Warehouse System Research Articles

Enhancing clinical data warehousing with provenance data to support longitudinal analyses and large file management: The gitOmmix approach for genomic and image data.

Enhancing clinical data warehousing with provenance data to support longitudinal analyses and large file management: The gitOmmix approach for genomic and image data.

Design of Universal Platform Architecture for Complex Discrete Storage System

The flexible satellite batch production line is a complex discrete production system with multiple cross-disciplinary fields and mixed serial parallel tasks. As the source of the satellite batch production line process, the warehousing system has urgent needs such as uncertain production scale and rapid iteration and optimization of business processes. Therefore, the requirements and architecture of complex discrete warehousing systems such as flexible satellite batch production lines are studied. The physical system of intelligent equipment is abstracted as a digital model to form the underlying module, and a digital fusion framework of “business domain + middleware platform + intelligent equipment information model” is constructed. The granularity of microservice splitting is calculated based on the dynamic correlation relationship between user access instances and database table structures. The general warehousing functions of the platform are divided to achieve module customization, addition, and configuration. An open discrete warehousing system based on microservices is designed. Software architecture and design develop complex discrete warehousing systems based on the SpringCloud framework. This architecture achieves the decoupling of business logic and physical hardware, enhances the maintainability and scalability of the system, and greatly improves the system’s adaptability to different complex discrete warehousing business scenarios.

Bio‐Inspired Advanced Warehouse System for Data Handling and Management

Bio‐Inspired Advanced Warehouse System for Data Handling and Management

Rapid Data Ingestion through DB-OS Co-design

Sequential data access for the rapid ingestion of large fact tables from storage is a pivotal yet resource-intensive operation in data warehouse systems, consuming substantial CPU cycles across various components of DBMSs and operating systems. Although bypassing these layers can eliminate access latency, concurrent access to the same table often results in redundant data fetching due to cache-bypassing data transfers. Thus, a new design for data access control is necessary to enhance rapid data ingestion in databases. To address this concern, we propose a novel DB-OS co-design that efficiently supports sequential data access at full device speed. Our approach, zicIO, liberates DBMSs from data access control by preparing required data just before DBMSs access it, while alleviating all known I/O latencies. The core of zicIO lies in its DB-OS co-design, which aims to (1) automate data access control and (2) relieve redundant data fetching through seamless collaboration between the DB and the OS. We implemented zicIO and integrated it with four databases to demonstrate its general applicability. The evaluation showed performance enhancements of up to 9.95x under TPC-H loads.

Warehouse Optimization Strategies for Improved Supply Chain Efficiency

This study focuses on the improvement of supply chain efficiency by warehouse system optimization. Combined with the development trend of information technology, the application prospect of new technologies such as intelligent warehousing, automation equipment and warehouse management system (WMS) in warehouse optimization is discussed. Specific optimization strategies, including warehouse location optimization, partition storage, moving line planning and job process reengineering, are proposed to improve storage efficiency from multiple dimensions. In order to fully evaluate the effect of these optimization measures, the evaluation index system of this study includes key aspects such as reduced operating costs, improved inventory turnover, reduced order response time and improved customer satisfaction. Through these indicators, the study evaluated the positive role of warehouse optimization in improving overall operational efficiency. In addition, this study also focuses on the impact of warehousing optimization on all aspects of the supply chain, including the optimization effects of supplier collaboration, logistics distribution and information sharing. By improving the collaborative efficiency of these links, the optimized storage system can significantly improve the overall operational efficiency of the supply chain. The purpose of this study is to provide theoretical guidance with practical value for enterprises, help enterprises to enhance the competitiveness of supply chain in the fierce market competition, and maintain sustainable development advantages.

Research on Cross-border E-commerce Supply Chain Risk Management and Countermeasures -- Taking TikTok as an Example

With the rapid development of globalization and the Internet, cross-border e-commerce has become an important form of international trade, possessing significant growth potential. However, its supply chain is relatively complex and faces numerous risks, including delivery issues, policy changes, and warehouse management challenges. Although existing research has focused on the identification and management of supply chain risks, in-depth analyses of specific enterprises, such as TikTok Shop, remain limited. This study takes TikTok Shop as a case to explore risk management and response strategies within its cross-border e-commerce supply chain. Through literature review, SWOT analysis, and other methodologies, the main risks and their underlying causes encountered in TikTok Shops supply chain operations are systematically analyzed. The research finds that TikTok Shop primarily faces issues of unstable logistics, changes in international policies, and inadequate warehouse management. In response to these risks, this paper proposes strategies such as establishing corresponding risk response mechanisms and optimizing the warehouse system, aiming to enhance the stability and competitiveness of the supply chain. These findings provide strategic insights and references for cross-border e-commerce enterprises.

A strategic perspective on automated warehouse systems in retail: insights from a multiple case study

PurposeAs the warehouse is increasingly viewed as a strategic component, retailers’ willingness to invest in automated warehouse systems (AWS) has increased. These investment decisions are influenced by well-known operational factors, but strategic factors, which have received limited attention in warehousing literature, also play a pivotal role. Addressing this gap, this study investigates how strategic factors influence AWS investment decisions in retail.Design/methodology/approachBased on a theoretical foundation of technology adoption, strategic intent, and automation strategy, an abductive multiple case study is conducted with eight purposefully selected retailers that had implemented or were in the process of implementing a large AWS.FindingsThe study ranks 10 competitive priorities and 21 AWS evaluation aspects and shows how the firm’s strategic intent and the AWS investment decisions can be connected via the formulation of a warehouse automation strategy. The findings reveal the content for such a strategy – including 7 categories and 17 considerations – related to, for example, technology innovativeness, efficiency versus adaptiveness, technology-supplier relationships, control and ownership, and risk exposure. The study empirically shows how manager characteristics and owner strategies influence retailers’ AWS investment decisions. Four strategic intent profiles are abductively developed: reliability and delivery service; profitable deliveries; scalable logistics for volume growth; and platform building for logistics services. The study also provides evidence of a reciprocal relationship between strategic intent and AWS investment decisions.Research limitations/implicationsThe study is conducted with a limited number of Swedish retailers, indicating a need for additional studies to test the findings across different contexts.Practical implicationsThe study offers a framework for formulating a warehouse automation strategy. As a foundation for developing the framework, the study shares empirical insights from retailers in the forefront of AWS implementation.Originality/valueThe study contributes as a conversation changer by showing the importance of shifting from a tactical-operational focus to a strategic perspective on warehouse configuration in general and on AWS investment decisions in retail in particular.

Augmented reality technologies application in the warehouse system

Augmented reality technologies application in the warehouse system

PENERAPAN TEKNOLOGI BUSINESS INTELLIGENCE DALAM MENINGKATKAN STRATEGI PENJUALAN DENGAN METODE OLAP PADA CAFÉ LE KAHVE

This research aims to develop a data warehouse system and information dashboard based on Business Intelligence (BI) technology with the OLAP method to improve sales strategies at Le Kahve, a coffee shop company. The BI implementation uses Pentaho Data Integration for the ETL (Extract, Transform, Load) process and Microsoft Power BI for dashboard visualization. The ETL process is carried out to collect, process, and link sales data obtained from the Point of Sales (POS) system in Excel format. The data is then processed into a data warehouse using a star schema, which facilitates multidimensional analysis. Through the OLAP method, sales data is analyzed across various dimensions such as product, time, and payment method. The data visualization results in the form of a dashboard enable the company to quickly view sales performance and make more effective decisions. This dashboard provides information on best-selling products, product categories, and sales trends over time. The research results show that by implementing BI and OLAP, the company can improve operational efficiency, accelerate analysis, and support decision-making to enhance sales strategies and company competitiveness.

Determining The Conditions For The Efficient Use Of Resources In The Warehouse System

Abstract. Problem. For manufacturing enterprises, it is very important to reduce production and logistics costs, and one of the resource-intensive elements of this system is warehousing. Therefore, the need to establish conditions for the efficient use of material and warehouse resources is identified. Goal. The goal is to reduce the total cost of operating the built warehouse system by introducing efficient conditions for the use of resources. Methodology. A structural model of the warehouse system elements has been developed, which takes into account the main features of technological interaction of subsystems for processing incoming, internal and external flows of containers, packaging and finished products. To determine the conditions for efficient use of resources in the functioning of the warehouse system, it is proposed to use the evaluation parameter - the total cost of the warehouse system of a production enterprise. The parameters of external influence are the following intensities of flows determined by the warehouse process of a manufacturing enterprise: input, internal, and output. It is proposed to consider three options for the use of human and material resources. Originality. The article develops forecasting models of a degree type for three proposed three variants of use of material and human resources in functioning of warehouse systems of production enterprises. The obtained dependencies allow obtaining decisionmaking conditions depending on the intensity of incoming, internal and outgoing cargo flows. Practical value. Determining the conditions for using resources at the lowest possible total cost. The least costly option is to use two operators in each warehouse subsystem and one mechanism

Review of Application of AI in Amazon Warehouse Management

This paper outlines the research perspectives, objectives and methods to explore the ideas and applications of artificial intelligence in optimizing supply chain inbound management. By leveraging AI algorithms for demand forecasting, inventory optimization, and transportation planning, Amazon has improved efficiency, reduced costs, and increased customer satisfaction. The integration of robotic warehousing systems, automated guided vehicles, and shortest path algorithms has revolutionized warehouse operations, enabling faster order fulfillment and resource utilization. Despite challenges such as data security, technological uncertainty, and employee resistance, companies can address these issues through enhanced data protection measures, flexible technology architecture, and promoting collaboration between AI and employees. The future of AI in supply chain management will require continuous adaptation to technological advances, efficient path planning solutions for multiple robots, and cultivating harmonious relationships between AI systems and human workers. Embracing AI-driven innovation offers great potential for optimizing inventory processes, improving operational efficiency, and achieving sustainable growth in the digital age. Through a comprehensive literature review and case study analysis, it aims to provide valuable insights into the role of artificial intelligence in the transformation of supply chain operations.

Analisis Risiko Warehouse Management System Dalam Supply Chain Management Di PT. Magnesium Gosari Internasional

This research aims to analyze risk management in the Warehouse Management System (WMS) within Supply Chain Management at PT. Magnesium Gosari Internasional using the ISO 31000:2009 framework. The research methodology employs a qualitative approach through direct observation, in-depth interviews, and documentation studies. Data analysis was conducted by identifying, assessing, and evaluating risks based on their probability and impact levels. The research identified 21 risks classified into 5 main categories: Receiving, Putaway, Demand, Storage, and Shipment. Of the total identified risks, 7 risks fall into the low-risk category, 5 risks in the medium-risk category, and 9 risks in the high-risk category. This research provides risk treatment recommendations that can be implemented to enhance the effectiveness of risk management in the company's warehouse system.

Operating policies for robotic cellular warehousing systems

Operating policies for robotic cellular warehousing systems

Multidimensional Analysis of Booking Data in Hospitality Industry Using Data Warehousing Techniques

Understanding customer behaviors is essential for optimizing pricing strategies, enhancing guest experiences, and effectively meeting demand in the hospitality industry. This study presents the development of a data warehouse system designed to analyze hotel booking behaviors. Using ETL processes, reservation data from diverse sources is consolidated and standardized to enable comprehensive analysis. Then, multidimensional analyses of booking frequency and transaction value reveal key customer preferences and behavioral patterns. A real-world dataset comprising 119,390 records spanning from July 1, 2015, to August 31, 2017, was utilized to validate the system. Multidimensional analyses revealed that 70% of bookings occurred during peak seasons, with transaction values averaging 25% higher compared to off-peak periods. Additionally, customers who booked via direct channels displayed a 20% higher retention rate. The results validate the proposed system's capability to provide actionable intelligence, driving effective business strategies and supporting predictive modeling in the hospitality industry.

Optimizing Procurement Efficiency by Implementing K-Means and Random Forest in Kopegtel Samarinda’s Warehouse System

Procurement is a company’s activity to purchase goods or equipment needed in operations. In the management process, a procurement management system is often used to facilitate this management, such as at CV Indocitra Multi Artha, which uses the “Sistem Warehouse Kopegtel Samarinda.” The system provides significant assistance to the company, but large requests can be overwhelming to be handled by the manager and can cause an overload information problem. Research was conducted to deal with these problems by implementing a data mining algorithm as a procurement recommendation system. K-means and Random Forest algorithms were chosen as methods for the research. The algorithm is processed within two critical steps, first by K-Means to get cluster data and then by predicting it with Random Forest to get a recommendation for whether the object should be bought or not. Hyperparameter tuning was performed to optimize the model’s performance, yielding an F1-Score of 86.95%, representing the balance between precision and recall, and an ROC AUC value of 82.34%. These substantial metric outcomes indicate that the model can provide practical recommendations

Impact of encounters on patient app use: results of a tethered mobile personal health record usage pattern analysis.

The adoption of tethered mobile personal health records provides not only medical information to patients but also various convenience functions related to hospital use, thereby increasing accessibility to healthcare services and promoting patient engagement. We analyse the tethered mobile personal health records app's usage logs to determine how it can contribute to improving medical service accessibility and patient engagement. Log data, that comprised menu type, log time, and date, were collected from the mobile personal health records app of a tertiary referral hospital. Clinical information, including patients' demographics and visit type, was collected from the clinical research warehouse system. The usage log was analysed in terms of the type of visit, service function, and time period. Outpatients accounted for 34% of the total app usage and was the most app-accessed visit type. The most utilized menu functions were lab test and visit schedule for visits or non-visits. For Inpatient and Health check-ups, menu usage patterns showed a focus on lab test results. While investigations and other menu usage showed double peaks in the morning and afternoon, peak usage of lab test results correlated with inpatient blood sampling times, which was around 9 am. App menus to access health information, particularly blood tests, emerged as the most accessed menu. Hence, when compared with blood sampling times and hospital information system usage patterns, encounters occurring in the hospital majorly impacted patient app use. For improved patient engagement, improving lab test function should be the priority.

Research on the configuration of AS/RS warehouse system based on AGV transportation

Research on the configuration of AS/RS warehouse system based on AGV transportation

Optimizing Packing Operations with an Integrated Barcode Warehouse System

General background: Business competition in the globalization era demands companies enhance operational efficiency, particularly in warehouse management. Specific background: Packing operations significantly influence the supply chain's effectiveness, affecting goods availability, delivery speed, and customer satisfaction. However, challenges like manual errors, slow item identification, and lack of integration between warehouse management systems and modern technologies persist. Knowledge gap: Despite the increasing use of warehouse management systems, the potential of integrating barcode technology to optimize packing operations has not been fully explored. Aims: This study aims to examine the optimization potential of packing operations using an integrated barcode-based warehouse management system. Results: By employing the SDLC waterfall model for application development, the integrated system showed improvements in item identification efficiency, minimized packing errors, and enhanced inventory accuracy. Novelty: The proposed approach demonstrates how barcode technology can streamline warehouse management processes, which is not thoroughly documented in existing literature. Implications: These findings provide insights for companies to develop effective strategies and technologies to improve warehouse operations, ensuring a stronger position in the competitive market. Highlights: Barcode integration improves packing speed and accuracy. The system reduces manual errors in warehouse operations. Enhances inventory management for better supply chain performance. Keywords: Packing Optimization, Warehouse Management, Barcode Integration, Efficiency, Inventory Accuracy

Task allocation and path planning for multi-robot systems in intelligent warehousing

Faced with today's increasingly complex market demands, traditional manual warehouse systems are becoming inadequate, necessitating the urgent intelligent transformation and upgrading of warehouse systems. In this context, this paper aims to design a task allocation and path planning strategy for a multi-robot warehouse system to efficiently accomplish mixed single-robot and multi-robot types of warehouse tasks. The study proposes a warehouse task allocation strategy that incorporates traffic flow impact factors into the auction algorithm, optimizing task allocation by predicting robot density in various areas of the environment. For multi-robot formation tasks, a three-robot formation model based on the virtual structure method is designed. Additionally, a two-layer path planning strategy is proposed: the outer layer conducts global path planning based on the Floyd algorithm, while the inner layer resolves various collision issues through traffic rule constraints, achieving local optimal path planning. Simulation experiments conducted on the MATLAB platform show that the multi-robot system can flexibly handle mixed types of warehouse tasks, effectively reducing collision risks between robots and stagnation in dense areas, thereby improving the safety and efficiency of the multi-robot system. This study provides a reference for future research and practical applications of multi-robot systems.

Robotic warehouse systems considering dynamic priority

Robotic warehouse systems considering dynamic priority