Supply Chain Design Research Articles

An optimization model for the design of supply chains considering disruptions from pandemic uncertainty and infection trends

Supply chain design has been an extensively researched area of study especially given how companies all around the world seek to optimize system performance. The past years have highlighted the importance of creating lean supply chains in order to reduce costs and improve efficiency. However, the uncertainties brought by the COVID-19 pandemic have challenged global supply chains with constraints on labor and capacity. Decision makers are now focusing on creating a balance between agile and lean systems because of these disruptions. A mixed integer nonlinear programming (MINLP) model for the design of supply chains under a pandemic scenario has been developed in this regard. The model considers uncertainties brought about by the imposition of government restrictions on the operations of supply chains. Specifically, the model integrates forecasts on the likely degree of restrictions that may be imposed on a given network. It then examines the interdependency between infection trends and uncertainties on customer demand, operational capacity, and facility connections. Scenarios involving different infection trend types were used to test model behavior and variable relationships. The numerical experiments yielded results showing that supply chains need to anticipate and hedge against increasing infection trends. Failure to do so could lead to incurring as much as 18% in additional total system costs. The experiments further demonstrated that additional facilities with low setup costs but higher operating costs could be selected as insurance for possible declines in operating capacity or abrupt spikes in demand.

Supply chain challenges in the post–Covid Era

In this paper, we reflect on the supply chain issues and operational challenges that we experienced through the various stages of the Covid‐19 pandemic. We identify some phenomena that were attributable in some way to the pandemic, and apply core principles of operations management, and a simple numerical model, to explain and understand their occurrence. We highlight some key lessons and discuss implications for supply chain design and planning, to prepare for the next global disruption.

Planning the location of facilities in the supply chain using the firefly meta-innovation algorithm

Abstract Analysis of supply chain location issues and decision-making regarding the location of facilities in the supply chain is one of the most important issues in the decision-making of governments, organizations and companies. Undoubtedly, the correct location of facilities has very important effects on economic benefits, providing appropriate services and customer satisfaction. Supply chain issue is one of the most widely used issues in today’s competitive world and location issues are among the most used issues in designing supply chain networks to improve and reduce costs and increase competitiveness. The facilities under consideration include warehouses and distribution centers, which have been solved with the aim of reducing transportation costs. And then the two methods are compared. The problem is solved in small, medium and large dimensions and finally it was concluded that the firefly algorithm had a better performance than the genetic algorithm.

Model Ideal Sistem Manajemen Rantai Pasok Agribisnis Kakao: Pendekatan Soft System Methodology (SSM)

Jambewangi village cocoa farmers market their cocoa beans individually and in small quantities, so they’re in a low bargaining position. In addition to not optimally the role of each institution, they also haven’t coordinated and cooperated to promote cocoa. Researchers need to research to design institutional synergies in the agribusiness supply chain. The research was conducted in Jambewangi villages, the center of cocoa production in Banyuwangi. The supply chain design is based Soft System Methodology (SSM) approach through information from sources, farmers, and experts. Based on the SSM analysis, it can be concluded that: 1. There is a fundamental problem with the cocoa in Jambewangi village that farmers only produce unfermented cocoa beans, and sell them individually, so their bargaining position is low. Several institutions in the village haven’t played an optimal role in joint marketing, 2. There is no synergy between institutions at the farmer level, so they can’t protect farmers in their bargaining position. 3. If supply chain actors synergize and carry out their respective roles in the supply chain, uniform, and SNI-standardized cocoa beans can be produced and are able to produce processed chocolate products such as butter, chocolate bars, and powder.

Demand forecasting accuracy in the pharmaceutical supply chain: a machine learning approach

PurposeDemand foresting significantly impacts supply chain (SC) design and recovery planning. The more accurate the demand forecast, the better the recovery plan and the more resilient the SC. Given the paucity of research about machine learning (ML) applications and the pharmaceutical industry’s need for disruptive techniques, this study aims to investigate the applicability and effect of ML algorithms on demand forecasting. More specifically, the study identifies machine learning algorithms applicable to demand forecasting and assess the forecasting accuracy of using ML in the pharmaceutical SC.Design/methodology/approachThis research used a single-case explanatory methodology. The exploratory approach examined the study’s objective and the acquisition of information technology impact. In this research, three experimental designs were carried out to test training data partitioning, apply ML algorithms and test different ranges of exclusion factors. The Konstanz Information Miner platform was used in this research.FindingsBased on the analysis, this study could show that the most accurate training data partition was 80%, with random forest and simple tree outperforming other algorithms regarding demand forecasting accuracy. The improvement in demand forecasting accuracy ranged from 10% to 41%.Research limitations/implicationsThis study provides practical and theoretical insights into the importance of applying disruptive techniques such as ML to improve the resilience of the pharmaceutical supply design in such a disruptive time.Originality/valueThe finding of this research contributes to the limited knowledge about ML applications in demand forecasting. This is manifested in the knowledge advancement about the different ML algorithms applicable in demand forecasting and their effectiveness. Besides, the study at hand offers guidance for future research in expanding and analyzing the applicability and effectiveness of ML algorithms in the different sectors of the SC.

A multi-period sustainable hydrogen supply chain model considering pipeline routing and carbon emissions: The case study of Oman

This paper presents a mathematical model for a multi-period hydrogen supply chain design problem considering several design features not addressed in other studies. The model is formulated as a mixed-integer program allowing the production and storage facilities to be extended over time. Pipeline and tube trailer transport modes are considered for carrying hydrogen. The model also allows finding the optimal pipeline routes and the number of transport units. The objective is to obtain an efficient supply chain design within a given time frame in a way that the demand and carbon dioxide emissions constraints are satisfied and the total cost is minimized. A computer program is developed to ease the problem-solving process. The computer program extracts the geographical information from Google Maps and solves the problem using an optimization solver. Finally, the applicability of the proposed model is demonstrated in a case study from Oman.

The transformation of supply chain collaboration and design through Industry 4.0

ABSTRACT This paper analyzes how Industry 4.0 transforms supply chain collaboration and design. Therefore, we identify and analyze 151 academic articles. The study uncovers holistic strategic changes Industry 4.0 and associated technologies imply for cross-company collaboration in supply chains. These include changes in the initiation of buyer-supplier transactions, information exchange and handling, and how Industry 4.0 transforms relationships and cooperation. Subsequently, implications for aspects concerning supply chain design, such as ecosystem development are given. The systematic literature review is the first to analyze the body of literature on supply chains in Industry 4.0 to synthesise how cross-company collaboration is transformed in detail. The paper provides a comprehensive overview of the current state of research and develops several suggestions for future research and managerial practice, such as the role of risk-benefit sharing and information sharing, allowing to develop new forms of collaborative value creation in Industry 4.0.

Optimization and assessment of carbon capture, transport and storage supply chains for industrial sectors: The cost of resilience

This study investigates the optimal design of carbon capture, transport and storage (CCTS) supply chains for decarbonizing industrial emitters. It builds upon previous analyses, which determine the cost-optimal design of CCTS supply chains while complying with specified emissions reduction scenarios, and determines optimal designs in terms of minimum cost and maximum resilience. The resilience of a supply chain is defined as its ability to permanently store the captured CO2 during the time horizon of interest, and it is quantified in terms of expected amount of carbon not stored under a fixed number of possible simultaneous failures. A mixed-integer linear program is developed to design CCTS supply chains that ensure a specified level of resilience while minimizing total system costs and CO2 emissions. The model is illustrated by determining the optimal decarbonization strategy in terms of costs and level of resilience for the Swiss waste-to-energy sector from 2025 to 2050. Overall, resilience is achieved with a cost increase with respect to the cost-optimal solution ranging from about 5% (backup truck connections to minimize the cost of the supply chain) to about 70% (backup pipeline connections to minimize the emissions of the supply chain) when the storage site is in the North Sea. When the storage site is available in Switzerland, the cost increase is much reduced and ranges from about 1% to 10% with respect to the cost-optimal solution.

Design and management of humanitarian supply chains: challenges, solutions, and frameworks

The design and management of the humanitarian supply chain are the most critical aspects of the humanitarian aid supply chain. Despite enormous interest among the academic community and the practitioners, the design of a humanitarian supply chain is still not well understood. Most of the publications have attempted to address the mechanisms of the humanitarian relief operations. However, the elements of the humanitarian supply chain designs are not well understood in an integrated manner. In this special issue, we have accepted the articles based on six factors that shape the design and management of the humanitarian supply chain and the influencing factors (see Fig. 4). We have noted the research gaps and offered rich directions for future research.

Additive manufacturing: A framework for supply chain configuration

Using Resource Dependence Theory (RDT), this paper explores the changing state of Supply Chain (SC) complexities and dependencies resulting from Additive Manufacturing (AM) adoption, analysing implications for competitiveness. We utilised an adapted SC configuration framework to develop embedded case studies across the Aerospace, Automotive and Power Generation industries. The sample included fifteen companies deploying metal AM across three SC tiers. Using an abductive logic, our findings reveal that the complexity and dependency-reduction potential of AM depends on economic, industry, geographical, organisational and technological factors. We developed a conceptual framework for AM SC configuration and four propositions, which provide further insights into the interplay between SC complexities, dependencies and competitiveness. By utilising RDT, we contribute to the AM SC configuration literature by highlighting the mediating role that dependencies play in achieving competitiveness, as well as strategies adopted by firms to mitigate uncertainty. We also highlight the interplay between ‘relationship and governance’ and three other SC configuration dimensions in relation to competitiveness. Insights into the changing state of complexities and dependencies identified in this study could also support managerial decisions in AM SC design.

Life cycle optimization of the supply chain for biobased chemicals with local biomass resources

Developing a biobased chemical industry that copes with the current environmental challenges relies on understanding the influence of the different stages of the value chain on economic and environmental performance. This paper investigates the use of a life cycle optimization framework to assess trade-offs between environmental and economic criteria for designing supply chains for biobased polyethylene terephthalate as a case study. A multi-objective optimization model was formulated to account for the environmental impacts (through life cycle assessment) and total costs (through life cycle costing) of the case study. High environmental priority (w = 0.9) in the design of the supply chain resulted in environmental gains (in 8 out of 9 midpoint indicators) with low increments (<10 %) of the total supply chain costs. The hotspot analysis supported identifying the stages (processing plants) of the supply chain where environmental and economic improvements are required. As the environmental priority increased, a rebound effect was evidenced where the mitigation of the environmental impact in different processing plants negatively affected the economic performance of other processing plants. Therefore, close collaboration with all value chain actors is required to achieve the most optimal configuration for supply chains of biobased polyethylene terephthalate.

Impact of special-denomination label constraints on fresh produce supply chains

Special Denomination labels for fresh food products, such as “local”, “fair-trade”, “organic” and similar market-narrowing movements have gained a lot of attention over the last few decades. While these movements have some clear benefits and beneficiaries, they also may increase the complexity of the management of the underlying supply chains resulting in unintended consequences such as increased food waste, higher prices, and reduced food availability for certain geographical areas and population segments. The implementation of local food movements may result in the exclusion of farmers of certain areas, enticing these farmers to move their operations to be close to large urban centers, increasing the competition for scarce resources such as land, water and labor. In this paper, we explore the use of optimization-based tools to assess the impact of different levels of implementation of these special denomination labels to fresh food, with particular emphasis on the “local food” label and the distance used to determine compliance with this label. The tools presented seek to contribute to the emergence and maturation of the design of efficient supply chains for the direct procurement of special denomination crops, particularly fresh vegetables by making the most efficient connections between a market demand and the suppliers of products with the required characteristics. A case study is used to analyze the impact of the definition of this label on the potential suppliers of a specific market. The results show that the implementation of distinct definitions of local food have different impacts on the potential supplying sources being considered, depending on their distance to the market and the other potential supply sites. The results also show that the relaxation of strict local-food label and the use of other proxy measures of local food may be a better approach to reduce CO2 emissions associated with the supply chain of fresh produce.

A digital platform for the design of patient-centric supply chains

Chimeric Antigen Receptor (CAR) T cell therapies have received increasing attention, showing promising results in the treatment of acute lymphoblastic leukaemia and aggressive B cell lymphoma. Unlike typical cancer treatments, autologous CAR T cell therapies are patient-specific; this makes them a unique therapeutic to manufacture and distribute. In this work, we focus on the development of a computer modelling tool to assist the design and assessment of supply chain structures that can reliably and cost-efficiently deliver autologous CAR T cell therapies. We focus on four demand scales (200, 500, 1000 and 2000 patients annually) and we assess the tool’s capabilities with respect to the design of responsive supply chain candidate solutions while minimising cost.

To Discriminate General Election system in Thailand by using K-Means Clustering

Thailand uses the ballot paper in the general election since 1933. In the present day, technology has been involved in daily basics. This study aims to explore the hypothesis of the election system in Thailand which can use the technology in the election process name as electronic voting (e-voting) or still need to use the traditional method. Before implementation, it should study in terms of the area that is ready to implement the new method and where still need to use the current one. This study takes the relevant factors to analyze with the data of each area collected from various sources. The clustering method used in this study is k-means. Then to find the acceptable k cluster the silhouette method is used. The result is 2 cluster is a perfect fit with the 11 factors that used in this study. The first cluster is Bangkok which is the capital city to be matched with the e-voting method and the second cluster is the remainder province in Thailand (76 provinces). This can be used for the next study in terms of supply chain design for e-voting and developing the ballot paper logistics.

Risk-based multistage stochastic mixed-integer optimization for biofuel supply chain management under multiple uncertainties

CO2 emissions from the transportation sector account for 22% of global emissions. Blending biofuels with fossil fuels is a promising method for reducing these emissions and ensuring energy security. One of the most important and challenging aspects of the biofuel production process is the design of supply chains that integrate various types of biomasses under multiple uncertainties. The aim of this study is to address this issue by proposing a multi-stage stochastic program for strategic and tactical planning in multi-feedstock biofuel supply chain networks under uncertainties of biomass supply and biofuel demand. The proposed model minimizes the total annualized cost by selecting raw materials, positioning production facilities, placing storage facilities, and defining deferral paths for each feedstock. In addition, a new dynamic method of the conditional value at risk is adopted to consider the risk hedge. Deterministic, stochastic, and risk-based models are compared, and case studies based on the South Korean context show the effectiveness of the model and the algorithm. Computational results indicate that considering risk can increase biofuel expected production cost by up to 88%. It also shows that biomass transportation is the most affected part of the supply chain in risk assessment. Furthermore, it indicates that a value of 0.5 is the most effective risk aversion factor.

Distributionally robust optimization for the closed‐loop supply chain design under uncertainty

AbstractThe closed‐loop supply chain network (CLSCN) contains reverse flows that collect products from customers and recycle or remanufacture usable parts. The CLSCN design problem is becoming more and more prominent under the context of Sustainable Development and Circular Economy. Parameters associated with a CLSCN including customer demands, transportation costs, or disposal rates are usually subject to uncertainty. Furthermore, natural or man‐made disruptions may cause part of the CLSCN to malfunction. We herein propose a hybrid stochastic and distributionally robust optimization (DRO) approach to hedge against discrete disruption scenarios and uncertain customer demands. We also tailor a Benders decomposition‐based algorithm to efficiently solve the resulting large‐scale mixed integer linear programming reformulations. Computational experiments demonstrate that the proposed algorithm can outperform commercial solvers such as CPLEX, and the DRO approach can produce solutions with low average costs and low variance in out‐of‐sample tests.

A proprietary component manufacturer’s global supply chain design: The impacts of tax and organizational structure

This paper studies the supply chain structure design for a proprietary component manufacturer (PCM) in a global environment. The PCM, producing component with patent protection and/or superior quality, chooses to enter into an overseas market to enhance revenue. There are three supply chain structures available to the PCM: (1) monopoly where the PCM establishes a downstream manufacturing subsidiary to produce and sell the end product, (2) component supplier where the PCM supplies component to a local original equipment manufacturer (OEM), and (3) dual distributor where the PCM supplies component to the OEM but also produces a competing end product. Our analysis suggests that two important features in a global environment, i.e., the global tax disparity and the organizational structure, have significant impacts on the PCM’s supply chain structure design and profits. If the downstream has a tax advantage, it is optimal for the PCM to directly enter into the downstream market with the structure of monopoly. As the downstream tax rate increases, the other two structures, i.e., component supplier and dual distributor, become more attractive. We show that centralization makes the dual distributor and monopoly structures more desirable. However, the OEM may be driven out of the market and the dual distributor structure is infeasible when the downstream tax rate is high enough. Contrary to our traditional wisdom, decentralization may benefit the PCM under certain circumstances.

E-commerce supply chain network design using on-demand warehousing system under uncertainty

During the COVID-19 pandemic, e-commerce retailers have had trouble satisfying the growing demand because of limited warehouse capacity constraints. Fortunately, an on-demand warehousing system has emerged as a new alternative to mitigate warehouse capacity issues. In recent years, several studies have focused on the supply chain problem considering on-demand warehousing. However, there is no study that deals simultaneously with inherent uncertainties and the property of commitment, which is the main advantage of on-demand warehousing. To fill these research gaps, this paper presents an e-commerce supply chain network design problem considering an on-demand warehousing and decisions for commitment periods. We propose the two-stage stochastic programming model that captures the inherent uncertainties to formulate the presented problem. We solve the proposed model utilizing sample average approximation combined with the Benders decomposition algorithm. Of particular note, we develop a method to generate effective initial cuts for improving the convergence speed of the Benders decomposition algorithm. Computational results show that the developed method could find an effective feasible solution within a reasonable computational time for problems of practical size. Furthermore, we show the significant cost-saving effects, based on experiment results, that occur when an on-demand warehousing system is used for designing supply chain networks.

An integrated mixed integer linear programming model for resilient and sustainable natural gas supply chain

ABSTRACT Disruptions in the natural gas supply chain result in reduced throughput and associated emissions and losses, causing significant economic, environmental, and social impacts. Therefore, it is crucial to design supply chains that are resilient and sustainable to prevent or reduce the effects of disruptions. This paper proposes a novel Mixed-Integer Linear Programming model, which optimizes the natural gas supply chain in terms of resilience and sustainability, by examining the impact of an additional workflow design (contingency pipeline) located between the shutdown inlet and outlet nodes in the transmission echelon. The model is applied to a “real world” case, using data collected from gas companies operating in Nigeria. Both steady and transient states of the system are examined in this study through a set of scenarios. The best final solution was found to yield 93.6% performance increase when compared to target throughput and 63% performance increase with the introduction of the contingency when compared with the baseline scenario.

ENERGY EFFICIENCY OPTIMIZATION OF LAST MILE SUPPLY SYSTEM WITH REVERSE LOGISTICS CONSIDERATION

Last mile supply system takes great importance in the designed supply chain management, especially in the big urban areas, where various goods delivery locations should be tackled. Transportation routes and vehicles play a critical share in the optimization of the energy spent in this system because it is considered a complicated case due to its high solutions possibilities. Also, part of these transport processes is considered reverse logistics, where the goods take the way back, starting from the customer. Using a metaheuristic optimization is usually a good way to increase operations efficiency and save time and energy, next to raising sustainability. Within this paper, the last mile supply system within urban areas focusing on the goods' delivery and collection tasks is presented. The model design is described, mathematical optimization modelling is detailed, and a case study to investigate the impact of using diesel and electric trucks on energy efficiency is solved. After an introduction and theoretical background that includes a brief literature review, the designed system and used methodology are described. The designed system incorporates cloud computing, real routes of vehicles, analysis of collected data, energy consumption optimization, and time windows. Also, a mathematical model is developed with the aim of optimizing the total energy consumption. Real thirty locations in Budapest in the VII district are described and used as a case study for finding the solutions of the optimized taken routes and energy consumption by the genetic algorithm for both diesel and electric trucks. In the end, the results are analyzed and compared against a random solution to clarify the presented optimization's effectiveness.