Agents’ incentives under buy-back contracts in a two-stage supply chain

This paper investigates how the behavior of individual decision makers can affect the performance of a supply chain. We study a two-stage supplier–retailer system, using a buy-back contract. Each firm's actions are executed by an agent. The retailer's purchasing agent and the supplier's sales agent are compensated based on certain performance measures, and they act accordingly. We study the impacts of their behavior in both the supplier-as-leader and retailer-as-leader settings. We find that, unless their incentives are carefully constructed, the agents can strongly distort the system's behavior. Specifically, “channel stuffing” (packing the distribution channel with excess inventory) can occur in both settings. Only when the agents are compensated based on net profit do they act in accord with their firms’ objectives. These results may help explain some recent scandals.     

Evaluation of robustness of supply chain information-sharing strategies using a hybrid Taguchi and multiple criteria decision-making method

The advances in information technology have prompted the development of many supply chain information-sharing strategies, including electronic point of sales (EPOS), vendor-managed inventory (VMI), e-shopping, emergency transshipments, and so on. However, variations in the business environment can produce uncertainty and increase decision-making complexity for enterprises selecting from various supply chain information-sharing strategies. An effective and efficient supply chain strategy should be capable of reducing costs and raising customer-service levels, and should be capable of enhancing the robustness of the supply chain. In this study, the robustness of different supply chain strategies under various uncertain environments is studied using the simulated beer game. Techniques included Taguchi methodology and multiple criteria decision-making methods (MCDMs), including simple multiple attribute rating technology (SMART), technique for order performance by similarity to ideal solution (TOPSIS), and grey relational analysis (GRA). The signal-to-noise (S/N) ratio for each criterion is calculated to indicate the robustness of performance. This S/N ratio is used to determine an overall evaluation among various supply chain information-sharing strategies. The simulation results show that e-shopping has the most robust performance in uncertain environments.     

Development of a process mining system for supporting knowledge discovery in a supply chain network

In today's competitive environment, business organizations are forced to maintain their competitive advantage by their ability to cut costs, increase revenue and uncover hidden issues. In order to enhance the visibility and transparency of value added information in a supply chain network, a process mining system is proposed for discovering a set of fuzzy association rules based on the daily captured logistics operation data, within the network. The proposed methodology provides all levels of employees with the ability to enhance their knowledge and understanding of the current business environment. Once interesting association rules have been extracted, organizations can identify the root-causes of quality problems in a supply chain and improve performance by fine-tuning the configuration of process parameters in specified processes. The application of the proposed methodology in a case company has also been studied. The prototype system has been developed and evaluated after performing a spatial analysis. The results obtained indicate that the system is capable of extracting high-quality and actionable information in the case company.     

Non-cooperative strategies for production and shipment lot sizing in one vendor-multi-buyer system

The supply chain structure examined in this paper consists of a single vendor (or manufacturer) with multiple heterogeneous buyers (or retailers). A continuous deterministic model is presented. To satisfy buyers demands, the vendor will deliver the product in JIT shipments to each buyer. The production rate is constant and sufficient to meet the buyers’ demands. The product is shipped in discrete batches from the vendor's stock to buyers’ stocks and all shipments are realized instantaneously. Special production-replenishment policies of the vendor and the buyers are analyzed. That is, the production batch is transferred to each buyer in several sub-batches in each production distribution cycle (PDC).This paper offers game model without prices, where agents minimize individual costs. It is a non-cooperative (1+N)-person game model with agents (a single vendor and N-buyers) choosing numbers and sizes of transferred batches. The model describes inventory patterns and cost structure of PDC. It is proved that there exist Nash equilibria in several types of sub-games of the considered game.     

Ordering and pricing policies in a manufacturing and distribution supply chain for fashion products

In the paper, we develop a model of manufacturing and distribution supply chains that are operating to meet price-sensitive random demand for products with short life cycles such as fashion products. Two specific scenarios are considered. The manufacturer-controlled scenario is one where the distributor shares price-sensitive random demand with the manufacturer, and the manufacturer controls the supply chain stocking decisions and bears the risk of overstocking costs. The distributor-controlled scenario works in the opposite direction. Prevailing wisdom suggests that the manufacturer should control supply chain decisions (e.g., via vendor-managed inventory). Our results indicate that such an arrangement is against the interest of a distributor selling short life-cycle products. Furthermore, we find that the total supply chain profit is generally higher when the distributor controls the supply chain stocking decisions and bears the risk of overstocking costs.     

A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty

Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including production cost, hiring, firing and training cost, raw material and end product inventory holding cost, transportation and shortage cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers’ zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient production planning in a supply chain.     

A strategic-tactical model for the supply chain design in the delocalization context: Mathematical formulation and a case study

In this paper, we propose a mathematical model for the design of supply chains in the delocalization context. Our main objective is to develop a strategic-tactical supply chain design model that integrates all the relevant components that characterize the delocalization problem. We adopt the activity based approach to model the problem and we focus on the logistic decisions of activity location, technology choice, supplier selection, etc., and the financial decisions of transfer pricing, transportation costs allocation, etc. The mathematical formulation is illustrated by a case study from the automotive sector. A comparison between the model solution and the real decisions is used to prove the applicability and the utility of the proposed model.