Supply Chain Planning Research Articles

Special issue Editorial: Supply chain planning & management challenges and opportunities driven by climate change and other disruptions and how to accelerate societal transition to equitable, sustainable and livable societies

Special issue Editorial: Supply chain planning & management challenges and opportunities driven by climate change and other disruptions and how to accelerate societal transition to equitable, sustainable and livable societies

Enhancing Supply Chain Management Through Artificial Intelligence: A Case Study of JD Logistics

With the intensification of global economic competition, enterprises face the challenge of improving the supply chain management efficiency, and AI technology, as an emerging field in computer science, can provide effective solutions. As a leading e-commerce logistics service provider in China, JD Logistics has accumulated a wealth of logistics technology capabilities and digital transformation experience within and outside the JD Group. Using JD Logistics as a case study, this paper discusses the application of AI technology in supply chain management and its impact on enterprise responsiveness and operational efficiency. AI technology has significantly improved the supply chain operational efficiency and responsiveness of JD Logistics by applying intelligent supply chain planning, predictive analysis and intelligent decision-making. The study systematically summarizes the key role of AI technology in optimizing supply chain management through a literature review and specific case studies, providing an important reference and practical experience for enterprises to achieve digital transformation.

Socio-Economical Analysis of a Green Reverse Logistics Network under Uncertainty: A Case Study of Hospital Constructions

This study addresses the critical issue of managing construction and demolition waste in urban environments. Effective waste management is not only essential for minimizing costs but also for enhancing sustainability and reducing environmental impact. In this context, the research introduces a green reverse logistics model designed for C&D waste management, integrating both sustainability considerations and current regulatory frameworks, such as LEED. A key innovation of this model is the incorporation of electric vehicles for waste collection, compared to traditional diesel vehicles, as part of the logistical process, as carbon emission is a significant concern. By evaluating the limitations and opportunities associated with electric vehicles, alongside robust optimization to manage uncertainties in waste collection, the model seeks to balance environmental, social, and economic objectives. It further incorporates decision-making tools like fuzzy logic to optimize multi-objective outcomes across various waste facilities, including separation labs, incineration centers, recycling centers, and landfills. A case study conducted in Tehran validates the model, highlighting the socio-economic and environmental benefits of using electric vehicles in waste collection. Sensitivity analysis indicates that hybrid and socially focused policies perform best under high-impact scenarios, although results can differ with varying data sets. Despite the complexity of managing reverse logistics networks, this research provides valuable insights for supply chain planners. It suggests potential future directions, such as the application of metaheuristic algorithms and improved stochastic planning methods.

Analysis of optimization models under different approaches to deal with uncertainty regarding pre-disaster planning in food bank supply chains

Purpose: Pre-positioning is a crucial choice in pre-disaster humanitarian logistics planning that consists of deciding in advance how much aid and where should it be located to enable effective and prompt operations in the case of an emergency. To support managers making such decisions, we propose four mathematical formulations that, considering the uncertainty on the demand to satisfy, seek to optimize aid prepositioning (before the event) and further distribution (after the event) in order to minimize unmet demand (MUD). The purpose of this paper is to evaluate and compare the performance of these formulations on a real case to discuss when and why should each approach be applied.Design/methodology/approach: The two first formulations adopt the cardinality-constrained (CC) approach to handle uncertainty. These formulations differ in their objective functions, the first formulation’s objective seeks to MUD, whilst the second incorporates equity in the way that demand is satisfied. The two remaining formulations are scenario-based (SB) and as in the previous two formulations, seek to MUD with and without equity considerations, respectively.Findings: Applying our formulations to a case study, we compare the differences between the solutions produced by the proposed formulations and the solutions that would have been produced without uncertainty (perfect information) to have a better understanding of their performance and their behavior. A discussion of the strengths and weaknesses of each model is provided to help managers choose the model that best suits their needs.Originality/value: The formulations are applied to a case study where a food bank is faced with the arrival of a hurricane in Mexico. As far as our knowledge, it is the first work in literature to deal with humanitarian logistics under a cardinality-constrained approach.

Разработка подхода к интегрированному планированию в цепочках поставок приборостроительной отрасли

Purpose: the article proposes an approach to integrated planning in supply chains of the instrument-making industry, aimed at improving the efficiency of coordination between their main participants. Methods: the study used methods of system analysis, statistical analysis and expert assessments, which made it possible to evaluate current approaches to planning and propose an approach to integrated planning based on modern digital technologies used in the performance of the planning function. To obtain the results of the study, official statistical data on the productivity and economic indicators of the instrument-making industry, data on logistics operations and supply chains of various enterprises in the industry were used. Results: the main scientific result is the approach to the development of integrated planning in supply chains developed by the author. The study also provided practical recommendations on the use of management methods at different stages of the formation of an integrated planning system. Practical significance: the practical significance of the study lies in the possibility of applying the proposed recommendations at enterprises of the instrument-making industry to improve the efficiency of supply chain management. The implementation of the developed methods will allow enterprises to adapt to dynamic market conditions, reduce costs and improve coordination of the actions of private logistics chain participants. The results of the study can be used to develop and improve planning systems in organizations in the instrument-making industry.

Supply Chain Planning and Performance of Road Transport Sector Agencies in Kenya.

Supply Chain Planning and Performance of Road Transport Sector Agencies in Kenya.

Communication Strategies for Interdisciplinary Collaboration: Bridging complex expertise

For consulting agencies, service design projects often face tight timelines, requiring rapid adaptation to clients' complex industries. Effective collaboration is crucial, especially in supply chain planning and healthcare logistics. How can strategic communication and interdisciplinary collaboration help leverage client expertise and achieve meaningful outcomes?

An agent-based cooperative co-evolutionary framework for optimizing the production planning of energy supply chains under uncertainty scenarios

Nowadays, energy and power companies compete to get the raw materials and equipment they need on time, as project times lengthen, costs spiral, stock-out continues to plague plans to a decarbonized energy future. The risks reflect the impact of uncertainty and volatility on the resilience of the supply chains. Therefore, there is a need for the enhancement of the production planning in Energy Supply Chains (ESCs), as it enables affordable energy supplies and supports the companies transition to a clean, secure and sustainable energy mix. This study aims to understand the interactive behavior among individuals and optimize their production planning under uncertainty scenarios. In particular, we propose a novel framework to couple an Agent-based Modelling (ABM) and a Co-evolutionary Algorithm (CEA), to realize its capacity to solve a Many-objective Optimization Problem (MaOP) where the profits of multiple agents are concurrently maximized in their interactive transaction processes under normal conditions and uncertain disruption events.For demonstration, we illustrate the proposed approach by considering a five-layer oil and gas ESC model, where uncertainties from multiple sources and the structural dynamics challenge the balance between supply and demand. The results obtained by an integration of a Cooperative Co-evolutionary Particle Swarm Optimizer (CCPSO) algorithm into ABM show the pricing and orders of the target agents are optimized while the loss of ESC resilience is minimized under uncertainty scenarios, proving its capacity of improving the diversity and the convergence, compared to the classic evolutionary algorithms.

A MILP-based approach to address the production and distribution planning of large industrial gas supply chains

This paper focuses on addressing the Production Routing Problem (PRP) of industrial gas supply chains (SC). The work introduces a Mixed-Integer Linear Programming (MILP) based approach to effectively tackle this challenging problem. The proposed framework aims to increase company profits by optimizing both production and distribution activities simultaneously. For the distribution part, a route generation algorithm is employed to initially generate a set of feasible routes, ensuring that only routes with practical significance are considered. Subsequently, the mathematical model is developed considering only the set of routes generated. The formulation includes decisions associated with plants production levels, plant selection to supply the different consumers, clients to be visited per trip and day, quantity to be delivered and routes to be used. As part of the problem, it is considered that trucks can make several trips per day, as well as trips of several days. To take into account the last feature, the approach accounts for the explicit effect of the lead time on production and distribution decisions. In this way, the work allows for different delivery times for customers included in the same route, according to their inventories and usage levels. The model considers fleet unavailability until they return to the plants after finishing a trip. Finally, two case studies, one motivating and the other industrial size, are presented and solved. The results obtained demonstrate the effectiveness of the proposed approach to solve the case studies in reasonable CPU times.

From operational to strategic modelling: A continuous multi-scale approach for last-mile analysis

Supply chain planning requires decision-making at all levels, especially in the new normal of intervened supply networks. The integration of strategic network design and operational routing decisions has been widely studied in the literature as the location-routing problem (LRP). However, the LRP does not consider the differences in the planning horizon of each sub-problem, nor have geospatial elements been included in the distribution of demand. This paper aims to redesign the conceptual modelling of the last mile to inform strategic network design problems. A continuous multi-scale approach (CMA) is proposed by taking elements from the districting problem (DP) and continuous approximation (CA). This approach includes stochastic demand in the analysis and the effects of time windows and failed deliveries. The validation of CMA in a case of rural parcel deliveries in Belgium shows an estimate of the distance travelled similar to traditional routing algorithms in scenarios with high demand density. Likewise, the effects of time windows on the spatial configuration of multi-scale districting are explored. This approach provides insights for decision-making in strategic and tactical planning, such as sonification, differentiated services to satisfy consumer preferences, and fleet management. The limitations of the CMA lie in its implementation in purely operational scenarios since it does not offer detailed routing information. Subsequent research aims to exploit the potential of strategic last-mile modelling with effective integration into network design problems.

Sustainability in Supply Chain Planning

Sustainability in supply chain planning has emerged as a vital component for attaining long-term company success and environmental stewardship in today's quickly expanding global market. This is because sustainability is integral to the planning process. Sustainable supply chain planning incorporates environmental, social, and economic issues into the heart of supply chain management strategies. This is in response to the growing awareness among companies of the need to reduce their ecological imprint. When establishing a sustainable supply chain, it is necessary to conduct an exhaustive analysis of the whole supply chain lifetime, beginning with the procurement of raw materials and ending with the disposal of products at their end of life. It is necessary to put into practice procedures that cut down on waste, lessen the amount of carbon emissions, and increase the efficiency with which resources are used. The selection of suppliers who adhere to sustainable practices, the optimisation of transportation routes to minimise fuel usage, and the utilisation of technology that support the ideas of a circular economy are all important initiatives. Through the implementation of these components, organisations have the ability to reduce the risks connected with environmental requirements and interruptions in supply chain operations.

The theory of interpersonal behaviour for conventional enterprises' adoption of the halal supply chain in Malaysia

PurposeThis paper aims to apply the theory of interpersonal behaviour (TIB) and its determinants to determine conventional enterprises' intentions toward halal supply chains (HSCs) adoption in Malaysia.Design/methodology/approachA survey was conducted with a judgmental sampling and over 150 responses were gathered. Besides, the literature on the factors that influence an enterprise’s adoption of a halal supply chain, and TIB is evaluated to determine the influential determinants that lead to conventional entrepreneurs’ desire to participate in the halal supply chain in Malaysia.FindingsThe study findings suggest that the intention to use a halal supply chain is determined not only by social factors, affect, facilitating conditions and attitude, but also by the added construct of perceived expected benefits.Research limitations/implicationsConsidering this research to be limited in terms of coverage geographically and the theory rendered the context should be given proper attention when interpreting future outcomes. Furthermore, future researchers can extend the direct relationship by employing habit construct when conducting a longitudinal study.Practical implicationsThis paper serves as a guide to ensure the best planning of halal supply chains in both theory and practice.Originality/valueThis study expands on the use of TIB in the context of conventional enterprises’ intention toward halal supply chains in Malaysia.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0334

Stimulating investments in manufacturing: can policy create supply chains from a void?

PurposeThe purpose of this paper is to evaluate the effectiveness of a policy, the 2014 UK Supply Chain Plan, which aimed to create a local supply chain in a sector (offshore wind) where the central manufacturing node capabilities and knowledge are not possessed locally. It aims to address the following question: can policy create a new manufacturing supply chain from a void?Design/methodology/approachA qualitative field research approach is used to derive an original set of theoretical propositions explaining the motivation behind the Supply Chain Plan policy. The outcomes of this policy are examined 10 years later in order to provide an opportunity to observe the impact of the policy.FindingsThe conclusion is that the policy has been successful in increasing local content, and some of that local content has benefited local manufacturers. However, a lot of the increase in local content has been achieved in non-manufacturing areas or in new areas. The main issue, i.e. the lack of a central manufacturing capability, remains unaddressed. The impact of the local content policy on cost is undocumented.Originality/valueThere is an increasing amount of interest in regional/local supply chains after the COVID-19 pandemic and increasing geopolitical tensions. This paper's originality is to document with an industry case study the fact that manufacturing knowledge and capabilities are the central growth engine of supply chains and that creating a new manufacturing supply chain competing with well-established clusters is not a simple matter that can be achieved through a local content policy. Such policies raise critical questions about the policy makers' implicit valuation of manufacturing technology.

Digital governance for supplier opportunism: The mediating role of supplier transparency

Curtailing supplier opportunism is a critical concern for both business practitioners and supply chain researchers. However, ambiguity remains regarding the effective deployment of digital technologies (DTs) within supply chain practices to address this issue. Drawing on the asset orchestration perspective, we consider two DT deployment manners (i.e., breadth and depth) and examine their impacts on supplier transparency and supplier opportunism. We investigate 323 Chinese firms and use structural equation modeling to test the proposed hypotheses. We also conduct a scenario-based experiment involving 144 business managers to improve the robustness of our results. We find that although two DT deployment manners positively affect suppliers' cost and relationship transparency, only relationship transparency exhibits a statistically significant negative association with suppliers’ weak opportunism, rather than strong opportunism. More interestingly, of the two DT deployment manners, relationship transparency merely mediates the breadth – weak opportunism association. We advance research on supply chain governance by elucidating the nuanced role of DT deployment in curbing supplier opportunism. We also offer significant insights for firms seeking to reassess their digital supply chain plans, improve engagement with suppliers, and curb unethical practices.

Integrated planning of hydrogen supply chain and reinforcement of power distribution network for accommodating fuel cell electric vehicles

Fuel cell electric vehicles (FCEVs) hold great promise for achieving sustainable and environmentally friendly transportation. However, their widespread adoption depends on the efficient development of the hydrogen supply chain (HSC) required for refueling these vehicles. Given the interdependence between the HSC and power distribution networks (PDNs), this paper presents an innovative model to jointly plan the HSC and reinforce PDNs. In this new model, the inherent uncertainties related to wind energy generation, load demand, and FCEV consumption are modeled. Furthermore, coordinating the planning of hydrogen storage and battery energy storage systems is incorporated. The results underscore the critical importance of simultaneous planning for the HSC and PDN reinforcement. Moreover, the findings reveal that hydrogen storage alone can provide sufficient energy arbitrage, leading to a 9.6% reduction in total costs compared to the scenario where storage is disregarded.

Supply chain digitalization and sports industry performance in Chinese context: a mediated moderated model

PurposeIn era of industry 4.0 all the organizations are investing their resources so they can adopt the latest technologies with no exception of supply chain. Accordingly, the present study attempted to examine the influence of digitalization of supply chain and performance in context of Chinese sports industry. Additionally, it has considered the supply chain integration and time management as a mechanism along with boundary condition of supply chain resilience.Design/methodology/approachThe study followed positivism philosophy and adopted quantitative methods. The data were collected by using the questionnaire from the Chinese sport industry organizations’ professionals related to logistics and supply chain. A total 746 questionnaires were distributed and 570 of them subjected to SPSS and Smart PLS for data analysis. Hypotheses were tested using PLS-SEM through the Smart-PLS 4.FindingsThe results of the study revealed that supply chain digitalization positively influences the both internal and external supply chain integration. Moreover, supply chain planning and scheduling influenced by both internal and external supply chain integration lead by supply chain digitalization. Ultimately resulting in improved sports industry performance. However, the supply chain resilience did not found as a significant moderator.Originality/valueThe study is few of the attempts that have tested a comprehensive mechanism through which the supply chain digitalization lead towards the higher performance. Also, the study results signify that the digitization can help the organizations to accomplish superior performance specifically in sports industry.

Integrated supply chain plan under multiple distribution networks: an implementation of mixed integer linear programming

Integrated supply chain plan under multiple distribution networks: an implementation of mixed integer linear programming

Perancangan Jaringan Rantai Pasok Dan Sistem Informasi Pemasaran Pada Program Sistem Ekonomi Jaringan Usaha Koperasi Untuk Kerjasama Usaha Koperasi

Cooperatives are an opportunity for economic actors to continue to develop with business activities carried out in groups to achieve common goals among fellow cooperative members, so strategic planning in the form of a design for a Government Program, especially in the Cooperative Sector, is a form of consideration and input for developing a resource. The resources owned by cooperatives in Bandung City by providing digital trade, from the Cooperative Business Network Economic System Program, abbreviated as SEJUK, with the aim of creating business cooperation between cooperatives in Bandung City. Information collection and management carried out in the form of interviews, literature studies, design carried out using quantitative research methods, marketing information system design based on Unified Modeling Leangues (UML) and supply chain planning based on Value Stream Mapping (VSM). From the design results, it is clear that supply chain network design and marketing information systems are connected in managing cooperatives to carry out goods procurement and business promotion activities by utilizing marketing information systems to provide digital trade channels for cooperatives participating in the SEJUK Program. so that the impact given by participating in the SEJUK Program needs to be improved and enhanced in the future in providing a forum for cooperatives to carry out business collaboration through the SEJUK Program

Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives

Every organization typically comprises various internal components, including regional branches, operations centers/field offices, major transportation hubs, and operational units, among others, housing a population susceptible to disaster impacts. Moreover, organizations often possess resources such as staff, various vehicles, and medical facilities, which can mitigate human casualties and address needs across affected areas. However, despite the importance of managing disasters within organizational networks, there remains a research gap in the development of mathematical models for such scenarios, specifically incorporating operations centers/field offices and external stakeholders as relief centers. Addressing this gap, this study examines an optimization model for both before and after disaster planning in a humanitarian supply chain and logistical framework within an organization. The affected areas are defined as regional branches, operational units, major transportation hubs, operations centers/field offices, external stakeholders, and medical facilities. A mixed-integer nonlinear model is formulated to minimize overall costs, considering factors such as penalty costs for untreated injuries and demand, delays in rescue and relief item distribution operations, and waiting costs for the injured in emergency medical vehicles and air ambulances. The model is implemented using GAMS software 47.1.0 for various test problems across different scales, with the Grasshopper Optimization Algorithm proposed for larger-scale scenarios. Numerical examples are provided to show the effectiveness and feasibility of the proposed model and to validate the metaheuristic approach. Sensitivity analysis is conducted to assess the model’s performance under different conditions, and key managerial insights and implications are discussed.

Interaction Between Automation and Humans in Supply Chain Planning

Automation is transforming supply chain planning (SCP) processes, replacing human tasks with technological solutions. While automation offers efficiency gains, the interaction between humans and automated systems presents behavioral challenges. This study investigates how decision-makers in SCP processes learn to correct errors when interacting with automated demand forecasting systems versus human planners. Drawing from psychology and behavioral theories, we examine the effects of interaction type (automated system vs. human) on learning, operationalized as performance improvement over time. Further, we analyze whether this relationship is moderated by cognitive psychological traits (positive attitude towards technology, technology anxiety/dependence) and socio-psychological traits (social influence based on subjective norms, social influence based on image) of the human decision-maker. Our article contributes to supply chain management research by introducing automation-human interaction, providing a temporal learning perspective on performance, and integrating cognitive and socio-psychological moderators. Insights are offered on how to facilitate effective human-automation collaboration by managing socio-psychological influences. Limitations and future research opportunities, including cultural contexts and artificial intelligence, are discussed.