Data-driven machine criticality assessment – maintenance decision support for increased productivity

Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldomupdated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity

Machine criticality assessment for productivity improvement: Smart maintenance decision support

Purpose\ua0The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity.Design/methodology/approach\ua0An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety.Findings\ua0The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization.Originality/value\ua0Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities

Real-Time data-driven average active period method for bottleneck detection

Prioritising improvement and maintenance activities is an important part of the production management and development process. Companies need to direct their efforts to the production constraints (bottlenecks) to achieve higher productivity. The first step is to identify the bottlenecks in the production system. A majority of the current bottleneck detection techniques can be classified into two categories, based on the methods used to develop the techniques: Analytical and simulation based. Analytical methods are difficult to use in more complex multi-stepped production systems, and simulation-based approaches are time-consuming and less flexible with regard to changes in the production system. This research paper introduces a real-Time, data-driven algorithm, which examines the average active period of the machines (the time when the machine is not waiting) to identify the bottlenecks based on real-Time shop floor data captured by Manufacturing Execution Systems (MES). The method utilises machine state information and the corresponding time stamps of those states as recorded by MES. The algorithm has been tested on a real-Time MES data set from a manufacturing company. The advantage of this algorithm is that it works for all kinds of production systems, including flow-oriented layouts and parallel-systems, and does not require a simulation model of the production system

Cyber-Physical Production Testbed: Literature Review and Concept Development

Many researchers use virtual and simulation-based testbed technology for research in production and maintenance optimization. Although, the virtual environment produces good results, it cannot imitate the unexpected changes that occur in actual production. There are very few physical testbeds emulating actual production environment. The aim of this paper is to present a concept of a cyber-physical production testbed based on review of Cyber-Physical Systems (CPS) testbeds in research. The testbed consists of a semi-automatic production line equipped with system monitoring tools, data analysis capabilities and commercial software. This testbed will be used for demonstration of data acquisition for production and maintenance prioritization. Additionally, the testbed will be used for research in IoT platforms for production optimization

Organisational Constraints in Data-driven Maintenance: a case study in the automotive industry

Technological development and innovations has been the focus of research in the field of smart maintenance, whereas there is less research regarding how maintenance organisations adapt the development. This case study focuses to understand what constraints maintenance organisations in the transition into applying more data-driven decisions in maintenance. This paper aims to emphasize the organisational challenges in data-driven maintenance, such as trustworthiness of data-driven decisions, data quality, management and competences. Through a case study at a global company in the automotive industry these challenges are highlighted and discussed through a questionnaire survey participated by 72 people and interviews with 7 people from the maintenance organisation

An algorithm for data-driven shifting bottleneck detection

Manufacturing companies continuously capture shop floor information using sensors technologies, Manufacturing Execution Systems (MES), Enterprise Resource Planning systems. The volumes of data collected by these technologies are growing and the pace of that growth is accelerating. Manufacturing data is constantly changing but immediately relevant. Collecting and analysing them on a real-time basis can lead to increased productivity. Particularly, prioritising improvement activities such as cycle time improvement, setup time reduction and maintenance activities on bottleneck machines is an important part of the operations management process on the shop floor to improve productivity. The first step in that process is the identification of bottlenecks. This paper introduces a purely data-driven shifting bottleneck detection algorithm to identify the bottlenecks from the real-time data of the machines as captured by MES. The developed algorithm detects the current bottleneck at any given time, the average and the non-bottlenecks over a time interval. The algorithm has been tested over real-world MES data sets of two manufacturing companies, identifying the potentials and the prerequisites of the data-driven method. The main prerequisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run

Data Analytics in Maintenance Planning – DAIMP

Manufacturing industry plays a vital role in the society, which is evident in current discussions on industrialization agendas. Digitalization, the Industrial Internet of Things and their connections to sustainable production are identified as key enablers for increasing the number of jobs in Swedish industry. To implement digitalized manufacturing achieving high maintenance performance becomes utmost necessity. A substantial increase in systems availability is crucial to enable the expected levels of automation and autonomy in future production. Maintenance organizations needs to go from experiences based decision making in maintenance planning to using fact based decision making using Big Data analysis and data-driven decision support. Currently, there is lack of maintenance-oriented research based on empirical data, which hinders the increased use of engineering methods within the area.The DAIMP project addresses the problem with insufficient availability and robustness in Swedish production systems. The main challenges include limited productivity, challenges in capability of introducing new products, and challenges in implement digital production. The DAIMP project connects data collection from a detailed machine level to system level analysis. DAIMP project aimed at reaching a system level analytics to detect critical equipment, differentiate maintenance planning and prioritize the most important equipment in real-time. Furthermore, maintenance organizations will also be supported in moving from descriptive statistics of historical data to predictive and prescriptive analytics.The main goals of the project are: Agreed data parameters and alarm structures for analyses and performance measures Increased back-office maintenance planning using predictive and prescriptive analysis Increased use of dynamic and data-driven criticality analysis Increased prioritization of maintenance activitiesThe goals were further divided into specific goals and six work packages were designed to execute the project.WP1 focused on the purchase phase and getting data structures and collaboration with equipment vendors correct from start.WP2 focused on the ramp-up phase of new products and production lines when predictive and prescriptive analytics are important to handle unknown disturbances.WP3 focused on the operational phase and to provide data-driven decision support for directing maintenance efforts to the critical equipment from a systems perspective.WP4 focused on designing maintenance packages for different equipment with inputs from WP3, including both reactive, preventive, and improving activities.WP5 focused on the evaluation and demonstration for different project resultsWP6 focused on coordination project managementIn WP1, models were developed to understand the missing element for the capability assessment from initiation of the machine tool procurement to the end of lifecycle. The information exchange and process of machine tool procurement from the end-users perspective was assessed. Additionally, the alarm structure is created using the capability framework and the ability model. In WP2, diagnostic, predictive and prescriptive algorithms were developed and validated. The algorithms were developed using manufacturing execution system (MES) data to provide system level decision making using data analytics. Improved quality of decisions by data-driven algorithms. Moved from experienced based decision to algorithmic based decisions. Identified the required amount data sets for developing machine learning algorithm. In WP3, data-driven machine criticality assessment framework was developed and validated. MES and computerised maintenance management system (CMMS) data were used to assess criticality of machines. It serves as data-driven decision support for maintenance planning and prioritization. It provided guidelines to achieve systems perspective in maintenance organization. In WP4, a component classification was developed. It provides guidelines for designing preventive maintenance programs based on the machine criticality. It uses CMMS data for component classification. In WP6, three demonstrator cases were performed at (i) Volvo Cars focusing on system level decision support at ramp up phase, (ii) Volvo GTO focusing on global standardization and (iii) a test-bed demo of data-driven criticality assessment at Chalmers. Lastly, as part of WP6, an international evaluation was conducted by inviting two visiting professors.The outcomes of the DAIMP project showed a strong contribution to research and manufacturing industry alike. Particularly, the project created a strong impact and awareness regarding the value maintenance possess in the manufacturing companies. It showed that maintenance will have a key role in enabling industrial digitalization. The project put the maintenance research back on the national agenda. For example, the project produced world-leading level in MES data analytics research; it showed how maintenance can contribute to productivity increase, thereby changing the mind-set from narrow-focused to having an enlarged-focus; showed how to work with component level problems to working with vendors and end-users

Data Analytics in Maintenance Planning – DAIMP

Manufacturing industry plays a vital role in the society, which is evident in current discussions on industrialization agendas. Digitalization, the Industrial Internet of Things and their connections to sustainable production are identified as key enablers for increasing the number of jobs in Swedish industry. To implement digitalized manufacturing achieving high maintenance performance becomes utmost necessity. A substantial increase in systems availability is crucial to enable the expected levels of automation and autonomy in future production. Maintenance organizations needs to go from experiences based decision making in maintenance planning to using fact based decision making using Big Data analysis and data-driven decision support. Currently, there is lack of maintenance-oriented research based on empirical data, which hinders the increased use of engineering methods within the area.The DAIMP project addresses the problem with insufficient availability and robustness in Swedish production systems. The main challenges include limited productivity, challenges in capability of introducing new products, and challenges in implement digital production. The DAIMP project connects data collection from a detailed machine level to system level analysis. DAIMP project aimed at reaching a system level analytics to detect critical equipment, differentiate maintenance planning and prioritize the most important equipment in real-time. Furthermore, maintenance organizations will also be supported in moving from descriptive statistics of historical data to predictive and prescriptive analytics.The main goals of the project are: Agreed data parameters and alarm structures for analyses and performance measures Increased back-office maintenance planning using predictive and prescriptive analysis Increased use of dynamic and data-driven criticality analysis Increased prioritization of maintenance activitiesThe goals were further divided into specific goals and six work packages were designed to execute the project.WP1 focused on the purchase phase and getting data structures and collaboration with equipment vendors correct from start.WP2 focused on the ramp-up phase of new products and production lines when predictive and prescriptive analytics are important to handle unknown disturbances.WP3 focused on the operational phase and to provide data-driven decision support for directing maintenance efforts to the critical equipment from a systems perspective.WP4 focused on designing maintenance packages for different equipment with inputs from WP3, including both reactive, preventive, and improving activities.WP5 focused on the evaluation and demonstration for different project resultsWP6 focused on coordination project managementIn WP1, models were developed to understand the missing element for the capability assessment from initiation of the machine tool procurement to the end of lifecycle. The information exchange and process of machine tool procurement from the end-users perspective was assessed. Additionally, the alarm structure is created using the capability framework and the ability model. In WP2, diagnostic, predictive and prescriptive algorithms were developed and validated. The algorithms were developed using manufacturing execution system (MES) data to provide system level decision making using data analytics. Improved quality of decisions by data-driven algorithms. Moved from experienced based decision to algorithmic based decisions. Identified the required amount data sets for developing machine learning algorithm. In WP3, data-driven machine criticality assessment framework was developed and validated. MES and computerised maintenance management system (CMMS) data were used to assess criticality of machines. It serves as data-driven decision support for maintenance planning and prioritization. It provided guidelines to achieve systems perspective in maintenance organization. In WP4, a component classification was developed. It provides guidelines for designing preventive maintenance programs based on the machine criticality. It uses CMMS data for component classification. In WP6, three demonstrator cases were performed at (i) Volvo Cars focusing on system level decision support at ramp up phase, (ii) Volvo GTO focusing on global standardization and (iii) a test-bed demo of data-driven criticality assessment at Chalmers. Lastly, as part of WP6, an international evaluation was conducted by inviting two visiting professors.The outcomes of the DAIMP project showed a strong contribution to research and manufacturing industry alike. Particularly, the project created a strong impact and awareness regarding the value maintenance possess in the manufacturing companies. It showed that maintenance will have a key role in enabling industrial digitalization. The project put the maintenance research back on the national agenda. For example, the project produced world-leading level in MES data analytics research; it showed how maintenance can contribute to productivity increase, thereby changing the mind-set from narrow-focused to having an enlarged-focus; showed how to work with component level problems to working with vendors and end-users

Data-driven algorithm for throughput bottleneck analysis of production systems

The digital transformation of manufacturing industries is expected to yield increased productivity. Companies collect large volumes of real-time machine data and are seeking new ways to use it in furthering data-driven decision making. A\ua0challenge for these companies is identifying throughput bottlenecks using the real-time machine data they collect. This paper proposes a data-driven algorithm to better identify bottleneck groups and provide diagnostic insights. The algorithm is based on the active period theory of throughput bottleneck analysis. It integrates available manufacturing execution systems (MES) data from the machines and tests the statistical significance of any bottlenecks detected. The algorithm can be automated to allow data-driven decision making on the shop floor, thus improving throughput. Real-world MES datasets were used to develop and test the algorithm, producing research outcomes useful to\ua0manufacturing industries. This research pushes standards in throughput bottleneck analysis, using an interdisciplinary approach based on production and data sciences