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1.	Savolainen, P., et al. (författare) Organisational Constraints in Data-driven Maintenance: a case study in the automotive industry 2020 Ingår i: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963 .- 2405-8963. ; 53:3, s. 95-100 Konferensbidrag (refereegranskat)abstract 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.
2.	Chen, Siyuan, 1997, et al. (författare) Understanding Stakeholder Requirements for Digital Twins In Manufacturing Maintenance 2023 Ingår i: Proceedings - Winter Simulation Conference. - 0891-7736. ; , s. 2008-2019 Konferensbidrag (refereegranskat)abstract Digital twin has emerged as a key technology in the era of smart manufacturing and holds significant potential for maintenance. However, gaps remain in understanding stakeholders’ requirements and how this technology support maintenance-related decisions. This paper aims to identify stakeholders’ requirements for digital twin implementation and examine the role of digital twin in supporting maintenance actions and decision-making process. Semi-structured interviews and a workshop involving manufacturing practitioners and researchers were conducted to attain these goals. Furthermore, an in-depth qualitative analysis of the interview data was carried out. The results shed light on the current state of digital twin adoption, implementation challenges, requirements, supported decisions and actions, and future demand characteristics. By integrating the findings from the literature review and interview analysis, this study outlines the requirements for the digital twins as expressed by industry stakeholders that will be used and tested in the drone factory digital twin model.
3.	Despeisse, Mélanie, 1985, et al. (författare) Battery Production Systems: State of the Art and Future Developments 2023 Ingår i: IFIP Advances in Information and Communication Technology. - 1868-4238 .- 1868-422X. ; 692, s. 521-535 Konferensbidrag (refereegranskat)abstract 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.
4.	Despeisse, Mélanie, 1985, et al. (författare) Challenges in Data Life Cycle Management for Sustainable Cyber-Physical Production Systems 2020 Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer International Publishing. - 1868-4238 .- 1868-422X. ; 592 IFIP, s. 57-65 Konferensbidrag (refereegranskat)abstract Rapid technological advances present new opportunities to use industrial Big Data to monitor and improve performance more systematically and more holistically. The on-going fourth industrial revolution, aka Industrie 4.0, holds the promise to support the implementation of sustainability principles in manufacturing. However, much of these opportunities are missed as social and environmental performance are still largely considered as an afterthought or add-on to business as usual. This paper reviews existing data life cycle models and discusses their usefulness for sustainable manufacturing performance management. Finally, we suggest possible directions for further research to promote more sustainable cyber-physical production systems.
5.	Despeisse, Mélanie, 1985, et al. (författare) Developing Data Models for Smart Environmental Performance Management in Production 2023 Ingår i: IFIP Advances in Information and Communication Technology. - 1868-4238 .- 1868-422X. ; 692 AICT, s. 3-15 Konferensbidrag (refereegranskat)abstract For manufacturing companies to prosper in the long term, they must demonstrate contribution to sustainable development by implementing greener practices using approaches such eco-efficiency and circular economy; i.e., creating social and economic value while minimising the environmental impact of production through efficient, closed-loop circulation of resources. In addition, industrial digitalization presents new opportunities to unlock new ways to measure complex systems’ performance and systematically improve towards circular economy and sustainability. This paper presents the results of a feasibility study aiming to develop a practical toolkit to implement environmental sustainability concepts at factory level. To achieve the project objective, we focused on data handling practices for environmental performance management, including process mapping, data inventory, data quality assessment, and gap analysis to identify existing strengths and define areas of improvement to boost the environmental performance of production systems.
6.	Duan, Xinjie, et al. (författare) A Data Scientific Approach Towards Predictive Maintenance Application in Manufacturing Industry 2022 Ingår i: Advances in Transdisciplinary Engineering. ; 21, s. 292-303 Konferensbidrag (refereegranskat)abstract 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.
7.	Joseph, Doyel, et al. (författare) A Predictive Maintenance Application for A Robot Cell using LSTM Model 2022 Ingår i: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963 .- 2405-8963. ; 55:19, s. 115-120 Konferensbidrag (refereegranskat)abstract Maintaining equipment is critical for increasing production capacity and decreasing production time. With the advent of digitalization, industries are able to access massive amounts of data that can be used to ensure their long-term viability and competitive advantage by implementing predictive maintenance. Therefore, this study aims to demonstrate a predictive maintenance application for a robot cell using real-world manufacturing big data coming from a company in the automotive industry. A hyperparameter tuned Long Short-Term Memory (LSTM) model is developed, and the results show that this model is capable of predicting the day of failure with good accuracy. The difficulties inherent in conducting real-world industrial initiatives are analyzed, and recommendations for improvement are presented. Copyright (C) 2022 The Authors.
8.	Karlsson, Alexander, et al. (författare) Multi-Machine Gaussian Topic Modeling for Predictive Maintenance 2021 Ingår i: IEEE Access. - : IEEE. - 2169-3536 .- 2169-3536. ; 9, s. 100063-100080 Tidskriftsartikel (refereegranskat)abstract In this paper, we propose a coherent framework for multi-machine analysis, using a group clustering model, which can be utilized for predictive maintenance (PdM). The framework benefits from the repetitive structure posed by multiple machines and enables for assessment of health condition, degradation modeling and comparison of machines. It is based on a hierarchical probabilistic model, denoted Gaussian topic model (GTM), where cluster patterns are shared over machines and therefore it allows one to directly obtain proportions of patterns over the machines. This is then used as a basis for cross comparison between machines where identified similarities and differences can lead to important insights about their degradation behavior. The framework is based on aggregation of data over multiple streams by a predefined set of features extracted over a time window. Moreover, the framework contains a clustering schema which takes uncertainty of cluster assignments into account and where one can specify a desirable degree of reliability of the assignments. By using a multi-machine simulation example, we highlight how the framework can be utilized in order to obtain cluster patterns and inherent variations of such patterns over machines. Furthermore, a comparative study with the commonly used Gaussian mixture model (GMM) demonstrates that GTM is able to identify inherent patterns in the data while the GMM fails. Such result is a consequence of the group level being modeled by the GTM while being absent in the GMM. Hence, the GTM are trained with a view on the data that is not available to the GMM with the consequence that the GMM can miss important, possibly even key, cluster patterns. Therefore, we argue that more advanced cluster models, like the GTM, can be key for interpreting and understanding degradation behavior across machines and ultimately for obtaining more efficient and reliable PdM systems.
9.	Kurrewar, Harshad, et al. (författare) A Machine Learning Based Health Indicator Construction in Implementing Predictive Maintenance: A Real World Industrial Application from Manufacturing 2021 Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer International Publishing. - 1868-4238 .- 1868-422X. ; 632 IFIP, s. 599-608 Konferensbidrag (refereegranskat)abstract Predictive maintenance (PdM) using Machine learning (ML) is a top-rated business case with respect to the availability of data and potential business value for future sustainability and competitiveness in the manufacturing industry. However, applying ML within actual industrial practice of PdM is a complex and challenging task due to high dimensionality and lack of labeled data. To cope with this challenge, this paper presents a systematic framework based on an unsupervised ML approach by aiming to construct health indicators, which has a crucial impact on making the data meaningful and usable for monitoring machine performance (health) in PdM applications. The results are presented by using real-world industrial data coming from a manufacturing company. In conclusion, the designed health indicators can be used to monitor machine performance over time and further be used in a supervised setting for the purpose of prognostic like remaining useful life estimation in implementing PdM in the industry.
10.	Lopes, Paulo Victor, 1996, et al. (författare) Data-Driven Smart Maintenance Decision Analysis: A Drone Factory Demonstrator Combining Digital Twins and Adapted AHP 2023 Ingår i: Proceedings - Winter Simulation Conference. - 0891-7736. - 9798350369663 ; 2023, s. 1996-2007 Konferensbidrag (refereegranskat)abstract The concept of Digital Twins has gained significant attention in recent years due to its potential for improving the performance of production systems. One promising area for Digital Twins is Smart Maintenance, enabling the simulation of different strategies without disrupting operations in the real system. This study proposes a high-level framework to integrate Digital Twins to support Smart Maintenance data-driven decision making in production lines. We implement, then, a case study of a lab scale drone factory to demonstrate how the production line performance evaluation is made under different what-if maintenance scenarios. The effects of this Smart Maintenance decision analysis approach were evaluated according to Key Performance Indicators from literature. The identified contributions are: (i) Digital Twin demonstrator focused on smart maintenance; (ii) implementation of smart maintenance data-driven decision analysis concepts; (iii) design and evaluation of what-if maintenance scenarios.
11.	Lundén, Nils, et al. (författare) Domain Knowledge in CRISP-DM: An Application Case in Manufacturing 2023 Ingår i: IFAC-PapersOnLine. - 2405-8963. ; 56:2, s. 7603-7608 Konferensbidrag (refereegranskat)abstract To keep up with shifting technology trends and remain competitive, more manufacturing companies are investigating how to utilize data analytics to improve their processes. An issue these companies often face today is the need for more competence to perform advanced analytics projects within their departments. By using a human-in-the-loop approach and efficiently utilizing current domain knowledge in combination with data analytics, the higher success of implementation can be achieved. A common approach today to perform data analytics projects is to use the general Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. This methodology does not consider the challenges specific to manufacturing and how to include domain expertise. This paper, therefore, suggests how the CRISP-DM methodology can be adapted to compensate for these issues. The adapted methodology is demonstrated in a case study for improving quality in the machining process by using interpretable machine learning models that can be used to assist experts when performing root cause analysis. This contributes to showing how to use domain experts’ knowledge better and how data analytics can be used in conjunction with domain-specific methods.
12.	Lundgren, Camilla, 1989, et al. (författare) Determining the impact of 5G-technology on manufacturing performance using a modified TOPSIS method 2022 Ingår i: International Journal of Computer Integrated Manufacturing. - 0951-192X .- 1362-3052. ; 35:1, s. 69-90 Tidskriftsartikel (refereegranskat)abstract 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.
13.	Rajashekarappa, Mohan, et al. (författare) A Data-Driven Approach to Air Leakage Detection in Pneumatic Systems 2021 Ingår i: 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing). - : IEEE. - 9781665401319 - 9781665401302 - 9781665429795 Konferensbidrag (refereegranskat)abstract During the transition phase of traditional manufacturing companies towards smart factories, they are likely to experience challenges like lack of prehistoric data recordings or events on which the machine learning models need to be trained. This paper introduces a novel approach of artificially induced anomalies for data labelling. Moreover, for newly installed systems or a setup, which has not seen any kind of malfunction yet, the combination of artificially induced anomalies by experiments and machine learning model help to proactively prepare for any kind of future hindrance of the production systems. Two experiments were performed for detection of air leakage. The first one was designed to identify 'sensitive feature' and understand the behaviour of the sensor readings with respect to different state of the machine. The second one was performed to capture more data points pertaining to leaking state of machine on a normal production day since the first one was conducted on a maintenance break). RUSBoosted bagged trees model was built as a supervised machine-learning model, which was yielded 98.73% accuracy, 99.40% precision, recall of 99.21%, and F1 score of 99.30% on test data for detecting pneumatic leakage. As a conclusion, previously unknown hidden patterns and insights regarding temperature feature along with a standardized and systematic methodology are the important deliverables of this study.
14.	Subramaniyan, Mukund, 1989, et al. (författare) A prognostic algorithm to prescribe improvement measures on throughput bottlenecks 2019 Ingår i: Journal of Manufacturing Systems. - : Elsevier BV. - 0278-6125. ; 53, s. 271-281 Tidskriftsartikel (refereegranskat)abstract 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.
15.	Suder, Asli, et al. (författare) Fuzzy Multiattribute Consumer Choice among Health Insurance Options 2016 Ingår i: Technological and Economic Development of Economy. - : Vilnius Gediminas Technical University. - 1392-8619 .- 2029-4913 .- 2029-4921. ; 22:1, s. 1-20 Tidskriftsartikel (refereegranskat)abstract 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.
16.	Syu, Fu Siang, et al. (författare) Usability and Usefulness of Circularity Indicators for Manufacturing Performance Management 2022 Ingår i: Procedia CIRP. - : Elsevier BV. - 2212-8271. ; 105, s. 835-840 Konferensbidrag (refereegranskat)abstract 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' 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's perspective. The conclusion also suggests directions for further research on tools and methods to support circular manufacturing.
17.	Turanoglu Bekar, Ebru, 1987, et al. (författare) An ANFIS Algorithm for Forecasting Overall Equipment Effectiveness Parameter in Total Productive Maintenance 2015 Ingår i: Journal of Multiple-Valued Logic and Soft Computing. - 1542-3980. ; 25:6, s. 535-554 Tidskriftsartikel (refereegranskat)abstract otal Productive Maintenance (TPM) is a successful technique used for corrective, preventive and predictive maintenance policies. It is important in identifying the success and overall effectiveness of the manufacturing process for long term economic viability of business. Overall equipment effectiveness (OEE) is commonly used and well-accepted metric for TPM implementation in many manufacturing industries. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to obtain forecasted results for OEE parameter in TPM through some predetermined inputs such as availability, performance efficiency and rate of quality. Triangular type of membership functions was determined as low, medium, and high for each input parameter in the ANFIS model. Fuzzy c-means clustering algorithm was used for determining of the membership degrees of membership functions for each input parameter. This study is important to forecast the risk by OEE in the TPM. With the predicted results of OEE performance an appropriate maintenance strategy can be developed and the production can be improved. This can also help reducing the risk level of breakdowns or failures at any critical equipment.
18.	Turanoglu Bekar, Ebru, 1987, et al. (författare) An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study 2020 Ingår i: Advances in Mechanical Engineering. - : SAGE Publications. - 1687-8132 .- 1687-8140. ; 12:5 Tidskriftsartikel (refereegranskat)abstract 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.
19.	Turanoglu Bekar, Ebru, 1987 (författare) Efficiency measurement based on novel performance measures in total productive maintenance (TPM) using a fuzzy integrated COPRAS and DEA method 2023 Ingår i: Frontiers in Manufacturing Technology. - : Frontiers Media SA. - 2813-0359. ; 3, s. 1-20 Tidskriftsartikel (refereegranskat)abstract 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.
20.	Turanoglu Bekar, Ebru, 1987, et al. (författare) Fuzzy COPRAS Method for Performance Measurement in Total Productive Maintenance: A Comparative Analysis 2016 Ingår i: Journal of Business Economics and Management. - : Vilnius Gediminas Technical University. - 1611-1699 .- 2029-4433. ; 17:5, s. 663-684 Tidskriftsartikel (refereegranskat)abstract 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.
21.	Turanoglu Bekar, Ebru, 1987, et al. (författare) Involving students in engineering course design: a combined approach based on constructive alignment and multi-criteria decision-making 2024 Ingår i: European Journal of Engineering Education. - 0304-3797 .- 1469-5898. ; 49:4, s. 647-666 Tidskriftsartikel (refereegranskat)abstract Empowering students to actively shape their learning is essential. Various student involvement models, such as design-based research, participatory design, and co-creation, emphasise students’ growing role in shaping educational activities. Engaging students in course design, as seen in student co-creation, can enhance agency, improve the student experience, and boost outcomes. Considering this, we propose a systematic approach combining multi-criteria decision-making with constructive alignment theory to involve students as co-creators in course design. This approach aims to engage students in the course design process and co-create intended learning outcomes, which can be regarded as the primary participatory phase in developing a co-created course. By involving students, our approach aims to gain insight into their needs, prioritise their views, and guide the formulation of appropriate course specifications throughout the course design process. The approach is applied to a new multi-disciplinary engineering course, and the results are summarised following its corresponding steps. Students’ feedback indicates that the approach positively influenced their motivation, engagement with course objectives, collaboration with teachers, and overall achievement of intended learning outcomes. This study’s significance lies in its contribution to higher education, offering a more integrated and systematic approach to support co-creation between educators and learners in academic course design.
22.	Turanoglu Bekar, Ebru, 1987, et al. (författare) Prediction of Industry 4.0’s Impact on Total Productive Maintenance Using a Real Manufacturing Case 2018 Ingår i: Proceedings of the International Symposium for Production Research 2018. - Cham : Springer International Publishing. - 9783319922676 ; , s. 136-149 Konferensbidrag (refereegranskat)abstract With rapid advancements in industry, technology and applications, many concepts have emerged in the manufacturing environment. It is generally known that the term ‘Industry 4.0’ was published to highlight a new industrial revolution. Maintenance is a key operation function since it is related to all the manufacturing processes and focuses not only on avoiding the equipment breakdown but also on improving business performance. Monitoring of manufacturing systems for maintenance helps to identify equipment condition and failures before equipment breakdowns. Total Productive Maintenance (TPM) is widely used maintenance strategy to gain a competitive advantage in the industry. In this context, this paper focuses on the incompletely perceived link between the Industry 4.0’s key technologies and TPM. Conjoint analysis, which is a multi-attribute decision making method based on experimental design, is implemented to quantify the impacts of Industry 4.0’s key technologies on pillars of TPM. Moreover, interaction between key technologies of Industry 4.0 and pillars of TPM demonstrates several opportunities for achieving synergies thus leading to a successful implementation of future interconnected smart factories. The major contribution of is to provide a guideline and technology roadmap for investment decision for industries that are under the transformation phase towards future smart factory and offers a space for further scientific discussion.
23.	Turanoglu Bekar, Ebru, 1987, et al. (författare) Using Adaptive Neuro-Fuzzy Inference System, Artificial Neural Network and Response Surface Method to Optimize Overall Equipment Effectiveness for An Automotive Supplier Company 2017 Ingår i: Journal of Multiple-Valued Logic and Soft Computing. - 1542-3980. ; 28:4-5, s. 375-407 Tidskriftsartikel (refereegranskat)abstract Total Productive Maintenance (TPM) is a successful technique that is important in identifying the success and overall effectiveness of the manufacturing process for long term economic viability of business. Overall equipment effectiveness (OEE) is commonly used and well-accepted metric for TPM implementation in many manufacturing industries. As OEE is an important performance measure for effectiveness of any equipment, careful analysis is required to know the effect of various components. An attempt has been done in this research to predict the OEE by using simulation software. The objective is to identify an optimal OEE level to maximize the time between failures and simultaneously minimize the mean repair time. The process of OEE is optimized by using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference system (ANFIS) to identify optimized zone for maximizing output. Finally it is determined the feasible values of inputs using Sequential Quadratic Programming (SQP) algorithm based on trained ANFIS predictive model. The result from this study can be used the inconvenient impact of the failures on the production process, it is strongly recommended to upgrade the operation management, i.e. TPM program, capacity analysis, parts replacement decisions, training programs for technicians/operators, spare parts requirement etc.
24.	Ulutagay, Gozde, et al. (författare) ANFIS Modeling for Forecasting Oil Consumption of Turkey 2016 Ingår i: Journal of Multiple-Valued Logic and Soft Computing. - 1542-3980. ; 26:6, s. 609-624 Tidskriftsartikel (refereegranskat)abstract In this study, the interrelationship between oil consumption and economic growth is examined via ANFIS modeling that is used to obtain long term forecasting results for oil consumption of Turkey through predetermined inputs, which are speciﬁed as population, gross domestic product (GDP), import and export. The data samples from 1965 to 2000 are conducted for developing the ANFIS model indicating the relationship between the oil consumption and the four inputs. Triangular types of membership functions are deﬁned as low, medium and high for each input parameter in the ANFIS prediction system. Afterwards, oil consumption of Turkey is predicted from 2012 to 2030 using double exponential forecasting technique. Hence, this study can act as a guideline for long term forecasting of future oil consump-tion of any other country.
25.	Öztayşi, Başar, et al. (författare) Fuzzy Analytic Hierarchy Process with Interval Type-2 Fuzzy Sets 2014 Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 59, s. 48-57 Tidskriftsartikel (refereegranskat)abstract The membership functions of type-1 fuzzy sets have no uncertainty associated with it. While excessive arithmetic operations are needed with type-2 fuzzy sets with respect to type-1’s, type-2 fuzzy sets generalize type-1 fuzzy sets and systems so that more uncertainty for defining membership functions can be handled. A type-2 fuzzy set lets us incorporate the uncertainty of membership functions into the fuzzy set theory. Some fuzzy multicriteria methods have recently been extended by using type-2 fuzzy sets. Analytic Hierarchy Process (AHP) is a widely used multicriteria method that can take into account various and conflicting criteria at the same time. Our objective is to develop an interval type-2 fuzzy AHP method together with a new ranking method for type-2 fuzzy sets. We apply the proposed method to a supplier selection problem.

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