A decision model for ranking suppliers in the presence of cardinal and ordinal data,weight restrictions,and nondiscretionary factors

Selecting an appropriate supplier for outsourcing is now one of the most important decisions of the purchasing department. This paper proposes a model for ranking suppliers in the presence of weight restrictions, nondiscretionary factors, and cardinal and ordinal data. A numerical example demonstrates the application of the proposed method.     

Order splitting in single sourcing with scheduled-release orders

Traditional inventory models assume that a buyer places one order with a supplier in each order cycle. A large number of researchers have studied the benefits of dual sourcing such that an order quantity is split and placed simultaneously with two suppliers. We show that many of the benefits of dual sourcing are due to order splitting rather than using two suppliers. We investigate order splitting with one supplier such that the first part of the order is sent out immediately but the second part of the order is released later (scheduled-release). Through extensive computational results, we show that in many situations where dual sourcing or the use of a cheaper supplier would be cost effective, single sourcing with order splitting using scheduled-release orders is better. The paper provides a quantitative rationale to continue with one supplier. We also summarize the qualitative reasons to prefer single sourcing or multiple sourcing.     

A decision model for supplier selection in the presence of dual-role factors

Best suppliers help manufacturers to gain and sustain a competitive advantage by improving product quality, reducing cost, shortening lead-time and so on. The crucial role played by suppliers in a supply chain renders the selection of a supplier or suppliers a significant work that attracts the attention of both researchers and practitioners. In the literature, data envelopment analysis (DEA) as a nonparametric technique is often applied to conduct a performance analysis. Little attention, however, has been paid to dual-role factors in the supplier selection process. This paper proposes a novel two-step approach to select suppliers in the presence of dual-role factors. As a combination of DEA and two-person zero-sum game theory, our work considers the strategic behaviour of the external environment and is able to handle the supplier selection in a competitive environment. Moreover, the proposed model circumvents the requirement to specify the status of a dual-role factor ahead of an evaluation and is easy to solve. Finally, the applicability of the procedure is illustrated by an application to a data set of 18 suppliers.     

Modeling a stochastic multi-objective supplier quota allocation problem with price-dependent ordering

One of the interesting subjects in supply chain management is supply management, which generally relates to the activities regarding suppliers such as empowerment, evaluation, partnerships and so on. A major objective of supplier evaluation involves buyers determining the optimal quota allocated to each supplier when placing an order. In this paper, we propose a multi-objective model in which purchasing cost, rejected units, and late delivered units are minimized, while the obtained total score from the supplier evaluation process is maximized. We assume that the buyer obtains multiple products from a number of predetermined suppliers. The buyer faces a stochastic demand with a probability distribution of Poisson regarding each product type. A major assumption is that the supplier prices are linearly dependent on the order size of each product. Since demand is stochastic, the buyer may incur holding and stockout costs in addition to the regular purchasing cost. We use the well-known L-1 metric method to solve the supplier evaluation problem by utilizing two meta-heuristic algorithms to solve the corresponding mathematical problems.     

The Effect of Acceptance Sampling and Risk Aversion on the Quality Delivered by Suppliers

Firms often use acceptance sampling to monitor the quality of the raw materials and components delivered by suppliers. In this paper, we use numerical methods to examine how a risk averse supplier reacts to the acceptance sampling plan used by a customer. We assume that the supplier produces and delivers a quality level that maximizes the supplier's expected utility. We examine the sensitivity of the optimal delivered quality to changes in the price, to changes in the supplier's level of risk aversion, and to changes in the parameters of the customer's sampling plan. We conclude that risk averse suppliers deliver higher quality, that higher capability suppliers do not necessarily deliver higher quality, and that the optimal quality is sensitive to the lot size. We also conclude that since the risk of rejection motivates suppliers to improve quality, customers have an economic justification for using acceptance sampling even when there is no statistical justification.     

Integrated analytical hierarch process and mathematical programming to supplier selection problem with quantity discount

In this article an integration of analytical hierarchy process and non-linear integer and multi-objective programming under some constraints such as quantity discounts, capacity, and budget is applied to determine the best suppliers and to place the optimal order quantities among them. This integration-based multi-criteria decision making methodology takes into account both qualitative and quantitative factors in supplier selection. While the analytical hierarchy process matches item characteristics with supplier characteristics, non-linear integer programming model analytically determines the best suppliers and the optimal order quantities among the determined suppliers. The objectives of the mathematical models constructed are maximizing the total value of purchase (TVP), minimizing the total cost of purchase (TCP) or maximizing TVP and minimizing TCP simultaneously. In addition, several “what if” scenarios are facilitated and the quality of the resulting models is evaluated on real-life data.     

Suppliers selection in the presence of both cardinal and ordinal data

Traditionally, supplier selection models are based on cardinal data with less emphasis on ordinal data. However, with the widespread use of manufacturing philosophies such as just-in-time (JIT), emphasis has shifted to the simultaneous consideration of cardinal and ordinal data in supplier selection process. To select the best suppliers in the presence of both cardinal and ordinal data, this paper proposes an innovative method, which is based on imprecise data envelopment analysis (IDEA). A numerical example demonstrates the application of the proposed method.     

基于改进支持向量机的高端装备制造业供应商分类研究

针对现有供应商分类方法应用于高端装备制造业供应商所存在的局限性,从相互依赖视角构建了高端装备制造业供应商分类指标体系,提出了基于改进支持向量机的高端装备制造业供应商分类模型。该模型根据供应商误分代价不同,设计代价敏感支持向量机分类器,利用粒子群算法优化分类器的参数,并采用概率输出方法对多个优化的二类分类器的结果进行组合以实现多类分类。实验结果表明,该模型提高了现有方法的分类效果,可以降低总体误分代价,有效识别出对高端装备制造企业具有重大影响的供应商,为高端装备制造企业实施供应商分类管理提供了依据。     

Are more suppliers better?: generalising the Guo and Ganeshan procedure

In multiple supplier inventory models, where several suppliers are used to replenish the stock of one item, computation of mean and variance of supplier lead times requires the knowledge of the moments of order statistics from the parent lead time distribution. This article presents a general procedure of finding the moments of supplier lead times in multiple supplier inventory models. The procedure is based on using the Generalised Lambda Distribution (GLD) to approximate the lead time distribution. Numerical examples are provided to validate our procedure. The proposed procedure has the advantage that the computations involved are very simple and can be used for any continuous unimodal lead time distribution.     

A deterministic,multi-item inventory model with supplier selection and imperfect quality

This paper considers the scenario of supply chain with multiple products and multiple suppliers, all of which have limited capacity. We assume that received items from suppliers are not of perfect quality. Items of imperfect quality, not necessarily defective, could be used in another inventory situation. Imperfect items are sold as a single batch, prior to receiving the next shipment, at a discounted price. The demand over a finite planning horizon is known, and an optimal procurement strategy for this multi-period horizon is to be determined. Each of products can be sourced from a set of approved suppliers, a supplier-dependent transaction cost applies for each period in which an order is placed on a supplier. A product-dependent holding cost per period applies for each product in the inventory that is carried across a period in the planning horizon. Also a maximum storage space for the buyer in each period is considered. The decision maker, the buyer, needs to decide what products to order, in what quantities, with which suppliers, and in which periods. Finally, a genetic algorithm (GA) is used to solve the model.     

Impact of supply base heterogeneity in electronic markets

Internet based marketplaces have enabled industrial buyers to locate suppliers from geographically diverse locations. This has resulted in increased variations in certain supplier parameters such as capacity and cost among the participating suppliers. However, the impact of this increased heterogeneity on the procurement practices are not well understood. In this paper we consider three supplier parameters that can affect the price the buyer pays and the number of suppliers that the buyer will select for award of contract. These attributes are capacity, production cost and demand for supplier’s capacity. We show how these parameters impact the price that a supplier quotes. We also show how the buyer will determine the optimum number of suppliers using a reverse auction mechanism when he does not have perfect knowledge of the suppliers’ parameters. Our model suggests that buyers need to adjust some of the input parameters while procuring capacity from a heterogeneous supply base. For instance, buyers need to pre-qualify more suppliers if the supply base has greater heterogeneity.     

竞争环境下随机产出供应商的融资策略研究

考虑由一个制造商和两个竞争性供应商组成的供应链系统,其中一个供应商存在资金短缺的问题,自有资金不足以完成正常生产任务,需要通过融资获得资金完成生产。针对供应链内部和外部两种融资模式,分别构建了供应链成员的收益模型,计算了不同融资方式下的决策结果,并在此基础上对两种融资模式进行了比较分析。结果表明,资金短缺的供应商生产不确定性较低时,供应链成员所能获得的利润较高,并且内部融资下资金短缺的供应商生产积极性高于外部融资。同时发现,对融资方式偏好的不一致将导致制造商利润损失,此时制造商可以采取一定的激励措施引导供应商选择合适的融资方式以提高整体收益。     

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.     

Heuristics for sourcing from multiple suppliers with alternative quantity discounts

In this paper, we analyze the impact of supplier pricing schemes and supplier capacity limitations on the optimal sourcing policy for a single firm. We consider the situation where the total quantity to be procured for a single period is known by the firm and communicated to the supplier set. In response to this communication, each supplier quotes a price and a capacity limit in terms of a maximum quantity that can be supplied to the buyer. Based on this information, the buyer makes a quantity allocation decision among the suppliers and corresponding to this decision is the choice of a subset of suppliers who will receive an order. Based on industry observations, a variety of supplier pricing schemes from the constituent group of suppliers are analyzed, including linear discounts, incremental units discounts, and all units discounts. Given the complexity of the optimization problem for certain types of pricing schemes, heuristic solution methodologies are developed to identify a quantity allocation decision for the firm. Through an extensive computational comparison, we find that these heuristics generate near-optimal solutions very quickly. Data from a major office products retailer is used to illustrate the resulting sourcing strategies given different pricing schemes and capacity limitations of suppliers in this industry. We find for the case of capacity constrained suppliers, the optimal quantity allocations for two complex pricing schemes (linear discount, and incremental units discount) are such that at most one selected supplier will receive an order quantity that is less than its capacity.     

Optimal purchasing policy in a two-component assembly system with different purchasing contracts for each component

This paper considers an assembly system where a firm produces a single product which is assembled using two types of components (component 1 and component 2). The components are provided by individual suppliers (supplier 1 and supplier 2). We assume that the firm makes different procurement contracts with supplier 1 and supplier 2. To supplier 1, the firm specifies the maximum inventory level of component 1 and makes a commitment to purchase the component as long as its inventory level is below this target level. To supplier 2, the firm has the option of purchasing or rejecting component 2 at each instant supplier 2 provides it. Formulating our model as a Markov decision problem, we identify a component 2 purchasing policy which maximizes the firm’s profits subject to the costs of rejecting component 1, holding component 2, and purchasing component 2. We also investigate how the changes in the sales price and cost parameters affect the optimal purchasing policy. Finally, we present numerical study for the optimal performance evaluation.This material is based upon work supported by the Korea Science and Engineering Foundation (KOSEF) through the Northeast Asia e-Logistics Research Center at University of Incheon.     

Supplier selection using axiomatic fuzzy set and TOPSIS methodology in supply chain management

The current paper presents a comprehensive methodology for supplier selection. In the first stage, the linguistic values expressed as trapezoidal fuzzy numbers are used to assess the weights of the criteria. The Axiomatic Fuzzy Set clustering (AFS) method, which handles ambiguity and fuzziness in the supplier selection problem effectively, is applied to cluster the suppliers and evaluate each potential supplier that aims at obtaining initial supplier ranking. In the second stage, the Fuzzy Analytic Hierarchy Process (FAHP) model is constructed to determine the weight of various quantitative and qualitative criteria. To address multiple decision criteria in supplier ranking, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to select the final suppliers. A numerical example composed of 30 suppliers and 6 criteria is studied, and the experimental results show that the proposed evaluation framework is suitable for supplier selection decisions even with the dependent criteria/attributes.     

Manufacturer cooperation in supplier development under risk

Supplier development involves efforts undertaken by manufacturing firms to improve their suppliers’ capabilities and performance. These improvement efforts can be targeted at a variety of areas such as quality management, product development, and cost reduction. Since supplier development requires investments on the part of the manufacturer, it is important to optimally allocate investment dollars among multiple suppliers to minimize risk while maintaining an acceptable level of return. This paper presents a set of optimization models that address this issue. We consider two scenarios: single-manufacturer and multiple suppliers (SMMS) and two-manufacturer and multiple suppliers (TMMS). In the SMMS case, we suggest optimal investments in various suppliers by effectively considering risk and return. The TMMS case investigates whether manufacturers with differing capabilities could gain risk reduction benefits from cooperating with each other in supplier development. Through illustrative applications, we identify conditions in which both cooperation and non-cooperation are beneficial for manufacturers. Under conditions of cooperation, we propose optimal investments for manufacturers to achieve high levels of risk reduction benefits.     

竞争性供应商选择电商平台销售模式的互动机理研究

通过构建两个供应商和两个电商平台之间的博弈模型,探讨竞争性供应商选择电商平台销售模式的互动机理。研究发现:(1)竞争性供应商在电商平台销售模式选择上存在互动机理。(2)在短期均衡中,当佣金费率较高或较低时,供应商的销售模式选择不受竞争对手影响;当佣金费率适中时,供应商的销售模式选择将采取跟随策略,与竞争对手的选择保持一致。(3)在长期均衡中,当佣金费率较低时,两个供应商均会选择代销模式;当佣金费率较高时,两个供应商均会选择转销模式;当供应商自身的佣金费率较高(较低),竞争对手的佣金费率较低(较高)时,供应自身选择转销(代销)模式,竞争对手会选择代销(转销)模式。     

基于双层规划的集群式供应链供应商激励机制研究

对存在多级供应商的集群式供应链中稀缺资源供应的激励问题进行了研究。在假设市场某种零部件稀缺,制造商的产量直接与零部件供应量链相关,而零部件供应量直接与一级供应商努力程度相关的情况下,建立了相应的单多双层规划模型,分别研究了制造商针对供应商结盟与不结盟的情况下的最优激励机制。最后进行了仿真,结果表明本文的激励机制能较好激励供应商并能有效防范其结盟。     

Analysis of a Continuous Review Stock-control Model with Multiple Suppliers

This paper analyses the use of multiple suppliers to replenish one stock item. The replenishment order is split into several portions, one for each supplier, and orders for these portions are placed simultaneously with the respective suppliers. Approximate expressions are derived for the mean and variance of the effective lead time, which is the minimum of the individual lead times of each supplier. These results give effective lead-time-demand mean and variance that are smaller than those of the individual suppliers. The results can be used to lower the operating cost of the stocking policy.