Strategic Supply Chain Design Research Articles

Development of a robust multi-objective model for green capacitated location-routing under crisis conditions

Location-Routing Problem (LRP) is a strategic supply chain design problem aimed at meeting customer demands. LRPs involve selecting one or more depot sites from a set of potential locations and determining the best routes to connect them to demand points. With the rising awareness about the environmental impacts of transportation over the past years, the use of green logistics to mitigate these impacts has become increasingly important. To compensate for a gap in the literature, this paper presents a robust bi-objective mixed-integer linear programming (MILP) model for the green capacitated location-routing problem (G-CLRP) with demand uncertainty and the possibility of failure in depots and routes. The final result of this Robust Multi-Objective Model is to set up the depots and select the routes that offer the highest reliability (Maximizing network service) while imposing the lowest cost and environmental pollution. A Nondominated Sorting Genetic Algorithm (NSGA-II) is used to solve the large-sized instances of the modeled problem. The paper also provides a numerical analysis and a sensitivity analysis of the solutions of the model.

Design of a biofuel supply network under stochastic and price-dependent biomass availability

This article presents a framework for profit-maximizing strategic bio-energy supply chain design by taking into account variability in biomass as a response to price set as well as uncertainty in biomass yield. We present our model as a two-stage stochastic integer program for a multi-period integrated design of a network in which the here-and-now strategic decisions include biorefinery locations and size as well as base biomass price. To efficiently solve our model, we suggest an L-shaped-based algorithm along with a Sample Average Approximation approach. Finally, we demonstrate our results in a case study in Texas using realistic data. Within our framework, we present the relationship between biomass and biofuel price, as well as the optimal network design for the biofuel producer.

Building supply chain quality management theory from case study in China

In view of the inconsistency on the definition and key components of supply chain quality management (SCQM), this study is conducted to make up the deficiency. The research team collected the data using semi-structural interviews of four supply chains from food, toy, automobile and pharmaceutical industries in China. Based on within- and cross-case analysis, six key components of SCQM (supply chain leadership on quality, internal quality integration, strategic supply chain design for quality, upstream quality management system, downstream quality management system and product recall system) were identified. Finally, a definition and its holistic framework of SCQM were proposed. Compared with the existing research, the components identified in this study were more comprehensive, and it can facilitate the operationalisation of the construct and help to develop a high quality measurement scale. Practically, the SCQM framework proposed in this study will provide guidelines for the managers to build up an effective SCQM system.

Building supply chain quality management theory from case study in China

In view of the inconsistency on the definition and key components of supply chain quality management (SCQM), this study is conducted to make up the deficiency. The research team collected the data using semi-structural interviews of four supply chains from food, toy, automobile and pharmaceutical industries in China. Based on within- and cross-case analysis, six key components of SCQM (supply chain leadership on quality, internal quality integration, strategic supply chain design for quality, upstream quality management system, downstream quality management system and product recall system) were identified. Finally, a definition and its holistic framework of SCQM were proposed. Compared with the existing research, the components identified in this study were more comprehensive, and it can facilitate the operationalisation of the construct and help to develop a high quality measurement scale. Practically, the SCQM framework proposed in this study will provide guidelines for the managers to build up an effective SCQM system.

Incorporating dynamic cellular manufacturing into strategic supply chain design

For increasing the efficiency of the supply chain (SC), it is necessary to take into account the interactions and relationships between the stages of procurement of raw materials, manufacturing the products, and distributing them. An integrated framework is proposed in this paper for companies interested in meeting the demand for different products in the customer zones by establishing a number of plants and distributors at the candidate sites and in having SC design with reconfiguration capability based on changes in demand and more proper economic opportunities. For this purpose, a geographically distributed cell design is proposed for the selection of the proper location for each of the facilities and the production process of the products. A mixed integer linear programming model is presented here for the integration of the sectors for procurement, production, and distribution of the products in the SC. In light of the NP-hard class of the cell formation problem, a new algorithm titled hybrid genetic ant lion optimization (HGALO) algorithm is presented for finding the optimal or near-optimal solutions. A comparison is also made here between the proposed algorithm and the genetic algorithm (GA) for demonstration of the efficiency of the proposed algorithm. The quality of the solutions generated based on the HGALO algorithm demonstrates the capability and effectiveness of the algorithm in finding high quality solutions.

Optimal trade credit and replenishment policies for supply chain network design

Classical supply chain models usually overlook the impact of financial factors on the overall supply chain performance. In practice, however, it has been demonstrated that financial flow, as one of the three major flows, significantly affects the operational decisions. When it is difficult to obtain funds from the financial institutes, trade credit is frequently adopted as an important financial tool that affects the firm’s ordering behavior. In this paper, with the consideration of trade credit, we propose an integrated supply chain network design model that simultaneously determines the location of warehouses, the assignments of retailers to warehouses, the policy of warehouse-retailer inventory management, and the deferred payment time allowed by the warehouses for each retailer to minimize the system-wise location, transportation, multi-echelon inventory, and financing costs. The impacts of trade credit on warehouse locations and warehouse-retailer assignments are examined analytically and numerically. Through numerical studies, we show that the optimal network will be more consolidated with higher financing costs and significant cost reduction can be achieved by considering financial decisions at the strategic supply chain design stage.

Economic optimisation of European supply chains for CO2 capture, transport and sequestration

Diminishing the anthropogenic generation of greenhouse gases is one of the key challenges of the twenty-first century. Considering the current state of affairs, it is barely impossible to reduce emissions without relying on CO2 capture and sequestration technologies. In a situation where a large-scale infrastructure is yet to be developed, mathematical programming techniques can provide valuable tools to decision makers for optimising their choices. Here, a mixed integer linear programming framework for the strategic design and planning of a large European supply chain for carbon geological storage is presented. The European territory is discretised so as to allow for a spatially explicit definition of large emission clusters. As regards CO2 capture, post-combustion, oxy-fuel combustion and pre-combustion are considered as possible technological options, whereas both pipelines (inshore and offshore) and ships are taken into account as possible transport means. The overall network is economically optimised over a 20 years’ time horizon to provide the geographic location and scale of capture and sequestration sites as well as the most convenient transport means and routes. Different scenarios (capturing up to 70% of European CO2 emissions from large stationary sources) are analysed and commented on. Results demonstrate the good European potential for carbon sequestration and give some indications on the total cost for CO2 capture, transport and sequestration. Capture costs are found to be the major contribution to total cost, while transport and sequestration costs are never higher than 10% of the investment required to set in motion and operate the whole network. The overall costs for a European carbon capture, transport and storage supply chain were estimated in the range of 27–38 €/ton of CO2.

Simple heuristic for the strategic supply chain design of large-scale networks: A Brazilian case study

We propose a simple heuristic method for the strategic Supply Chain Network Design (SCND) problem of a Brazilian tire company with four layers, multi-product in a single-period. Although several solution methods have been developed to solve this problem, mainly based on heuristic techniques, they have serious limitations in the number of warehouses, products, and consumer zones they can handle. This issue restrains their practical applications to the specific addressed real-world problem, involving hundreds of elements in these dimensions. The heuristic approach, combining a multi-start mechanism and a rounding strategy, is specially developed to solve large-scale problems in reasonable time. The rounding strategy solves the models proposed by the multi-start mechanism. This integration allows the required diversification in the solution search, avoiding stagnation in a local optimum. The heuristic method is first evaluated using randomly generated instances, with different sizes in terms of the number of suppliers, factories, warehouses, products, and customer zones. The obtained results show the solution method is competitive, compared with previous developed approaches, with the advantage of solving larger instances efficiently and effectively. The heuristic is then applied to the company, aiming to restructure its supply chain due to the overture of a new factory. The proposed heuristic approach proves appropriate to solve this practical application, involving an unprecedented number of warehouses, products, and customers.

An integrated model for strategic supply chain design: Formulation and ABC-based solution approach

This study develops a mixed integer nonlinear programming (MINLP) model to design supply chains. In view of the limitations of many available strategic supply chain design models, this model involves three major supply chain stages, including procurement, production, and distribution, and their interactions; it takes into account bill of materials constraints for modeling complex supply chain inter-relationships. In addition, in accordance with the fact that companies nowadays develop product families, our model addresses multi-product supply chain design to respond to diverse customer requirements. Recognizing their importance, this study identifies and formulates constraints related to facility pairwise relationships and supplier priority along with the classical constraints from the available literature. To efficiently solve such a highly constrained, large scale MINLP model, we develop an approach based on an artificial bee colony (ABC) algorithm. Bicycle design and production is used to demonstrate the potential of the MINLP model for designing supply chains and the performance of the ABC-based solution approach in solving the model. The proposed model and solution approach can be considered as two fundamental components of an expert system in the broad sense. Thus, this study is expected to stimulate more future research on the development of practical expert systems for designing supply chains.

Optimizing Facility Location Decisions Considering Health Impact on Local Population

Minimizing environmental impacts due to logistics activities have long been recognized as part of a way to promote a sustainable supply chain. Facility location decisions play a critical role in strategic design of supply chain and hence have a long-term impact on its cost and environmental performance. In this study, Operations research and Life-cycle impact assessment techniques are used to estimate the burden on local population's health caused by facility location decisions. Specifically, our primary goal is to minimize the impacts on local population's health quantified in terms of Disability Adjusted Life Year (DALY). A mixed integer linear programming model is developed based on a case study located in Can Tho city, Vietnam. Geographic information system (GIS) mapping software is used to estimate the number of affected population. The results of the study show that population density along different parts of transportation routes is the main factor influencing the selection of optimal solutions.

Agility and Proximity Considerations in Supply Chain Design

Strategic supply chain design decisions are critical to the long-term success of a business. Traditional facility location models for supply chain design focus on the trade-offs between the costs and benefits of proximity, i.e., the distance between facilities and customers. These strategic-focused models do not consider the supply chain’s agility, i.e., its ability to quickly respond to unexpected fluctuations in customer needs. In this paper, we study the problem of designing a supply chain distribution network under demand uncertainty and analyze how the optimal design characteristics of proximity and agility depend on various input parameters. We are able to draw managerial insights on how agility considerations may invalidate well-established and widely accepted qualitative results derived from traditional models. In particular, we show that it is optimal to increase the density of distribution centers (DCs) when the shortage penalty cost increases, and to decrease the density of DCs when a certain unit transportation cost parameter increases. Through these findings, our work conveys the message that traditional, proximity-based facility location models can be inadequate for designing modern responsive supply chains, and calls for the need to develop a new class of models for the task. This paper was accepted by Yossi Aviv, operations management.

A supply chain design approach considering environmentally sensitive customers: the case of a German manufacturing SME

Sustainable business development is one of the main topics of research and management in recent years. Since the environmental pillar is a part of the sustainability concept, companies are forced to (re-)design their supply chain according to environmental issues. Both government and other stakeholders, e.g. non-governmental organisations and customers, pay a lot of attention on a company’s environmental performance. Hence there is a risk of losing reputation if a company does not comply with environmental norms. We focus on the impact of customers’ requirements regarding the environmental performance of a product on strategic supply chain design decisions of the manufacturer of the product. Thus, we consider the case of a German manufacturing company and present a mixed-integer linear programming supply chain design model with a demand function that is influenced by sustainability requirements. The company is assumed to be able to improve the environmental performance of the products sold and affect the customer demand positively by designing an environmentally conscious supply chain.

Present and Further Researches for Robust Supply Chain Design

Uncertainty is inherent feature of supply chain and it can be appeared variety of situations in the internal and external supply chain. Supply chain can’t fulfill the inherent feature when an uncertainty obstructs the flow of information, raw material, finance and disruption of supply chain network. After years of emphasis on responsiveness businesses is now experiencing their uncertainty to supply chain so there is a need for an integrated framework to support the analysis and design of robust supply chains. The purpose of this research is to define the distinct meaning of robust supply chain and propose the further research area for strategic design of supply chain to perform the inherent feature of supply chain under uncertainty.

Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk

The strategic design of a robust supply chain has to determine the configuration of the supply chain so that its performance remains of a consistently high quality for all possible future conditions. The current modeling techniques often only consider either the efficiency or the risk of the supply chain. Instead, we define the strategic robust supply chain design as the set of all Pareto-optimal configurations considering simultaneously the efficiency and the risk, where the risk is measured by the standard deviation of the efficiency. We model the problem as the Mean–Standard Deviation Robust Design Problem (MSD-RDP). Since the standard deviation has a square root expression, which makes standard maximization algorithms based on mixed-integer linear programming non-applicable, we show the equivalency to the Mean–Variance Robust Design Problem (MV-RDP). The MV-RDP yields an infinite number of mixed-integer programming problems with quadratic objective (MIQO) when considering all possible tradeoff weights. In order to identify all Pareto-optimal configurations efficiently, we extend the branch-and-reduce algorithm by applying optimality cuts and upper bounds to eliminate parts of the infeasible region and the non-Pareto-optimal region. We show that all Pareto-optimal configurations can be found within a prescribed optimality tolerance with a finite number of iterations of solving the MIQO. Numerical experience for a metallurgical case is reported.

A dynamic multi-commodity inventory and facility location problem in steel supply chain network design

Logistics network design is a major strategic issue due to its impact on the efficiency and responsiveness of the supply chain. This paper focuses on strategic and tactical design of steel supply chain (SSC) networks. Ever-increasing demand for steel products enforces the steel producers to expand their production and storage capacities. The main purpose of the paper includes preparing a countrywide production, inventory, distribution, and capacity expansion plan to design an SSC network. The SSC networks consist of iron ore mines as suppliers, raw steel producer companies as producers, and downstream steel companies as customers. Demand is assumed stochastic with normal distribution and known at the beginning of planning horizon. To achieve the service level of interest, a potential production capacity along with two kinds of safety stocks including emergency and shared safety stocks are suggested by the authors. A mixed integer nonlinear programming (MINLP) model and a mixed integer linear programming (MILP) model are presented to design dynamic multi-commodity SSC networks. To evaluate the performance of the MILP model, a real case of SSC network design is solved. Furthermore, solving two proposed models by using a commercial solver for a set of numerical test cases shows that the MILP model outperforms MINLP in medium- and large-scale problems in terms of computational time. Finally, the complexity of the linear model is investigated by relaxing some major assumptions.

Scenario-based strategic supply chain design and analysis for the forest biorefinery using an operational supply chain model

Supply chain (SC) design involves decisions for the long term, e.g. determining products, process technologies, number, location and capacity of different SC nodes, production rates, as well as suppliers, markets and partners. The forest biorefinery (FBR) is emerging as a new possibility for improving forestry companies’ business models, however introduces significant technological, economic and financial challenges—which can be systematically addressed in strategic SC design. In order to reduce the burden of such challenges, partnership with companies whose expertise brings value and experience to the forestry companies’ new business model is essential for FBR implementation. In this regard, redesigning the forestry SC in order for it to be aligned and consistent with the partner's SC, and in other words, designing a new integrated SC is of crucial importance. This paper presents a scenario-based approach to strategic SC design for the FBR, designing the SC based on the impacts of the design on operational SC activities. Two kinds of scenarios are used; market scenarios representing market volatility and SC network scenarios (referred to as alternatives) representing different biorefinery options/strategies. In order to analyze the impacts of SC alternatives on operational activities, a tactical/operational SC model examines the advantages and disadvantages of each alternative by exploiting the capability of the SC for flexibility in the case of the market scenario realization. This demonstrates the impact of each scenario on SC profitability. It will also show how integration scenarios may result in better economic performances compared to the case when the forestry company implements the biorefinery on its own and hence, it reveals how forestry companies can benefit from the created synergies.

Agility and Proximity Considerations in Supply Chain Design - An Analytical Approach

Strategic supply chain design decisions are critical to the long-term success of a business. Traditional facility location models for supply chain design focus on the trade-offs between the costs and benefits of proximity, i.e., the distance between facilities and customers. These strategic-focused models do not consider the supply chain's agility, i.e., its ability to quickly respond to unexpected fluctuations in customer needs. In this paper, we study the problem of designing a supply chain distribution network under demand uncertainty and analyze how the optimal design characteristics of proximity and agility depend on various input parameters. We are able to draw managerial insights on how agility considerations may invalidate well-established and widely-accepted qualitative results derived from traditional models. In particular, we show that it is optimal to increase the density of DCs when the shortage penalty cost increases, and to decrease the density of DCs when a certain unit transportation cost parameter increases. Through these findings, our work conveys the message that traditional, proximity-based facility location models can be inadequate for designing modern responsive supply chains, and calls for the need to develop a new class of models for the task.

Design of global supply chain network with operational risks

Facility location decisions play a vital role in the strategic design of the global supply chain. Global supply chains are vulnerable towards different types of risks factor that causes disturbance in the allocation of certain goods at the required place and time, and with the required quality and quantity. This paper presents a model of the multi-stage global supply chain network problem incorporating a set of risk factors (such as: late shipment, exchange rates, quality problems, logistics and transportation breakdown, and production risks), their expected values and probability of their occurrence, and associated additional cost. Different scenarios are considered to demonstrate the applicability of the model. Optimal decisions regarding the facility locations and inter-echelon quantity flows in the global supply chain are based on initial information for the risk factors. Further, if any change in the expected value of risk factor has occurred, the inter-echelons shifting take place to minimize the overall cost of the supply chain.

A novel rolling horizon strategy for the strategic planning of supply chains. Application to the sugar cane industry of Argentina

Abstract In this article, we propose a new method to reduce the computational burden of strategic supply chain (SC) planning models that provide decision support for public policy makers. The method is based on a rolling horizon strategy where some of the integer variables in the mixed-integer programming model are treated as continuous. By comparing with rigorous solutions, we show that the strategy works efficiently. We illustrate the capabilities of the approach presented by its application to a SC design problem related to the sugar cane industry in Argentina. The case study involves determining the number and type of production and storage facilities to be built in each region of the country so that the ethanol and sugar demand is fulfilled and the economic performance is maximized.

Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty

Fossil fuel depletion and the increase of greenhouse gases emissions has been pushing the search for alternative fuels for automotive transport. The European Union has identified biofuel technology as one option for reducing its dependence on imported energy. Ethanol is a promising biofuel, but great uncertainty on the business profitability has recently determined a slowdown in the industry expansion. In particular, geographical plant location, biomass price fluctuation and fuel demand variability severely constrain the economic viability of new ethanol facilities. In this work a dynamic, spatially explicit and multi-echelon Mixed Integer Linear Program (MILP) modeling framework is presented to help decision-makers and potential investors assessing economic performances and risk on investment of the entire biomass-based ethanol supply chain. A case study concerning the corn-to-ethanol production supply chain in Northern Italy is used to demonstrate the effectiveness of the proposed modeling approach. The mathematical pattern addresses the issue of optimizing the ethanol supply network over a ten years’ time period under uncertainty on biomass production cost and product selling price. The model allows optimizing economic performances and minimize financial risk on investment by identifying the best network topology in terms of biomass cultivation site locations, ethanol production plant capacities, location and transport logistics.