Coordination of a supply chain with one manufacturer and multiple competing retailers under simultaneous demand and cost disruptions

This paper develops a coordination mechanism for a supply chain consisting of one manufacturer and n Cournot competing retailers when the production cost and demands are simultaneously disrupted. This differs from traditional supply chain coordination models under a static case and the case with only demand or cost disruption. The coordination mechanism with revenue sharing is considered, and the effects of production cost and demand disruptions on revenue sharing contract are discussed to investigate the optimal strategies of players with disruptions. The penalty cost is introduced explicitly to obtain the production deviation cost caused by the disruptions. In this study, it is obtained that the coordination contract considering the production deviation cost differs from that without disruption. Besides that, the disruptions may affect the order quantities, wholesale prices as well as revenue sharing contract. Then, the optimal strategies for different disruption levels under the centralized decision-making mode are proposed. Concerning the decentralized mode, the improved revenue sharing contract can be used to coordinate the decentralized decision-making supply chain effectively. Finally, the theoretical results are illustrated by conducting some numerical examples.     

Supply network dynamics as a source of new business

Supply networks where operational control extends well over organizational boundaries have emerged in industries producing relatively complex and customized products with tight profit margins. Products like ships, automobiles and telecommunication systems incorporate complex design and engineering skills that are produced through a tier-structured, multi-level supply networks. Efficiency in these networks has stemmed from specialization and cost efficiency in individual value adding operations. This paper demonstrates how supplier networks have evolved and how the inherent dynamics of these networks generate constantly new business opportunities for fast moving companies with a clear focus on operational efficiency. We use action research methodology on cases from the shipbuilding and constructions industry to document some of the dynamic features of supply networks. This insight is then applied to the electronics manufacturing services business to explain the fundamentals of successful operations in this highly competitive business with ever narrowing margins. In this dynamic market of contract manufacturing companies with constant focus on the reduction of production lead times by incorporating value added operations either physically or logically to maintain and recreate profitable business. To succeed in doing this, issues related to industrial parks, local tacit knowledge and reverse/repair logistics must be managed in cross-organizational manner. We conclude that there is an ever-changing limit to the expansion of supplier networks through specialization and cost efficiency, and that at one point contracting and integrating parts of the supply network will create operationally outperforming business models that further boost the inherent dynamics of supply networks.     

Managing the supply chain during disruptions: Developing a framework for decision-making

Business-to-business firms have traditionally encountered disruptions, but the scale of the COVID-19 pandemic was extraordinary as it interrupted global supply chains by causing unprecedented shocks to supply and demand. Firms experienced extraordinary pressure and struggled to minimize the immediate and long-term impact of supply chains disruptions. Applying chaos theory, this study employs a single-case method to understand the disruptions to the business-to-business oil and gas supply chain. We make three major contributions. First, we examine firm decision-making during significant disruptions. Second, we use chaos theory to better understand the decision-making process. Finally, we develop a framework to explicate the decision-making process and provide guidelines for decision-making during disruptions. Our findings provide theoretical insights and have important implications for practitioners addressing supply chain disruptions during crises.     

Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty

Models that aim to optimize the design of supply chain networks have become a mainstream in the supply chain literature. This paper aims to fill a gap in the literature by introducing a mathematical model that integrates financial considerations with supply chain design decisions under demand uncertainty. The proposed Mixed-Integer Linear Programming (MILP) problem enchases financial statement analysis through financial ratios and demand uncertainty through scenario analysis. The applicability of the model is illustrated by using a case study along with a sensitivity analysis on financial parameters expressing the business environment. The model could be used as an effective and convenient strategic decision tool by supply chain managers.     

Performance-based strategic resource allocation in supply networks

One driving force behind continued logistics planning research is firms’ need to maintain competitive performance and efficient resource allocation. Operations research has been instrumental in advancing logistics planning research particularly in the petroleum industry. The impact and potential of incorporating performance imperatives however, have been largely unaddressed while the problem's general complexity has grown. This paper focuses on performance planning through resource allocation in supply networks by developing a profit-maximizing network model for distribution planning, and explicitly incorporating order performance criteria for distribution centers and delivery vehicles. The approach has been tested on forty realistic-sized problems, and tradeoff models provide insight and guidelines for assessing the interaction among expected profits, customer service, and operational efficiency for resource acquisition and redistribution. Managerial implications are also provided.     

Fashion retail forecasting by evolutionary neural networks

Recent literature on nonlinear models has shown that neural networks are versatile tools for forecasting. However, the search for an ideal network structure is a complex task. Evolutionary computation is a promising global search approach for feature and model selection. In this paper, an evolutionary computation approach is proposed in searching for the ideal network structure for a forecasting system. Two years’ apparel sales data are used in the analysis. The optimized neural networks structure for the forecasting of apparel sales is developed. The performances of the models are compared with the basic fully connected neural networks and the traditional forecasting models. We find that the proposed algorithms are useful for fashion retail forecasting, and the performance of it is better than the traditional SARIMA model for products with features of low demand uncertainty and weak seasonal trends. It is applicable for fashion retailers to produce short-term retail forecasting for apparels, which share these features.     

An empirical analysis of supply chain risk management in the German automotive industry

The purpose of this paper is the empirical analysis of supply chain risk management practices. The analysis is based on a survey with 67 manufacturing plants conducted in the German automotive industry. After investigating the vulnerability of supply chains in general and examining key drivers of supply chain risks, the paper identifies supply chain risks by analyzing their likelihood to occur and their potential impact on the supply chain. The results are visualized in the probability-impact-matrix distinguishing between internal and external supply chain risks. Furthermore, instruments for dealing with supply chain risks are investigated. Therefore, the impact of supply chain risk management on performance is tested. In order to distinguish between companies with a high degree of supply chain risk management and those with no or only limited implementation the plants are grouped by means of a cluster analysis based on factors reflecting the instruments of supply chain risk management. In particular, groups are created representing two different approaches to deal with supply chain risks, i.e. reactive and preventive supply chain risk management. The clusters are investigated concerning differences in terms of performance criteria. The analyses reveal that companies with a high implementation degree show a better supply chain performance. Furthermore, the results show that the group using reactive supply chain risk management has higher average value in terms of disruptions resilience or the reduction of the bullwhip effect, whereas the group pursuing preventive supply chain risk management has better values concerning flexibility or safety stocks.     

Identifying risk issues and research advancements in supply chain risk management

The purpose of this paper is to investigate the research development in supply chain risk management (SCRM), which has shown an increasing global attention in recent years. Literature survey and citation/co-citation analysis are used to fulfil the research task. Literature survey has undertaken a thorough search of articles on selected journals relevant to supply chain operations management. Meanwhile, citation/co-citation analysis uses Web of Sciences database to disclose SCRM development between 1995 and 2009. Both the approaches show similar trends of rising publications over the past 15 years. This review has piloted us to identify and classify the potential risk associated with different flows, namely material, cash and information flows. Consequently, we identify some research gaps. Even though there is a pressing need and awareness of SCRM from industrial aspect, quantitative models in the field are relatively lacking and information flow risk has received less attention. It is also interesting to observe the evolutions and advancements of SCRM discipline. One finding is that the intellectual structure of the field made statistically significant increase during 2000-2005 and evolved from passively reacting to vague general issues of disruptions towards more proactively managing supply chain risk from system perspectives.     

The Effects of Average Norm Model Regulation: The Case of Electricity Distribution in Sweden

Following the reform of energy sectors, some countries have used engineering norm models in incentive regulation of network utilities. In 2003, Sweden adopted this approach to regulation of electricity distribution networks. This paper examines whether the norm models represent the real networks and create incentives for performance improvement. We analyse data from 138 network concession holders between 2000 and 2007. The results show that norm models are not adequate representations of real networks. Also, utilities that perform better than their norm models tend to behave opportunistically. Finally, we find that private utilities respond more strongly to incentives.     

Supply chain risk, simulation, and vendor selection

This paper considers three types of risk evaluation models within supply chains: chance constrained programming (CCP), data envelopment analysis (DEA), and multi-objective programming (MOP) models. Various risks are modeled in the form of probability and simulation of specific probability distribution in risk-embedded attributes is conducted in these three types of risk evaluation models. We model a supply chain consisting of three levels and use simulated data with representative distributions. Results from three models as well as simulation models are compared and analysis is conducted. The results show that the proposed approach allows decision makers to perform trade-off analysis among expected costs, quality acceptance levels, and on-time delivery distributions. It also provides alternative tools to evaluate and improve supplier selection decisions in an uncertain supply chain environment.     

Designing a reverse logistics network for optimal collection, recovery and quality-based product-mix planning

This study proposes an integrated, reverse logistics supply chain planning process with modular product design that produces and markets products at different quality levels. A mixed integer programming (MIP) model formulates the overall planning process required to maximize profit by considering the collection of returned products, the recovery of modules and the proportion of the product mix at different quality levels. This paper proposes the collection of returnables (end-of life, defective, product under warranty) through retail outlets combined with the recovery of modules from the collected products using a network of recovery service providers. The proposed modular product design approach would create a design criterion that provides an improved recovery process at a lower cost. This study uses a total supply chain view that considers the production, transportation and distribution of products to customers, while a numerical problem illustrates the applicability of the models.     

Product substitution in different weights and brands considering customer segmentation and panic buying behavior

Due to recent supply disruptions, there has been a rapid increase in panic buying. This study considers a wholesaler selling two brands of a product with multiple weights and produced by different manufacturers to multiple retailers. The products are substituted based on weight (same brand) and a competitor’s brand over two periods. In the first period (panic situation), the wholesaler attempts to retain the inventory to satisfy the retailer. In the second period (supply disruption), retailers are willing to accept the substitute products. The wholesaler segregates the retailers into high and low indexed customers, where the high indexed retailers (provide higher profit) order greater quantities than the low indexed retailers. The objective of this model is to determine the optimal numbers for ordering quantities and substitutions to maximize total profit. Moreover, supply disruption for both single and multiple brands is analyzed, along with the influence of different degrees of supply disruption and panic rate on decisions and profits. Finally, we compare the models with and without customer-segmented substitution and brand substitution. In addition, other model extensions are discussed, such as an increase in the price in the second period.     

集群供应链网络的联结模式与共治框架

集群供应链作为一类新型网络组织形式,是依托于产业集群又突破产业集群地域限制的供应链网络的集合体,集群供应链网络治理问题对集群竞争优势创造与集群持续发展有着重要作用。本文首先阐明了供应链网络与集群供应链网络的区别,进而对集群供应链网络的结构及类型进行了分析,在此基础上研究了集群供应链网络的联结模式,提出集群供应链网络共治的参与者、集群供应链网络共治框架体系及各参与主体在集群供应链共治中的作用分工,以期对集群供应链网络的治理活动起到借鉴作用。     

Multi-objective ant colony optimisation: A meta-heuristic approach to supply chain design

This paper proposes a new approach to determining the Supply Chain (SC) design for a family of products comprising complex hierarchies of subassemblies and components. For a supply chain, there may be multiple suppliers that could supply the same components as well as optional manufacturing plants that could assemble the subassemblies and the products. Each of these options is differentiated by a lead-time and cost. Given all the possible options, the supply chain design problem is to select the options that minimise the total supply chain cost while keeping the total lead-times within required delivery due dates. This work proposes an algorithm based on Pareto Ant Colony Optimisation as an effective meta-heuristic method for solving multi-objective supply chain design problems. An experimental example and a number of variations of the example are used to test the algorithm and the results reported using a number of comparative metrics. Parameters affecting the performance of the algorithm are investigated.     

An integrated production–distribution model for the dynamic location and allocation problem with safety stock optimization

The design and management of a multi-stage production–distribution system is one of the most critical problems in logistics and in facility management. Companies need to be able to evaluate and design different configurations for their logistic networks as quickly as possible. This means coordinating the entire supply chain effectively in order to minimize costs and simultaneously optimize facilities location, the allocation of customer demand to production/distribution centers, the inbound and outbound transportation activities, the product flows between production and/or warehousing facilities, the reverse logistics activities, etc.Full optimization of supply chain is achieved by integrating strategic, tactical, and operational decision-making in terms of the design, management, and control of activities. The cost-based and mixed-integer programming model presented in this study has been developed to support management in making the following decisions: the number of facilities (e.g. warehousing systems, distribution centers), the choice of their locations and the assignment of customer demand to them, and also incorporate tactical decisions regarding inventory control, production rates, and service-level determination in a stochastic environment. This paper presents an original model for the dynamic location–allocation problem with control of customer service level and safety stock optimization. An experimental analysis identifies the most critical factors affecting the logistics cost, and to finish, an industrial application is illustrated demonstrating the effectiveness of the proposed optimization approach.     

Analysing an activity in context: A case study of the conditions for vehicle maintenance

The IMP approach is concerned with analysis of complex industrial networks. How to design case studies relying on these theoretical assumptions poses a challenge. In this paper we address this challenge by discussing a case study, focusing on the conditions for maintenance of heavy vehicles. The aim of the paper is to suggest an approach for analysis of embedded activities in industrial networks. The case study takes its starting point in the perspective of an actor considering how to develop vehicle maintenance services for its customers and points at the need to enable understanding of the conditions for vehicle maintenance, which necessitates identification and analysis of the variety across transport service settings.     

A framework to facilitate institutional arrangements for smallholder supply in developing countries: An agribusiness perspective

The industrialization of agriculture and the potential problem of small-scale farmer marginalization calls for a fresh approach to the design of agribusiness supply chain arrangements in developing countries. The objective of the paper is to contribute to a better understanding of institutional arrangements that can promote stable smallholder agribusiness contracting arrangements in a developing country context. A case study approach, incorporating a transaction cost framework, is used to test whether trust can significantly change the contract characteristics of supply. The results suggest that although the presence of trust can influence the contract characteristics of a supply arrangement, it may not be significant because of other factors in a developing country context. Bearing this in mind, a number of institutional arrangements are recommended in order to promote more stable contract conditions.     

Modeling defaults of companies in multi-stage supply chain networks

The interest in supply chain networks and their analysis as complex systems is rapidly growing. The physical approach to the topic draws on the concept of heterogenous interacting agents. The interaction among agents is considered as a repeated process of orders and production. The dynamics of production in the supply chain network which we observe is nonlinear due to the random failures in processes of orders and production. We introduce an agent-based model of a supply chain network which represents in more detail the real economic environment in which firms operate. We focus on the influence of local processes on the global economic behavior of the system and study how the proposed modifications change the general properties of the model. We observe collective bankruptcies of firms, which lead to self-emerging network structures. Our results give insight into the dynamics of default processes in supply chain networks, which have important implications both for risk managers and policy makers. Based on the simulations we show that agent-based modeling is a powerful tool for optimization of supply chain networks.     

Assessing the impact of cost optimization based on infrastructure modelling on CO2 emissions

Traditionally, logistics design is driven by a need to reduce costs and improve customer service. Recently, the environmental concerns from transport have been increasingly discussed. The traffic levels and associated energy consumption are influenced by supply chain structure, modal split and vehicle utilization. This paper aims to assess the impact of the traditional cost optimization approach to strategic modelling on overall logistics costs and CO₂ emissions by taking into account the supply chain structure (number of depots) and different freight vehicle utilization ratios (90%, 75% and 60%). The simulation model, based on a European case study from the automotive industry, considers strategic and operational level decisions simultaneously. The analysis shows that the optimum design based on costs does not necessary equate to an optimum solution for CO₂ emissions, therefore there is a need to address economical and environmental objectives explicitly as part of the logistics design.     

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.