supply can have a severe impact on company performance. Their mitigation and management is therefore an important task. Reasons for interruptions can be machine breakdowns, material shortages, natural disasters, and labour strikes. Sourcing from multiple suppliers is a strategy to deal with and reduce supply disruption risk. We study a supply chain with one buyer facing Poisson demand who can procure from a set of potential suppliers who are not perfectly reliable. Each supplier is fully available for a certain amount of time (ON periods) and then breaks down for a certain amount of time during which it can supply nothing at all (OFF periods). The problem is modeled by a Semi-Markov decision process (SMDP) where demands, lead times and ON and OFF periods of the suppliers are stochastic. The objective is to minimize the buyer's long run average cost, including purchasing, holding and penalty costs. In a numerical study, we investigate the trade-off between single and multiple sourcing, as well as keeping inventory and having a back-up supplier. The results illustrate the benefit from dual souring compared to single sourcing and show the influence of the suppliers’ characteristics cost, speed and availability on the optimal policy. Further, the value of full information about the supplier status switching events is analyzed and the performance of the optimal policy is compared to an order-up-to-S policy. As the optimal policy is very complex, a simple heuristic providing good results compared to the optimal solution is developed.

Dual-channel component replenishment problem in an assemble-to-order system

This article considers a component inventory problem in an assemble-to-order system where the assemble-to-order manufacturer faces a single-period stochastic demand for a single product created from multiple components. After the demand is realized, required components that are not available from inventory can be obtained using two sourcing channels that have different prices and lead times. The considered problem is formulated and solved analytically and the structural properties of its optimal solutions are explored. Computational experiments are performed to demonstrate the efficiency of the solution methods and comparisons are made between the performance of assemble-to-order systems with single- and dual-channel procurement approaches. It is also demonstrated that the proposed approach can be extended to the problem that considers multiple sourcing channels for components.

Reducing lead time risk through multiple sourcing: the case of stochastic demand and variable lead time

This paper studies a buyer sourcing a product from multiple suppliers under stochastic demand. The buyer uses a (Q, s) continuous review, reorder point, order quantity inventory control system to determine the size and timing of orders. Lead time is assumed to be deterministic and to vary linearly with the lot size, wherefore lead time and the associated stockout risk may be influenced by varying the lot size and the number of contracted suppliers. This paper presents mathematical models for a multiple supplier single buyer integrated inventory problem with stochastic demand and variable lead time and studies the impact of the delivery structure on the risk of incurring a stockout during lead time.

Supplier selection by F-compromise method: a case study of cement industry of NE India

In this paper, we initially conducted a brief review of supplier selection methods to find most cited multi-criteria decision making method, present trend of supplier selection and most cited criteria for supplier selection. Our study reveals that irrespective of several limitations of analytic hierarchy process (AHP), AHP and its integrated model is most preferred supplier selection method. Present research trend of supplier selection gives more emphasises on multiple sourcing instead of single sourcing. Based on our initial study, a suitable integrated model is proposed. In this integrated model, fuzzy analytic hierarchy process (FAHP) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje; in Serbian) is used to select suppliers from a predefined supplier base. Multi-objective genetic algorithm (MOGA) is used further to allocate order among suppliers. Finally, a case study is discussed to use proposed method.

Resource Co-specialization and Supplier Selection in Concurrent Sourcing

Recent research on concurrent sourcing has identified the preconditions for the simultaneous use of multiple sourcing modes (i.e. make-and-buy). However, little progress has been made to address this question: in the presence of such preconditions, how do firms decide how much to make and how much to buy? Our paper fills this gap by identifying two mechanisms that explain the make-buy balance in concurrent sourcing. First, opportunities for resource co-specialization within the firm enhance the benefits of internalization (i.e. make). Second, the supplier portfolio design affects the make-buy balance. In particular, a firm using a small number of suppliers benefits from relational contracting and consequently relies more on externalization (i.e. buy). Using a panel dataset of in-house lawyers at Fortune 500 firms, we demonstrate that concurrent sourcing strategies are tilted in favor of ‘make’ when firms have resources for co- specialization in the form of R&D, advertising, international presence, and legal expertise in their top management team. We also find that firms rely more on ‘make’ when they have a large number of suppliers of legal services. This paper contributes to theories of the firm by identifying reasons for variation in concurrent sourcing strategies, and by extending the enquiry to the domain of corporate functions.

Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach

This paper investigates an order allocation problem of a manufacturer/buyer among multiple suppliers under the risks of supply disruption. A mixed integer non-linear programming (MINLP) model is developed for order allocation considering different capacity, failure probability and quantity discounts for each supplier. We have shown that the formulated problem is NP-hard in nature and genetic algorithm (GA) approach is used to solve it. The model is illustrated through a numerical study and the result portrays that the cost of supplier has more influence on order quantity allocation rather than supplier’s failure probability.

Single or Multiple Sourcing: A Method for Determining the Optimal Size of the Supply Base

Consider a situation where a buyer has to procure an item from outside suppliers and is faced with the decision whether to procure the item from a single supplier or from multiple suppliers. Supply risk has become, in the recent years, a key consideration for a manager while taking such decisions and often mitigation of such risks is done either by building up inventory or having multiple suppliers. In this paper, we address the problem of determining the optimal number of suppliers to be engaged in order to minimize the effects of supply risks on the focal firm. In the literature, the events leading to supply risks have been classified into three categories viz. super events, semi-super events and unique events. However, none have considered all the three types of events together into a single model in order to determine the optimal size of the supply base. In the present work, we have considered all three types of events and calculated the probability of complete supply disruption. We formulate the problem as a cost minimization problem so as to find the optimal size of the supplier base that minimizes the effects of such supply disruptions. The mathematical formulation of the problem is combinatorial in nature. When a decision tree is used, a moderate size problem results in an unmanageable number of decision alternatives. In this paper, we propose mathematical theorems and rules that helps avoid considering many non-optimal decision alternatives for evaluation. The proposed solution procedure is very simple and reduces the number of decision alternatives to be evaluated significantly, thus saving time and effort in solving the problem.

An improved model for supplier selection under capacity constraint and multiple criteria

Abstract Ghodsypour and O'Brien [2001. The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. International Journal of Production Economics 73(1), 15–27] study multiple sourcing problem with multiple criteria and capacitated suppliers. Considering the buyer's quality requirements as a constraint, they develop a mixed integer non-linear programming model to find the least-cost cyclic ordering policy for the buyer. They propose to solve the model by enumerating over all possible supplier combinations. Although the problem is interesting and their analysis is correct, we point out two issues with their assumptions, namely, capacitated suppliers and cyclic ordering policy. We discuss two different capacitated supplier settings: (i) long-run average annual capacity, and (ii) exact annual capacity. First, under long-run average annual capacity assumption we propose a model which provides the same or a better solution and is much easier to solve than their model. Then, we discuss how to modify our and their models to handle exact annual capacity assumption.

Sourcing strategies in supply risk management: An approximate dynamic programming approach

In recent years, supply chains have become increasingly globalized. As a consequence, the world's supply of all types of parts has become more susceptible to disruptions. Some of these disruptions are extreme and may have global implications. Our research is based on the supply risk management problem faced by a manufacturer. We model the problem as a dynamic program, design and implement approximate dynamic programming (ADP) algorithms to solve it, to overcome the well-known curses of dimensionality. Using numerical experiments, we compare the performance of different ADP algorithms. We then design a series of numerical experiments to study the performance of different sourcing strategies (single, dual, multiple, and contingent sourcing) under various settings, and to discover insights for supply risk management practice. The results show that, under a wide variety of settings, the addition of a third or more suppliers brings much less marginal benefits. Thus, managers can limit their options to a backup supplier (contingent sourcing) or an additional regular supplier (dual sourcing). Our results also show that, unless the backup supplier can supply with zero lead time, using dual sourcing appears to be preferable. Lastly, we demonstrate the capability of the proposed method in analyzing more complicated realistic supply chains.

Multi-sourcing as an entry deterrence strategy

This paper studies the rationale for multiple sourcing. In a simple model of outsourcing that embodies technology transfer and the threat of competition from the supplier(s) due to imitation, we show that multiple sourcing helps to deter entry by the suppliers into the final goods market and enhances profitability of the outsourcing firm. Our explanation for multiple outsourcing differs from the standard arguments, which either reduce the double marginalization problem or eliminate supply bottlenecks.