Efficiency measurement based on novel performance measures in total productive maintenance (TPM) using a fuzzy integrated COPRAS and DEA method

Total Productive Maintenance (TPM) has been widely recognized as a strategic tool and lean manufacturing practice for improving manufacturing performance and sustainability, and therefore it has been successfully implemented in many organizations. The evaluation of TPM efficiency can assist companies in improving their operations across a variety of dimensions. This paper aims to propose a comprehensive and systematic framework for the evaluation of TPM performance. The proposed total productive maintenance performance measurement system (TPM PMS) is divided into four phases (e.g., design, evaluate, implement, and review): i) the design of new performance measures, ii) the evaluation of the new performance measures, iii) the implementation of the new performance measures to evaluate TPM performance, and iv) the reviewing of the TPM PMS. In the design phase, different types of performance measures impacting TPM are defined and analyzed by decision-makers. In the evaluation phase, novel performance measures are evaluated using the Fuzzy COmplex Proportional Assessment (FCOPRAS) method. In the implementation phase, a modified fuzzy data envelopment analysis (FDEA) is used to determine efficient and inefficient TPM performance with novel performance measures. In the review phase, TPM performance is periodically monitored, and the proposed TPM PMS is reviewed for successful implementation of TPM. A real-world case study from an international manufacturing company operating in the automotive industry is presented to demonstrate the applicability of the proposed TPM PMS. The main findings from the real-world case study showed that the proposed TPM PMS allows measuring TPM performance with different indicators especially soft ones, e.g., human-related, and supports decision makers by comparing the TPM performances of production lines and so prioritizing the most important preventive/predictive decisions and actions according to production lines, especially the ineffective ones in TPM program implementation. Therefore, this system can be considered a powerful monitoring tool and reliable evidence to make the implementation process of TPM more efficient in the real-world production environment

An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study

Recent development in the predictive maintenance field has focused on incorporating artificial intelligence techniques in the monitoring and prognostics of machine health. The current predictive maintenance applications in manufacturing are now more dependent on data-driven Machine Learning algorithms requiring an intelligent and effective analysis of a large amount of historical and real-time data coming from multiple streams (sensors and computer systems) across multiple machines. Therefore, this article addresses issues of data pre-processing that have a significant impact on generalization performance of a Machine Learning algorithm. We present an intelligent approach using unsupervised Machine Learning techniques for data pre-processing and analysis in predictive maintenance to achieve qualified and structured data. We also demonstrate the applicability of the formulated approach by using an industrial case study in manufacturing. Data sets from the manufacturing industry are analyzed to identify data quality problems and detect interesting subsets for hidden information. With the approach formulated, it is possible to get the useful and diagnostic information in a systematic way about component/machine behavior as the basis for decision support and prognostic model development in predictive maintenance

Fuzzy multiattribute consumer choice among health insurance options

People buy insurance to protect themselves against possible financial loss in the future. Health insurance provides protection against the possibility of financial loss due to health care use. A selection among health insurance options is a multiattribute decision making problem including many conflicting criteria. This problem can be better solved using the fuzzy set theory since human decision making is generally based on vague and linguistic data. We propose an integrated methodology composed of fuzzy AHP and fuzzy TOPSIS to select the best health insurance option. The considered option types, Health Savings Account (HSA), Flexible Spending Accounts (FSA), and Health Reimbursement Arrangement (HRA) are evaluated using eight different criteria under fuzziness. A sensitivity analysis is also realized. First published online: 18 Jun 201

Fuzzy COPRAS method for performance measurement in total productive maintenance: a comparative analysis

Modern manufacturing firms should be supported by effective maintenance to become successful in their operations. One of the approaches for improving the performance of maintenance activities is to implement a total productive maintenance (TPM) strategy. Overall equipment effectiveness (OEE) is the key measure of TPM. According to the results of the literature review, the performance elements measured by the OEE tool are not sufficient to describe the effectiveness of TPM implementation. Hence, we aim at developing and evaluating new performance measures oriented towards the quantification of TPM implementation effectiveness under fuzzy environment. For the evaluation of each performance measure, at first, the nominal group technique has been used. Then to determine whether these performance measures are statistically significant, conjoint analysis based experimental design has been applied. In the second step, COmplex PRo-portional ASsessment of alternatives with Grey relations (COPRAS-G) and the fuzzy COPRAS method has been developed to evaluate these performance measures in TPM. Proposed fuzzy COPRAS method gives the reassuring results of ranking newly developed performance measures in TPM

A Data Scientific Approach Towards Predictive Maintenance Application in Manufacturing Industry

Most industries have recently started to harness the power of data to assess their performance and improve their production systems for future competitiveness and sustainability. Therefore, utilization of data for obtaining insights through data-driven approaches is invading every domain of industrial applications. Predictive maintenance (PdM) is one of the highest impacted industrial use cases in data-driven applications due to its ability to predict machine failures by implementing machine learning algorithms. This study aims to propose a systematic data scientific approach to provide valuable insights by analysing industrial alarm and event log data, which might further be used for investigation in root cause understanding and planning of necessary maintenance activities. To do that, a Cross-Industry Standard Process for Data Mining (CRISP-DM) is followed as a reference model in this study. The results are presented by first understanding the relationship between alarms and product types being processed in the selected machines by using exploratory data analysis (EDA). Along with this, the behavior of problematic alarms is identified. Afterward, a predictive analysis formulated as a multi-class classification problem is performed using various Machine Learning (ML) models to predict the category of alarm and generate rules to be used for further investigation in maintenance planning. The performance of the developed models is evaluated based on the different metrics and the decision tree model is selected with the higher accuracy score among them. As a theoretical contribution, this study presents an implementation of predictive modeling in a structured way, which uses a systematic data scientific approach based on industrial alarm and event log data. On the other hand, as a practical contribution, this study provides a set of decision rules that can act as decision support for further exploration of possible in-depth root causes through the other contextual data, and hence it gives an initial foundation towards PdM application in the case company

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

A prognostic algorithm to prescribe improvement measures on throughput bottlenecks

Throughput bottleneck analysis is important in prioritising production and maintenance measures in a production system. Due to system dynamics, bottlenecks shift between different production resources and across production runs. Therefore, it is important to predict where the bottlenecks will shift to and understand the root causes of predicted bottlenecks. Previous research efforts on bottlenecks are limited to only predicting the shifting location of throughput bottlenecks; they do not give any insights into root causes. Therefore, the aim of this paper is to propose a data-driven prognostic algorithm (using the active-period bottleneck analysis theory) to forecast the durations of individual active states of bottleneck machines from machine event-log data from the manufacturing execution system (MES). Forecasting the duration of active states helps explain the root causes of bottlenecks and enables the prescription of specific measures for them. It thus forms a machine-states-based prescriptive approach to bottleneck management. Data from real-world production systems is used to demonstrate the effectiveness of the proposed algorithm. The practical implications of these results are that shop-floor production and maintenance teams can be forewarned, before a production run, about bottleneck locations, root causes (in terms of machine states) and any prescribed measures, thus forming a prescriptive approach. This approach will enhance the understanding of bottleneck behaviour in production systems and allow data-driven decision making to manage bottlenecks proactively

Determining the impact of 5G-technology on manufacturing performance using a modified TOPSIS method

A digital transformation is currently taking place in society, where people and things are connected to each other and the Internet. The number of connected devices is projected to be 28 Billion in 2025, and our expectations on digitalization set new requirements of mobile communication technology. To handle the increased amount of connected devices and data generated, the next generation of mobile communication technology is under deployment: 5G-technology.The manufacturing industry follows the digital transformation, aiming for digitalized manufacturing with competitive and sustainable production systems.5G-technology meets the connectivity requirements in digitalized manufacturing, with low latency, high data rates, and high reliability. Despite these technological benefits, the question remains: Why should the manufacturing industry invest in 5G-technology? This study aims to determine the impact of 5G-technology on manufacturing performance; based on a mixed-methods approach including a modified TOPSIS method to ensure robustness of the results. The results show that 5 G-technology will mainly impact productivity, maintenance performance, and flexibility. By linking 5G-technology to the performance of the manufacturing system, instead of focusing on network performance, the benefits of using 5G-technology in manufacturing become clear, and can thus facilitate investment and deployment of 5G-technology in the manufacturing industry

Usability and Usefulness of Circularity Indicators for Manufacturing Performance Management

Advances in industrial digitalization present many opportunities for process and product data exploitation in manufacturing, unlocking new systemic measures of performance beyond a single machine, process, facility area and even beyond the factory gates. However, existing data models and manufacturing systems\u27 performance measures are still focused on productivity, quality and delivery time, which could potentially lead to an accelerated linear economy. To shift to more circular industrial systems, we need to identify and assess circularity opportunities in ways that align the goals of sustainable and industrial development. In this study, micro-level circular indicators were reviewed, selected, analysed and tested in a manufacturing company to evaluate their usability and usefulness to guide process improvements. The aim is to enable circular and eco-efficient solutions towards sustainable production systems. Usability and usefulness of the indicators are essential to their integration into established environmental and operations management systems. The main contribution of this study is in the identification of key features making circularity indicators usable and useful from a manufacturer\u27s perspective. The conclusion also suggests directions for further research on tools and methods to support circular manufacturing

Battery Production Systems: State of the Art and Future Developments

This paper discusses the state of the art in battery production research, focusing on high-importance topics to address industrial needs and sustainability goals in this rapidly growing field. We first present current research around three themes: human-centred production, smart production management, and sustainable manufacturing value chains. For each theme, key subtopics are explored to potentially transform battery value chains and shift to more sustainable production models. Such systemic transformations are supported by technological advances to enable superior manufacturing performance through: skills and competence development, improved production ergonomics and human factors, automation and human-robot collaboration, smart production planning and control, smart maintenance, data-driven solutions for production quality and its impact on battery performance (operational efficiency and durability), circular battery systems supported by service-based business models, more integrated and digitalized value chains, and increased industrial resilience. Each subtopic is discussed to suggest directions for further research to realise the full potential of digitalization for sustainable battery production