Operations Management Research最新文献

This paper aims to enhance the understanding of how small and medium-sized enterprises (SMEs) can bolster their resilience to supply chain disruptions by engaging and aligning cross-functional staff in the process of developing supply chain resilience (SCRES). Employing process theory, the study adopts a multiple case-study methodology involving 18 Danish production SMEs across two iterative phases: an exploratory phase encompassing eight case companies, and a subsequent refinement phase involving an additional ten case companies. Utilizing a mixed-method approach comprising semi-structured interviews, card sorting exercises, observational studies, and a questionnaire survey, the research proposes a four-stage process for enhancing SCRES. This process includes: 1) mapping the supply chain, 2) identifying vulnerabilities and capabilities within each function, 3) prioritizing and creating cross-organizational alignment, and 4) developing action plans. The refined approach, validated through the ten Danish SMEs in the refinement phase, offers a practical and relevant framework for companies seeking to mitigate vulnerabilities and enhance capabilities in their supply chains. By strengthening SMEs' resilience against supply chain disruptions, this approach serves as a potential model for other companies striving to achieve SCRES.

In the age of digital transformation, maintenance operations are crucial for leveraging the potential of Industry 4.0 and 5.0. Yet, this domain remains significantly under-optimized in terms of strategic maintenance planning and enhancing asset performance. The advent of smart technologies offers a myriad of innovative avenues; however, harnessing these effectively requires systematic planning that incorporates these new, various and quite diversified, smart practices. Thus, this paper proposes a new methodological approach to maintenance planning, based on the Reliability-Centered Maintenance method, aimed at providing an operative tool for organizations to foster the evolution of their maintenance plans towards the paradigm of digitalization. This novel method enables the identification of hidden opportunities of improvement not identifiable through the use of the traditional approach through the proposal of an Opportunity Index, to use together with the Criticality Index in asset selection, and a Digitalization Score to use during Failure Mode, Effects, and Criticality Analysis. The proposed method is applied to transform the maintenance planning of a production line, thus identifying the opportunities of the approach and testing its feasibility.

Sustainable value chain management (SVCM) incorporates the social, economic, and environmental aspects (known as the triple-bottom-line) of production systems, offering significant potential for sustainable operations. By broadening system boundaries and including triple-bottom-line sustainability indicators, SVCM can improve the existing literature on sustainable operations management. Life cycle sustainability assessment (LCSA) identifies sustainability hotspots within value chains but is often underutilized in the practical design of sustainable operations. This paper presents a three-phase framework that combines SVCM and LCSA to enhance sustainable operations, using electricity production as a case study due to its substantial carbon footprint. The authors reviewed 443 articles from an initial 1649 documents on electricity production technologies, emphasizing the use of life cycle assessment (LCA) models to achieve responsible operations in the energy sector. The study highlights the benefits of the proposed integrated framework in achieving sustainable operations through sustainability reporting, stakeholder engagement, transparent procurement, global value chain management, corporate social responsibility, integrated decision-making, circular economy, and carbon footprint management. Future research should focus on developing circular production systems, integrating socioeconomic indicators, and aligning sustainable development goals with value chain hotspots.

This study addresses the capacitated lot-sizing problem in the poultry industry for egg production planning, aiming to minimize production, transportation, and inventory costs. This problem has already been investigated with data certainty and formulated as a mathematical model and a heuristic algorithm has been applied to solve it due to high complexity. In this study, we reformulate the same problem as a new mixed integer linear programming model to achieve optimal solution in a relatively short time without the need for heuristic algorithms. To evaluate the model performance, it is executed using the available data, and its efficiency is validated by comparing the obtained results. Subsequently, the uncertainty of weekly demand is considered, leading to potential shortage or surplus in storage. To address this uncertainty, the chance-constraints method is employed with various attitudes, and several production plans are proposed accordingly. The performance of these plans is compared using random data, and the most suitable programs are identified. The presented decision-making tool can provide production planning that meets customer demand with high reliability while also minimizing surplus inventory in the warehouse.

When a disaster strikes, there is always a demand for life-supporting commodities, whose slow and ineffective delivery can result in huge human and financial losses. Warehouse location and the storage of necessary relief commodities (RCs) before a disaster, and the proper distribution of RCs among affected people following a disaster can improve performance and reduce latency when responding to a given disaster. Hence, many researchers have focused on these fields while overlooking some crucial actual conditions as a result of the complexity of the problem. Consequently, this study develops a location-inventory-distribution problem in disaster relief supply chain (DRSC) considering the gradual injection of the limited pre-disaster budgets, the time value of money, and various evaluation criteria for locating warehouses. In this regard, a novel multi-objective two-stage scenario-based stochastic programming model under a pre-disaster multi-period planning time horizon (PTH) is presented. In each period, pre-disaster warehouse location and inventory management are addressed in the first stage, and the post-disaster distribution of the stocked RCs is planned in the second stage. Utilizing new priority-weighted service utility and balance measures, the model strives to optimize deprivation cost, demand coverage, and fair service. The maximization of warehouses’ utility is done according to various criteria and using a data envelopment analysis (DEA) model integrated with the model. The applicability and performance of the model are validated via a real-world case study followed by various tests and sensitivity analyses. The outcomes show that the model significantly improves logistics and deprivation costs, satisfied demands, fair service, and warehouses’ utility.

Conflicts and wars profoundly impact infrastructure, exacerbating the adversity already caused by natural disasters. Therefore, it is imperative that the reconstruction process be both effective and efficient to expedite a return to normalcy. This study aims to enhance the efficacy of reconstruction efforts through improved construction supplier evaluation and selection. It introduces an innovative hybrid multi-objective decision-making model that integrates a broad spectrum of economic, technical, and humanitarian criteria. The model is designed to optimally select and assign construction suppliers in regions affected by human and natural conflicts and crises. Fifteen criteria have been incorporated into the evaluation process to validate its effectiveness and maximize its contribution to local communities. This methodology streamlines decision-making and enhances transparency in conflict zones, aligning with the interests of all stakeholders. The study incorporates advanced methodologies, including Fuzzy Goal Programming (F-GP), Geographic Information System (GIS)-based Risk Assessment, and Fuzzy Analytic Hierarchy Process (F-AHP), leveraging real-world data and a case study. Additionally, a sensitivity analysis examines the impact of varying inputs on the model's output. The findings attest to the model's utility in conflict-affected regions and its potential applicability in stable settings.

Empirical investigations of how the reshoring of manufacturing is affected by Industry 4.0 technologies, supply chain disruptions, and made-in effects are rare in the extant academic literature. This paper contains an empirical analysis of how these variables affect reshoring and reshoring intentions. Results from a 2022 questionnaire survey including 152 offshoring manufacturing firms show that reshoring and reshoring intentions are associated positively with investments in automation in manufacturing, and with employee made-in. Results also showed that while Covid-19 associated disruptions increased firms’ reshoring intentions equally across firm sizes, smaller and larger firms reacted quite differently towards more well-known disruption types: larger firms decreasing reshoring intentions with higher levels of uncertainty and smaller firms increasing reshoring intentions with higher levels of uncertainty. These results point to the importance of creating consciousness about the dynamics of production localization and how firm-level and situation-specific contingencies may interfere with Industry 4.0 technology-, supply chain disruption-, and made-in effects on strategic reshoring decisions.

Traditional agriculture has jeopardized national resources given the limited availability of natural resources. On the other hand, artificial intelligence (AI) has resulted in more efficient resource utilization. Nowadays, animal agriculture is much more sustainable with the help of artificial intelligence. Furthermore, the rate of AI maturity in animal agriculture provides a roadmap for optimizing its integration into it, which is of great concern to enterprise managers and policymakers. According to the literature, there is no AI maturity model in the animal agriculture sector to assess the latter. The current study was carried out in four phases. First, the literature shed light on the dimensions of AI and its applications in animal agriculture. Second, animal agricultural experts ranked the AI dimensions using the Best-Worst Method (BWM). In the third phase, a model was developed to assess AI maturity across all dimensions of AI technology and AI applications in animal agriculture. Finally, a company maturity assessment tested the proposed model by questionnaire. The research findings show that health monitoring is the most important AI application in animal agriculture. Also, the company under study showed great individual identification maturity. The research is original in that it determines the importance of AI in animal agriculture and introduces an AI maturity model in the animal agriculture sector.

Demand-Driven Material Requirements Planning (DDMRP) was designed to improve supply chain performance in complex and uncertain environments. Literature on the topic suggests that production replenishment orders should be dispatched for execution based on the buffers’ penetration ratio of the products ordered, which is a measure of protection against stock depletion. However, the actual performance impact of this dispatching rule remains largely unknown as is the impact of different lot transfer policies. A simulation analysis was carried out to compare the performance of the lowest net flow position, the highest buffer penetration ratio, earliest operation due date and first-come first-served rules under synchronized and unsynchronized lot transfer policies. Results of our study show that the choice of dispatching rules is contingent on the setting of top-of-yellow and top-of-green, which determine the re-order quantity, and on the demand mix of products. The earliest operation due date rule shows great potential to outperform the rule typically applied in a DDMRP context specifically for a high demand mix. These findings provide important insights for improving industrial practice and for guiding future research on DDMRP.

Leveraging on ten case studies, the paper examines the Supply Chain Finance (SCF) innovation process through a multiple stakeholder perspective (buyers, suppliers, and SCF providers). The aim is to identify the phases of the process impacted by Artificial Intelligence (AI), as well as its benefits and challenges. AI affects several activities in the Initiation phase of the innovation process, supporting the SCF provider’s commercial activities and contributing to assessing the buyer’s creditworthiness, detecting fraud, or proposing the right SCF solution. In the Implementation phase, AI supports assessing the supplier’s credit rating, categorizing and onboarding suppliers, and fastening the administrative tasks. Formulating 9 propositions, this study supports the theory related to the SCF by providing empirical evidence about the role of AI in the SCF innovation process and also identifying the resulting benefits and challenges for all the actors involved.