Agile Factor Flexible Industry 4.0 with MES Manufacturing Execution System Along with ERP Back-End Integration: Ready-Made Garments as a Case Study

Recently domestic manufacturing companies have been experiencing worsening profitability and stunted growth due to the long-term economic recession and the rapid rise of developing countries such as China and Southeast Asia. These difficulties force many companies to concentrate their core competencies on new value creation and innovation in order to gain momentum for new growth. Enterprise Resource Planning (ERP) has been considered as one of viable solutions. Among the various modules in ERP, shop floor control function in the production management module is rather limited. In order to overcome this problem, Manufacturing Execution System (MES) has been used as a subsystem which has a strong information gathering power and flexibility. Both systems interact closely with each other. In particular, ERP requires fast, accurate shop floor information at MES. This paper describes how to synchronize relevant information between ERP and MES with theory of constraints (TOC). The processing time information transmitted from the MES workplace is received at the ERP workplace. In the process, the received processing time is causing information distortion in ERP, when the information gathering standard of MES is different from the ERP information interpretation standard. The Drum-Buffer-Rope theory of TOC was applied to resolve this problem, therefore, information synchronization between both systems was made. As a precondition, the standard time of the upper ERP system was rearranged according to the capacity constraints resource. As a result, standard time restructuring has affected changes in labor costs. Standard labor costs have come close to actual ones, and information synchronization of MES transmission data has improved the reliability of standard product costs, such that it enabled various company-wide restructuring actions to be much more effective.

In this paper, the system integration of an Advanced Planning and Scheduling (APS) system with Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES) for a Semiconductor Backend Assembly environment is described. The company is one of the worldwide market leaders in semiconductor packaging technology. SIMTech was responsible for the implementation of the Planning & Scheduling functions through its APS application which is called Gintic Scheduling System (GSS). GSS system has been integrated with the other two key manufacturing software systems, namely ERP and MES, with the CIM framework. This paper emphasizes the system integration between the GSS and the customer's existing ERP and MES systems. The paper starts with a broader overview of the APS, ERP and MES systems with the focus on the linkage of APS with ERP and APS with MES. It then introduces the brief background information of the implementation and the integration methodology. Detailed work of the integration is laid out in the fond of integration model structure and illustrated by the system integration mechanism. Various data types that need to be integrated are also elaborated. Further discussions continue with the practical implementation issue like the required frequency of data integration and also the two approaches of data transfer. Finally the paper shares the experiences and lessons the team has learned through the implementation process and concludes the essence of the system integration work.

Production quality in terms of process efficiency and quality in the manufacturing industry must always be improved in order to maintain customer confidence. PT XYZ as a heavy equipment assembly company is one of the companies that depends on process reliability for a smooth production process. Based on this, the problems raised in this study focus on increasing the efficiency of the process of installing bolts on slew bearings. By using Overall Equipment Efficiency (OEE) and Process Capability (CpK) as the main benchmarks for measuring the quality of the production process where the latest data shows the average OEE value is at 18.53%, while the CpK value for the bolt tightening process is at 0 ,67. The OEE and CpK figures obtained show that the process quality is still not optimal and needs to be improved. The purpose of this research is to identify and prevent as many factors as possible that can lead to process failure. The methods used to evaluate processes and to identify where and how a process might fail are Failure Mode and Effect Analysis (FMEA) and Logic Tree Analysis (LTA). Both methods are used to identify and prevent as many factors as possible that can lead to a process failure. The results of research using the FMEA and LTA methods show that in the process of installing slew bearing bolts there is a process that needs to be improved because the RPN value is quite high, namely above 125. Some suggestions for improvement such as the use of a manipulator arm on a torque tool and the implementation of a manufacturing execution system (MES) can reduce the RPN value from above 125 to 28, where the process obtained is better than before.

With the further integration of the informatization and industrialization, it is a key factor for enterprise to develop them by enterprise information construction. At present, with the development of computer, enterprise informatization generally has formed three layers architecture, which contains: ERP (Enterprise Resource Planning), MES (Manufacturing Execution System) and PCS (Process Control System). MES plays an irreplaceable position in the enterprise informatization. However, different needs of the design for these systems. Then, they lead system not to share information, business process effectively and work together with others. Then finally lead to "Information Island". It has made people to pay more attention to the topic, which is how to implement the exchange of information real-time and accurately between ERP, PCS and MES. So, we analyze some major exchange ways of the present information between MES and other systems in this paper. Then, we propose a flexible and configurable EAI architecture-AiOintegration (All-in-One integration), which has integrated the current methods of exchanging between system information. And AiOintegration implemented information exchange of cross-language and cross-platform between with MES and other systems.

This paper investigates the deployment of Manufacturing Execution Systems (MES) within SAP environments in manufacturing enterprises. Through an extensive literature review, it examines the critical aspects of integrating MES with SAP Enterprise Resource Planning (ERP) systems, encompassing implementation strategies, benefits, challenges, and best practices. The study underscores the significance of real-time data integration between MES and SAP for enhanced decision-making and resource utilization. It further highlights the role of MES in facilitating lean manufacturing principles and the essential nature of change management in implementation projects. The findings emphasize the necessity of aligning MES implementation with overarching business objectives and maintaining continuous performance monitoring. Practical implications for manufacturers contemplating MES implementation in SAP environments are discussed, including the importance of strategic planning, robust data integration, employee training, and ongoing system optimization. The paper concludes by identifying areas for future research, such as the impact of emerging technologies on MES implementation. Keywords: Manufacturing Execution Systems (MES), SAP, Enterprise Resource Planning (ERP),Manufacturing enterprises, System integration, Real-time data, Lean manufacturing, Change management, Implementation strategies, Performance monitoring, Data integration, Employee training, System optimization, Emerging technologies

The manufacturing processes in a product are different according to the characteristics of the product and the information within the process is also varied. To implement the flexible MES, the Data Acquisition System (DAS) considering the characteristics of manufacturing system is needed. In this paper, we introduced the design of the equipment information acquisition system that can process the information of shop floor in real-time and adaptive to the changes of shop floor. The proposed system is the bridge between the shop floor and the Manufacturing Execution System (MES). It provides the MES the shop floor information between the work order and the results of work performance. The proposed system provides a data parser module to process equipment status in a flexible manner and a data mapper module to couple the equipment status to a manufacturing process and a SOA-based data integration module to link the processed shop floor information to upper system such as MES and ERP. The system is highly configurable system and easy to maintain even if new equipment or manufacturing process is inserted or re-arranged

Presented in the paper is MES (manufacturing execution system) architecture, which is suitable for managing FMS (flexible manufacturing system) lines under an ERP (enterprise planning system)environment. An IDEF0-model of an 'order handling' shop floor having an FMS line is developed to identify functional requirements of MES, and then a two-tier MES architecture satisfying the functional requirements is proposed. The proposed MES is composed of a Main-MES (for the main shop floor) and an FMS-MES (for the FMS line). A BOP (bill of processes) is used as a means to represent process plans, and a LS-Net (loading schedule network) is used as a mechanism for representing and manipulating loading schedules. Object models of BOP and LS-Net are presented, and the effectiveness of the proposed MES is demonstrated by applying it to two FMS lines, a stamping-die machining line and a mechanical part machining line.

The constant demand for food and beverages to sustain human life drives fierce competition among manufacturers, focusing on product excellence in terms of timeliness, quality, and pricing. The key to competitiveness depends in optimizing manufacturing processes by efficiently utilizing company resources. To ensure the overall optimization and reliable flow of manufacturing processes, a systematic evaluation process must be used, Overall Equipment Efficiency (OEE) stands out as a prominent performance measurement metric in manufacturing process efficiency. OEE serves as a valuable diagnostic tool, exposing areas for improvement and losses transparently. Accurate OEE measurement necessitates the implementation of an automated data collection system with minimum human dependencies, human intervention, and conducting on-the-fly calculations to informed the stakeholder/user. Data quality and accuracy in OEE measurement is very critical. Low quality and accuracy data could lead to false decision. OEE categorizes losses into six groups loss to pinpoint significant factors for potential improvement. Once OEE could be maintain at high level with high data accuracy and right improvement point, an optimum manufacturing process, and cost effective in manufacturing expenses will be achieve. Base on the result comparison for OEE result before and after the system implementation, positive improvement in OEE could reach 8.06%. This scenario be adopted by other company, and could become a model for 1st phase journey in company digital transformation.

This research work investigates optimization of the overall equipment efficiency (OEE) of loaders and rigid frame trucks in Southern Coastal Mine Stripping fleet of Namibia. The objectives of the research were achieved through direct observation and recording in a natural setting. The collected data were duration of various cycle loss times components, loader and truck cycle times. The primary source of data is a time motion study conducted over day shifts. The secondary source is existing data from the mine. The collected data were recorded in print and transferred to corresponding digital spread sheet format in the Microsoft Excel® package for more efficient calculation and analysis. The results of the analyses revealed that OEE estimated for truck operation is 63.12% while that of loader is 24.4%. The estimated availability for truck by OEE is 60.67% while that of loader is 43.30% against the bench mark of 90%, the estimated performance for truck by OEE is 94.58% while that of loader is 59.94% against the bench mark of 90% and the estimated quality for truck by OEE is 110% while that of loader is 94% against the bench mark of 95%. From an availability perspective, queuing and bunching of trucks should be minimized.

This paper describes the continuous application of simulation from system design to system operation including the integration of the simulation in a manufacturing execution system (MES). This is done using the example of a real-world manufacturing system, a complex flexible laser welding facility. The results of a simulation study during system design are presented and it is derived how these results strengthened the decision to implement a simulation-based MES. Some technical details of the connection between the Enterprise Resource Planning (ERP) system, the MES, and the Programmable Logic Control (PLC) are explained, as well as the usage of the MES and the simulation by the operations planners (dispatchers).

Enterprise Resource Planning (ERP) is an integrated system that has been used in several companies, including this Trading and Distribution Company. The company has implemented an ERP system at a certain time. The implementation of the ERP system not only broken off until the testing phase of the deployment process but also required some periodical analysis to improve the effectiveness. Lack of human resource skills and the business process of the company could be several major problems. Consequently, a specified analysis is needed n terms of improving the implementation of ERP in a company. The purpose of this research is to analyze the critical success factors (CSF) on ERP implementation in the company with the aim of identifying success factors that can be used to improve the performance system implementation, especially the human resource. Extended Technology Acceptance Model (TAM) was adopted to run the analysis. The research used quantitative data analysis in seeking core problems. Several variables defining CSF of ERP has built based on the TAM model. The result indicates that each variable in the model is a critical factor determining the success of the system's implementation. The result of the study could be exemplary guidance to find the CSF on ERP implementation.

This paper presents a novel design of a smart and efficient Manufacturing Execution System (MES) to acquire data from the shop floor in a factory. With increasing complexities in the production process, improving industrial productivity using production information systems is a challenge for all industries in the world. Manufacturing Execution Systems (MES), since their introduction in the factory environment, are gaining popularity due to their ability to control and monitor production process using integrated data. However, employers/managers are still confronting with many complications in understanding huge information on the production process and reacting quickly based on such information. In this paper, we propose the design of NIRMAN Factory Information System (FIS)-A Smart MES to enable the control and monitoring of multiple factors of the production process like assembly line, personnel, quality control, safety, etc. NIRMAN FIS, a MES based on Android Smartphone, presents data about factory environment and factory operations on the manager's smartphone screen and helps him to react and take important decisions quickly and flexibly. Index Terms: Sectional Efficiency, Factory Efficiency, Assembly Line, Threshold Limit, Productivity, Morale, Factory Environment, Data Acquisition, Analysis, Automatic and Manual Control, Bluetooth, Arduino, Android, Remote Monitoring.

Within the framework of Industry 4.0, the transition from stand-alone systems to an interconnected digital enterprise plays a crucial role. Increasing demands for flexibility, quality, and shorter product development cycles drive the adoption of advanced digital solutions such as product data management (PDM) systems, manufacturing execution systems (MES), advanced planning and scheduling (APS) tools, and the digital twin (DT) concept. At the same time, digitalisation trends (e.g., Smart Factory) reveal critical weaknesses of manufacturing companies, with insufficient planning and scheduling capabilities being one of the most common issues. While medium- and long-term planning is typically handled within ERP systems, operational scheduling is often still managed through inflexible spreadsheet-based solutions. APS systems are therefore gaining importance as effective tools with relatively fast return on investment, although their deployment requires alignment between ERP and MES layers as well as a thorough understanding of business and production processes. The combined implementation of PDM, APS, MES, and DT enhances decision-making, enables rapid adaptability to change, and supports the development of learning organisations. The article focuses on key principles and technologies of digital transformation with emphasis on data integration using selected tools.

Enterprise Resource Planning (ERP) systems offer various functionalities to support an organization’s core functions. However, many anticipated benefits often need to materialize due to business context changes and users’ high expectations. Continuous adaptation and improvement are necessary to address user disappointments. This research focuses on ERP systems, exploring key factors influencing the success of their implementation. Recognizing challenges in ERP system implementation, this study provides a comprehensive literature review, identifying essential and contemporary Critical Success Factors (CSFs) influenced by technological advancements. Addressing challenges specific to the wood industry, this research introduces additional industry-adapted CSFs, including industry adaptability, integration with production machinery, effective warehouse management, and supply chain tracking. Furthermore, this paper emphasizes the need for continuous adaptation and improvement of ERP systems, especially in light of current trends and technological achievements. This study recommends a holistic approach, considering traditional or essential CSFs while adapting to new trends. Critical success factors in ERP implementation in the next decade involve considering cloud technology, artificial intelligence and machine learning, data security, mobile access, IoT integration, user experience, and training. The main objective of this paper is to identify the latest CSFs in ERP implementation. This research highlights essential success factors in ERP implementation, and contemporary trends in ERP implementation with a particular focus on the specifics of wood industry. While organizations should aim to maximize the potential of ERP systems, they should also acknowledge the crucial role played by human intervention in the effective and responsible implementation of artificial intelligence.

Overall equipment efficiency (OEE) has been proposed as a comprehensive index for individual equipment performance. However, there are limitations for evaluating only OEE of single machine in semiconductor manufacturing. This study aims to propose overall tool group efficiency (OGE) indices to observe the equipment performance at the tool group level. In addition, rather than evaluating OEE for a fixed period of observation time, we also proposed a mechanism with statistical efficiency control charts to continuously monitor OGE over time. This study constructed a framework for monitoring equipment performances from critical tool groups to single machines via longitudinal analysis. Then, root-cause analysis of machine statuses is employed to identify possible performance detractors. Following the proposed framework, improvement actions can be thus triggered on those critical machine statuses to promote tool group productivity. We used a case study based on real data from a fab in Taiwan for illustration and concluded this study with discussion of future research directions.