Optimal Order Batching in Warehouse Management: A Data-Driven Robust Approach

Optimizing warehouse processes has direct impact on supply chain responsiveness, timely order fulfillment, and customer satisfaction. In this work, we focus on the picking process in warehouse management and study it from a data perspective. Using historical data from an industrial partner, we introduce, model, and study the robust order batching problem (ROBP) that groups orders into batches to minimize total order processing time accounting for uncertainty caused by system congestion and human behavior. We provide a generalizable, data-driven approach that overcomes warehouse-specific assumptions characterizing most of the work in the literature. We analyze historical data to understand the processes in the warehouse, to predict processing times, and to improve order processing. We introduce the ROBP and develop an efficient learning-based branch-and-price algorithm based on simultaneous column and row generation, embedded with alternative prediction models such as linear regression and random forest that predict processing time of a batch. We conduct extensive computational experiments to test the performance of the proposed approach and to derive managerial insights based on real data. The data-driven prescriptive analytics tool we propose achieves savings of seven to eight minutes per order, which translates into a 14.8% increase in daily picking operations capacity of the warehouse.

Application of Passive Wireless RFID Asset Management in Warehousing of Cross-Border E-Commerce Enterprises

As an important part of modern logistics, warehousing provides a guarantee for the sustainable stability of enterprises, so as to realize the economy of enterprise production and transportation and so as to realize the economy of enterprise production and transportation. With the advent of the information age, the traditional logistics management model has been difficult to adapt to the current increasingly fierce market competition environment. In the management of cross-border e-commerce enterprises, warehouse management must establish a systematic and information-based logistics warehouse management system to improve the level of logistics management. Passive wireless RFID asset management provides a new idea for the construction of warehousing management system of cross-border e-commerce enterprises. Through the application of RFID technology, the warehousing design scheme and process of cross-border e-commerce enterprises can be optimized and controlled to the greatest extent, so as to strengthen the optimization and integration of supply chain and improve the market competitiveness of logistics enterprises. Based on the wireless passive RFID asset management technology, this paper analyses the construction and optimization of the logistics warehouse management system of cross-border e-commerce enterprises. The simulation results show that after the application of wireless passive RFID asset management technology, the warehouse operation efficiency of cross-border e-commerce enterprises is improved, the employee utilization rate is relatively reduced, and at the same time, it can also save a lot of manpower and reduce the labour cost.

OPTIMIZATION PROCESSES IN WAREHOUSE MANAGEMENT

This article aims to show the essence and importance of optimizing warehouse processes. Efficiency itself is an inseparable element of development, it drives the industry and allows for constant development. The idea behind the optimization of warehouse processes is an important element of business development, thanks to optimization you can save both time and money, and the pursuit of self-improvement should guide every organization striving for excellence in terms of services offered and the manner and time of order execution.

EVOLUTIONS OF GROUP MANAGEMENT DEVELOPMENT OF MOBILE ROBOTIC PLATFORMS IN WAREHOUSING 4.0.

The subject of this research is the technology of management of mobile robot groups in the concept of Industry 4.0 and its composition. The purpose of this article is to find ways to implement an effective strategy for building and managing mobile robotic platforms in Warehousing, as a key tool of Lean Production. To achieve this goal, it is necessary to solve the following tasks: to analyze the management of supply chains in Smart Manufacturing, within Industry 4.0 and its impact on achieving the goals of Lean Production; to study the evolution of technologies used in Warehousing in the dynamics of the Industrial Revolution; to analyze the evolution of Warehouse Management Systems (WMS) as one of the most important components on the basis of which the requirements for automation of Warehousing automation in Smart Manufacturing with group management of mobile robotic platforms are implemented and achieved; to compare the impact of the technologies used by Warehousing 4.0 and Warehouse Management Systems on the key indicators of Lean Production. Results: One of the promising ways to achieve the effectiveness of the implementation of Lean Production tools in WMS systems is the use of Collaborative Robot System technology, which makes it possible to ensure a high density of product storage in Warehousing. However, modern mobile robotic platforms have their limitations both in the methods of loading and unloading products, and in the design. Therefore, the authors see the task in improving the design of mobile robotic platforms, which will develop a new intelligent group method of loading and unloading products, increasing the storage density for a variety of goods. Conclusions: The paper compares the impact of Warehousing 4.0 and Warehouse Management Systems on key Lean Production tools, which shows how the introduction of new group management technologies for robotic platforms in Warehousing 4.0 and Warehouse Management Systems (WMS) affects the effectiveness of Lean Production tools such as Heijunka, Just-in-time, 5S. This suggests that the introduction of new models and methods of managing complex warehouses with high density and chaotic storage of products, through the use of mobile robotic autonomous systems, will significantly optimize the process of supply chain management in Smart Manufacturing.

A machine learning approach for predictive warehouse design

Warehouse management systems (WMS) track warehousing and picking operations, generating a huge volumes of data quantified in millions to billions of records. Logistic operators incur significant costs to maintain these IT systems, without actively mining the collected data to monitor their business processes, smooth the warehousing flows, and support the strategic decisions. This study explores the impact of tracing data beyond the simple traceability purpose. We aim at supporting the strategic design of a warehousing system by training classifiers that can predict the storage technology (ST), the material handling system (MHS), the storage allocation strategy (SAS), and the picking policy (PP) of a storage system. We introduce the definition of a learning table, whose attributes are benchmarking metrics applicable to any storage system. Then, we investigate how the availability of data in the warehouse management system (i.e. varying the number of attributes of the learning table) affects the accuracy of the predictions. To validate the approach, we illustrate a generalisable case study which collects data from sixteen different real companies belonging to different industrial sectors (automotive, manufacturing, food and beverage, cosmetics and publishing) and different players (distribution centres and third-party logistic providers). The benchmarking metrics are applied and used to generate learning tables with varying number of attributes. A bunch of classifiers is used to identify the crucial input data attributes in the prediction of ST, MHS, SAS, and PP. The managerial relevance of the data-driven methodology for warehouse design is showcased for 3PL providers experiencing a fast rotation of the SKUs stored in their storage systems.

IoT Assisted Machine Learning Model for Warehouse Management

In the digital world of today, any enterprise that deals with the amounts of data in Warehouse Management Systems (WMS) are an important component. Furthermore, the amount of data being raisedand its complexity have become more challenging to maintain the WMS efficiency. Therefore, a device is required, which can manage such complexities autonomously with no human intervention. In this paper, Hybrid Machine Learning with the Internet of Things (HML-IoT) improves isolated doors. Furthermore, operating machine performance in the factory of hazardous goods. Decision-Making Algorithm (DMA) Data from the customer’s holding space’s dangerous goods warehouses shall be checked using separated doors. Thispaper’s significant aspect is that inventory and inventory operation’s organizational performance can be increased, further logistics costs minimized utilizing the fair use of isolated doors. Finally, the HML-IoT model integrated hazardous goods warehouse with isolated doors has been contrasted with the current one, demonstrating that the previous one has greater efficacy.

A POSSIBILIDADE DE UTILIZAÇÃO DO SISTEMA WMS PARA MELHORIA DA GESTÃO DE ARMAZENAGEM: O CASO DE UMA EMPRESA DO RAMO DE CONSTRUÇÃO DA ZONA DA MATA MINEIRA

A necessidade de expansão das atividades e o ganho de vantagem competitiva é uma realidade no atual cenário mercadológico. Nesse contexto, os processos internos da organização repercutem em seu desempenho no mercado. Assim, a preocupação com a gestão da armazenagem se mostra de grande relevância, uma vez que essa possui o poder de garantir o atendimento das novas demandas. O presente artigo trata-se de pesquisa aplicada, qualitativa, de caráter descritivo, com desenvolvimento de estudo de caso, tendo como propósito estudar e identificar os problemas que afetam o sistema de gestão de armazenagem e estoque do Centro de Distribuição da rede de lojas de uma empresa de revenda de materiais de construção. O objetivo é analisar a gestão de armazenagem a fim de diagnosticar problemas existentes, e indicar possíveis ganhos com adoção de um sistema Warehouse Management System (WMS). Através de entrevistas com colaboradores, supervisores dos setores, visitas ao local e relatórios com dados analíticos. Enquanto resultados, a identificação de diversos problemas envolvendo a Gestão da Armazenagem da organização, concluindo que a mesma não conta com um sistema informatizado para tal serviço e que seu estoque não possui endereçamento dos produtos, o que acarreta dificuldades para separação de pedidos, realização de inventário, e acaba por comprometer a acuracidade de estoque da mesma. Como forma de proposição de melhoria para a atual situação da armazenagem e melhor planejamento do estoque, foi realizada a classificação da Curva ABC, e, foi demonstrada as vantagens, sugerida a aquisição e implantação de sistema WMS, que se mostrou capaz de atenuar as principais problemática que atualmente acomete a organização.