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 reverse logistics network design under uncertainty

Localising facilities and assigning product flows in a reverse logistics environment is a crucial but difficult strategic management decision, certainly when value decay plays an important part. Despite numerous publications regarding closed-loop supply chain design, very few addressed the impact of lead times and the high level of uncertainty in reverse processes. In this article, a single product reverse logistics network design problem with multiple layers and multiple routings is considered. To this end, a new advanced strategic planning model with integrated queueing relationships is built that explicitly takes into account stochastic delays due to various processes like collection, production and transportation, as well as disturbances due to various sources of variability like uncertain supply, uncertain process times, unknown quality, breakdowns, etc. Their impact is measured by transforming these delays into work-in-process, which affects profit through inventory costs. This innovative modeling approach is difficult to solve because of both combinatorial and nonlinear continuous relationships. The differential evolution algorithm with an enhanced constraint handling method is proposed as an appropriate heuristic to solve this model close to optimality. A number of scenarios for a realistic case illustrate the power of this optimisation tool.     

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.     

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.     

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.     

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 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.     

An intelligent physarum solver for supply chain network design under profit maximization and oligopolistic competition

We propose an efficient bio-inspired algorithm for design of optimal supply chain networks in a competitive oligopoly markets. The firms compete in manufacture, storage and distribution of a product to several markets. Each firm aims at maximisation of its own profit by optimising the design capacity and product flow in the supply chain. We model the supply chain network as a multi-layer graph of manufacturing nodes, distribution nodes and storage centres. To optimise the network, we adopt the mechanisms of a foraging behaviour of slime mould Physarum polycephalym. First, we extend the original Physarum model to deal with networks with multiple sources and sinks. Second, we develop a novel method to solve the user equilibrium (UE) problem by exploiting the adaptivity of the Physarum model: we update the link costs according to the product flow. Third, we refer to an equivalent transformation between system optimum problem and UE problem to determine the optimal product flows and design capacities of a supply chain. At last, we present an approach to update the amount of product supplied by each firm. By comparing our solutions with that in Nagurney (2010b) on several numerical examples, we demonstrate the efficiency and practicality of the proposed method.     

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.     

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.     

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.     

Bayesian network modelling for supply chain risk propagation

Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management.     

The design of an agent for modelling supply chain network dynamics

A useful insight into managerial decision making can be found from simulation of business systems, but existing work on simulation of supply chain behaviour has largely considered non-competitive chains. Where competitive agents have been examined, they have generally had a simple structure and been used for fundamental examination of stability and equilibria rather than providing practical guidance to managers. In this paper, a new agent for the study of competitive supply chain network dynamics is proposed. The novel features of the agent include the ability to select between competing vendors, distribute orders preferentially among many customers, manage production and inventory, and determine price based on competitive behaviour. The structure of the agent is related to existing business models and sufficient details are provided to allow implementation. The agent is tested to demonstrate that it recreates the main results of the existing modelling and management literature on supply chain dynamics. A brief exploration of competitive dynamics is given to confirm that the proposed agent can respond to competition. The results demonstrate that overall profitability for a supply chain network is maximised when businesses operate collectively. It is possible for an individual business to achieve higher profits by adopting a more competitive stance, but the consequence of this is that the overall profitability of the network is reduced. The agent will be of use for a broad range of studies on the long-run effect of management decisions on their network of suppliers and customers.     

Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain

Recently, the applications of Blockchain technology have begun to revolutionise different aspects of supply chain (SC) management. Among others, Blockchain is a platform to execute the smart contracts in the SC as transactions. We develop and test a new model for smart contract design in the SC with multiple logistics service providers and show that this problem can be presented as a multi-processor flexible flow shop scheduling. A distinctive feature of our approach is that the execution of physical operations is modelled inside the start and completion of cyber information services. We name this modelling concept ‘virtual operation’. The constructed model and the developed experimental environment constitute an event-driven dynamic approach to task and service composition when designing the smart contract. Our approach is also of value when considering the contract execution stage. The use of state control variables in our model allows for operations status updates in the Blockchain that in turn, feeds automated information feedbacks, disruption detection and control of contract execution. The latter launches the re-scheduling procedure, comprehensively combining planning and adaptation decisions within a unified methodological framework of dynamic control theory. The modelling complex developed can be used to design and control smart contracts in the SC.     

Pricing and effort decisions for a supply chain with uncertain information

This paper analyses the pricing and effort decisions of a supply chain with single manufacturer and single retailer. The manufacturer produces a kind of product and then wholesales the product to the retailer, who in turn retails it to customers over a single selling season. The retailer can influence demand through her sales effort. This research depicts the consumer demand, the manufacturing cost and the sales effort cost as uncertain variables. Considering the demand expansion effectiveness of sales effort, one centralised and three decentralised game models are built on the basis of the expected value criterion, and the equilibrium solutions are obtained. We investigate the effects of the parameters’ uncertainty degrees on the pricing and effort decisions. The results indicate that the manufacturer benefits from improvement in demand and cost uncertainties when he has at least bargaining power in the supply chain. The results also imply that the uncertainty degree of sales effort elasticity has an outstanding influence on the pricing and effort decisions, whereas the uncertainty degree of price elasticity has a modest impact on these decisions. We also study the effects of the parameters’ uncertainty degrees on the supply chain from the consumers’ perspective. The results suggest that with a power retailer, the retail price should always be on the high end. Consequently, consumers do not necessarily benefit from a power retailer. When the manufacturer and the retailer have equal bargaining power, consumers do not necessarily benefit from the supply chain, either.     

Supply chain network design with multi-time-period stochastic demands for optimizing dual objectives on economic and environmental factors

ABSTRACT

Traditional supply chain management focuses on operational and financial factors, such as improving operational efficiency and reducing cost. Since global warming has become critical, researchers and managers of the supply chain have paid more attention to environmental issues. With the environmental factor, the complexity in designing an optimal supply chain network essentially increases. This research considers a multi-objective (minimal total cost and CO₂ emissions) supply chain network design with environmental factor (SCNDEF), which includes the features of multiple echelons, stochastic demands, production/storage capacity, environmental investment, and multiple time periods. In order to help managers make decisions about the SCNDEF, this research performs sensitivity analyses to investigate the effect of key parameters, such as customers’ satisfaction level and limits on CO₂ emissions, on the performance of the supply chain network. Numerical examples are given to verify the proposed methodology. The results show that a good SCNDEF can not only improve the performance of the entire supply chain but also achieve sustainable environmental protection. These research findings are intended to serve as practical reference for industry.     

A bi-level network interdiction model for solving the hazmat routing problem

In the current work, we considered the problem of hazardous material distribution where the distributer chooses the routes on the network, and a regulatory agency controls the behaviour of the distributer to traverse the specified routes. In these circumstances, the distributer sets to select some routes to minimise the total distributing costs. Mostly, this occurs due to selecting risky arcs in which more individuals are exposed to risk. To prevent this and increase the capability to deal with the risk of hazardous material transportation through roads, the regulatory agency obliges carriers to traverse through the most secure arcs, though imposing more distribution costs. The problem is modelled as a bi-level routing problem. The bi-level model is difficult to solve and may be ill-posed. Two meta-heuristic algorithms are proposed to solve the bi-level model, and some randomly generated problems are applied to show the applicability and efficiency of the proposed algorithms.     

改进的蚁群算法及其在卫星网络路由计算中的应用

介绍了蚁群算法的原理，然后对现有蚁群算法进行了一些改进，使它能够快速地收敛以满足高速变化的卫星网络拓扑结构．采用改进的虚拟拓扑策略解决了卫星网络拓扑高速变换的问题．将改进的蚁群算法应用于其上，并给出了相应的性能评估．所提出的改进的虚拟拓扑策略，能够大大减少一个系统周期内卫星网的时间片个数．应用于此基础上的改进的蚁群算法也体现了较好的性能．     

Sustainable supply chains for supply chain sustainability: impact of sustainability efforts on supply chain risk

Supply chain managers across the globe are finding it difficult to manage the increasingly complex supply chains despite adopting a variety of risk mitigation strategies. Firms on the other hand have also been adopting various kinds of environmental and social sustainability practices in recent times to reduce carbon footprint and improve their image on the social front. However, very few studies in the extant literature have examined the impact of sustainability practices on supply chain risk. We address this important gap in literature by empirically testing this relationship, using primary data from six manufacturing sectors and 21 different countries including developed as well as emerging markets across the globe. Our findings indicate that risk mitigation strategies do not always reduce the actual supply chain risk experienced by firms, whereas sustainability efforts help reduce supply chain risk, especially in emerging market contexts. In addition, we find that, while reactive risk mitigation strategies on their own fail to reduce supply chain risk, they are effective when used in conjunction with sustainability efforts. We also find that preventive risk mitigation efforts are only effective in mature supply chains such as the OECD countries.