Manufacturing Execution System Research Articles

Potential Integration Of Machine Learning Algorithm And Manufacturing Execution System In The Lean Six Sigma Method To Improve Operational Excellence At ABC Farma Company

This research focuses on the implementation of Operational Excellence (OE) in the pharmaceutical industry, especially in companies that transformed from PT Askes (Persero) to the Health Insurance Organizing Agency (BPJS). One approach adopted is Lean Six Sigma (LSS) which is supported by the Manufacturing Execution System (MES) to improve the efficiency and quality of the production process. This research takes a case study on product A with a focus on improvements to hardness issues. Through the Define, Measure, Analyze, Improve, and Control (DMAIC) stages, traditional methods and advanced analysis based on Machine Learning (ML) algorithms are used to improve production processes. The results showed success in achieving significant improvements in the productivity index and quality of product A production processes, making a valuable contribution in the context of Operational Excellence research in the pharmaceutical industry. The LSS method, which has been modified by integrating with ML algorithms and MES, provides productivity aspects consisting of LT, PCE and TD, as well as quality aspects consisting of better Pp, Ppk and sigma levels. The modified LSS method also provides the potential for COGM savings of 2,66 billion Rupiah and reduces the risk of production process failure to less than 200 batches for every 1 million production batches. Based on this, it can be concluded that the development of the Lean Six Sigma method which has been integrated with ML and MES algorithms has the positive potential to increase OE at ABC Farma.

Edge architecture for the automation and control of flexible manufacturing lines

The automation and control of industrial machines and processes is a task traditionally performed by PLCs and SCADA systems. However, with the advent of Industry 4.0 paradigm, which merges OT and IT domains, this task can also be performed inside the IT domain. This paper proposes an edge architecture where a flexible manufacturing line is led by a state machine programmed and running on the IT domain inside the Manufacturing Execution System. Each industrial asset has been modelled as a black-box, where security and hard real-time functionalities are programmed and controlled inside each black-box by traditional OT tools. Functionalities and variables required to synchronize each black-box, have been modelled as inputs and outputs. These input and output variables and methods are available using Industry 4.0 industrial communication protocols, such as OPC UA or MQTT. Then, a state machine programmed on a high-level IT language model inside the MES, automates the manufacturing line reacting to the inputs and outputs of each black-box. This architecture has been successfully validated automating a manufacturing line composed by a conveyor, a robot and a vision based quality control system.

Design and implementation of a machine log for PBF-LB/M on basis of IoT communication architectures and an ETL pipeline

Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M) has gained more industrial relevance and already demonstrated applications at a small series scale. However, its widespread adoption in various use cases faces challenges due to the absence of interfaces to established Manufacturing Execution Systems (MES) that support customers in the predominantly data-driven quality assurance. Current state-of-the-art PBF-LB/M machines utilize communication architectures, such as OPC Unified Architecture (OPC UA), Message Queuing Telemetry Transport (MQTT) and Representational State Transfer Application Programming Interface (REST API). In the context of the Reference Architecture Model Industry 4.0 (RAMI 4.0) and the Internet of Things (IoT), the assets, particularly the physical PBF-LB/M machines, already have an integration layer implemented to communicate data such as process states or sensor values. Missing is an MES component acting as a communication and information layer. To address this gap, the proposed Extract Transform Load (ETL) pipeline aims to extract relevant data from the fabrication of each build cycle down to the level of scan vectors and additionally to register process signals. The suggested data schema for archiving each build cycle adheres to all terms defined by ISO/TC 261—Additive Manufacturing (AM). In relation to the measurement frequency, all data are reorganized into entities, such as the AM machine, build cycle, part, layer, and scan vector. These scan vectors are stored in a runtime-independent format, including all metadata, to be valid and traceable. The resulting machine log represents a comprehensive documentation of each build cycle, enabling data-driven quality assurance at process level.

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber–Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation.

Critical Role of Manufacturing Execution Systems in Digital Transformation of Manufacturing Industry

The manufacturing industry is undergoing a significant digital transformation, propelled by advancements in technology. Central to this evolution is the implementation of Manufacturing Execution Systems (MES), which act as a vital link between the shop floor and enterprise-level systems. This paper explores the critical role of MES in the digital transformation of the manufacturing industry, focusing on how it enhances operational efficiency, improves product quality, and enables real-time decision-making. Manufacturing Execution Systems are software platforms designed to monitor, control, and optimize production processes from the initiation of orders to the delivery of finished goods. MES provides real-time data on production activities, enabling manufacturers to make informed decisions, reduce production costs, and increase overall productivity. The integration of MES with advanced technologies such as the Internet of Things (IoT), Big Data analytics, and cloud computing has further amplified its impact, leading to smarter and more agile manufacturing processes. One of the key benefits of MES is its ability to improve operational efficiency. MES offers real-time visibility into the production process, allowing manufacturers to identify and resolve bottlenecks, minimize downtime, and optimize resource utilization. Predictive maintenance capabilities, enabled by IoT integration, prevent unexpected equipment failures and reduce maintenance costs. MES also plays a crucial role in enhancing product quality. By monitoring each stage of production, MES ensures adherence to quality standards and facilitates traceability. In the event of a quality issue, MES allows for quick identification of the root cause and implementation of corrective actions. Real-time quality checks at critical points in the production process help detect and address defects promptly. Moreover, MES supports real-time decision-making by providing up-to-date data that allows production managers to respond swiftly to changes in demand or production conditions. This agility is essential in today's fast-paced manufacturing environment. In conclusion, Manufacturing Execution Systems are indispensable in the digital transformation of the manufacturing industry. By improving efficiency, product quality, and decision-making capabilities, MES equips manufacturers to succeed in the era of Industry 4.0. This paper highlights the benefits and challenges of MES implementation, offering valuable insights for manufacturers aiming to leverage MES in their digital transformation efforts

Implementation of Manufacturing Execution System (MES) to Increase Productivity and Competitiveness in the Manufacturing Sector

Abstract: Industry 4.0's underlying digital technologies are already essential in helping manufacturing businesses, especially small and medium-sized enterprises (SMEs), to adapt to the dynamics of the new normal. Manufacturing execution system (MES) serves as the factory information hub and is made possible by internet of things (IoT) ready devices that generate data and cyber physical systems that control the interconnection between equipment and computational assets for industries to achieve sustained competitiveness and address post- pandemic market volatility by utilizing real-time information. This paper focuses on the implementation for MES with the help of three case studies. Three case studies were used to empirically test the theories put out on MES to provide an overview of "How MES is efficient in loss reduction” through findings from cross-case synthesis of the data that was gathered.

Uncertainty Quantification of Data-driven Quality Prediction Model For Realizing the Active Sampling Inspection of Mechanical Properties in Steel Production

Pre-production quality defect inspection is a crucial step in industrial manufacturing, and many traditional inspection strategies suffer from inefficiency issues. This is especially true for tasks such as mechanical performance testing of steel products, which involve time-consuming processes like offline sampling, specimen preparation, and testing. The inspection volume significantly impacts the production cycle, inventory, yield, and labor costs. Constructing a data-driven model for predicting product quality and implementing proactive sampling inspection based on the prediction results is an appealing solution. However, the prediction uncertainty of data-driven models poses a challenging problem that needs to be addressed. This paper proposes an active quality inspection approach for steel products based on the uncertainty quantification in the predictive model for mechanical performance. The objective is to reduce both the sampling frequency and the omission rate on the production site. First, an ensemble model based on improved lower and upper bound estimation is established for interval prediction of mechanical performance. The uncertainty of the specific value prediction model is quantitatively estimated using interval probability distributions. Then, a predictive model for the mechanical performance failure probability is built based on the prediction interval size and probability distribution. By determining an appropriate probability threshold, the trade-off between prediction accuracy and defect detection accuracy (recall rate) is balanced, enabling the establishment of an active sampling strategy. Finally, this functionality is integrated into the manufacturing execution system of a steel factory, realizing a mechanical performance inspection approach based on proactive sampling. The proposed approach is validated using real production datasets. When the probability threshold is set to 30%, the prediction accuracy and recall rate for failure mechanical performance samples are 75% and 100%, respectively. Meanwhile, the sampling rate is only 5.33%, while controlling the risk of omission. This represents a 50% reduction in sampling rate compared to the inspection rules commonly used in actual production. The overall efficiency of product quality inspection is improved, and inspection costs are reduced.

A data-driven approach toward a machine- and system-level performance monitoring digital twin for production lines

Efficient performance monitoring in production systems holds paramount importance as it enables organizations to optimize their manufacturing processes, enhance productivity, and maintain a competitive edge in the market. Typically, machine and system level performance monitoring systems are investigated independently, whereas an integrated approach that considers both levels can offer valuable insights and benefits. This paper introduces a data-driven approach for evaluating and improving the performance of production lines by monitoring the performance of both individual machines and their interactions as a system. The approach begins with a rigorous methodology for classifying machine states recorded by the Manufacturing Execution System (MES) into finer-grained substates, enabling a comprehensive analysis of machine cycle time variability. Subsequently, these substates are leveraged as a foundation for constructing performance monitoring models at both the machine and system levels, employing probabilistic automata for the machine level and logistic regression for the system level. The system-level performance monitoring model is constructed to predict a Flow metric that enables the prediction of abnormal behaviors and deviations from production targets. This data-driven approach serves as a foundational ingredient of a system-level digital twin, designed to provide production lines with insights that enable proactive implementation of measures aimed at optimizing overall manufacturing efficiency. Through an industrial test case from the automotive industry, the results demonstrate the capability of performance monitoring, capturing errors within confidence intervals, and establishing predictive cause-and-effect relationships between machines within the production system.

Making Cognitive Ergonomics in the Human–Computer Interaction of Manufacturing Execution Systems Assessable: Experimental and Validation Approaches to Closing Research Gaps

Cognitive ergonomics and the mental health of production workers have attracted increasing interest in industrial companies. However, there is still not much research available as it is regarding physical ergonomics and muscular load. This paper designs an experiment to analyze the cognitive ergonomics and mental stress of shop floor production workers interacting with different user interfaces of a Manufacturing Execution System (MES) that is adjustable for analyzing the influence of other assistive systems, too. This approach is going to be designed with the Design of Experiments (DoE) method. Therefore, the respective goals and factors are going to be determined. The environment will be the laboratories of the University of Applied Sciences Amberg-Weiden and its Campus for Digitalization in Amberg. In detail, there will be a sample assembly process from the automotive supplier industry for demonstration purposes. At this laboratory, the MES software from the European benchmark SAP is installed, and the respective standard Production Operator Desk is going to be used with slight adaptions. In order to make the cognitive ergonomics measurable, different approaches are going to be used. For instance, body temperature, heart rate and skin conductance as well as subjective methods of self-assessment are planned. The result of this paper is a ready-to-run experiment with sample data for each classification of participants. Further, possible limitations and adjustments are going to be discussed. Finally, an approach to validating the expected results is going to be shown and future intentions are going to be discussed.

An Interpretable Time Series Forecasting Model for Predicting NOx Emission Concentration in Ferroalloy Electric Arc Furnace Plants

Considering the pivotal role of ferroalloys in the steel industry and the escalating global emphasis on sustainability (e.g., zero emissions and carbon neutrality), the demand for ferroalloys is anticipated to increase. However, the electric arc furnace (EAF) of ferroalloy plants generates substantial amounts of nitrogen oxides (NOx) because of the high-temperature combustion processes. Despite the substantial contributions of many studies on NOx prediction from various industrial facilities, there is a lack of studies considering the environmental condition of the EAF in ferroalloy plants. Therefore, this study presents a deep learning model for predicting NOx emissions from ferroalloy plants and further can provide guidelines for predicting NOx in industrial sites equipped with electric furnaces. In this study, we collected various historical data from the manufacturing execution system of electric furnaces and exhaust gas systems to develop a prediction model. Additionally, an interpretable artificial intelligence method was employed to track the effects of each variable on the NOx emissions. The proposed prediction model can provide decision support to reduce NOx emissions. Furthermore, the interpretation of the model contributes to a better understanding of the factors influencing NOx emissions and the development of effective strategies for emission reduction in ferroalloys EAF plants.

A multi-MLP prediction for inventory management in manufacturing execution system

Artificial intelligence (AI) positively remodels industrial processes, notably inventory management (IM), from planning, scheduling, and optimization to logistics. Intelligent technologies such as AI have enabled innovative processes in the production line of manufacturing execution systems (MES), particularly in predicting IM. This study proposes a Multi-MLP model with LightGBM feature selection technique for MES IM prediction to enable high prediction accuracy, minimal computation cost, low prediction error, and minimum time cost. The proposed model is evaluated using publicly available Product Backorder datasets to prove its reliability. Investigating varying feature selection techniques results in identifying appropriate data features relevant to building an AI-based solution for the IM prediction in MES. The experiment results demonstrate efficient decision-making of the proposed system with a low error prediction MAE of 0.2331, MSE of 0.1225, and RMSE of 0.3504.

Machine Learning used in the field of Pharmacy

The application of intelligence in technology is expanding to include machine-prevalent methods. It could reduce expenses and save time, all the while additionally enhancing our comprehension of how different formulations and process parameters interact. Artificial intelligence, which falls under the realm of computer science is concentrated on problem-solving through programming. It has evolved into a science of problem solving with applications in industries like technology, medicine and more. The research paper covers a range of topics such as discovering peptides from sources, managing and treating rare diseases ensuring proper drug adherence and dosage as well as discussing barriers, to implementing AI in the pharmaceutical industry. It also touches upon automated control procedures, manufacturing execution systems and using AI for treatment predictions.

Integrating Ideological and Political Education in Manufacturing Execution System (MES) Curriculum: Design, Implementation, and Practical Pathways

"Manufacturing Execution System (MES)" is an important professional course in the course system of automation specialty. According to the actual teaching situation, this paper will analyze the teaching design and implementation of ideological and political education in the course of "Manufacturing Execution System (MES)", deeply excavate the teaching elements of ideological and political education, develop new teaching objectives and teaching contents, and explore the practical path of ideological and political education in professional courses.

Methodology for Implementing a Manufacturing Execution System in the Machinery and Equipment Industry

In today's volatile, uncertain, and complex business environments, manufacturing companies must not only adapt to market demands but also minimize the time between problem occurrence and resolution. The implementation of lean manufacturing systems has been crucial in this regard. However, traditional approaches have shown notable inefficiencies that can be effectively addressed through digitalization. By embracing digital solutions, manufacturing companies can ensure efficient continuous improvement, driving performance to higher levels. This study aims to find a digital solution for a specific company that faces daily challenges associated with low visibility into production. An investigation revealed that the Lean tools used by the company were outdated, directly affecting the generated information and consequently, decision-making. The integration of a Manufacturing Execution System into the factory's dynamics was the solution found. In this context, a step-by-step methodology is proposed to guide the implementation. As a result, a prototype of the system was designed. The validation of the system by end-users demonstrates the success of the proposed methodology.

An IoT-Enabled Real-Time Dynamic Scheduler for Flexible Job Shop Scheduling (FJSS) in an Industry 4.0-Based Manufacturing Execution System (MES 4.0)

An IoT-Enabled Real-Time Dynamic Scheduler for Flexible Job Shop Scheduling (FJSS) in an Industry 4.0-Based Manufacturing Execution System (MES 4.0)

Artificial Intelligence Manufacturing Execution System (MES) Unit Control in Automation Application Fusion Industry and Education Platform Design Innovation Exploration

Artificial Intelligence Manufacturing Execution System (MES) Unit Control in Automation Application Fusion Industry and Education Platform Design Innovation Exploration

Enhancing Energy Efficiency and Real-time Monitoring in Industrial Environments through an Integrated Software Solution: NIGHTWATCH

Globally, the demand for energy continues to escalate due to factors such as heating, lighting, transportation, and fuel supply for various devices. In a context where energy has garnered such paramount importance, achieving energy efficiency has necessitated a comprehensive approach encompassing activities related to energy generation, transmission, and consumption. Any positive or negative developments in the realm of energy significantly impact both human and environmental factors, which constitute the focal points of sustainable development. The presented study, operates within industrial settings and interfaces with energy analyzers, collecting and analyzing data such as energy consumption, instantaneous current, and voltage. This initiative seeks to provide real-time visibility into energy-related operations on factory premises. Additionally, the software's capabilities extend to retrospective data analysis, enabling informed insights for future extrapolations. Furthermore, the integration of energy consumption data from the analyzers into the Manufacturing Execution System (MES) facilitates energy tracking on a per-job basis. The software's dashboard component empowers users to establish customized threshold values for monitored energy parameters. When these thresholds are exceeded or when values deviate from expected levels, the software triggers alerts and notifications via email and other communication channels, ensuring timely dissemination of pertinent information. Consequently, the software allows businesses to evaluate unit costs associated with specific job orders. Additionally, the study aims to establish a correlation between energy consumption data at the job-order level and machine-level energy consumption. This correlation could foster the augmentation of efficient energy utilization, thereby enhancing competitiveness and overall efficacy.

Time Series Manufacturing Data Edge Monitoring and Visualization to Support Industrial Maintenance Teams

Traditional manufacturing control systems such as Manufacturing Execution Systems (MES) or SCADA (Supervisory Control And Data Acquisition) were not designed for Industry 4.0 paradigm. Industry 4.0 implies that more data variables must be automatically monitored and data must be captured at a higher frequency: from one value of a few key variables to values of several variables captured at frequencies of seconds. Thus, new architectures and tools are required to merge Information Technology (IT) and Operation Technology (OT) fields and to meet Industry 4.0 requirements. This paper proposes a lightweight architecture based on micro-services and time series data requirements to connect to manufacturing process controllers, and to capture, store, monitor and visualize relevant data about the process. Moreover, a reference implementation based on Open Source tools is presented. This implementation has been validated by members of the maintenance team of a factory from Lecta, a paper manufacturer. The implementation has proven to be a new valuable tool providing further insights and customized alarms of the manufacturing process.

Effectiveness Analysis of Hydraulic Torque Wrench Machine Using Failure Mode and Effect Analysis (FMEA) and Logic Tree Analysis Study Case on Heavy Equipment Manufacturing

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.

Digital transformation for improved energy and product efficiency in tire production: a case study

The concept of digital transformation has become increasingly significant with advancements in technology. In this study, digital transformation design and implementation were performed in a tire production plant to improve the product and energy efficiency for sustainable production. Initially, production data were obtained by implementing Shop Floor Control infrastructure. In the second stage, a Manufacturing Execution System (MES) was placed on the upper layer and connected to Enterprise Resource Planning (ERP) software on the top layer. Cloud database technology was used for the MES and ERP layers, with servers located outside the plant and a side-to-side VPN providing a plant network connection. The automation architecture was designed for the end-to-end monitoring of the entire production site and was applied to the Banbury Unit. The performance data of the system were compared in terms of energy efficiency, product efficiency, and overall efficiency. The Banbury unit was used 15% more efficiently, recovering 2.4 MWh of energy daily with the proposed system. Additionally, the scrap product rate decreased by 5%, resulting in an annual cost recovery of $39,500.