1.
Gopalakrishnan, Maheshwaran, 1987, et al.
(författare)
Machine criticality assessment for productivity improvement: Smart maintenance decision support
2019
Ingår i: International Journal of Productivity and Performance Management. - : EMERALD GROUP PUBLISHING LTD. - 1741-0401 .- 1758-6658. ; 68:5, s. 858-878
Tidskriftsartikel (refereegranskat) abstract
Purpose The 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 An 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 The 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 Machine 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.
2.
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.
3.
Johansson, Björn, 1975, et al.
(författare)
Power Level Sampling of Metal Cutting Machines for Data Representation in Discrete Event Simulation
2015
Ingår i: International Journal of Production Research. - : Informa UK Limited. - 0020-7543 .- 1366-588X. ; 53:23, s. 7060-7070
Tidskriftsartikel (refereegranskat) abstract
An extension to the application area for discrete event simulation (DES) has been ongoing since the last decade and focused only on economic aspects to include ecologic sustainability. With this new focus, additional input parameters, such as electrical power consumption of machines, are needed. This paper aim at investigating how NC machine power consumption should be represented in simulation models of factories. The study includes data-sets from three different factories. One factory producing truck engine blocks, one producing brake disc parts for cars and one producing forklift components. The total number of data points analysed are more than 2,45,000, where of over 1,11,000 on busy state for 11 NC machines. The low variability between busy cycles indicates that statistical representations are not adding significant variability. Furthermore, results show that non-value-added activities cause a substantial amount of the total energy consumption, which can be reduced by optimising the production flow using dynamic simulations such as DES.
4.
Karlsson, Nadine, 1988, et al.
(författare)
QUANTIFYING THE EFFECTS OF PRODUCTION MAINTENANCE DECISIONS USING DISCRETE EVENT SIMULATION
2014
Ingår i: Swedish Production Symposium, 2014, Gothenburg.
Konferensbidrag (refereegranskat) abstract
Use of simulation to analyze and plan maintenance activities is still limited compared to planning production activities. The paper discusses a simulation based approach to quantify the effects of maintenance decision making by identifying the related performance indicators. The aim of the paper is to quantify the production maintenance related decisions, in terms of Key Performance Indicators (KPIs) determined trough interwievs and simulation. The approach is exemplified in a manufacturing case-study. The results show that use of simulation tool has the potential to be a strategic decision support tool for production maintenance in the production system.
5.
Bokrantz, Jon, 1988, et al.
(författare)
Smart Maintenance: Instrument Development, Content Validation and an Empirical Pilot
2020
Ingår i: International Journal of Operations and Production Management. - 1758-6593 .- 0144-3577. ; 40:4, s. 481-506
Tidskriftsartikel (refereegranskat) abstract
Purpose: Scholars and practitioners within industrial maintenance management are focused on understanding antecedents, correlates and consequences of the concept of “Smart Maintenance”, which consists of the four dimensions data-driven decision-making, human capital resource, internal integration and external integration. In order to facilitate this understanding, valid and reliable empirical measures need to be developed. Therefore, this paper aims to develop a psychometric instrument that measures the four dimensions of Smart Maintenance. Design/methodology/approach: The results from two sequential empirical studies are presented, which include generating items to represent the constructs, assessment of content validity, as well as an empirical pilot test. With input from 50 industrial experts, a pool of 80 items that represent the constructs are generated. Thereafter, using data from 42 industrial and academic raters, the content validity of all items is assessed quantitatively. Finally, using data from 59 manufacturing plants, the dimensionality and factor structure of the instrument are tested. Findings: We demonstrate content validity and provide evidence of good model fit and psychometric properties for one-factor models with 8-11 items for each of the four constructs, as well as a combined 24-item four-factor model. Originality/value: We provide recommendations for scholarly use of the instrument in further theory-testing research, as well as its practical use to assess, benchmark and longitudinally evaluate Smart Maintenance within the manufacturing industry.
6.
Gustafsson, Bertil, 1949, et al.
(författare)
Design and Problem Oriented Education based on the Application of Knowledge – Developing Chalmers' Master’s Programme in Production Engineering
2011
Ingår i: Proceedings of the Swedish Production Symposium 2011. ; , s. 442-449
Konferensbidrag (refereegranskat) abstract
Solving real industrial problems will doubtlessly, more than ever before, be a question of transferring well-founded scientific theory into sustainable practice. A major success factor is the training of future professional engineers to achieve appropriate skills for such transfer. Furthermore, to succeed with high quality training under considerable economic constraints, while simultaneously meeting the students' demand for highly customized curricula, is indeed challenging. This paper describes Chalmers' Master’s Programme in Production Engineering (MPPEN), a theoretically high-levelled and industrially relevant education for future industrial experts, fulfilling the requirements of the Bologna declaration. At this point, MPPEN has been running for three years and evaluations show that the students are highly satisfied with the programme. However, experiences, in combination with a desire to obtain a stronger focus on occupational alignment, have lead to a recent improvement process. This paper identifies design factors for future successful production education and reports how MPPEN has adapted to these requirements by reorganising courses, programme architecture, and course plans. MPPEN is an important part of the Chalmers Production Area of Advance and the programme is responsible for educating production engineers, developing and managing manufacturing processes and production systems, using a holistic view and sustainability thinking.
7.
Ito, Adriana, 1985, et al.
(författare)
Prioritisation of root cause analysis in production disturbance management
2022
Ingår i: International Journal of Quality and Reliability Management. - : Emerald Group Holdings Ltd.. - 0265-671X .- 1758-6682. ; 39:5, s. 1133-1150
Tidskriftsartikel (refereegranskat) abstract
Purpose: Manufacturing companies struggle to manage production disturbances. One step of such management deals with prioritising those disturbances which should undergo root cause analysis. The focus of this work is on two areas. First, investigating current challenges faced by manufacturing companies when prioritising root cause analysis of production disturbances. Second, identifying the stakeholders and factors impacted by production disturbances. Understanding the current challenges and identifying impacted stakeholders and factors allows the development of more efficient prioritisation strategies and, thus, contributes to the reduction of frequency and impact of disturbances. Design/methodology/approach: To achieve the intended purpose of this research, a qualitative approach was chosen. A series of interviews was conducted with practitioners, to identify current challenges. A series of focus groups was also held, to identify the impacted stakeholders and factors by disturbances. Findings: Various challenges were identified. These are faced by manufacturing companies in their prioritisation of production disturbances and relate to the time needed, criteria used, centralisation of the process, perspective considered and data support. It was also found that a wide range of stakeholders is impacted by production disturbances, surpassing the limits of production and maintenance departments. Furthermore, the most critical factors impacted are quality, work environment, safety, time, company results, customer satisfaction, productivity, deliverability, resource utilisation, profit, process flow, plannability, machine health and reputation. Originality/value: The current situation regarding root cause analysis prioritisation has not been identified in previous works. Moreover, there has been no prior systematic identification of the various stakeholders and factors impacted by production disturbances.
8.
Ito, Adriana, 1985, et al.
(författare)
Production disturbances handling: Where are we and where are we heading?
2021
Ingår i: Proceedings of the International Conference on Industrial Engineering and Operations Management. - : IEOM Society. - 2169-8767. ; , s. 12-23
Konferensbidrag (refereegranskat) abstract
Half of manufacturing companies' production capacity is estimated to be compromised due to disturbances. With the upcoming Industry 4.0, this problem is expected to be minimized through technological solutions. The aim of this article is to propose alternatives to handle production disturbances by means of technological support, to minimize their occurrence and impacts. To this purpose, we conducted a literature review and a series of interviews with five companies. We distinguish six stages for handling production disturbances: Detection, diagnosis, mitigation/correction, root cause analysis, prevention, and prediction. Our results indicate that all these stages are expected to benefit from Industry 4.0 technologies significantly. Furthermore, our results point out that practitioners perceive the stages of prevention and prediction with the highest potential for improvement. However, focus on the diagnosis and root cause analysis stages is also necessary since those stages are coupled to the prevention and prediction. The contributions of this article are twofold. Firstly, it provides a holistic view of the stages and technologies to handle production disturbances in Industry 4.0, from which practitioners can extract directions for implementation. Secondly, the paper provides focus for further research in the field of disturbance management with the identification of the current challenges.
9.
Lundgren, Camilla, 1989, et al.
(författare)
A Strategy Development Process for Smart Maintenance Implementation
2021
Ingår i: Journal of Manufacturing Technology Management. - 1741-038X. ; 32:9, s. 142-166
Tidskriftsartikel (refereegranskat) abstract
Technological advancements are reshaping the manufacturing industry toward digitalized manufacturing. Despite the importance of top-class maintenance in such systems, many industrial companies lack a clear strategy for maintenance in digitalized manufacturing. The purpose of this paper is to facilitate the implementation of maintenance in digitalized manufacturing by proposing a strategy development process for the Smart Maintenance concept. A process of strategy development for smart maintenance is proposed, including six steps: benchmarking, setting clear goals, setting strategic priority, planning key activities, elevating implementation and follow-up. The proposed process provides industry practitioners with a step-by-step guide for the development of a clear smart maintenance strategy, based on the current state of their maintenance organization. This creates employee engagement and is a new way of developing maintenance strategies.
10.
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.
