The optimal number of suppliers considering the costs of individual supplier failures

This paper utilizes the decision tree approach to determine the optimal number of suppliers in the presence of supplier failure risks. Previous proposed models have considered only two states of nature: all suppliers fail to deliver and not all suppliers fail to deliver. In practice, however, there is clearly a partial loss associated with the failure of any individual supplier. We present models that allow a more realistic decision-making process by taking into consideration the independent risks of individual supplier failures when the probability of failure for each of the suppliers is equal as well as the case where the probability of failure from each of the suppliers is not equal. We also consider various levels of supplier failure probability and possible procurement or operating cost savings gained from using less reliable suppliers. The results indicate that when suppliers are highly reliable, sole sourcing is the lowest cost approach under all experimental conditions. However, as the suppliers become less reliable, additional suppliers may be required to obtain the lowest cost. Finally, it was shown that only in the extreme conditions of unreliable suppliers, high loss to operational cost per supplier, and low ability to mitigate the failure from a partial set of suppliers, having a large number of suppliers is an effective strategy.     

Optimal Inventory Control with Dual‐Sourcing,Heterogeneous Ordering Costs and Order Size Constraints

We consider a dual‐sourcing inventory system, where procuring from one supplier involves a high variable cost but negligible fixed cost whereas procuring from the other supplier involves a low variable cost but high fixed cost, as well as an order size constraint. We show that the problem can be reduced to an equivalent single‐sourcing problem. However, the corresponding ordering cost is neither concave nor convex. Using the notion of quasi‐convexity, we partially characterize the structure of the optimal policy and show that it can be specified by multiple thresholds which determine when to order from each supplier and how much. In contrast to previous research, which does not consider order size constraints, we show that it is optimal to simultaneously source from both suppliers when the beginning inventory level is sufficiently low. We also show that the decision to source from the low‐cost supplier is not monotonic in the inventory level. Our results require that the variable costs satisfy a certain condition which guarantees quasi‐convexity. However, extensive numerical results suggest that our policy is almost always optimal when the condition is not satisfied. We also show how the results can be extended to systems with multiple capacitated suppliers.     

Push or Pull? Auctioning Supply Contracts

Consider a buyer, facing uncertain demand, who sources from multiple suppliers via online procurement auctions (open descending price‐only auctions). The suppliers have heterogeneous production costs, which are private information, and the winning supplier has to invest in production capacity before the demand uncertainty is resolved. The buyer chooses to offer a push or pull contract, for which the single price and winning supplier are determined via the auction. We show that, with a pull contract, the buyer does not necessarily benefit from a larger number of suppliers participating in the auction, due to the negative effect of supplier competition on the incentive of supplier capacity investment. We thus propose an enhanced pull mechanism that mitigates this effect with a floor price. We then analyze and compare the outcomes of auctions for push and (enhanced) pull contracts, establishing when one form is preferred over the other based on the buyer's profits. We also compare our simple, price‐only push and pull contract auctions to the optimal mechanisms, benchmarking the performance of the simple mechanisms as well as establishing the relative importance of auction design and contract design in procurement auctions.     

多部件采购外包的两阶段均衡投标策略

本文考虑一个由单个制造商和多个供应商群体组成的供应链——该制造商需要采购多个部件,对于每个部件在市场上均存在多个供应商。不同于传统的从各供应商群体分别采购各个部件,制造商考虑将所有部件的采购整体外包给某个供应商。在第一阶段,制造商使用一级价格密封投标的逆向拍卖来确定赢得整体采购合约的供应商。接下来,第一阶段投标的获胜者生产其所能供应的部件,并使用逆向拍卖向第一阶段中未获胜的其他竞标者采购其余部件。我们分析了供应商在两个阶段的均衡竞价策略,并比较了制造商在亲自逐件采购和外包整体采购两种情况下的期望采购成本。我们证明了在两种机制下制造商的总的期望采购成本相等。     

Supplier Selection for Framework Agreements in Humanitarian Relief

In this study, we consider the supplier selection problem of a relief organization that wants to establish framework agreements (FAs) with a number of suppliers to ensure quick and cost‐effective procurement of relief supplies in responding to sudden‐onset disasters. Motivated by the FAs in relief practice, we focus on a quantity flexibility contract in which the relief organization commits to purchase a minimum total quantity from each framework supplier over a fixed agreement horizon, and, in return, the suppliers reserve capacity for the organization and promise to deliver items according to pre‐specified agreement terms. Due to the uncertainties in demand locations and amounts, it may be challenging for relief organizations to assess candidate suppliers and the offered agreement terms. We use a scenario‐based approach to represent demand uncertainty and develop a stochastic programming model that selects framework suppliers to minimize expected procurement and agreement costs while meeting service requirements. We perform numerical experiments to understand the implications of agreement terms in different settings. The results show that supplier selection decisions and costs are generally more sensitive to the changes in agreement terms in settings with high‐impact disasters. Finally, we illustrate the applicability of our model on a case study.     

Dual Sourcing Under Random Supply Capacities: The Role of the Slow Supplier

Sourcing from multiple suppliers with different characteristics is common in practice for various reasons. This paper studies a dynamic procurement planning problem in which the firm can replenish inventory from a fast and a slow supplier, both with uncertain capacities. The optimal policy is characterized by two reorder points, one for each supplier. Whenever the pre‐order inventory level is below the reorder point, a replenishment order is issued to the corresponding supplier. Interestingly, the reorder point for the slow supplier can be higher than that of the fast even if the former has a higher cost, lower reliability, and smaller capacity than the latter, suggesting the possibility of ordering exclusively from an inferior slow supplier in the short term. Moreover, the firm may allocate a larger portion of the long‐term total order quantity to the slow supplier than to the fast, even if the former does not possess any cost or reliability advantage over the latter. Such phenomena, different from the observations made in previous studies, happen when the demand is uncertain and the supply is limited or unreliable. Our observations highlight the importance of incorporating both demand uncertainty and supplier characteristics (i.e., cost, lead time, capacity and uncertainty) in a unified framework when formulating supplier selection and order allocation strategies.     

Optimal Procurement Design of an Assembly Supply Chain with Information Asymmetry

We study a decentralized assembly supply chain in which an assembler (she) assembles a set of n components, each produced by a different supplier (he), into a final product to satisfy an uncertain market demand. Each supplier holds private cost information to himself, for which the assembler only has a subjective estimate. Furthermore, the assembler believes that the suppliers' costs follow a joint discrete probability distribution. The assembler aims to design an optimal menu of contracts to maximize her own expected profit. The assembler's problem is a complex multi‐dimensional constrained optimization problem. We prove that there exists a unique optimal menu of contracts for the assembler, and we further develop an efficient algorithm with a complexity of O(n) to compute the optimal contract. In addition, we conduct a comprehensive sensitivity analysis to analyze how environmental parameters affect individual firm's performance and the value of information to the assembler, to each supplier, and to the supply chain. Our results suggest that each supplier's private cost information becomes more valuable to the assembler and each supplier when the average market demand increases or when the final product unit revenue increases. Surprisingly, when a supplier's cost volatility increases and its mean remains the same, the value of information to the assembler or to each supplier does not necessarily increase. Furthermore, we show that when the suppliers' cost distributions become more positively correlated, the suppliers are always worse off, but the assembler is better off. However, the value of information for the assembler might increase or decrease.     

SHORT‐TERM E‐PROCUREMENT STRATEGIES VERSUS LONG‐TERM CONTRACTS

In this paper we compare the following procurement strategies based on their expected costs: strategic partnership, which is based on a long‐term relationship with a single supplier; online search, which is a short‐term strategy; and a combined strategy, which is some combination of the first two strategies. In addition, we determine for the online search and combined strategy the optimal number of suppliers to contact for a price quote, and analyze how it depends on the various cost and demand parameters. The main contribution of this paper is that it does not assume a single procurement strategy, but rather compares three alternatives.     

Managing Supplier Competition and Sourcing Sequence for Component Manufacturing

We consider a setting in which a manufacturer sequentially sources two components and uses reverse auction to select a supplier with the lowest bidding price for each component. The manufacturer chooses a quantity to order from each supplier and a price for selling the final product. We show that the interplay between the direct competition faced by suppliers in providing their respective components and the sequence whereby the manufacturer sources components influence system performance in a subtle, and sometimes dramatic, way. As the direct competition for the early sourced component intensifies, the profit of its supplier will deteriorate while the profits of the other firms will improve. As the direct competition for the late sourced component intensifies, however, the profit of its supplier may improve, and the profits of the other supplier, the manufacturer, and the system can all decrease. Compared with when the manufacturer simultaneously sources the components, sequentially sourcing the components can benefit the manufacturer and every supplier. Furthermore, all the channel parties can unanimously agree on a specific sourcing sequence. All of these signify the importance for manufacturers to take appropriate measures to manage their sourcing procedures and the competition environments faced by their suppliers.     

供应商产出随机与供应中断下的双源采购决策

研究在需求与供应不确定条件下，一个制造商向两个存在产出随机和供应中断可能性的供应商采购零部件时的最优决策问题。论文分别针对两种情况：（1）只考虑供应商产出随机，（2）同时考虑供应商产出随机与供应中断可能性，建立了两种情况下制造商与两个供应商之间的博弈模型，证明了在两种情况下，制造商均存在最优订货量、供应商存在最优生产量使自身利润最优。研究发现，供应商产出稳定性的改善（即产出随机性降低）不仅可以使自身获得更大的利润，也使制造商获得更大的利润。供应商最优生产量随着自身产出随机性降低先增后减。同时，供应商供应中断可能性的降低会为自身带来更大的收益，也为制造商带来更大的收益。     

Supply Chain Sourcing Under Asymmetric Information

We study a supply chain with two suppliers competing over a contract to supply components to a manufacturer. One of the suppliers is a big company for whom the manufacturer's business constitutes a small part of his business. The other supplier is a small company for whom the manufacturer's business constitutes a large portion of his business. We analyze the problem from the perspective of the big supplier and address the following questions: What is the optimal contracting strategy that the big supplier should follow? How does the information about the small supplier's production cost affect the profits and contracting decision? How does the existence of the small supplier affect profits? By studying various information scenarios regarding the small supplier's and the manufacturer's production cost, we show, for example, that the big supplier benefits when the small supplier keeps its production cost private. We quantify the value of information for the big supplier and the manufacturer. We also quantify the cost (value) of the alternative‐sourcing option for the big supplier (the manufacturer). We determine when an alternative‐sourcing option has more impact on profits than information. We conclude with extensions and numerical examples to shed light on how system parameters affect this supply chain.     

存在信息更新时的双源采购和后备生产结合策略研究

在现实的采购运作中，双源采购和后备生产是两种最常见的风险应对策略。本文在考虑信息更新的情况下，探讨了一个两阶段动态采购决策模型：第一阶段，制造商向存在供应风险的一个或两个主供应商订货；第二阶段，制造商根据主供应商风险信息的更新，决定是否向供应可靠但价格较高的后备供应商订货。本文得到了两阶段的最优采购策略，分析发现，当固定采购成本较低时：若潜在市场需求较小，双源采购将排斥后备生产；若潜在市场需求较大，双源采购和后备生产共存；若潜在市场需求适中，后备生产可能排斥双源采购，两者也可能共存。特别地，在潜在市场需求适中时，可靠性改进概率的增大或后备供应商生产成本的降低，将使后备生产趋于排斥双源采购；反之，可靠性改进概率的减小、固定采购成本的降低或后备生产成本的增加，将使两者趋于共存。     

An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems if suppliers have capacity or other different constraints two problems will exist: which suppliers are the best and how much should be purchased from each selected supplier? In this paper an integrated approach of analytic network process (ANP) and multi-objective mixed integer linear programming (MOMILP) is proposed. This integrated approach considers both tangible and intangible factors in choosing the best suppliers and defines the optimum quantities among selected suppliers to maximize the total value of purchasing (TVP), and to minimize the total cost and total defect rate and to balance the total cost among periods. The priorities are calculated for each supplier by using ANP. Four different plastic molding firms working with a refrigerator plant are evaluated according to 14 criteria that are involved in the four clusters: benefits, opportunities, costs and risks (BOCR). The priorities of suppliers will also be used as the parameters of the first objective function. This multi-objective and multi-period real-life problem is solved by using previous techniques and a reservation level driven Tchebycheff procedure (RLTP). Finally the most preferred nondominated solutions are determined by considering the decision maker's (DM's) preferences and the results obtained by these techniques are compared.     

Integrated Order Scheduling and Packing

We consider an integrated production–distribution scheduling model in a make‐to‐order supply chain consisting of one supplier and one customer. The supplier receives a set of orders from the customer at the beginning of a planning horizon. The supplier needs to process all the orders at a single production line, pack the completed orders to form delivery batches, and deliver the batches to the customer. Each order has a weight, and the total weight of the orders packed in a batch must not exceed the capacity of the delivery batch. Each delivery batch incurs a fixed distribution cost. The problem is to find jointly a schedule for order processing and a way of packing completed orders to form delivery batches such that the total distribution cost (or equivalently, the number of delivery batches) is minimized subject to the constraint that a given customer service level is guaranteed. We consider two customer service constraints—meeting the given deadlines of the orders; or requiring the average delivery lead time of the orders to be within a given threshold. Several problems of the model with each of those constraints are considered. We clarify the complexity of each problem and develop fast heuristics for the NP‐hard problems and analyze their worst‐case performance bounds. Our computational results indicate that all the heuristics are capable of generating near optimal solutions quickly for the respective problems.     

Dual Sourcing in a Serial System

We consider a finite‐horizon, periodic‐review model for a serial system with two uncapacitated external suppliers. While one external supplier (regular supplier) ships to the most upstream stage, the other one (emergency supplier) ships directly to a downstream stage. For this system, with general lead‐times we show that the optimal cost function is multimodular with respect to inventories in transit and inventories at different stages and obtain results that show how the optimal order quantities change with respect to these inventories. Our analysis also leads to sufficient conditions under which modified echelon‐basestock‐type policies are optimal.     

Supplier Selection via Tournaments

In this paper, we study the performance of a sourcing mechanism gaining popularity in industrial procurement environments; a tournament. Under a tournament, a buyer initially procures her parts from two suppliers with possibly different quality levels, for T time periods, i.e., she parallel sources. During this time, the buyer is able to observe noisy signals about the suppliers' quality. At time T, she selects the supplier with the highest observed performance and awards it the remainder of her business. We characterize the optimal duration of the tournament as a function of various market parameters, including information and investment costs. Furthermore, we demonstrate that a tournament can be more profitable for the buyer than selecting the highest quality supplier at time T = 0 and sole sourcing entirely.     

Supplier competition and its impact on firm׳s sourcing strategy

Most research on firms׳ sourcing strategies assumes that wholesale prices and reliability of suppliers are exogenous. It is of our interest to study suppliers׳ competition on both wholesale price and reliability and firms׳ corresponding optimal sourcing strategy under complete information. In particular, we study a problem in which a firm procures a single product from two suppliers, taking into account suppliers׳ price and reliability differences. This motivates the suppliers to compete on these two factors. We investigate the equilibria of this supplier game and the firm׳s corresponding sourcing decisions. Our study shows that suppliers׳ reliability often plays a more important role than wholesale price in supplier competition and that maintaining high reliability and a high wholesale price is the ideal strategy for suppliers if multiple options exist. The conventional wisdom implies that low supply reliability and high demand uncertainty motivate dual-sourcing. We notice that when the suppliers׳ shared market/transportation network is often disrupted and demand uncertainty is high, suppliers׳ competition on both price and reliability may render the sole-sourcing strategy to be optimal in some cases that depend on the format of suppliers׳ cost functions. Moreover, numerical study shows that when the cost or vulnerability (to market disruptions) of one supplier increases, its profit and that of the firm may not necessarily decrease under supplier competition.     

Role of Random Capacity Risk and the Retailer in Decentralized Supply Chains with Competing Suppliers

This research considers a supply chain under the following conditions: (i) two heterogeneous suppliers are in competition, (ii) supply capacity is random and pricing is endogenous, (iii) consumer demand, with and without an intermediate retailer, is price dependent. Specifically, we examine how uncertainty in supply capacity affects optimal ordering and pricing decisions, supplier and retailer profits, and the incentives to reduce such uncertainty. When two suppliers sell through a monopolistic retailer, supply uncertainty not only affects the retailer's diversification strategy for replenishment, but also changes the suppliers’ wholesale price competition and the incentive for reducing capacity uncertainty. In this dual‐sourcing model, we show that the benefit of reducing capacity uncertainty depends on the cost heterogeneity between the suppliers. In addition, we show that a supplier does not necessarily benefit from capacity variability reduction. We contrast this incentive misalignment with findings from the single‐supplier case and a supplier‐duopoly case where both suppliers sell directly to market without the monopolistic retailer. In the latter single‐supplier and duopoly cases, we prove that the unreliable supplier always benefits from reducing capacity variability. These results highlight the role of the retailer's diversification strategy in distorting a supplier's incentive for reducing capacity uncertainty under supplier price competition.     

Supply Diversification with Responsive Pricing

We study sourcing and pricing decisions of a firm with correlated suppliers and a price‐dependent demand. With two suppliers, the insight—cost is the order qualifier while reliability is the order winner—derived in the literature for the case of exogenously determined price and independent suppliers, continues to hold when the suppliers' capacities are correlated. Moreover, a firm orders only from one supplier if the effective purchase cost from him, which includes the imputed cost of his unreliability, is lower than the wholesale price charged by his rival. Otherwise, the firm orders from both. Furthermore, the firm's diversification decision does not depend on the correlation between the two suppliers' random capacities. However, its order quantities do depend on the capacity correlation, and, if the firm's objective function is unimodal, the total order quantity decreases as the capacity correlation increases in the sense of the supermodular order. With more than two suppliers, the insight no longer holds. That is, when ordering from two or more suppliers, one is the lowest‐cost supplier and the others are not selected on the basis of their costs. We conclude the paper by developing a solution algorithm for the firm's optimal diversification problem.     

An integrated multiobjective decision making process for supplier selection and order allocation

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems if suppliers have capacity or other different constraints two problems will exist: which suppliers are the best and how much should be purchased from each selected supplier? In this paper an integrated approach of analytic network process (ANP) and multi-objective mixed integer linear programming (MOMILP) is proposed to consider both tangible and intangible factors in choosing the best suppliers and define the optimum quantities among selected suppliers to maximize the total value of purchasing and minimize the budget and defect rate. The priorities are calculated for each supplier by using ANP. Four different plastic molding firms working with a refrigerator plant are evaluated according to 14 criteria that are involved in the four clusters: benefits, opportunities, costs and risks (BOCR). Also the priorities of suppliers will be used as the parameters of the first objective function. This multi-objective real-life problem was solved by using ε$\epsilon$-constraint method and a reservation level driven Tchebycheff procedure. Finally, the most preferred nondominated solutions were determined by considering decision maker's (DM) preferences and the results obtained by these techniques are compared.