Interactions of agents in performance based supply chain management

To acquire a competitive advantage in the expanding market, manufacturing companies should be able to manage their supply chain as much effective as possible. Measuring the supply chain performance is one of the main indicators of business success. Supply chain management (SCM) involves managing the flow of materials from suppliers to manufacturing units. This paper proposes a SCM model with performance measurement capabilities. The model is designed in such a way that it well suits to multi agent systems and related implementations. This paper highlights the components of the model especially pointing out the key parameters of performance indicators.     

A new network epsilon-based DEA model for supply chain performance evaluation

Supply chain performance evaluation problems are inherently complex problems with multilayered internal linking activities and multiple entities. Data Envelopment Analysis (DEA) has been used to evaluate the relative performance of organizational units called Decision Making Units (DMUs). However, the conventional DEA models cannot take into consideration the complex nature of supply chains with internal linking activities. Network DEA models using radial measures of efficiency are used for supply chain performance evaluation problems. However, these models are not suitable for problems where radial and non-radial inputs and outputs must be considered simultaneously. DEA models using Epsilon-Based Measures (EBMs) of efficiency are proposed for a simultaneous consideration of radial and non-radial inputs and outputs. We extend the EBM model and propose a new Network EBM (NEBM) model. The proposed NEBM model combines the radial and non-radial measures of efficiency into a unified framework for solving network DEA problems. A case study is presented to exhibit the efficacy of the procedures and to demonstrate the applicability of the proposed method to a supply chain performance evaluation problem in the semiconductor industry.     

Coordination of cooperative advertising models in a one-manufacturer two-retailer supply chain system

This paper considers cooperative advertising issues of a monopolistic manufacturer with competing duopolistic retailers. Four possible game structures (or power configurations), i.e., Stackelberg–Cournot, Stackelberg–Collusion, Nash–Cournot and Nash–Collusion, are discussed. Under each of four game structures, we develop a decision model for the three partners to design the optimal cooperative advertising policies. Through a comparison among the four models, we reveal how cooperative advertising policies and profits of all participants are affected by various competitive behaviors, and then determine whether the partners have any incentives to transit to a different structure. Also presented in the paper are a centralized decision model and a proposed cost-sharing contract, which is able to achieve perfect coordination of the considered channel, where the utility of risk preference is used to determine the fraction of local advertising costs shared by the manufacturer.     

A decision framework for the analysis of green supply chain contracts: An evolutionary game approach

The overwhelming growth in the struggle between economy and ecology has led managerial research to promote methodologies to reap profits ensuring sustainability of the ecosystem thus venturing into the novel domain incorporating sustainability with supply chain management and coordination. In this paper, we look for a synergetic alliance between the environmental and commercial benefits by establishing coordination between the producer and the retailer to adjudicate their strategies to trigger green practices with the focus on maximizing economic profits by leveraging upon the product’s greenness. A two player game has been developed to stand synonymous to the situation considered. Special considerations have been devised to augment the analysis with the consumer psychology towards the green products to establish a practical outlook involving the greenness and economic returns. Evolutionary dynamics has been adopted extensively to locate the optimal and the most stable point offering the best economic gains. The economically suitable solutions are then substantiated on an existing sustainability metric and hence we aim to offer a holistic view into supply chain dynamics from the perspective of environmental management. The insights drawn provide managerial implications and advantages of having environment as a key focus along with business motives.     

Price and leadtime competition,and coordination for make-to-order supply chains

This paper develops a game theoretic model of a one-manufacturer and one-retailer supply chain facing an outside integrated chain (manufacturer) to study the price and leadtime competition and investigate coordination of the supply chain, where the make-to-order production mode is employed and consumers are sensitive to retail price and leadtime. We find that decentralization of the supply chain increases its leadtime while decreases the rival’s leadtime; and the decentralization increases the retail prices. The existence of the outside competitor raises the leadtime. A higher reservation price or brand differentiation increases the retail prices but decreases the leadtimes; a higher transportation cost or lower leadtime sensitivity increases the retail prices and the leadtimes. The coordination of the supply chain facing integrated rival harms the integrated rival. We design contracts to coordinate the supply chain under leadtime-decision-first scenario and wholesale-price-decision-first scenario, respectively. Further, we find that the sequence of decisions affects the validity of the all-unit quantity discount scheme in coordinating the supply chain.     

Ordering,wholesale pricing and lead-time decisions in a three-stage supply chain under demand uncertainty

This paper develops a game theoretic model of a three-stage supply chain consisting of one retailer, one manufacturer and one subcontractor to study ordering, wholesale pricing and lead-time decisions, where the manufacturer produces a seasonal/perishable product. We explicitly model the effects of the lead-time and the length of selling season on both demand uncertainty and inventory-holding costs. We present the equilibrium outcome of the decentralized supply chain. When the lead-time increases, we find that the retailer increases the order quantity, the manufacturer offers a lower unit-wholesale price and the subcontractor decreases its unit-wholesale price if the manufacturer subcontracts part of the retailer’s order. In the endogenous lead-time setting, we illustrate the effects of some factors such as unit holding cost and capacity on the equilibrium outcome. We find that a higher unit holding cost implies a lower optimal lead-time and order quantity while higher unit-wholesale prices; the basic demand uncertainty increases the optimal lead-time and order quantity while decreases the unit-wholesale prices. The effects of distribution form on equilibrium outcome/profits are investigated by employing a numerical example. The profit loss of decentralization decreases (increases) with the basic demand uncertainty and manufacturer’s capacity (mean demand).     

Modeling supplier selection and the use of option contracts for global supply chain design

As supply chains become more and more dependent on the efficient movement of materials among facilities that are geographically dispersed there is more opportunity for disruption. One of the common disruptions is the loss of production capability at supplier sites. We formulate a two-stage stochastic program and a solution procedure to optimize supplier selection to hedge against these disruptions. This model allows for the effective quantitative exploration of the trade-off between cost and risks to support improved decision-making in global supply chain design. A realistic case study is explored.     

Co-op advertising and pricing models in manufacturer–retailer supply chains

Cooperative (co-op) advertising plays a significant role in marketing programs in conventional supply chains and makes up the majority of promotional budgets in many product lines for both manufacturers and retailers. Nevertheless, most studies to date on co-op advertising have only assumed that the market demand is only influenced by the advertising level but not in any way by the retail price. That is why our work is concerned with co-op advertising and pricing strategies in distribution channels consisting of a manufacturer and a retailer. Four different models are discussed which are based on three non-cooperative games (i.e., Nash, Stackelberg retailer and Stackelberg manufacturer) and one cooperative game. We identify optimal co-op advertising and pricing strategies for both firms mostly analytically but we have to resort to numerical simulations in one case. Comparisons are then made about various outcomes, especially the profits, for all cases. This leads to consider more specifically the cooperation case in which profits are the highest for both the retailer and the manufacturer, and how they should share the extra joint profit achieved by moving to cooperation. We solve this bargain problem using the Nash bargaining model.     

A tri-level programming model based on Conditional Value-at-Risk for three-stage supply chain management

For the problem of supply chain management, the existing literature mainly focuses on the research of the single-stage supply chain or the two-stage supply chain that consists of a manufacturer and a retailer. To our best knowledge, little attention has been paid to the study of a more extensive supply chain that consists of a material supplier, a manufacturer and a retailer, which is a more practical and interesting case. Therefore, based on the Conditional Value-at-Risk (CVaR) measure of risk management, this paper proposes a tri-level programming model for the three-stage supply chain management. In this model, the material supplier and the manufacturer maximize their own profit while the retailer maximize his/her CVaR of expected profit. Further, we show that the proposed tri-level programming model can be transferred into a bilevel programming model, which can be solved by the existing methods. Numerical results show that the proposed model is efficient for improving the risk management of the three-stage supply chain.     

Strategies in supply chain management for the Trading Agent Competition

Negotiating with suppliers and with customers is a key part of supply chain management. However, with recent technological advances, the mechanisms available to carry out such activities have become increasingly sophisticated, and the environment in which these activities take place has become highly dynamic. As a consequence, the overall planning of these complex trades, and the coordination of the various production and scheduling activities, need to be carefully considered by the businesses involved in the supply chain management. In order to guide the overall planning, production, scheduling, and allocation of resources, especially designed strategies are increasingly used by the businesses. In this setting, it is crucial that the intended behaviour, and through that, the desired outcomes, of these strategies be precisely understood. Using an empirical analysis, this paper investigates two fundamental strategies in supply chain management: buy-to-build and build-to-order.     

Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management

Aggregate production-distribution planning (APDP) is one of the most important activities in supply chain management (SCM). When solving the problem of APDP, we are usually faced with uncertain market demands and capacities in production environment, imprecise process times, and other factors introducing inherent uncertainty to the solution. Using deterministic and stochastic models in such conditions may not lead to fully satisfactory results. Using fuzzy models allows us to remove this drawback. It also facilitates the inclusion of expert knowledge. However, the majority of existing fuzzy models deal only with separate aggregate production planning without taking into account the interrelated nature of production and distribution systems. This limited approach often leads to inadequate results. An integration of the two interconnected processes within a single production-distribution model would allow better planning and management. Due to the need for a joint general strategic plan for production and distribution and vague planning data, in this paper we develop a fuzzy integrated multi-period and multi-product production and distribution model in supply chain. The model is formulated in terms of fuzzy programming and the solution is provided by genetic optimization (genetic algorithm). The use of the interactive aggregate production-distribution planning procedure developed on the basis of the proposed fuzzy integrated model with fuzzy objective function and soft constraints allows sound trade-off between the maximization of profit and fillrate. The experimental results demonstrate high efficiency of the proposed method.     

Risk assessment and management for supply chain networks: A case study

The aim of this study is to show how a timed Petri nets framework can be used to model and analyze a supply chain (SC) network which is subject to various risks. The method is illustrated by an industrial case study. We first investigate the disruption factors of the SC network by a failure mode, effects and criticality analysis (FMECA) technique. We then integrate the risk management procedures into design, planning, and performance evaluation process of supply chain networks through Petri net (PN) based simulation. The developed PN model provides an efficient environment for defining uncertainties in the system and evaluating the added value of the risk mitigation actions. The findings of the case study shows that the system performance can be improved using risk management actions and the overall system costs can be reduced by mitigation scenarios.     

Collaborative design and analysis of supply chain network management key processes model

This study focuses on collaboratively designing a structured and comprehensive supply chain (SC) network management key processes model and analyzing the relative importance of these key processes for semiconductor industry. The collaborative design and analysis are performed by a multidisciplinary team consisting of over 20 members from both academia and industry. This research is based on experiences of these team members who joined a successful e-SCM project, used as a case study in this research, between the world's largest semiconductor foundry and the world's largest assembly and testing service provider. This study adopts focus group methodology for collaborative design and fuzzy analytic hierarchy process (FAHP) for collaborative analysis. The result of the design is a structured and comprehensive key processes model consisting of four dimensions: strategy and planning, manufacturing, logistics, and risk management (SMLR) with a total of 15 key processes included in these four dimensions. The resulting weightings from FAHP analysis can identify the most critical one dimension and four key processes since they account for approximately half of the overall weighting in their level. The SMLR model provides a structured and comprehensive reference model for future SC network management project executives, ensuring that all key processes are supported to avoid extremely costly failure. The resulting weightings provide these managers with the relative importance of these key processes and can help them make critical decisions in allocating limited resources to support the most critical processes. To confirm the results and further explore the managerial implications, a second session of focus group meeting was conducted and practices of the top three key processes in the semiconductor manufacturing industry were used to illustrate what actions can be performed to improve these processes and hence benefit the entire SC network. The research results can serve as a foundation for related academic researches.     

Pricing and production decisions in dual-channel supply chains with demand disruptions

This paper develops a two-period pricing and production decision model in a one- manufacturer-one-retailer dual-channel supply chain that experiences a disruption in demand during the planning horizon. While disruption management has long been a key research issue in supply chain management, little attention has been given to disruption management in a dual-channel supply chain once the original production plan has been made. Generally, changes to the original production plan induced by a disruption may impose considerable deviation costs throughout the supply chain system. In this paper, we examine how to adjust the prices and the production plan so that the potential maximal profit is obtained under a disruption scenario. We first study the scenario where the manufacturer and the retailer are vertically integrated with demand disruptions. Then we further assume that the manufacturer bears the deviation costs and obtain the manufacturer’s and the retailer’s individual optimal pricing decision, as well as the manufacturer’s optimal production quantity in a decentralized decision-making setting. We derive conditions under which the maximum profit can be achieved. The results indicate that the optimal production quantity has some robustness under a demand disruption, in both centralized and decentralized dual-channel supply chains. We also find that the optimal pricing decisions are affected by customers’ preference for the direct channel and the market scale change, in both centralized and decentralized dual-channel supply chains.     

Modeling enablers of supply chain risk mitigation in electronic supply chains: A Grey–DEMATEL approach

Supply chains are becoming perpetually complex and potentially vulnerable due to increased globalization and vertical integration. Supply chain managers are conscious that the success of any supply chain lies in its proficiency to manage risks efficiently and effectively. Supply chain risk management (SCRM) signifies proactive practices of managing vulnerability of supply chains. Various firms espouse strategic level measures for risk mitigation and implement practices for enhancing supply chain resilience. In order to enhance supply chain resilience, the enablers of supply chain risk mitigation need to be acknowledged and implemented into practice. This research emphasizes on ascertaining the major enablers of supply chain risk mitigation with emblematic focus on electronic supply chains. A blend of Grey theory and DEMATEL approaches has been employed in this research to find out cause/effect relationships among the enablers of supply chain risk mitigation. This exercise helps to find which can be the driver to initiate the effects of other driven enablers. Sensitivity analysis was also conducted of the results to ensure reliability of solutions. The results show that that the enablers of supply chain risk mitigation are intertwined and one enabler can be the cause/effect of one or more enablers of supply chain risk mitigation. Dynamic assortment planning is found to be the decisive causal enabler, as it initiates the effects of many other enablers of supply chain risk mitigation, followed by accurate demand forecasting and flexible supply contracts. Cause–effect relationships plotted facilitate managers to ascertain primary causal enablers that need imperative attention in dealing with vulnerability issues of supply chain. Managers can take proactive steps to address and implement primary causal enables of risk mitigation into practice for reducing total risk impacts of the supply chain.     

Review of supply chain performance measurement systems: 1998–2015

In today’s competitive business environment, supply chain performance is one of the most critical issues in various industries. It is argued that supply chain performance measurement is fundamental to efficient supply chain management. Over the past two decades, several frameworks and systems have been developed to meet this need. This study reviews the literature in the field of supply chain performance measurement and assembles an overview of those systems, approaches, techniques and criteria. For this purpose, 83 of 374 related articles from 1998 to 2015 were selected for final review using the Scopus and ISI databases. Findings disclose that performance measurement in supply chain contexts is still a fruitful area of research. The study also provides an overview of the performance measures employed in supply chain systems. These findings present a solid basis for future academic and practitioner work in the field of supply chain performance measurement.     

The comparison of two procurement strategies in the presence of supply disruption

The emergency procurement strategy and the optimal allocation procurement strategy are widely used for managing supply disruption risks. In this paper, we investigate two competing manufacturers using these procurement strategies in the presence of supply disruption risks. The joint pricing and ordering decisions of both manufacturers are analyzed using the game theoretic framework. The structural property of the manufacturer with the optimal allocation procurement strategy is characterized by the reliability threshold value, which further determines the equilibrium outcomes for both manufacturers. We find the reliability threshold is a generalization of the supplier’s reliability level, which involves all the critical factors that influence manufacturers’ procurement decisions under a competitive scenario. The optimal allocation procurement strategy for manufacturer profit maximization in a non-competitive scenario does not necessarily generate competitive advantage in a competitive scenario; under a wide range of parameters, the emergency procurement strategy can produce larger profit for the manufacturer than the optimal allocation procurement strategy when all suppliers are unreliable. The effects of reliability level and costs on procurement decisions are examined using comparative studies and numerical computations.     

Knowledge management in supply chain: An empirical study from France

Supply chain management has assumed a leading operations strategy position in both manufacturing and service industries, and over the past 10 years companies have seriously implemented supply chain management strategies in their organizations. Knowledge management (KM) is a major enabler of supply chain management, and is a critical element in information intensive and multi-cultured enterprise environments. Realizing the importance of knowledge management in supply chain (SC), an attempt has been made in this paper to propose a conceptual framework for KM in SC and to validate the framework with help of an empirical study conducted with French companies. Finally, a summary of findings and conclusions is presented for KM in SC.     

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.     

A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path

A supply chain network design needs to consider the future probability of reconfiguration due to some problems of disaster or price changes. The objective of this article is to design a reconfigurable supply chain network by optimizing inventory allocation and transportation routing. A two-stage programming is composed according to Benders decomposition by allocating inventory in advance and anticipating the changes of transportation routings; thus the transportation routing is stochastic in nature. In addition, the fuzzy shortest path is developed to solve the problem complexity in terms of the multi-criteria of lead time and capacity with an efficient computational method. The results and analysis indicate that the proposed two-stage programming with fuzzy shortest path surpasses the performance of shortest path problem with time windows and capacity constraint (SPPTWCC) in terms of less computational time and CPU memory consumption. Finally, management decision-making is discussed among other concluding remarks.