Supply Chain Network Configuration Research Articles

Integrated decisions for global supply chain network configuration: a dynamic multi-modal perspective

ABSTRACT This paper proposes a mathematical model to optimally design a multi-modal global production and logistics network consisting of multiple plants, warehouses, and marketplaces. The proposed model is a mixed integer linear program (MILP), which optimally selects different facilities and warehouses required to be active in each time period to fulfil the demand of various marketplaces. The objective function is cost minimization pertaining to setup, production, inventory holding, and transportation. Along with this, the model also decides the optimal quantities to be transported among the plants, warehouses, and marketplaces. The model also optimally selects the transportation mode to be deployed. The paper then expands the proposed model to include a time-cost trade off by imposing a penalty cost for delays and compares and critiques the results of the two scenarios. The proposed MILP has been validated on a randomly generated data set consisting of 4 plants, 6 warehouses, 4 marketplaces, and 3 time periods (4P-6W-4 M-3T) using LINGO 10 optimization package. The illustration considers three modes of transportation namely, road, rail, and air. Last, the paper concludes with discussing managerial implications of the work along with suggesting some research directions for future.

Designing a manufacturing network with additive manufacturing using stochastic optimisation

Two of the major problems Traditional Manufacturing (TM) supply chains face are setting requisite reactive strategies to address the uncertainties in demand and the optimal placement of these buffering capacities in order to be both responsive and cost-effective. With Additive Manufacturing (AM) stepping into large-scale production at different firms, we address the aforementioned supply chain dilemmas by considering the potential role of AM in a TM supply chain network where AM facilities can act as a recourse to the TMs and, also, as a dedicated source providing responsive and cost-effective sourcing alternatives. We develop an analytical allocation rule based on cost differentials, which provides optimal sourcing decisions through sequential demand replenishment and facilitates an efficient performance evaluation of possible network configurations. We first model the scenario as a three-stage stochastic optimisation problem. We then solve it using the allocation rule and present an illustration of our analysis and the optimal supply chain network configuration. Furthermore, we derive some insights as to how different problem characteristics affect the value and usage of AM.

Selection of green suppliers with the approach of integrating economic, environmental and social decisions (Case study of Sazeh Gostar Saipa Photovoltaic Company)

Supply chain network design is one of the key issues in strategic chain planning that refers to the supply chain network configuration and as an infrastructure issue in its management, will have lasting effects on other tactical and operational decisions. In other words, the proper design of the supply chain network leads to the achievement of an optimal structure, which makes effective and competitive supply chain management possible. In this study, a problem of selecting a green supplier in terms of sustainability, under uncertainty based on three economic, environmental and social responsibility dimensions are studied by a case study of Sazeh Gostar Saipa Photovoltaic Company active in the pv industry. One of the environmental dimensions of the problem is the design of an efficient and flexible model for evaluating and selecting suppliers based on environmental indicators. One of the features of the proposed model is the use of environmental criteria in the process of selecting a green supplier and also the flexibility of the model in the number of sub-criteria. In this research, the theory of Rough sets has been used to find the weight of sub-environmental criteria. The obtained results confirm the efficiency of the multi-objective mathematical planning model in this research in evaluating suppliers and also using the theory of Rough sets to weight environmental indicators to achieve the above objectives.

Assessment of intermediate storage and distribution nodes in personalised medicine

Chimeric Antigen Receptor (CAR) T cell therapies are a type of patient-specific cell immunotherapy demonstrating promising results in the treatment of aggressive blood cancer types. CAR T cells follow a 1:1 business model, translating into manufacturing lines and distribution nodes being exclusive to the production of a single therapy, hindering volumetric scale up. In this work, we address manufacturing capacity bottlenecks via a Mixed Integer Linear Programming (MILP) model. The proposed formulation focuses on the design of candidate supply chain network configurations under different demand scenarios. We investigate the effect of an intermediate storage upstream of the network to: (a) debottleneck manufacturing lines and (b) increase facility utilisation. In this setting, we assess cost-effectiveness and flexibility of the supply chain and we evaluate network performance with respect to: (a) average production cost and (b) average response treatment time. The trade-off between cost-efficiency and responsiveness is examined and discussed.

A multi-objective optimization approach for green and resilient supply chain network design: A real-life case study

This study presents a robust multi-objective optimization model to configure a green global supply chain network structure under disruption. The proposed model is adapted to a global medical device manufacturing system. Economic and environmental issues are considered in designing the network, and mitigation strategies are employed to obtain a resilient supply chain network. To deal with the computational tractability of this non-linear and multi-objective optimization problem, a novel hybrid heuristic is developed that incorporates improved strength Pareto evolutionary algorithm 2 (SPEA2). Computational results indicate that the proposed global supply chain network configuration can respond to its global customers’ demand in agile as well as green manner. Based on our results, the importance of the SC agility is highlighted by increasing the budget of uncertainty, and some of well-known mitigation strategies are in contradiction to the agile production paradigm.

Social cost-benefit assessment as a post-optimal analysis for hydrogen supply chain design and deployment: Application to Occitania (France)

A lot of recent studies have concluded that hydrogen could gradually become a much more significant component of the European energy mix for mobility and stationary fuel cell system applications. Yet, the challenge of developing a future commercial hydrogen economy still remains through the deployment of a viable hydrogen supply chain and an increasing fuel cell vehicle market share, which allows to narrow the existing cost difference regarding the conventional fossil fuel vehicle market. In this paper, the market penetration of hydrogen fuel cell vehicles, as substitutes for internal combustion engine vehicles has been evaluated from a social and a subsidy-policy perspective from 2020 to 2050. For this purpose, the best compromise hydrogen supply chain network configuration after the sequential application of an optimization strategy and a multi-criteria decision-making tool has been assessed through a Social Cost-Benefit Analysis (SCBA) to determine whether the hydrogen mobility deployment increases enough the social welfare. The scientific objective of this work is essentially based on the development of a methodological framework to quantify potential societal benefits of hydrogen fuel cell vehicles. The case study of the Occitania Region in France supports the analysis. The externality costs involve the abatement cost of CO2, noise and local pollution as well as platinum depletion. A subsidy policy scenario has also been implemented. For the case study considered, the results obtained that are not intended to be general, show that CO2 abatement dominates the externalities, platinum is the second largest externality, yet reducing the benefits obtained by the CO2 abatement. The positive externalities from air pollution and noise abatement almost reach to compensate for the negative costs caused by platinum depletion. The externalities have a positive effect from 2025. Using a societal cost accounting framework with externalities and subsidies, hydrogen transition timing is reduced by four years for the example considered.

An integrative framework of supply chain flexibility

PurposeIt is crucial to recognize that supply chain flexibility is important to build sustainable competitive edge in coping with uncertainty. This study puts forth an empirically supported integrative framework to understand supply chain flexibility from market-oriented and network-oriented perspectives, interrelationships among its dimensions and the effect of supply chain complexity as a contingent factor.Design/methodology/approachThe online survey data were collected from 201 manufacturing companies and checked for common method variance, validity and reliability. Structural equation modeling was then used to test the hypotheses.FindingsThe results of this study demonstrate that there are interrelationships among market-oriented and network-oriented supply chain flexibility dimensions. It is the flexibility embedded in the supply chain network configuration that plays critical roles in superior performance in flexibility capabilities to create customer values. The interrelated effect among flexibility dimensions is contingent on the magnitude of the supply chain complexity corresponding to the number of supply chain tiers.Originality/valueThis study takes a step to advance understanding of supply chain flexibility from an integrative point of view consisting of market-oriented and network-oriented perspectives. This study is expected to provide practitioners the foundation to implement supply chain flexibility considering the interrelationships among flexibility dimensions. Our results suggest that to carry out a supply chain flexibility strategy, the vertical complexity of the supply chain structure needs to be taken into consideration.

An integrated open-loop supply chain network configuration model with sustainable supplier selection: fuzzy multi-objective approach

Supply chain management is an essential part of most companies to reduce costs and increase the profit for a competitive business environment. The success of companies is directly connected to the performance of the supply chain. The sustainability of businesses broadly depends on the purchasing decision, and the process becomes more complex with resource limitations. In addition, relationships with suppliers are considered for optimal production and risk minimization in the supply chain network. This research aims to design and optimize a multi-echelon, multi-period, and multi-objective open-loop supply chain (OLSC) network design. The objectives are deemed to be the total costs, the minimization of environmental costs, and the maximization of the importance level of suppliers. Furthermore, a fuzzy approach based on interval type-2 fuzzy sets is designed to evaluate the supplier performance according to sustainable qualitative criteria. After determination of the weights of criteria and evaluation of suppliers by interval type-2 fuzzy numbers, the final score of suppliers is calculated based on the weights of criteria and evaluation scores. Three fuzzy programming approaches are utilized to solve the developed OLSC model. A numerical example is provided to validate the model and effectiveness of the proposed evaluation procedure. The results demonstrate the feasibility of the proposed evaluation procedure and the achievement of a compromise in the developed model.

Impact of Tariffs on Global Supply Chain Network Configuration: Models, Predictions, and Future Research

The past three decades have witnessed tremendous growth of supply chain activities around the globe thanks to the lowered trade barriers and free trade agreements among countries. The global supply chain management literature developed during this time provided theoretical frameworks and decision support tools for operational and supply chain decisions in the global context. Recent development in trade barrier debates as well as increasing uncertainties in countries’ trade policies have forced companies to rethink their global operational strategies. In this short paper, we draw insights from the existing global supply chain literature and in particular, focus on interpreting recent research contributions published in Manufacturing & Service Operations Management (M&SOM) on the topic to gain understanding of the implications of trade policies, especially tariffs, for firms’ global facility network design decisions. We also provide a discussion of important dimensions that were absent from the existing literature but are pertinent to providing a richer understanding of implications of trade policies for today’s interconnected supply chains. Future research that takes into consideration those previously less explored dimensions has the potential to offer insights not only to industry practitioners but also, to the policy makers. Developing an appreciation of potential reactions from complex connected supply chains in various industries would help policy makers better anticipate the impact of trade policy change.

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.

Structural complexity and robustness of supply chain networks based on product architecture

Supply chain networks are dynamic and complex, but also vulnerable. The supply chain network configuration is a context for action and its design should depend on the manufactured product. Hence, this study develops a framework to assess topological complexity and robustness of the supply chain networks taking into consideration the product architecture. The framework includes a morphological analysis and evaluates quantitative indicators of complexity and robustness based on network characteristics. Additionally, overall metrics for complexity and robustness are presented. As a result, a decision matrix is drawn to allow the comparison between different product architectures and their corresponding supply chain network. Moreover, a cost analysis is performed to analyse the trade-offs between complexity vs. cost and robustness vs. cost. The results suggest that complexity is required to achieve robustness and an increase in cost is needed to attain a balanced level of complexity and robustness. The presented framework can be used during supply chain and product design phases to compare different product architectures and supply chain structures and to consider the implications of robustness, complexity and cost of the proposed designs.

Environmental and Economic Optimization of Algal Biofuel Supply Chain with Multiple Technological Pathways

This article optimizes the design and configuration of algal biofuel supply chain networks (SCN) under economic and environmental objectives. Minimization of the total supply chain cost and the total life cycle greenhouse gas emission are the economic and environmental objectives, respectively. The SCN has been modeled by a multiobjective mixed integer linear programming approach which incorporates multiple production pathways and time periods, seasonality factors, water evaporation, recycling opportunities, and other major traits of the algal biofuel SCN. The model determines the optimal strategic and tactical level decisions of all SCN echelons. A fuzzy solution-based e-constraint method has been utilized to obtain Pareto-optimal solutions that illustrate the trade-off between economic and environmental objectives. The performance of the model has been assessed in a case study carried out in seven states of the U.S which intends to develop the algal biofuel SCN from the year 2018 to the year 2024. Essen...

Understanding the value of upstream inventory information sharing in supply chain networks

• We developed CTMC models to study the effect of upstream inventory information sharing in SCNs. • A two-echelon SCN with one retail store and two production facilities was used as a test bed. • The marginal benefits of sharing additional inventory information were examined. • The impact of backorder limits on SCN performance was also assessed. • The study reveals that inventory information can be an effective substitute for physical inventory. Global competition has caused a paradigm shift in a firm’s outlook from a product-centric to a customer-centric view. With this shift, the availability of inventory at a store is a prime concern for firms in a supply chain network (SCN) as it affects customer goodwill and market share. Information sharing among the SCN partners is a key strategy to address this issue. In this study, we focus on the impact of sharing upstream inventory information in a SCN on its overall performance. A two-echelon SCN configuration with one retail store and two production facilities was used as an experimental test bed. To assess the marginal benefits of sharing additional information, three levels of information sharing were considered along with the base case of no information sharing. The information shared ranged from stock-out information at the lowest level to inventory and backorder levels at the highest level. Continuous Time Markov Chain models of the SCN were developed and analyzed to gain insights into the value of inventory information sharing. Numerical experiments were conducted to show that inventory information can be an effective substitute for physical inventory and to assess the impact of backorder limits on the SCN performance.

Development of Shale Gas Supply Chain Network under Market Uncertainties

The increasing demand of energy has turned the shale gas and shale oil into one of the most promising sources of energy in the United States. In this article, a model is proposed to address the long-term planning problem of the shale gas supply chain under uncertain conditions. A two-stage stochastic programming model is proposed to describe and optimize the shale gas supply chain network. Inherent uncertainty in final products’ prices, such as natural gas and natural gas liquids (NGL), is treated through the utilization of a scenario-based method. A binomial option pricing model is utilized to approximate the stochastic process through the generation of scenario trees. The aim of the proposed model is to generate an appropriate and realistic supply chain network configuration as well as scheduling of different operations throughout the planning horizon of a shale gas development project.

Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach

Supply chains can be vulnerable to sudden disruptions, especially when it emphasizes efficient operation. In this regard, supply chain resilience (SCR) has received attention recently to cope with disruptions and improve competitiveness. This paper presents a novel methodology to measure resilience between different configurations of a supply chain network (SCN), based on a number of influential factors. For this reason, data envelopment analysis (DEA) is employed to identify the best-practice and less-performing SCN configurations among a group of alternatives. On this basis, the extent to which a current configuration can improve its resiliency is also measured. The methodology is applied to the case of E1, a liquefied petroleum gas (LPG) company in Korea. Topological and operational measures were used as variables to assess resilience. The results suggest that the LPG supply chain in the case study requires an addition in the number and capacity of supply nodes in its network.

Multi-echelon closed-loop supply chain network design and configuration under supply risks and logistics risks

Present day supply chains are dynamically expanding with the increase in globalisation but susceptible towards a number of risks too. The risks cause disturbances in supply chain and hinder the movement of products across their stage which ultimately leads to overall increases in supply chain costs. In this paper, a multi-echelon closed-loop supply chain model is considered, where supplier's quality risks (production issues), logistics risks (transportation issues) are mathematically modelled using mixed integer linear programming (MILP) formulation. The paper highlights benefits of adopting the supply chain network design under risk. The comparison of risk and no risk situation is illustrated for a generic closed-loop supply chain network. The findings suggest that risk consideration in supply chain network design is very useful, and its cost is not much higher to the organisation too. The supply chain resilience can be achieved with a moderate risk premium. The firms which can pay 10-13% more will be able to sustain in risk events and their network configuration will be robust under supply and logistics risks happenings.

Multi-echelon closed-loop supply chain network design and configuration under supply risks and logistics risks

Present day supply chains are dynamically expanding with the increase in globalisation but susceptible towards a number of risks too. The risks cause disturbances in supply chain and hinder the movement of products across their stage which ultimately leads to overall increases in supply chain costs. In this paper, a multi-echelon closed-loop supply chain model is considered, where supplier's quality risks (production issues), logistics risks (transportation issues) are mathematically modelled using mixed integer linear programming (MILP) formulation. The paper highlights benefits of adopting the supply chain network design under risk. The comparison of risk and no risk situation is illustrated for a generic closed-loop supply chain network. The findings suggest that risk consideration in supply chain network design is very useful, and its cost is not much higher to the organisation too. The supply chain resilience can be achieved with a moderate risk premium. The firms which can pay 10-13% more will be able to sustain in risk events and their network configuration will be robust under supply and logistics risks happenings.

A multi-objective intelligent water drop algorithm to minimise cost Of goods sold and time to market in logistics networks

The Intelligent Water Drop (IWD) algorithm is inspired by the movement of natural water drops (WD) in a river. A stream can find an optimum path considering the conditions of its surroundings to reach its ultimate goal, which is often a sea. In the process of reaching such destination, the WD and the environment interact with each other as the WD moves through the river bed. Similarly, the supply chain problem can be modelled as a flow of stages that must be completed and optimised to obtain a finished product that is delivered to the end user. Every stage may have one or more options to be satisfied such as suppliers, manufacturing or delivery options. Each option is characterised by its time and cost. Within this context, multi–objective optimisation approaches are particularly well suited to provide optimal solutions. This problem has been classified as NP hard; thus, this paper proposes an approach aiming to solve the logistics network problem using a modified multi–objective extension of the IWD which returns a Pareto set.Artificial WD, flowing through the supply chain, will simultaneously minimise the cost of goods sold and the lead time of every product involved by using the concept of Pareto optimality. The proposed approach has been tested over instances widely used in literature yielding promising results which are supported by the performance measurements taken by comparison to the ant colony meta-heuristic as well as the true fronts obtained by exhaustive enumeration. The Pareto set returned by IWD is computed in 4 s and the generational distance, spacing, and hyper–area metrics are very close to those computed by exhaustive enumeration. Therefore, our main contribution is the design of a new algorithm that overcomes the algorithm proposed by Moncayo-Martínez and Zhang (2011).This paper contributes to enhance the current body of knowledge of expert and intelligent systems by providing a new, effective and efficient IWD-based optimisation method for the design and configuration of supply chain and logistics networks taking into account multiple objectives simultaneously.

A Modal Interval Based Genetic Algorithm for Closed-loop Supply Chain Network Design under Uncertainty

This paper proposes a modal interval based genetic algorithm to solve the closed-loop supply chain network configuration puzzle under uncertainty. In this special network, the retailer dominates the collection and remanufacturing activities, and sets up dedicate remanufacturing centers to remanufacture the components disassembled from the end of life excavators. This paper applies the modal intervals to characterize the uncertain parameters and combine the modal interval analysis with the genetic algorithm to solve the proposed modal interval linear programming problem. Moreover, three different decision criteria are adopted to analyze the optimal decisions of the remanufacturer. The results confirm that the proposed method can successfully determine the location of different facilities and the allocation of the products and components.

An integrated sustainable partner selection approach with closed-loop supply chain network configuration

Reverse logistics network design is a crucial issue in which it is important to take into account the selection of the most appropriate partner with sustainability concerns. This partner can be a supplier or a third-party reverse logistics provider (3PRLP). However, research works that consider reverse logistics (RL) network design, partner selection, and sustainability issues simultaneously are rather limited till now. This paper proposes an integrated sustainable approach for partner selection and closed-loop supply chain (CLSC) network configuration, particularly in the case of outsourcing reverse logistics process to third- party provider. We propose a trade-off between sustainability criteria for both supplier and 3PRL provider selection. A multi-objective mixed-integer programming (MILP) model is also proposed to configure CLSC network and to select the best partners. The model minimizes the total cost of sourcing, and the total greenhouse gas emissions, while it maximizes the total value of reverse logistics, and the number of new job opportunities. A numerical example is also presented to illustrate the proposed approach.