基于安全多方计算的产能约束供应商协同供货研究

针对供应链中核心制造商对产能约束的多个零部件供应商的“末位惩罚”问题, 构建基于安全多方计算的多个供应商协同模型, 并分析了分散决策和供应商协同决策下各个成员的最优决策. 通过Monte Carlo 模拟仿真方法和数例分析表明, 采用基于安全多方计算的供应链信息共享机制, 可在保护供应商的私有产能信息不公开的同时还能进行集中决策, 从而明显降低供应商的损失成本和整个供应链的全局损失成本, 最终让供应链达到帕累托改善.

     

Impact of decision sequence of pricing and quality investment in decentralized assembly system

The quality and price of suppliers’ products directly influence the potential demand in the market. This paper studies the impact of decision sequence in a two-echelon assembly system with demand uncertainty: suppliers first decide wholesale price and quality investment for their components, and then the manufacturer decides product price after the uncertainty in demand is resolved. We consider three scenarios in the stage of suppliers’ decisions. In case 1, both suppliers simultaneously determine quality investment followed by the simultaneous setting of wholesale prices. In case 2, both suppliers make quality investment and wholesale price decisions simultaneously. In case 3, one supplier acts as a leader and moves first to announce quality investment and wholesale price, and the other supplier moves later. We compare all decision models from each firms’ perspective. Our analysis reveals that the second decision sequence is the best option for the manufacturer and the integrated supply chain and the first decision sequence is the best option for suppliers as a whole, but there is no clear dominating choice for each supplier. The optimal choices of suppliers are mainly determined by the cost structure. We examine and discuss the relation between system parameters and the incentives of suppliers in choosing decision sequence.     

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.     

供应商能力有约束的混合型多属性决策方法

研究了一种混合型多属性决策方法,用于多资源模型的供应商选择问题,并将供应商选择过程分为2个阶段.首先用熵系数模型确定评价指标的客观权重,利用逼近理想解排序法模型排定方案优劣次序;然后建立多目标规划模型确定采购数量在入选供应商之间的分配;最后给出了一个算例,算例结果表明该方法可行且有效.     

Evaluation of sourcing contracts in wood supply procurement using simulation

Procurement operations in forest companies are exposed to various risks, which may increase procurement costs. Examples of risks are the contract's unreliability and contract breach. Deterministic planning models cannot perfectly reflect the complexities of real-world applications in the presence of sourcing risks when the future is uncertain. In practice, forest industries use contracts to guarantee the wood supply. Monthly forecasts are prepared for the delivery volume and are primarily based on the experience of procurement staff and the total volume of contracts. Missed deliveries in the contracts lead to a mismatch between the supply and demand for wood fiber and increase the costs of procurement as a result of high inventory costs or expensive purchases from the open market. Previous studies on simulating the impact of sourcing risks on procurement operations have been conducted; however, none have addressed the selection of procurement contracts in the presence of sourcing risks. In forest industry, numerous suppliers are available with sourcing contracts. Each contract possesses its own characteristics such as flexibility, volume, schedule, and price of delivery. A Monte Carlo simulation approach is implemented to analyze the behavior of a deterministic planning approach. Random events are generated by formulating different types of sourcing risks, having either short- or long-term impact. The simulation is embedded with a deterministic planning model in each period. Results showed that management of sourcing risks is easier with flexible contracts than with fixed contracts, despite their higher purchasing cost.     

Integrated product line design and supplier selection: A multi-objective optimization paradigm

Product line design is commonly used to provide higher product variety for satisfying diversified customer needs. To reduce the cost and development time and improve quality of products, companies quite often consider sourcing. Conventionally, product line design and supplier selection are dealt with separately. Some previous studies have been attempted to consider product line design and supplier selection simultaneously but two shortcomings were noted. First, the previous studies considered several objectives as a single objective function in the formulation of optimization models for the integrated problem. Second, positions of product variants to be offered in a product line in competitive markets are not clearly defined that would affect the formulation of marketing strategies for the product line. In this paper, a methodology for integrated product line design and supplier selection is proposed to address the shortcomings in which a multi-objective optimization model is formulated to determine their specifications and select suppliers for maximizing the profit, quality and performance as well as minimizing the cost of the product line. In addition, joint-spacing mapping is introduced to help estimate market share of products and indicate positions of product variants. The proposed methodology can provide decision makers with a better tradeoff among various objectives of product line design, and define market positions of product variants explicitly. The results generated based on the methodology could help companies develop product lines with higher profits, better product quality and larger market share to be obtained. A case study of a product line design of notebook computers was performed to illustrate the effectiveness of the proposed methodology. The results have shown that Pareto optimal product line designs and the specifications of product variants can be determined. Suppliers of components and modules can be selected with considerations of minimum sourcing cost, and maximum performance and quality of product variants. Prices and positions of the product variants can also be determined.     

Supply chain modeling in uncertain environment with bi-objective approach

Supply chain is viewed as a large-scale system that consists of production and inventory units, organized in a serial structure. Uncertainty is the main attribute in managing the supply chains. Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships among various entities exist in the SC. Uncertainty may result from customer’s demand variability or unreliability in external suppliers. In this paper we develop an inventory model for an assembly supply chain network (each unit has at most one immediate successor, but any number of immediate predecessors) which fuzzy demand for single product in one hand and fuzzy reliability of external suppliers in other hand affect on determination of inventory policy in SCM. External supplier’s reliability has determined using a fuzzy expert system. Also the performance of supply chain is assessed by two criteria including total cost and fill rate. To solve this bi-criteria model, hybridization of multi-objective particle swarm optimization and simulation optimization is considered. Results indicate the efficiency of proposed approach in performance measurement.     

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.     

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.     

A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers

It is well known that “green” principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan.     

Shipment consolidation in a multiple-vendor–single-buyer integrated inventory model

This paper studies the case of a single buyer sourcing a product from multiple vendors. To reduce transportation and handling costs, the buyer has the option to assign vendors to groups. The vendor groups then deliver their batches jointly to the buyer. The paper shows that consolidating shipments may lead to a significant reduction in total cost. Numerical studies indicate that deliveries should be scheduled in such a way that the buyer receives large shipments at the beginning of the delivery cycle and small shipments at its end. The results also suggest that vendor groups with high (low) production capacities should be scheduled to deliver their shipments towards the end (beginning) of the delivery cycle.     

Multi-compartment Distribution in the Catering Supply Chain

Multi-compartment distribution can lead to significant cost reductions and environmental savings. In the Dutch catering supply chain lead times have to become shorter, the delivery frequency needs to increase and the product and process quality must improve to meet new and strict food safety regulations (HACCP). To anticipate these market developments a high quality and efficient distribution system is necessary. Therefore, it is necessary to have knowledge and insight regarding the logistics of delivering fresh produce, other chilled products, dry groceries and frozen products in one consignment to caterers. First, the current logistic performance of the Dutch catering chain was analysed and performance indicators and logistic scenarios were formulated. Then, a model was built for the simulation of these logistic scenarios. The two most promising alternative scenarios for all supply chain partners were tested in a real-life situation. The outcome of this pilot was used to validate and refine the model.     

An optimal batch size for a production system operating under periodic delivery policy

A manufacturing system that procures raw materials from suppliers in a lot and processes them into a finished product is considered in this research. An ordering policy is proposed for raw materials to meet the requirements of a production facility which, in turn, must deliver finished products demanded by outside buyers at fixed points in time. First, a general cost model is formulated considering both raw materials and finished products. Then, using this model, a simple procedure is developed to determine an optimal ordering policy for procurement of raw materials, and the manufacturing batch size, to minimize the total cost of meeting customer demands in time. The dependent relationships between production batch size and rawmaterial purchasing quantity, and various delivery patterns considered in recent literature are critically reviewed. The quality of the solution is evaluated. Numerical examples are provided.     

On ordering adjustment policy under rolling forecast in supply chain planning

Rolling forecast is a useful tool for lowering total cost with regard to practical inventory management. The details regarding a rolling forecast are obtained from a customer’s projected ordering data. The customer estimation of a rolling forecast may deviate from actual orders because of unstable conditions or customer’s deliberation. This study investigates what measures a customer might apply in responding to a situation where the rolling forecast deviates from the actual order. In addition, an appropriate ordering adjustment policy is proposed for better monitoring the supply chain performance with regard to a variant level of error concerning rolling forecast data. This study also considers the influence of lead time and inventory cost structure. We adopted a simulation approach, employing a model developed and examined in several different settings. The proposed ordering adjustment policies are determined by AVG, SD, and RMSE calculated from differences existing between historical forecasts and realized data. Levels of estimate error and estimate bias in a rolling forecast are included in the experimental procedure. Results reveal that the RMSE ordering adjustment policy is the most effective in situations of normal and downside estimation bias, whereas the AVG policy is more appropriate in the case of upside estimation bias. The level of estimation error is irrelevant to the selection of ordering adjustment policies, but it is positively associated with inventory costs. Stock-out costs and lead time are positively associated with inventory costs. Accuracy of the rolling forecast is therefore deemed to be essential in a situation involving a long lead time with high stock-out costs.     

A new order splitting model with stochastic lead times for deterioration items

In unreliable supply environments, the strategy of pooling lead time risks by splitting replenishment orders among multiple suppliers simultaneously is an attractive sourcing policy that has captured the attention of academic researchers and corporate managers alike. While various assumptions are considered in the models developed, researchers tend to overlook an important inventory category in order splitting models: deteriorating items. In this paper, we study an order splitting policy for a retailer that sells a deteriorating product. The inventory system is modelled as a continuous review system (s, Q) under stochastic lead time. Demand rate per unit time is assumed to be constant over an infinite planning horizon and shortages are backordered completely. We develop two inventory models. In the first model, it is assumed that all the requirements are supplied by only one source, whereas in the second, two suppliers are available. We use sensitivity analysis to determine the situations in which each sourcing policy is the most economic. We then study a real case from the European pharmaceutical industry to demonstrate the applicability and effectiveness of the proposed models. Finally, more promising directions are suggested for future research.     

Coordinating a two-supplier and one-retailer supply chain with forecast updating

We consider the component-purchasing problem for a supply chain consisting of one retailer and two complementary suppliers with different lead-times. The retailer purchases a specific component from each supplier for assembling into a fashionable product. After ordering from the long-lead-time supplier (Supplier 1) and before ordering from the short-lead-time supplier (Supplier 2), the retailer can update its demand forecast for the product. The retailer can partially cancel its order from Supplier 1 after forecast updating. By formulating the problem as a dynamic optimization problem, we explore the measures that can be deployed to coordinate the retailer’s ordering decisions with forecast updating. We analytically show that the supply chain can be coordinated if both suppliers offer a returns policy and Supplier 1 charges an order-cancelation penalty to the retailer. We find that the coordination mechanism is independent of demand distribution and the forecast updating process. We further show that it is easier for the suppliers to coordinate the supply chain if market observation indicates the future market demand is sufficiently large. We also study the case where demand is price-dependent and propose a generalized revenue-sharing contract to coordinate the supply chain. We discuss the academic and managerial implications of the theoretical findings.     

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.     

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.     

Extending social responsibility to small and medium-sized suppliers in supply chains: A fuzzy-set qualitative comparative analysis

Buying firms’ sales and reputation will be greatly damaged by any non-responsible behaviors on the part of suppliers, especially when those suppliers, like small and medium-sized enterprises (SMEs), have restricted resources and capabilities. To eradicate these risks, a growing number of buying firms have introduced socially responsible supplier development (SRSD). SRSD, including monitoring and evaluating suppliers, can provide them with incentives and assistance. Based on configuration theory and contingency theory, fuzzy-set qualitative comparative analysis (fsQCA) is adopted in this study to examine how SRSD practices adopted by buying firms, supply chain partnership, and market turbulence affect the corporate social responsibility (CSR) performance of their SME suppliers. We find that, as core factors, supplier monitoring, supplier assistance, and supply chain partnership can work together with peripheral conditions to achieve superior CSR performance. In addition, even at different levels of market turbulence, superior CSR performance can be realized through different causal configurations.     

Supply planning model for remanufacturing system in reverse logistics environment

This paper discusses the remanufacturing process of reusable parts in reverse logistics, where the manufacturer has two alternatives for supplying parts: either ordering the required parts to external suppliers or overhauling returned products and bringing them back to ‘as new’ conditions. We propose a general framework for this remanufacturing environment and a mathematical model to maximize the total cost savings by optimally deciding the quantity of parts to be processed at each remanufacturing facilities, the number of purchased parts from subcontractor. The model is newly introduced and developed in the reverse logistics literatures. The model is validated through a set of experimental data reflecting practical business situation and sensitivity analyses are conducted on various parameters to gain insight into the proposed model.