Methodological approaches to supply chain design1

This paper reviews methodological approaches to the design (or redesign) of the supply chain (SC), including comprehensive approaches (proposals concerning the entire process of designing the SC) and those that deal with four specific aspects of the process (definition of the SC objectives, reverse SC, finance, and generation and use of scenarios) that have a decisive influence on the whole design of the SC. The comprehensive approaches include those based on typologies of products, markets and SCs and those that propose a succession of the stages to follow through the design process. The discussion shows that the use of typologies is not adequate to face SC design and that the methods proposing a succession of stages may suit, provided that they are developed and presented in a manner appropriate to their use for practitioners. The discussion leads also to suggest several research lines.     

Blood supply chain network design considering blood group compatibility under uncertainty

This paper addresses the design of a blood supply chain (SC) network considering blood group compatibility. To this aim, a bi-objective mathematical programming model is developed which minimises the total cost as well as the maximum unsatisfied demand. Due to uncertain nature of some input parameters, two novel robust possibilistic programming models are proposed based on credibility measure. The data of a real case study are then used to illustrate the applicability and performance of the proposed models as well as validating the proposed robust possibilistic programming approach. The obtained results show the superiority of the developed models and significant cost savings compared to current existed blood SC network.     

Multi-level supply chain network design with routing

Recently, the multi-level and multi-facility industrial problem in supply chain management (SCM) has been widely investigated. One of the key issues, central to this problem in the current SCM research area is the interdependence among the location of facilities, the allocation of facilities, and the vehicle routing for the supply of raw materials and products. This paper studies the supply chain network design problem, which involves the location of facilities, allocation of facilities, and routing decisions. The proposed problem has some practical applications. For example, it is necessary for third party logistics (3PL) companies to manage the design of the network and to operate vehicle transportation. The purpose of this study is to determine the optimal location, allocation, and routing with minimum cost to the supply chain network. The study proposes two mixed integer programming models, one without routing and one with routing, and a heuristic algorithm based on LP-relaxation in order to solve the model with routing. The results show that a developed heuristic algorithm is able to find a good solution in a reasonable time.     

A simulation-optimisation approach for supply chain network design under supply and demand uncertainties

We investigate a three-echelon stochastic supply chain network design problem. The problem requires selecting suppliers, determining warehouses locations and sizing, as well as the material flows. The objective is to minimise the total expected cost. An important feature of the investigated problem is that both the supply and the demand are uncertain. We solve this problem using a simulation-optimisation approach that is based on a novel hedging strategy that aims at capturing the randomness of the uncertain parameters. To determine the optimal hedging parameters, the search process is guided by particle swarm optimisation procedure. We present the results of extensive computational experiments that were conducted on a large set of instances and that provide evidence that the proposed hedging strategy constitutes an effective viable solution approach.     

A strategic model for exact supply chain network design and its application to a global manufacturer

This paper presents a comprehensive model that captures significant strategic decisions involved in designing or redesigning high-performance supply chains from the perspective of the manufacturer. The problem considers deterministic demand by multiple clients, for multiple products, over the periods of a long-term horizon. The design decisions involve selection of suppliers, establishment or resizing of production facilities and distribution centres, possible subcontracting of related activities, and selection of transportation modes and routes. The problem is formulated by a Mixed Integer Linear Programming model. Its objective is to minimise the overall costs associated with procurement, production, inventory, warehousing, and transportation over the design horizon. Appropriate constraints model the complex relationships among the links of the supply chain. The proposed model has been applied to a large case study of a global manufacturing firm, providing valuable insights into the transformation of the firm’s current supply chain network, as well as into the potential of the proposed approach.     

Modelling and solving spare parts supply chain network design problems

Spare parts are key operational assets in order to minimise unexpected equipment downtimes that may significantly impact a company’s results. The spare parts supply chain network supports the entire spare parts operations management and it is essential to achieve the planned goals. However, most of the traditional literature on spare parts management has not focused on the underlying supply chain network. Thus, this paper studies the integration of supply chain network design and control with traditional spare parts management. In particular, a generic network optimisation modelling structure is proposed, with simultaneous optimisation of warehouse locations and inventory control decisions, allowing minimising the total costs associated with the spare parts supply chain network. The generic model is specified based on three inventory control policies widely employed in the industry, which are suitable for managing a great variety of spare parts, i.e. (s, Q), (R, s, S) and (S-1, S). Furthermore, a solution approach is proposed based on Generalised Benders Decomposition. Finally, numerical results from a real-world application case in the process industry are shown and discussed.     

Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty

In this study, a general comprehensive model is proposed for strategic closed-loop supply chain network design under interval data uncertainty. The proposed model considers various assumptions such as multiple periods, multiple products, and multiple supply chain echelons as well as uncertain demand and purchasing cost. In addition, bill of materials for each product is considered via a new approach in management of forward and reverse flows of products for producing new products and reusing or disassembling returned products. Uncertainty of parameters in the proposed model is handled via an interval robust optimisation technique. The model assumptions are well matched with decision making environments of food and high-tech electronics manufacturing industries. The factors that make these two industries similar are time-dependent properties of products such as prices and warehousing lifetime period. The computational results of solving the proposed model via LINGO 8 demonstrate efficiency of the proposed model in dealing with uncertainty in an agile manufacturing context.     

A distributed approximation approach for solving the sustainable supply chain network design problem

This paper introduces a comprehensive Mixed Integer Linear Programming (MILP) model for a sustainable supply chain network design problem, and an efficient Distributed Approximation Approach (DAA) to solve it approximately. We study a multi-echelon, multi-product and multi-modal supply chain with different transportation modes. Besides relevant costs in the supply chain such as procurement, production and distribution cost, we also explicitly consider the environmental footprint, represented by carbon emissions and water consumption from production and transportation. The approximation approach is a decomposition-based method. First, the original problem is divided into a partner selection sub-problem and a transportation planning sub-problem. Then multiple filter mechanisms are used to remove potentially infeasible solutions, and an approximate value of the objective function is calculated for each of the remaining solutions to perform a further selection. The one with the lowest approximation is chosen to be applied with a branch-and-bound method. Finally, the algorithm is paralleled and implemented in Apache Spark distributed computing framework to further improve efficiency. Experimental results show that the proposed DAA can provide high quality solutions compared to the optimal solutions of the MILP model with mostly a negligible relative gap and solve large instances in much shorter time than CPLEX. Moreover, in our numerical study, we also compare the results of our model with another version of the model that does not take the environmental footprint into consideration. The results show that explicitly incorporating environmental footprint results in a substantial decrease of CO₂ emissions and water consumption at a negligible cost increase. This insight may be of interest to managers and other decision makers and policy makers.     

Robust closed-loop global supply chain network design under uncertainty: the case of the medical device industry

Improving performance of global supply chains requires careful consideration of various factors including distance from markets, access to resources, exchange and tax rates, import tariffs, and trade regulations. In this paper, a comprehensive optimization model is proposed to maximise the after-tax profit of a closed-loop global supply chain for medical devices under uncertainty. The uncertainty of the decision-making environment is modelled using the budget of uncertainty concept in interval robust optimization. International financial issues due to the Economic Cooperation Organisation Trade Agreement as well as national regulations including transfer pricing limitations, exchange rates, tax rates, and import tariffs are considered. The proposed model considers various realistic assumptions pertaining to medical device supply chains such as multiple products, multiple periods, multiple echelons, and limited warehousing lifetime. In addition, reverse flows of perished and defective products are considered to address environmental concerns and customers’ requirements as well as to gain economic advantages. To tackle this problem, an efficient memetic algorithm is developed that incorporates adaptive variable neighbourhood search as its local search heuristic. Computational results demonstrate the efficiency of the proposed model in dealing with uncertainty in an agile manufacturing context. In addition, several managerial insights are discussed based on the results.     

Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model

This article seeks to offer a systematic approach to establishing a reliable network of facilities in closed loop supply chains (CLSCs) under uncertainties. Facilities that are located in this article concurrently satisfy both traditional objective functions and reliability considerations in CLSC network designs. To attack this problem, a novel mathematical model is developed that integrates the network design decisions in both forward and reverse supply chain networks. The model also utilizes an effective reliability approach to find a robust network design. In order to make the results of this article more realistic, a CLSC for a case study in the iron and steel industry has been explored. The considered CLSC is multi-echelon, multi-facility, multi-product and multi-supplier. Furthermore, multiple facilities exist in the reverse logistics network leading to high complexities. Since the collection centres play an important role in this network, the reliability concept of these facilities is taken into consideration. To solve the proposed model, a novel interactive hybrid solution methodology is developed by combining a number of efficient solution approaches from the recent literature. The proposed solution methodology is a bi-objective interval fuzzy possibilistic chance-constraint mixed integer linear programming (BOIFPCCMILP). Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model in a supply chain environment and to help decision makers facilitate their analyses.     

A highly optimised tolerance-based approach for multi-stage,multi-product supply chain network design

In this paper we propose an algorithm called Highly Optimised Tolerance (HOT) for solving a multi-stage, multi-product supply chain network design problem. HOT is based on power law and control theory. The proposed approach takes its traits from the local incremental algorithm (LIA), which was initially employed to maximise the design parameter (i.e. yield), particularly in the percolation model. The LIA is somewhat analogous to the evolution by natural selection schema. The proposed methodology explores a wide search space and is computationally viable. The HOT algorithm tries to make the system more robust at each step of the optimisation. The objective of this paper is to reduce the total cost of supply chain distribution by selecting the optimum number of facilities in the network. To examine the effectiveness of the HOT algorithm we compare the results with those obtained by applying simulated annealing on a supply chain network design problem with different problem sizes and the same data sets.     

A fuzzy technique for supply chain network design with quantity discounts

This paper proposes a hierarchical technique for Supply Chain Network (SCN) efficiency maximisation under uncertainty composed of three steps. The first step extends a previous fuzzy cross-efficiency Data Envelopment Analysis approach, originally intended for suppliers’ selection, in order to evaluate and rank all the actors in each SCN stage under conflicting nondeterministic criteria. Afterwards, a fuzzy linear integer programming model is stated and solved for each pair of subsequent SCN stages to determine the quantities required from each stakeholder to maximise the overall SCN efficiency while satisfying the estimated demand and respecting the nodes capacity. Finally, a heuristics is applied to limit the exchange of small quantities in the SCN, in which the trade is not economically convenient according to quantity discounts. An illustrative example from the literature shows the technique effectiveness.     

Review of supply chain configuration and design decision-making for new product

The nature of competitive markets continuously pushes manufacturers to develop new products to meet the increasingly diversified customer demands. Manufacturers thus have to handle the complexities generated during the total life cycles of various product types, from product design to procurement, production, marketing and recycling. Though some management practices in mass customisation help to improve the performance of manufacturing systems, there are still some fundamental problems not covered. Joint decision-making of product and supply chain design, for example, is one of them. The existing industrial practice tends to treat these two problems separately. Decoupling these two problems decrease the design complexity but may lead to suboptimal decision outcome. To enhance understanding of the interconnected decisions for supply chain management and product design, this review collects related literature on this topic and focuses on the analysis of existing papers from an operation research perspective.     

Military supply chains and closed-loop systems: resource allocation and incentives in supply sourcing and supply chain design

The US military operates some of the largest and most complex supply chains in the world and their experience provides insight into a variety of supply chain issues. This paper taps that experience to study how the design of a supply chain is influenced by its economic environment. Consider these circumstances: the military is not a profit-seeking enterprise, they want to achieve a particular state of readiness and cost is secondary to that goal. Modern weapons systems are complex, expensive, push the envelope of technology, and the volume requisitioned is often small such that few manufacturers can afford to be in the business; and these expensive weapons systems operate in harsh operating conditions where wear and tear is substantial, consequently remanufacturing of systems and parts is a critical source of supply. Focusing on the supply chain of the US Army, this paper uses an economic model to study their responses to these conditions, how supply chain design is affected by these conflicting goals, and how those designs affect cost and efficiency.     

Designing a supply chain network for deteriorating inventory under preservation effort and trade credits

When designing a perishable goods supply chain network under trade credit arrangements, distribution companies must contend with deteriorating inventory and product preservation efforts to maximise profits. Key decisions include locating distribution centres (DCs), assigning retail stores to DCs, joint replenishment cycle time and investing in preservation technology. This paper addresses these factors from the position that as preservation effort increases, preservation technology cost increases and deterioration rate decreases. An algorithm based on piecewise nonlinear optimisation is provided for solving supply chain network design problems efficiently. In contrast to other studies that have used the approximation approach, the proposed approach solves the original problem accurately and efficiently. Numerical studies are conducted to demonstrate the solutions procedures and determine the effects of the parameters on decisions and profits. The results of this study and the proposed modelling approach are useful references for managerial decisions in designing a supply chain network the context of trade credit and inventory deterioration.     

A framework for designing a supply chain distribution network

Based on the analysis of the pertinent literature, we propose a framework to serve as a guide in designing a distribution network as a component of a supply chain (SC). It consists of three steps that are common to goods and services SCs: position of the decoupling point, structure of the network, and product delivery. For each step, we take into account the peculiarities of both goods and services separately in order to establish a list of available options and evaluate their suitability in relation to the relevant factors that characterise the products and the markets.     

Leagile supply chain: design drivers and business performance implications

In the context of a dynamic and hypercompetitive business environment, effective supply chain design helps organisations to align resources for improved flow of products and services and satisfy customers’ diverse needs. Scholars have proposed several mutually exclusive supply chain designs such as efficient versus responsive, and lean versus agile. Quantitative testing has revealed that supply chain designs of many firms do not match with what was conceptually expected. To address this mismatch, in this study, a new approach to supply chain leagility is introduced and the impact of uncertainty as the key design driver of supply chains on leagility is investigated. The partial least squares (PLS) was employed to analyse data collected from 299 Australian firms by administering a structured questionnaire. Results indicate that higher performance is achievable on minimising the deviation from a balanced supply chain in which aspects of both leanness and agility are equally embedded. Further, the level of uncertainty directly and positively affects the Deviation from Leagility (DFL) index.     

A reliable decision support system for fresh food supply chain management

The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. The DSS combines a unique tool for sales forecasting with order planning which includes an individual model selection system equipped with ARIMA, ARIMAX and transfer function forecasting model families, the latter two accounting for the impact of prices. Forecasting model parameters are chosen via two alternative tuning algorithms: a two-step statistical analysis, and a sequential parameter optimisation framework for automatic parameter tuning. The DSS selects the model to apply according to user-defined performance criteria. Then, it considers sales forecasting as a proxy of expected demand and uses it as input for a multi-objective optimisation algorithm that defines a set of non-dominated order proposals with respect to outdating, shortage, freshness of products and residual stock. A set of real data and a benchmark – based on the methods already in use – are employed to evaluate the performance of the proposed DSS. The analysis of different configurations shows that the DSS is suitable for the problem under investigation; in particular, the DSS ensures acceptable forecasting errors and proper computational effort, providing order plans with associated satisfactory performances.     

The impact of the cost of quality on serial supply‐chain network design

This paper presents a model for supply‐chain design that considers the Cost of Quality as well as the traditional manufacturing and distribution costs (SC‐COQ model). It includes three main contributions: (1) the SC‐COQ model internally computes quality costs for the whole supply chain considering the interdependencies among business entities, whereas previous works have assumed exogenously given Cost of Quality functions; (2) the SC‐COQ model can be used at a strategic planning level to design a logistic route that achieves a maximum profit while considering the overall quality level within a supply chain; and (3) we provide two solution methods based on simulated annealing and a genetic algorithm and perform computational experiments on test instances.     

Supply chain network design under the risk of uncertain disruptions

Facility disruptions in the supply chain often lead to catastrophic consequences, although they occur rarely. The low frequency and non-repeatability of disruptive events also make it impossible to estimate the disruption probability accurately. Therefore, we construct an uncertain programming model to design the three-echelon supply chain network with the disruption risk, in which disruptions are considered as uncertain events. Under the constraint of satisfying customer demands, the model optimises the selection of retailers with uncertain disruptions and the assignment of customers and retailers, in order to minimise the expected total cost of network design. In addition, we simplify the proposed model by analysing its properties and further linearise the simplified model. A Lagrangian relaxation algorithm for the linearised model and a genetic algorithm for the simplified model are developed to solve medium-scale problems and large-scale problems, respectively. Finally, we illustrate the effectiveness of proposed models and algorithms through several numerical examples.