Search results

Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.

The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.

This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.

This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.

By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.

This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.

This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management.

The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method.

The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies.

This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications.

Performance measurement involves gaining useful information about performance. The purpose is to develop a conceptual framework to assist wholesale managers in selecting useful information to evaluate operational performance. The proposed framework identifies core operations, measures of operational performance and factors that affect the performance of wholesale organisations.

The research essentially relies on the available literature to develop a conceptual framework and define related components. Research primary data were used to validate the framework components and to implement the framework in wholesale organisations by allocating performance factors to the different components of the proposed framework. Using a semi-structured interview design, ten face-to-face interviews were conducted with managers of ten different wholesale organisations.

The research identifies five core operations that affect the operational performance in wholesale organisations, four measures for evaluating efficiency and effectiveness in every core operation and 28 factors that influence operational performance in wholesale organisations.

Operational performance is monitored by evaluating the achievement of efficiency and effectiveness in operations. The research introduces a performance measurement framework that identifies the required information to evaluate the efficiency and effectiveness of core operations in wholesale organisations.

Industry 4.0 has put forward a smart perspective on managing supply chain networks and their operations. The current manufacturing system is primarily data-driven. Industries are deploying new emerging technologies in their operations to build a competitive edge in the business environment; however, the true potential of smart manufacturing has not yet been fully unveiled. This research aims to extensively analyse emerging technologies and their interconnection with smart manufacturing in developing smarter supply chains.

This research endeavours to establish a conceptual framework for a smart supply chain. A real case study on a smart factory is conducted to demonstrate the validity of this framework for building smarter supply chains. A comparative analysis is carried out between conventional and smart supply chains to ascertain the advantages of smart supply chains. In addition, a thorough investigation of the several factors needed to transition from smart to smarter supply chains is undertaken.

The integration of smart technology exemplifies the ability to improve the efficiency of supply chain operations. Research findings indicate that transitioning to a smart factory radically enhances productivity, quality assurance, data privacy and labour efficiency. The outcomes of this research will help academic and industrial sectors critically comprehend technological breakthroughs and their applications in smart supply chains.

This study highlights the implications of incorporating smart technologies into supply chain operations, specifically in smart purchasing, smart factory operations, smart warehousing and smart customer performance. A paradigm transition from conventional, smart to smarter supply chains offers a comprehensive perspective on the evolving dynamics in automation, optimisation and manufacturing technology domains, ultimately leading to the emergence of Industry 5.0.

The aim of this study is to develop a framework for the implementation of a Supplier Kanban System.

The research was conducted within a factory focusing on the warehousing and supplier-related operations using an action research methodology.

The study period spanning over six years was aimed to reduce the inventory and the manpower required to manage this inventory. This initiative led to a substantial average savings of more than 500,000 Malaysian ringgit annually.

The focus of the study is on a framework for implementing a Supplier Kanban System in a small or medium-sized company within the automotive industry.

This framework is designed to be self-sufficient such that the members of the organisation can use it without the help of external experts or consultants.

Reduction in inventory stored in the warehouse, the amount of redundant work carried out by the employees and other non-monetary improvements which are difficult to monetise resulted in a much better work environment and happier employees.

The change in the paradigm of the manufacturing value chain affects the Tier 1 and 2 suppliers in Malaysia. The lines and value chains posing challenges to the suppliers have brought the idea of the Supplier Kanban System. The Supplier Kanban System adopted the action research cycles and continuous learning cycles, and this process was documented and developed as the Framework for the Implementation of a Supplier Kanban System.

The aim of this study reported in this paper was to explore the application of operational excellence methodologies in a global context.

A qualitative interview approach was used to understand the current state, benefits, challenges, success factors, tools and techniques of operational excellence methodology implementation with relevance to logistics companies worldwide. About 16 interviews were undertaken with practitioners working in leading companies and with leading academics in Asia, Europe, Africa, North America, South America and Australia.

The findings show that operational excellence methodologies including Lean, Six Sigma, Lean Six Sigma and Agile can apply in logistics firms to improve operations and productivity and save costs. Top management support and involvement play an important role in the success of operational excellence projects in the logistics service.

The findings will be of interest to top and middle managers and logistics practitioners, with a dual aim of improving logistics performance and saving costs.

The present study has been one of the first global study attempts to explore the implementation of operational excellence methodologies in the logistics sectors.

The aim of this research is to underscore the pivotal role of warehouse management in the current turbulent global landscape exacerbated by the confluence of a health crisis and geopolitical instability in Europe. In today's interconnected global economy, the turbulence of the global supply chain causes a lack of its resilience among companies. Facing this critical crisis context, companies are refocusing on business processes and outsourcing support processes such as logistics. In this paper we have empirical and methodological objectives. Methodologically, we employ a qualitative research approach utilizing action research in a collaborative framework that involves academics and practitioners. The purpose of this methodology is to empirically investigate warehouse outsourcing as a solution for enhancing a company's performance and agility within the crisis context.

The authors’ action research based on case study approach is conducted through an immersion within the ALCL French multinational company located in Morocco. The authors mobilize the theory of constraints, which allows us to set up a process of identification and optimization of managerial constraints (Goldratt, 1990). The approach allows to set up a retroactive loop to increase the performance of the constraint.

The study shows that ALCL has a storage over-dimension constraint due to the decrease of physical flows caused by the global crisis. The results of action research protocol show that the optimization of warehousing constraint is achieved by the total outsourcing of the process.

The study provides new insights into how action research can improve management practices within companies and explore concrete solutions to the logistical challenges faced by businesses.

The objective of this research is to evaluate and rank the factors influencing omnichannel (OC) logistics, while also investigating the significant impact of big data analytics in improving these drivers of OC logistics.

Using exploratory sequential mixed method design, an in-person interview survey was conducted to identify and stratifies the enablers of OC retailing. These interviews were supplemented with a case study in an apparel firm to prioritise the enablers of OC logistics. Further, a survey was conducted to understand the role of big data analytics in improving drivers of OC logistics as well as the role of Individual capability and organisational capability in big data usage for omnichannel retailing.

Findings represent that information management is the most important driver followed by inventory management and network design for improving OC logistics. Further, significant relationship between big data analytics and drivers of omnichannel logistics has been reported.

This study identifies and classifies the drivers of OC retailing relating to their level of criticality in OC logistics which will assists practitioners to prioritise their tasks for the successful development of OC logistics. The study will also help practitioners to use BDA for developing the drivers of OC.

The study substantiates and adds to the BDA literature by emphasising the positive role of BDA in development of OC driver and highlighting the significant role of drivers of BDA in its usage.

The convergence of the digital supply chain (DSC) with sustainability presents promising opportunities and notable challenges for today's enterprises. This chapter explores the relationship between digital supply chain management (DSCM) and sustainability within the context of supply chain management. It begins by providing a detailed overview of the digital transformation in supply chain management, emphasizing its rapid evolution and its profound impact on sustainability. The chapter then delves into the various sustainability challenges that manifest within DSCs, including issues related to energy consumption, e-waste management, and environmental impact. It goes beyond environmental considerations, exploring social and ethical dimensions, such as the potential consequences of digitalization on socioeconomic disparities. Moreover, the chapter presents a comprehensive framework of best practices and strategies aimed at navigating these challenges. It highlights the utilization of digital technologies like artificial intelligence (AI), Internet of Things (IoT), and blockchain to enhance transparency, efficiency, and responsible sourcing. Real-world case studies are included to exemplify successful implementations of sustainable practices across diverse industries. In conclusion, the chapter emphasizes the imperative of a holistic, authentically sustainable supply chain transformation, underpinned by digital technologies, to advance sustainability objectives while adeptly addressing emerging challenges in the ever-evolving landscape of supply chain management.