1,720,976 research outputs found
Optimising centralisation in distribution networks for perishable products through mathematical modelling, parametric analysis, and machine learning
No full textThe success of distribution companies for perishable products is enabled by optimally configuring distribution networks, which allows for reducing total logistic costs while ensuring reduced product spoilage and high service levels. Since customer demand for perishable products varies over time, the network configuration should not be optimised once, but periodically reviewed. Among the decisions to be reviewed, determining whether to centralise or decentralise inventory (i.e. stock allocation in distribution centres) is crucial. However, the literature overlooks stock allocation decisions, and existing methodologies to compare the economic performance of centralised, decentralised, and hybrid policies neglect important cost items, also requiring advanced computational technologies and skills to be applied. This paper addresses these gaps by providing two contributions. First, a novel mathematical model is offered to compare five stock allocation policies (ranging from centralisation to decentralisation, crossing through three hybrid policies), identifying the most cost-effective one under a comprehensive economic analysis. Next, a parametric analysis is accomplished and a machine learning algorithm is trained to obtain a quick and easy-to-use Decision Support System (DSS). The DSS results in a decision tree, which was tested in a case study and provided managerial insights on how to review the stock allocation of perishable products
Centralized and decentralized supply chains: Performance maps for comparing the cost-effectiveness of distribution network configurations
No full textOptimizing Distribution Networks (DNs) is crucial for retailers, impacting service levels and logistics costs. A key DN configuration decision is the stock deployment policy, which entails choosing between centralized, decentralized, and hybrid DNs for each Stock Keeping Unit (SKU). Choosing the stock deployment policy is complex due to many variables influencing the decision (e.g., number of customers served, SKU purchasing costs, customer demand, etc.). Moreover, this decision must be revisited whenever customer demands changes, which can be time-consuming when DN resilience is challenged by geopolitical changes, market trends, and disruptive events. Dimensional Analysis (DA), and particularly the Buckingham Theorem (BT), shows capabilities to support retailers in guiding and streamlining stock deployment decisions. After modeling the stock deployment problem in a mathematical form, BT can identify its influential variables, extract knowledge on how variables mutually interact when affecting the stock deployment performance, and aid informed decision-making on the most cost-effective policy. Accordingly, BT enables creating performance maps which compare the characteristics of different DNs and SKUs, then suggesting similar stock deployment decisions for similar (scaled) DNs and SKUs. Despite the potential utility of these performance maps, no prior study has explored BT's capabilities for stock deployment decisions. This paper bridges this gap by proposing BT to create supportive maps for multidimensional scaling, similarity analysis, and economic performance prediction across centralized, decentralized, and hybrid DNs. The resultant maps provide retailers with visual decision support tools for associating similar DNs and SKUs with optimal stock deployment policies, ultimately improving DN performance and resilience
Operational Excellence through drone-based inventory monitoring: a mathematical model proposal
No full textManaging inventories is associated with high costs, which may account for about a third of the total logistics costs. These costs arise from different factors such as the consequences of Inventory Record Inaccuracy (IRI). IRI represents the discrepancies between the physical and digital inventories. These discrepancies generate great labor efforts to solve them, along with write-offs and oversells. This leads to economic and productivity losses. To reduce the impact of IRI, inventories are periodically controlled for audit compliance and correct the physical-digital discrepancies. This is usually done through costly, time-consuming, and labor-intensive approaches. The recent advances in drones have led to their adoption for logistic purposes, including inventory monitoring. Drone-based inventory monitoring entails several benefits compared to conventional approaches such as being automated and quicker. This may result in better accuracy and performance, contributing to operational excellence. Despite this, available literature mainly focuses on the technical and conceptual development of drone-based inventory monitoring systems. Less interest has been devoted to evaluating their economic viability compared to conventional labor-intensive approaches, particularly considering in the analysis reduction in labor and IRI-related issues. To this end, this work aims to develop a mathematical model to compare drone-based inventory monitoring with the labor-intensive conventional technique, considering the presence of write-offs and oversells. The model is tested on two case studies: a manufacturer and a third-party logistics (3PL). Warehouse managers may exploit the model for preliminary assessment of the economic benefits of drone-based inventory monitoring
Analyzing Forklift and Drone Applications in Sustainable Logistics: A Bibliometric Review
No full textNowadays organizations are under increasing pressure to adopt sustainable solutions for material handling and management due to growing environmental awareness. This drive has spurred advancements in innovative technologies, optimizing logistic processes to reduce energy usage and emissions, and enhancing overall supply chain sustainability. This approach extends to internal material handling with forklifts and the emerging use of drones for last-mile operations. Despite the importance of sustainable logistic practices, the literature lacks a comprehensive review of research on forklifts and drones in logistic activities. Through a Systematic Literature Network Analysis (SLNA), this paper addresses this gap by conducting a comparative analysis of two bibliometric reviews: one on forklifts' sustainable logistic applications and the other on drones'. This analysis aims to identify key contributors, countries, journals, and research themes in both areas. The SLNA results seek to highlight existing literature, productive countries, influential authors, and research themes. This paper provides theoretical contributions by identifying gaps in the current literature and highlighting key research themes related to sustainable logistic practices. Furthermore, it offers practical insights to industry stakeholders on integrating these practices using forklifts and drones, while also laying the groundwork for future research through a comparative analysis
A Conceptual Framework for Measuring the Impact of LARG Practices on Logistics Performance
No full textThe performance of industrial activities is being challenged by the complexity, uncertainty, and volatility of economic-industrial contexts. Moreover, organizations are increasingly compelled to address environmental issues by adopting more sustainable practices that reduce resource usage and waste. Within this context, logistics is also undergoing a strategic review to enable efficient, effective, flexible, and environmentally friendly physical flows. To achieve these objectives, the integration of Lean, Agile, Resilient, and Green (LARG) paradigms is proposed as a solution, offering specific problem-solving capabilities. While the evaluation of LARG practices has been discussed in various industrial contexts, such as supply chain management and manufacturing, its application to logistics remains understudied. Therefore, the purpose of this paper is to investigate the individual contributions of LARG practices and their relationship with logistics activities by assessing their level of integration. Based on this analysis, we propose a conceptual framework to evaluate the impact of LARG practices on logistics performance. The research findings can serve as a preliminary tool for measuring lean, agile, resilient, and green indicators within logistics activities and the transport sector
Sustainable Logistics 4.0: A Study on Selecting the Best Technology for Internal Material Handling
Full text linkLogistics is a vital activity for the economic growth of an organization as it manages the flow of materials and information within, into, and out of the organization, as well as reverse flow. Like many other industrial processes, logistics has also been impacted by the rise of Industry 4.0 technologies, which has highlighted the significance of Logistics 4.0. However, Logistics 4.0 is mainly focused on economic benefits, while overlooking environmental and social concerns. To address this, a method is proposed that takes into account the three goals of sustainable development when selecting the best technology for internal material handling activities. Firstly, a comprehensive literature review was conducted to examine the application of 4.0 technologies in logistics processes and their impact on economic, environmental, and social sustainability. Secondly, based on the findings of the review, a three-level analytic hierarchy process was proposed to identify the optimal 4.0 technology for internal logistics. To demonstrate the practicality of the proposed method, it was tested on three companies. The results showed that additive manufacturing, exoskeletons, and collaborative robots are the most suitable options for achieving sustainable development goals within Logistics 4.0
Trends and Recommendations for Enhancing Maturity Models in Supply Chain Management and Logistics
Full text linkMaturity models (MMs) are strategic tools used to assess and improve the current state of processes, objects, or people, with the goal of achieving continuous performance enhancement. While MMs are applied in various fields, their scope, design, and application criteria within Supply Chain Management and Logistics (SCML) lack comprehensive studies. This article aims to address this gap through a systematic literature review. The review analyzes 137 relevant articles using both bibliometric and content analysis techniques. The bibliometric analysis identifies major contributions, popular journals, and the classification and evolution of key keywords. The content analysis focuses on critical criteria related to the scope, design, and application of MMs. The findings reveal a growing emphasis on models assessing Industry 4.0 readiness and sustainability principles. However, several gaps are identified, including limited attention to optimizing and integrating logistic processes, underutilized and unvalidated MMs, and the absence of comprehensive improvement guidelines. Based on these trends and research gaps, this study proposes five recommendations for future developments that benefit both academics and practitioners. These recommendations aim to address the identified limitations and provide guidance for comprehensive and effective improvement strategies
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
