Additive manufacturing technology in spare parts supply chain: a comparative study

Additive Manufacturing (AM) technology has the potential to significantly improve supply chain dynamics. The purpose of this paper is to investigate the impact of AM on spare parts supply chain. Three supply chain scenarios are investigated in this paper, namely conventional supply chain, centralised AM-based supply chain and distributed AM-based supply chain. Based on system dynamics simulations, this paper specifically compares three supply chain scenarios, in terms of total variable cost and carbon emission. The results show the spare part supply chain utilising AM is indeed superior to the traditional one in sustainable performance. It is also expected that AM can facilitate the spare parts supply chain to achieve more economic benefits along with its development. To our knowledge, this paper is one of the early studies that explores the impact of AM on supply chain performance and quantitatively examines the superiority of utilising AM in spare parts supply chain. Some suggestions are also provided to help managers adopting AM in their spare parts supply chains.     

The influence of additive manufacturing on the configuration of make-to-order spare parts supply chain under heterogeneous demand

Additive manufacturing (AM), alongside technological developments, has been used in the production of spare parts with positive results for spare parts supply chains. In this study, we investigate spare parts supply chains serving heterogeneous demands from multiple service locations under the mode of make-to-order. We aim to compare different configurations (i.e. centralised and distributed) of spare parts supply chains in terms of their performance (e.g. sojourn time and cost) and to further propose suggestions to better configure AM-based spare parts supply chains by effectively allocating AM machines at service locations (SLs) or regional distribution centres (RDCs). In order to realise these research objectives, the simulation approach is used as the main research method. Different from the existing perception, our results illustrate that the distributed deployment of AM machines does not always guarantee a quick response, and that centralised configuration is desirable when the demand rate is relatively high due to the pooling effect. The distributed configuration, however, can still be suitable, considering the development of AM technology. Our results also indicate the possibility of a mixed configuration of AM-based supply chains with the potential for outperforming the purely centralised/distributed configuration. The criteria to design such a mixed configuration are also offered.     

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.     

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.     

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 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.     

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 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.     

Benefits of considering inventory in service parts logistics network design problems with time-based service constraints

We study the integrated logistics network design and inventory stocking problem as characterized by the interdependency of the design and stocking decisions in service parts logistics. These two sets of decisions are usually considered sequentially in practice, and the associated problems are tackled separately in the research literature. The overall problem is typically further complicated due to time-based service constraints that provide lower limits on the percentage of demand satisfied within specified time windows. We introduce an optimization model that explicitly captures the interdependency between network design (location of facilities, and allocation of demands to facilities) and inventory stocking decisions (stock levels and their corresponding stochastic fill rates), and present computational results from our extensive experiments that investigate the effects of several factors including demand levels, time-based service levels and costs. We show that the integrated approach can provide significant cost savings over the decoupled approach (solving the network design first and inventory stocking next), shifting the whole efficient frontier curve between cost and service level to superior regions. We also show that the decoupled and integrated approaches may generate totally different solutions, even in the number of located facilities and in their locations, magnifying the importance of considering inventory as part of the network design models.     

Two-echelon multi-item spare parts systems with emergency supply flexibility and waiting time constraints

We consider a two-echelon multi-item spare parts inventory system in which supply flexibility through both lateral transshipments and direct deliveries are used in response to stock-outs. We develop a simple heuristic to determine the stocking levels for each type of spare part at the central warehouse and local warehouses minimizing the expected total system cost subject to a target level for the average waiting time across items at each local warehouse. Numerical comparisons between the heuristic's objective function values and lower bounds obtained by applying Lagrangian relaxation techniques show that the heuristic performs well. A study to evaluate the relative merits of a two-echelon system as opposed to a single-echelon system is also presented. We find that the presence of lateral transhipment improves the performance of the single-echelon system considerably. A two-echelon system is only worth implementing when lateral transhipments are not possible.     

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 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.     

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.     

Modelling of sustainable food grain supply chain distribution system: a bi-objective approach

Growing food demand, environmental degradation, post-harvest losses and the dearth of resources encourage the decision makers from developing nations to integrate the economic and environmental aspects in food supply chain network design. This paper aims to develop a bi-objective decision support model for sustainable food grain supply chain considering an entire network of procurement centres, central, state and district level warehouses, and fair price shops. The model seeks to minimise the cost and carbon dioxide emission simultaneously. The model covers several problem peculiarities such as multi-echelon, multi-period, multi-modal transportation, multiple sourcing and distribution, emission caused due to various motives, heterogeneous capacitated vehicles and limited availability, and capacitated warehouses. Multiple realistic problem instances are solved using the two Pareto based multi-objective algorithms. Sensitivity analysis results imply that the decision makers should establish a sufficient number of warehouses in each producing and consuming states by maintaining the suitable balance between the two objectives. Various policymakers like Food Corporation of India, logistics providers and state government agencies will be benefited from this research study.     

Sensor-driven prognostic models for equipment replacement and spare parts inventory

Accurate predictions of equipment failure times are necessary to improve replacement and spare parts inventory decisions. Most of the existing decision models focus on using population-specific reliability characteristics, such as failure time distributions, to develop decision-making strategies. Since these distributions are unaffected by the underlying physical degradation processes, they do not distinguish between the different degradation characteristics of individual components of the population. This results in less accurate failure predictability and hence less accurate replacement and inventory decisions. In this paper, we develop a sensor-driven decision model for component replacement and spare parts inventory. We integrate a degradation modeling framework for computing remaining life distributions using condition-based in situ sensor data with existing replacement and inventory decision models. This enables the dynamic updating of replacement and inventory decisions based on the physical condition of the equipment.     

Optimal service parts contract with multiple response times and on-site spare parts

This study examines user outsourcing of spare parts management to vendors through a service contract. The user’s selection of a fixed-price service parts contract is formulated as a stochastic integer programming model that decides multiple response times and on-site spare parts, while considering component breakdown with uncertain failure rates. We analytically derive the optimality conditions for the continuous case and subsequently design an efficient algorithm. Numerical illustrations and analyses are conducted to evaluate decisions under various scenarios. Our analysis shows that when both failure rate and expedited contract cost are high, coupled with low part cost, users would prefer the purchase of spare parts for all components to expedited contracts. A fixed-price expedited contract has a lower marginal cost with respect to failure rate than a fixed-price next day contract and a usage-based contract. We also examine inventory behaviour for a single part, multiple types of parts, and multiple groups of parts. It is shown that there is a cost-saving pooling effect in spare parts for identical items, which significantly raises the likelihood of having on-site stored parts. The problem becomes more complex for multiple items, reflecting bundling effects between items for a given contract.     

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 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.     

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.     

Modelling and optimisation of biomass supply chains: a review

A growing number of researchers are attracted by the domain of bioenergies, due to the problems induced by greenhouse gas emissions and increasing energy demand. One possible way of producing biofuels in a renewable way is to use biomass, however the economic viability of a biorefinery system depends critically on the cost of its supplies. As biomass is not very expensive, logistics is responsible of an important fraction of this cost. Models and methods to optimise biomass supply chains are surveyed in this article, after introducing some technical terms and describing the main activities in these chains. This review aims to give a comprehensive overview of the research in this field with a focus on optimisation modelling issues and solution approaches. Recent advances in the current research and possible new directions are sketched.