This paper highlights the tight relationship between the picking and packing processes in warehouse management and the need to consider them as an integrated problem. The study describes and models ...this integrated problem as a mixed-integer programming model, to optimise overall labour costs by determining the assignment of the subsets of orders, i.e. batches, for picking and packing. To address the issue of model complexity, the paper presents a statistical-based framework for generating approximate models and selecting the optimal one through examination. Based on the examination results, a pair-swapping heuristic is additionally proposed to be combined as a hybrid algorithm. Numerical experiments based on a real-world case demonstrate the effectiveness of the framework-proposed and selected hybrid algorithm by comparison with other framework-proposed approximate models, a solver, and existing heuristics. Our findings indicate that the combined usage of integrated picking and packing processes planning and the hybrid algorithm proposed and selected within the statistical-based framework can effectively reduce the cost of warehouse management.
Automation and digitisation are the driving force of the Industrial Revolution 4.0. Industrial revolutions led to the mass production of goods, which increased the need for modern warehouses. Every ...year, the operation of warehouses becomes increasingly more complicated due to the increasing abundance of goods, thus the usual warehouse management strategies are no longer suitable. In order to cope with huge product flows, modern innovations should be used more extensively to manage these processes. Successful management will help provide quality service to rapidly changing business sectors. The Internet of Things (IoT) is a technology designed to process large amounts of data with maximum efficiency in real time. This technology can facilitate the implementation of smart identification, tracking, tracing, and management using radio frequency identification (RFID), infrared sensors, global positioning systems (GPS), laser scanners, and other detection tools. Such innovations as IoT have made a significant impact on warehousing operations. The aim of IoT is to perform administrative work, i.e., to efficiently manage warehouse data. IoT can be used to monitor and track goods, forecast demand trends, manage inventory, and perform other warehouse operations in real time. The key elements of a warehouse are sales and customer satisfaction. Implementing IoT improves financial performance, work productivity, and customer satisfaction. However, innovation requires additional investment in, for instance, implementation and maintenance. It is necessary to investigate how warehouse elements such as inventory accuracy or order processing time are affected by the internet of things in companies of different sizes. Research on the impact of IoT on warehouse management focuses on IoT advantages, disadvantages, mitigation risks, and the use of IoT in warehouses. The aim of this work is to research the impact of IoT on warehouse management in companies of different sizes and to determine whether the costs and benefits of IoT differ in the same scenario. As a result, the conceptual model for the adoption of IoT measures in warehouse companies was created, and its suitability was assessed by experts.
•Shortage of management skills and mind-sets presents a significant barrier to maximising Artificial Intelligence (AI) opportunities.•Operational management with previous exposure and familiarity of ...AI is critical for its adoption.•Compatibility of existing technical infrastructure, warehouse layout and structure play a major role in warehouse AI adoption.
The explosive rise in technologies has revolutionised the way in which business operate, consumers buy, and the pace at which these activities take place. These advancements continue to have profound impact on business processes across the entire organisation. As such, Logistics and Supply Chain Management (LSCM) are also leveraging benefits from digitisation, allowing organisations to increase efficiency and productivity, whilst also providing greater transparency and accuracy in the movement of goods. While the warehouse is a key component within LSCM, warehousing research remains an understudied area within overall supply chain research, accounting for only a fraction of the overall research within this field. However, of the extant warehouse research, attention has largely been placed on warehouse design, performance and technology use, yet overlooking the determinants of Artificial Intelligence (AI) adoption within warehouses. Accordingly, through proposing an extension of the Technology–Organisation–Environment (TOE) framework, this research explores the barriers and opportunities of AI within the warehouse of a major retailer. The findings for this qualitative study reveal AI challenges resulting from a shortage of both skill and mind-set of operational management, while also uncovering the opportunities presented through existing IT infrastructure and pre-existing AI exposure of management.
Business intelligence (BI) is now widely used, especially in the world of practice, to describe analytic applications. BI is currently the top-most priority of many chief information officers. BI has ...become a strategic initiative and is now recognized by CIOs and business leaders as instrumental in driving business effectiveness and innovation. BI is a process that includes two primary activities: getting data in and getting data out. Getting data in, traditionally referred to as data warehousing, involves moving data from a set of source systems into an integrated data warehouse. Getting data in delivers limited value to an enterprise; only when users and applications access the data and use it to make decisions does the organization realize the full value from its data warehouse. Thus, getting data out receives most attention from organizations. This second activity, which is commonly referred to as BI, consists of business users and applications accessing data from the data warehouse to perform enterprise reporting, OLAP, querying, and predictive analytics.
Aiming at the path planning problem of Automated Guided Vehicle (AGV) in intelligent storage, an improved Dijkstra algorithm that combines eight-angle search method and Dijkstra algorithm for path ...optimization is proposed. The grid method is used to model the storage environment, and the improved Dijkstra algorithm is used to optimize the route of the AGV. The simulation test of the AGV path planning process with Matlab shows that the AGV can effectively avoid obstacles by using the traditional Dijkstra algorithm and the improved Dijkstra algorithm, and then search for a collision-free optimized path from the start point to the end point; and the traditional Dijkstra algorithm In comparison, the path length planned by the improved Dijkstra algorithm is shorter and the turning angle is less, indicating that the improved algorithm is correct, feasible and effective, and has a strong global search ability.
PurposeThe evolution of technology from the most recent industrial age to the technology era better known as Industry 4.0 resulted in greater demand for horizontal, vertical and end-to-end digital ...integration. Prior studies show that Industry 4.0 adoption majorly influences the sustainability aspects in a supply chain network. The purpose of this paper is to identify the Industry 4.0 enablers of supply chain sustainability and further attempt to propose a research framework to bridge the theoretical gaps.Design/methodology/approachIn this research study, the authors have used a systematic literature review methodology in the field of Industry 4.0 and sustainable supply chain management. The list of papers was downloaded from Scopus (www.scopus.com) database. Through strict screening, only journal papers were selected for conducting the review of the literature.FindingsThe review brings out some interesting findings which will be helpful for the research community. There have been limited research in the area of managing supply chain network sustainability through Industry 4.0 technologies. The authors found only 10 papers out of a total of 53 papers which emphasize on smart manufacturing, smart production system, smart warehouse management system, smart logistics and sustainability. Most of the previous research studies have ignored the social aspects of supply chain sustainability. Finally, the authors identified 13 key enablers of Industry 4.0 playing an important role in driving supply chain sustainability.Practical implicationsThe strategies for Industry 4.0 should be refined and detailed to develop economic and social systems that can act flexibly to sudden changes in the system. Top management must be convinced for prioritizing investment support and creating a system that can facilitate technology convergence. Managers must also act on new models of employment and frame plans to continuously improve the system. In addition, managers must focus on establishing a collaborative platform to facilitate high-tech research and developments. Finally, it is essential to develop a performance management system for monitoring all actions in the supply chain network.Originality/valueIntegrating two independent subjects is the uniqueness of the current study. Here, Industry 4.0 and supply chain sustainability have been integrated to build the research framework, and in such a process, the authors have extended the existing knowledge base.
In the rapidly evolving e-commerce landscape, efficient packaging and logistics reduce costs and enhance customer satisfaction. This study addresses the problem of dynamic bin size optimization in ...e-commerce logistics by proposing a series of intelligent algorithms. Considering real-world constraints such as item separation requirements, a Mixed Integer Programming Model for Multi-Order Multi-Box Open-Dimension Rectangular Packing (MOMB-ODRPP) is formulated. The Stacked Clustering Algorithm (SCA) series, One-Dimensional Fixed Stacked Clustering Algorithm (ODF-SCA), Two-Dimensional Fixed Stacked Clustering Algorithm (TDF-SCA), and Variable Neighborhood Descent Spatial Ordering Algorithm (VND-SOA) series are employed to solve the MOMB-ODRPP model and improve order packing rates and optimize bin sizes. Computational experiments using real-world data from JD’s e-commerce operations reveal that the TDF-SCA algorithm series outperforms the ODF-SCA series by approximately 5% in Case 4. In contrast, the VND-SOA-S1 and VND-SOA-S2 algorithms achieve improvements of 0.83% and 0.76%, respectively, over the TDF-SCA-P2 algorithm in Cases 4 and 11. The comparative analysis highlights the practical implications of bin size optimization, with Case 11 providing a more viable option for standardizing bin sizes in e-commerce logistics.
•Proposed MOMB-ODRPP model for dynamic bin size optimization in e-commerce.•Developed ODF-SCA, TDF-SCA, and VND-SOA algorithms to solve MOMB-ODRPP.•Improved order packing rates by up to 5% using intelligent algorithms.
Micro, Small, and Medium Enterprises (MSMEs) operating in the food retailing sector encounter two main concerns with respect to their perishable inventory management system, i.e., the product's shelf ...life and investment in warehouse monitoring systems. New technologies like the Internet of Things (IoT), automated inventory control platforms, and automatic storage and retrieval systems offer effective solutions to these issues. However, MSMEs are reluctant to adopt these technologies due to their prior perception of higher implementation costs and the expected benefits. The present study aims to optimize IoT implementation in MSMEs' inventory management systems and to provide tangible proof of its feasibility and usefulness. In so doing, we propose a mathematical model and analyze the impact of IoT through two case studies. The model provides a cost-benefit analysis of IoT investments that aim to increase products' shelf life. We adopted the fractional program method, solved by particle swarm optimization on MATLAB software. The findings demonstrate the positive correlation between adopting IoT and reduced inventory costs supporting IoT deployment for improved perishability performance in MSMEs. The study offers several insights and practical guidelines in considering IoT deployment in MSMEs.
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