# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 1 Introduction  
Factory logistics encompasses the planning, implementation, and control of the efficient flow and storage of raw materials, work-in-progress inventories, finished goods, and related information within a manufacturing facility. It plays a crucial role in optimizing production processes, reducing costs, and enhancing overall operational efficiency [1, 2]. This field has garnered significant attention in recent years due to its profound impact on operational efficiency, cost reduction, and overall competitiveness in the manufacturing sector [3]. The importance of effective factory logistics is multifaceted, contributing to streamlined production processes, reduced waste, improved quality control, and enhanced flexibility in responding to market demands. Recent trends in factory logistics reflect the broader shift towards Industry 4.0, with the increasing integration of smart technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning. These emerging technologies are revolutionizing factory logistics in several ways: IoT enables the real-time tracking and monitoring of inventories and equipment, enhancing visibility and control [4]. AI and machine learning algorithms optimize routing, predict maintenance needs, and improve demand forecasting accuracy [5]. In material handling, automated guided vehicles (AGVs) and robotic process automation (RPA) are increasingly deployed, significantly improving efficiency and reducing errors [6]. The adoption of automation technologies, including automated guided vehicles (AGVs) androbotic process automation (RPA), is revolutionizing material handling and inventory management [7]. Additionally, there is a growing emphasis on real-time visibility and data analytics, enabling more informed decision-making and predictive maintenance strategies. However, the implementation of advanced logistics systems is not without challenges, including substantial initial investments, complexity in management, space constraints within facilities, and the need for specialized skills [8; 9]. While Industry 4.0 offers significant opportunities for improving factory logistics, it also introduces potential trade-offs between different priorities that manufacturers must carefully balance. Tensions exist between cost reduction, quality control, and flexibility in responding to market demands [9]. For example, implementing advanced automation systems can reduce labor costs but may require substantial upfront investment and potentially reduce flexibility to adapt to changing product specifications. Real-time quality monitoring enabled by Industry 4.0 technologies can improve product quality but may impact production speeds [10]. Manufacturers must therefore strategically evaluate these trade-offs and determine the optimal balance for their specific context and priorities. As Industry 4.0 continues to evolve, finding ways to simultaneously optimize multiple performance dimensions remains an important area for further research and practical innovation in factory logistics [11]. Understanding these trade-offs is crucial for implementing effective automation strategies that improve overall performance while maintaining adaptability in the dynamic logistics landscape [12]. Recent research has identified gaps in performance metrics that manufacturers need to leverage for effective predictive maintenance strategies, with significant consequences for factory logistics. While many studies focus on data analytics and machine learning methods for predictive maintenance, there is a lack of standardized metrics to evaluate the impact on overall production efficiency. This gap makes it challenging for manufacturers to quantify the benefits of predictive maintenance implementations [13]. Additionally, current performance metrics often fail to capture the complex interplay between maintenance activities and logistics operations.