| 1. |
- Hane Hagström, Malin, 1971, et al.
(author)
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Evaluating the effectiveness of machine acquisitions and design by the impact on maintenance cost – a case study
- 2020
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In: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963. ; 53:3, s. 25-30
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Conference paper (peer-reviewed)abstract
- Industry 4.0 and circular economy are paradigm shifts for the industry. More and more machines will be used and the capability to maintain the machines becomes vital. The maintainability of a machine is to a large extent set already in the design phase; the goal of this article is to use a case study to investigate the quality of the machine design from a maintenance perspective. The results show that maintenance cost is gradually increasing in the initial part of the machine life cycle, that the new machines have higher maintenance costs than the machines approaching end of life, and that design weakness is a significant contributor to the maintenance cost. To understand more clearly why, further research in knowledge management, complementary qualitative interviews and smart maintenance is suggested.
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| 2. |
- Ito, Adriana, 1985, et al.
(author)
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Improved root cause analysis supporting resilient production systems
- 2022
-
In: Journal of Manufacturing Systems. - : Elsevier BV. - 0278-6125. ; 64, s. 468-478
-
Research review (peer-reviewed)abstract
- Manufacturing companies struggle to be efficient and effective when conducting root cause analyses of production disturbances; a fact which hinders them from creating and developing resilient production systems. This article aims to describe the challenges and enablers identified in current research relating to the different phases of root cause analysis. A systematic literature review was conducted, in which a total of 14 challenges and 17 enablers are identified and described. These correlate to the different phases of root cause analysis. Examples of challenges are “need for expertise”, “employee bias”, “poor data quality” and “lack of data integration”, among others. Examples of enablers are “visualisation tools”, “collaborative platforms”, “thesaurus” and “machine learning techniques”. Based on these findings, the authors also propose potential areas for further research and then design inputs for new solutions to improve root cause analysis. This article provides a theoretical contribution in that it describes the challenges and enablers of root cause analysis and their correlation to the creation of resilient production systems. The article also provides practical contributions, with an overview of current research to support practitioners in gaining insights into potential solutions to be implemented and further developed, with the aim of improving root cause analysis in production systems.
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| 3. |
- Lundgren, Camilla, 1989, et al.
(author)
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How industrial maintenance managers perceive socio-technical changes in leadership in the Industry 4.0 context
- 2023
-
In: International Journal of Production Research. - : Informa UK Limited. - 0020-7543 .- 1366-588X. ; 61:15, s. 5282-5301
-
Journal article (peer-reviewed)abstract
- Innovations and advancements in technology create new opportunities to run and maintain manufacturing plants, which we refer to as digitalised manufacturing. This development is recognised as a socio-technical system (STS) change, where a change in the production system’s goals, technology, processes, people, or environment may lead to ripple effects between those sub-systems. Despite this, technology development and technology use cases account for most of the research within digitalised manufacturing, while little attention has been devoted to leadership practices considering digitalised manufacturing from a socio-technical perspective. This paper focuses on the maintenance organisation, whose mission in a company is to keep production systems functional. We aim to describe leadership in industrial maintenance from an STS perspective. This is a unique interview study where twenty maintenance managers from Swedish manufacturing industry offer their perspective on the changing leadership within maintenance, providing a unique insight into the challenges facing leaders of maintenance in digitalised manufacturing. We frame the empirical findings using an STS framework and propose an overall consideration model for leadership that supports the development of a functional maintenance organisation in the face of pervasive digitalisation.
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| 4. |
- Algabroun, Hatem, et al.
(author)
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Development of digitalised maintenance : a concept
- 2022
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In: Journal of Quality in Maintenance Engineering. - : Emerald Group Publishing Limited. - 1355-2511 .- 1758-7832. ; 28:2, s. 367-390
-
Journal article (peer-reviewed)abstract
- Purpose This paper presents a concept for digitalised maintenance (DM), maps the conceptualised DM to maintenance problems in industries and highlights challenges that might be faced when realizing this concept. Design/methodology/approach First, maintenance problems that are faced by the industry are presented, followed by a conceptualisation of DM. Next, a typical operational scenario is used as an exemplification to show system dynamics. The characteristics of this conceptualised DM are then mapped to the identified maintenance problems of industry. Then, interesting initiatives in this domain are highlighted, and finally, the challenges to realize this approach are discussed. Findings This paper identified a set of problems related to maintenance in industry. In order to solve current industrial problems, exploit emerging digital technologies and elevate future industries, it will be necessary to develop new maintenance approaches. The mapping between the criteria of DM and maintenance problems shows the potential of this concept and gives a reason to examine it empirically in future work. Originality/value This paper aims to help maintenance professionals from both academia and industry to understand and reflect on the problems related to maintenance, as well as to comprehend the requirements of a digitalised maintenance and challenges that may arise.
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| 5. |
- Alin, David, 1984, et al.
(author)
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Reducing the EPEI-Time Using Discrete Event Simulation
- 2009
-
In: Proceedings of the 2009 Swedish Production Symposium. ; :2, s. 295-301
-
Conference paper (peer-reviewed)abstract
- One of the cornerstones in LEAN production is ‘make to order’, which requires small batch sizes and, thus, short Every Part Every Interval (EPEI) times. EPEI-time is defined as the time it takes to produce all product variants, before the first variant in the cycle returns in the schedule. However, many companies are reluctant to reduce their EPEI-times due to the increased number of set-ups. This skepticism is also supported by parts of existing theory, while other research contributions mean that companies often can reduce batch-sizes without affecting productivity. This paper presents a case study which uses discrete event simulation (DES) to evaluate the relation between EPEI-time and productivity. The results show that it is possible to reduce the EPEI-time and still maintain productivity and service levels to customers, without any investments. Increased variation in the production schedule evened out the load among the machines and, hence, the time lost in set-ups was gained in more parallel work.
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| 6. |
- Andersson, Jon, 1985, et al.
(author)
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Environmental Activity Based Cost using Discrete Event Simulation
- 2011
-
In: Proceedings - Winter Simulation Conference. - 0891-7736. - 9781457721083 ; , s. 891-902
-
Conference paper (peer-reviewed)abstract
- Discrete event simulation (DES) provides engineers with a flexible modeling capability for extensive analysis of a production flow and its dynamic behavior. Activity based costing (ABC) modeling can pro-vide additional knowledge about the monetary costs related to the manufacturing processes in DES. In addition, ABC modeling has been proposed as a tool for environmental impact analysis. Thus, previous studies have separately brought ABC into DES and ABC into environmental impact analysis. Bringing all three areas together, an ABC environmental simulation could provide deeper understanding about envi-ronmental impacts in the manufacturing processes than a regular Life Cycle Assessment (LCA) analysis. This paper proposes to use ABC modeling in conjunction with DES to perform a more detailed economic and environmental impact cost analysis. It is emphasized that the time to perform both analysis in one simulation is shorter or equal to perform them separately. Moreover, the approach can resolve some LCA problems.
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| 7. |
- Andersson, Jon, 1985, et al.
(author)
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Environmental Impact Assessment for Manufacturing: Data Requirements for a Simulation-Based Approach
- 2012
-
In: Swedish Production Symposium. - 9789175197524 ; , s. 151-160
-
Conference paper (peer-reviewed)abstract
- The environmental footprint of products is an increasingly important measure for companies working to improve their sustainability performance, and the same measure has also become popular for marketing purposes. As a result, the demand for environmental product declarations and, thus, life cycle assessment (LCA) projects grows. To reap the full benefit from LCA studies in production systems analysis, LCA has more frequently been complemented with simulation of production flows (i.e. discrete event simulation) during the latest decade. Several examples of the DES-LCA combination in recent literature report substantial potential and successful implementations. However, a common problem is to establish efficient and credible procedures for collecting, analyzing, and representing the extensive amounts of input data required. The aim of this paper is therefore to provide recommendations for the management of environmental data in sustainability simulations. A review of seven previous DES-LCA projects provides a list of common sustainability parameters and experiences on how they should be collected and represented in simulation models. An important result is that deterministic representations appear to be enough for data not directly linked to production time. This finding makes it possible to replace time-consuming data gathering with collection of secondary data from public databases.
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| 8. |
- Andersson, Jon, 1985, et al.
(author)
-
Evaluation of methods used for life-cycle assessments in Discrete Event Simulation
- 2012
-
In: Proceedings - Winter Simulation Conference. - 0891-7736. - 9781467347792
-
Conference paper (peer-reviewed)abstract
- The incitements from society for life-cycle assessment (LCA) and credible ecolables are ever-increasingand often important for successful marketing of products. Robust assessment methods are important forcomparable, useful and trustworthy LCAs and ecolables. In order to improve the metrics of a product’secolable, is it important to fully understand its production system. Discrete Event Simulation (DES)models are able to provide more detailed information than traditional LCA approaches. Therefore,methods used to combining LCA in DES have been developed during the last decade. The combinedapproaches have matured and the experiences grown. This article compares six previous cases and aims tosummarize and discuss their experiences to aid future development. The results show where it isspecifically important to make good decisions throughout the modeling methodology, for example goaland scope definition, trustworthy input data for sensitive parts, and communicable impact categories.
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| 9. |
- Andersson, Jon, 1985, et al.
(author)
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Framework for Ecolabeling using Discrete Event Simulation
- 2012
-
In: Proceedings of the 2012 spring simulation multiconference.
-
Conference paper (peer-reviewed)abstract
- Ecolabled products have shown a competitive advantage to other products. Regulatory changes and market pressure creates an increased need for environmental impact assessments. The dominating method for environmental impact assessments - life cycle assessment (LCA) lacks support to properly analyze the dynamic aspects of business operations and production processes. This Paper proposes to use discrete event simulation to support more extensive and detailed environmental assessments on selected parts of the production process, keeping simplicity for parts of less importance and interest.
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| 10. |
- Baum, Jens, et al.
(author)
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Applications of Big Data analytics and Related Technologies in Maintenance - Literature-Based Research
- 2018
-
In: Machines. - : MDPI AG. - 2075-1702. ; 6:54, s. 1-12
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Journal article (peer-reviewed)abstract
- Digitalisation is argued to increase the efficiency of maintenance activities in a production system. One consequence of digitalisation is data deluge; this allows data analytics methods and technologies to be used. However, the actual data analytical methods and technologies used may differ, thus leading to many scientific papers on this topic. The purpose of our contribution is to find and cluster scientific papers regarding the implemented approaches relevant for use in production maintenance. Our research is based on a broad, systematic literature review consisting of a two-step search approach combined with additional filtering and classification. Based on the search results, we evaluate and visualise the potential impact of data analytics on the subject of maintenance. The results of this study broadly summarise the research activities in production maintenance, whilst indicating that the impact of data analytics will grow further. Specific methodological approaches are clearly favored
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| 11. |
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| 12. |
- Bergman, Becky, 1970, et al.
(author)
-
Forming effective culturally diverse work teams in project courses
- 2017
-
In: 13th international CDIO conference proceedings CDIO, June 18-22 2017, Calgary. - 1796-9964. - 9780889533998 ; 2017:13, s. 508-518
-
Conference paper (peer-reviewed)abstract
- A culturally diverse student population at Master’s level is a reality at many universities today, as it is at Chalmers University of Technology in Sweden. However, a common issue is the lack of interaction between home and international students, which counteracts university goals of fostering intercultural cooperation. This paper will discuss and evaluate a pilot project in one Master’s program in production engineering, where activities around group diversity were integrated into a company-based project course. Students were assigned groups where a mixture of backgrounds and expertise were prioritised.The project used a number of group dynamics activities including a pre-survey of expectations of group work; negotiating a group contract; and continuous peer group assessment in order to develop and reflect on the skills required in a culturally diverse work team.From reflective essays that students wrote, as well as interviews carried out, the project and tasks were evaluated. The following themes were identified: attitudes to diversity; the importance of well-functioning communication; and attitudes and roles within the group.Results show that students found this a challenging but useful environment to work in and found the group dynamics activities helpful in negotiating this environment. Continuous peer group assessment, in particular, was seen as helpful in providing a forum for feedback and discussion on individual performance in the group and challenges for the group as a whole.Recommendations include constructive alignment within the program in terms of clear goals, activities and assessment, in order to build up these skills and awareness, not only in a single course but throughout the program.
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| 13. |
- Bokrantz, Jon, 1988, et al.
(author)
-
A Methodology for Continuous Quality Assurance of Production Data
- 2016
-
In: Proceedings - Winter Simulation Conference. - 0891-7736. ; 2016-February, s. 2088-2099
-
Conference paper (peer-reviewed)abstract
- High quality input data is a necessity for successful Discrete Event Simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a day-to-day engineering tool requires high quality production data to be constantly available. Unfortunately, there are no detailed guidelines that describes how to achieve this. Therefore, this paper presents such a methodology, based on three concurrent engineering projects within the automotive industry. The methodology explains the necessary roles, responsibilities, meetings, and documents to achieve a continuous quality assurance of production data. It also specifies an approach to input data management for DES using the Generic Data Management Tool (GDM-Tool). The expected effects are increased availability of high quality production data and reduced lead time of input data management, especially valuable in manufacturing companies having advanced automated data collection methods and using DES on a daily basis.
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| 14. |
- Bokrantz, Jon, 1988, et al.
(author)
-
Adoption patterns and performance implications of Smart Maintenance
- 2023
-
In: International Journal of Production Economics. - : Elsevier BV. - 0925-5273. ; 256
-
Journal article (peer-reviewed)abstract
- To substantiate and extend emergent research on maintenance in digitalized manufacturing, we examine adoption patterns and performance implications of the four dimensions of Smart Maintenance: data-driven decision-making, human capital resource, internal integration, and external integration. Using data collected from 145 Swedish manufacturing plants, we apply a configurational approach to study how emergent patterns of Smart Maintenance are shaped and formed, as well as how the patterns are related to the operating environment and the performance of the manufacturing plant. Cluster analysis was used to develop an empirical taxonomy of Smart Maintenance, revealing four emergent patterns that reflect the strength and balance of the underlying dimensions. Canonical discriminant analysis indicated that the Smart Maintenance patterns are related to operating environments with a higher level of digitalization. The results from ANOVA and NCA showed the importance of a coordinated and joint Smart Maintenance implementation to the maintenance performance and productivity of the manufacturing plant. This study contributes to the literature on industrial maintenance by expanding and strengthening the theoretical and empirical foundation of Smart Maintenance, and it provides managerial advice for making strategic decisions about Smart Maintenance implementation.
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| 15. |
- Bokrantz, Jon, 1988, et al.
(author)
-
Data Quality Problems in Discrete Event Simulation of Manufacturing Operations
- 2018
-
In: Simulation. - : SAGE Publications. - 1741-3133 .- 0037-5497. ; 94:11, s. 1009-1025
-
Journal article (peer-reviewed)abstract
- High-quality input data are a necessity for successful discrete event simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a daily manufacturing engineering tool requires high-quality production data to be constantly available. In fact, there has been a major shift in the application of DES in manufacturing from production system design to daily operations, accompanied by a stream of research on automation of input data management and interoperability between data sources and simula- tion models. Unfortunately, this research stream rests on the assumption that the collected data are already of high qual- ity, and there is a lack of in-depth understanding of simulation data quality problems from a practitioners’ perspective. Therefore, a multiple-case study within the automotive industry was used to provide empirical descriptions of simulation data quality problems, data production processes, and relations between these processes and simulation data quality problems. These empirical descriptions are necessary to extend the present knowledge on data quality in DES in a prac- tical real-world manufacturing context, which is a prerequisite for developing practical solutions for solving data quality problems such as limited accessibility, lack of data on minor stoppages, and data sources not being designed for simula- tion. Further, the empirical and theoretical knowledge gained throughout the study was used to propose a set of practi- cal guidelines that can support manufacturing companies in improving data quality in DES.
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| 16. |
- Bokrantz, Jon, 1988, et al.
(author)
-
Handling of Production Disturbances in the Manufacturing Industry
- 2016
-
In: Journal of Manufacturing Technology Management. - 1741-038X. ; 27:8, s. 1054-1075
-
Journal article (peer-reviewed)abstract
- Purpose – A common understanding of what events to regard as production disturbances (PD) are essential for effective handling of PDs. Therefore, the purpose of this paper is to answer the two questions: how are individuals with production or maintenance management positions in industry classifying different PD factors? Which factors are being measured and registered as PDs in the companies monitoring systems? Design/methodology/approach – A longitudinal approach using a repeated cross-sectional survey design was adopted. Empirical data were collected from 80 companies in 2001 using a paper-based questionnaire, and from 71 companies in 2014 using a web-based questionnaire.Findings – A diverging view of 21 proposed PD factors is found between respondents in manufacturing industry, and there is also a lack of correspondence with existing literature. In particular, planned events are not classified and registered to the same extent as downtime losses. Moreover, the respondents are often prone to classify factors as PDs compared to what is actually registered. This diverging view has been consistent for over a decade, and hinders companies to develop systematic and effective strategies for handling of PDs.Originality/value – There has been no in-depth investigation, especially not from a longitudinal perspective, of the personal interpretation of PDs from people who play a central role in achieving high reliability of production systems.
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| 17. |
- Bokrantz, Jon, 1988, et al.
(author)
-
Lean Principles and Engineering Tools in Maintenance Organizations - A Survey Study
- 2014
-
In: Swedish Production Symposium 2014.
-
Conference paper (peer-reviewed)abstract
- This paper presents a questionnaire survey mapping how Lean principles and engineering tools are applied in a maintenance context in Swedish industry, based on a high-level strategic view from 76 respondents representing 71 companies. Results from the study cover different work practices according to Lean principles, to what extent risk assessment tools and software are used, to what degree companies are employing reliability engineers to conduct risk and reliability analysis, and how this relates to the safety of maintenance operations. The results indicate a gap between applying Lean in production and maintenance, and low use of valuable engineering tools.
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| 18. |
- Bokrantz, Jon, 1988, et al.
(author)
-
Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030
- 2017
-
In: International Journal of Production Economics. - : Elsevier BV. - 0925-5273. ; 191, s. 154-169
-
Journal article (peer-reviewed)abstract
- Despite extensive research on future manufacturing and the forthcoming fourth industrial revolution (implying extensive digitalisation), there is a lack of understanding regarding the specific changes that can be expected for maintenance organisations. Therefore, developing scenarios for future maintenance is needed to define long-term strategies for the realisation of digitalised manufacturing. This empirical Delphi-based scenario planning study is the first within the maintenance realm, examining a total of 34 projections about potential changes to the internal and external environment of maintenance organisations, considering both hard (technological) and soft (social) dimensions. The paper describes a probable future of maintenance organisations in digitalised manufacturing in the year 2030, based on an extensive three-round Delphi survey with 25 maintenance experts at strategic level from the largest companies within the Swedish manufacturing industry. In particular, the study contributes with development of probable as well as wildcard scenarios for future maintenance. This includes e.g. advancement of data analytics, increased emphasis on education and training, novel principles for maintenance planning with a systems perspective, and stronger environmental legislation and standards. The scenarios may serve as direct input to strategic development in industrial maintenance organisations and are expected to substantially improve preparedness to the changes brought by digitalised manufacturing.
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| 19. |
- Bokrantz, Jon, 1988, et al.
(author)
-
On the Interplay between Platform Concept Development and Production Maintenance
- 2017
-
In: Proceedings of the International Conference on Engineering Design, ICED. - 2220-4334 .- 2220-4342. ; Vol. 3: Product, Services and Systems Design
-
Conference paper (peer-reviewed)abstract
- To meet a broad customer-base, platforms can be used to achieve commonality and distinctiveness among a family of products. However, producibility of product variants are typically not ensured until late in the platform development phases. This may lead to increased production disturbances. To understand challenges in ensuring producibility of a product family in the early phases of platform development, this paper adopts the concept of lifecycle meetings to describe the interplay between platform concept development and production maintenance. Based on this description, we reason that to make early and credible cross product-production decisions, production system capabilities ought to be regarded as dynamic rather than static. While static implies as designed, dynamic implies change over time. In this paper, maintenance is regarded as one dynamic aspect of production. This reasoning is supported by a theoretical perspective and an illustrating case from the aerospace industry. The contribution of this paper may form the basis for future research on platform development and the effect of product variety on production systems.
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| 20. |
- Bokrantz, Jon, 1988, et al.
(author)
-
Perspectives on the Future of Maintenance Engineering Education
- 2021
-
In: IFIP Advances in Information and Communication Technology. - Cham : Springer International Publishing. - 1868-4238 .- 1868-422X. ; 634 IFIP, s. 209-216
-
Conference paper (peer-reviewed)abstract
- In this article, we aim to remedy the effects of skill-biased technological change within maintenance engineering and enable productivity gains from novel digital technologies such as Artificial Intelligence. We do this by outlining the critical role of education and the need for renewal and increased access to higher education within maintenance, followed by reviewing the literature on maintenance engineering education over the past two decades (2000–2020). In our systematic literature review, we identify nine key themes that have occupied maintenance researchers in their educational efforts, e.g. design and development of curricula, programs, and courses; identification of competence requirements and learning characteristics; and new educational formats such as gamification and innovative laboratory sessions using novel digital technologies. Following our review, we propose research- and policy-oriented recommendations for the future of maintenance engineering education.
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| 21. |
- Bokrantz, Jon, 1988, et al.
(author)
-
Realising the promises of artificial intelligence in manufacturing by enhancing CRISP-DM
- 2023
-
In: Production Planning and Control. - 0953-7287 .- 1366-5871. ; In Press
-
Journal article (peer-reviewed)abstract
- To support manufacturing firms in realising the value of Artificial Intelligence (AI), we embarked on a six-year process of research and practice to enhance the popular and widely used CRISP-DM methodology. We extend CRISP-DM into a continuous, active, and iterative life-cycle of AI solutions by adding the phase of ‘Operation and Maintenance’ as well as embedding a task-based framework for linking tasks to skills. Our key findings relate to the difficult trade-offs and hidden costs of operating and maintaining AI solutions and managing AI drift, as well as ensuring the presence of domain, data science, and data engineering competence throughout the CRISP-DM phases. Further, we show how data engineering is an essential but often neglected part of the AI workflow, provide novel insights into the trajectory of involvement of the three competences, and illustrate how the enhanced CRISP-DM methodology can be used as a management tool in AI projects.
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| 22. |
- Bokrantz, Jon, 1988, et al.
(author)
-
Smart Maintenance: a research agenda for industrial maintenance management
- 2020
-
In: International Journal of Production Economics. - : Elsevier BV. - 0925-5273. ; 224
-
Journal article (peer-reviewed)abstract
- How do modernized maintenance operations, often referred to as “Smart Maintenance”, impact the performance of manufacturing plants? This question is a pressing challenge for practitioners and scholars in industrial maintenance management, in direct response to the transition to an industrial environment with pervasive digital technologies. This paper is the second part of a two-paper series. We present an empirically grounded research agenda that reflects the heterogeneity in industrial adoption and performance of Smart Maintenance. Focus groups and interviews with more than 110 experts from over 20 different firms were used to identify contingencies, responses, and performance implications of Smart Maintenance. The findings were transformed into a contingency model, providing the basis for a research agenda consisting of five principal areas: (1) environmental contingencies; (2) institutional isomorphism; (3) implementation issues related to change, investments and interfaces; (4) the four dimensions of Smart Maintenance; and (5) performance implications at the plant and firm level. The agenda can guide the field of industrial maintenance management to move from exploratory work to confirmatory work, studying the validity of the proposed concepts as well as the magnitude and direction of their relationships. This will ultimately help scholars and practitioners answer how Smart Maintenance can impact industrial performance.
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| 23. |
- Bokrantz, Jon, 1988, et al.
(author)
-
Smart Maintenance: an empirically grounded conceptualization
- 2020
-
In: International Journal of Production Economics. - : Elsevier BV. - 0925-5273. ; 223
-
Journal article (peer-reviewed)abstract
- How do modernized maintenance operations, often referred to as “Smart Maintenance”, impact the performance of manufacturing plants? The inability to answer this question backed by data is a problem for industrial maintenance management, especially in light of the ongoing rapid transition towards an industrial environment with pervasive digital technologies. To this end, this paper, which is the first part of a two-paper series, aims to investigate and answer the question, “What is Smart Maintenance?”. The authors deployed an empirical, inductive research approach to conceptualize Smart Maintenance using focus groups and interviews with more than 110 experts from over 20 different firms. By viewing our original data through the lens of multiple general theories, our findings chart new directions for contemporary and future maintenance research. This paper describes empirical observations and theoretical interpretations cumulating in the first empirically grounded definition of Smart Maintenance and its four underlying dimensions; data-driven decision-making, human capital resource, internal integration, and external integration. In addition, the relationships between the underlying dimensions are specified and the concept structure formally modeled. This study thus achieves concept clarity with respect to Smart Maintenance, thereby making several theoretical and managerial contributions that guide both scholars and practitioners within the field of industrial maintenance management.
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| 24. |
- Bokrantz, Jon, 1988, et al.
(author)
-
Smart Maintenance: Instrument Development, Content Validation and an Empirical Pilot
- 2020
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In: International Journal of Operations and Production Management. - 1758-6593 .- 0144-3577. ; 40:4, s. 481-506
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Journal article (peer-reviewed)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.
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| 25. |
- Bokrantz, Jon, 1988, et al.
(author)
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The use of engineering tools and methods in maintenance organisations: mapping the current state in the manufacturing industry
- 2016
-
In: Procedia CIRP. - : Elsevier BV. - 2212-8271. ; 57, s. 556-561
-
Conference paper (peer-reviewed)abstract
- Digitalisation is the future of the manufacturing industry, and it will entail production systems that are highly automated, efficient, and flexible. The realisation of such systems will require effective maintenance organisations that adopt engineering approaches, e.g. engineering tools and methods. However, little is known about their actual extent of use in industry. Through a survey study in 70 Swedish manufacturing companies, this study shows to what extent engineering tools and methods are used in maintenance organisations, as well as to what extent companies have maintenance engineers performing work related to engineering tools and methods. Overall, the results indicate a potential for increasing the use of engineering tools and methods in both the operational and the design and development phase. This increase can contribute towards achieving high equipment performance, which is a necessity for the realisation of digital manufacturing
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| 26. |
- Boulonne, Adrien, 1987, et al.
(author)
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Simulation Data Architecture for Sustainable Development
- 2010
-
In: Proceedings of the 2010 Winter Simulation Conference. - 9781424498642 ; 42, s. 3435-3446
-
Conference paper (peer-reviewed)abstract
- Reducing costs, improving quality, shortening the time-to-market, and at the same time act and think sus-tainable are major challenges for manufacturing industries. To strive towards these objectives, discrete event simulation (DES) has proven to be an effective tool for production system decision support. Large companies continuously log raw data, and are therefore able to collect large quantities of re-source event information. However, usually it is difficult to reuse data for future DES projects. Thus, the aim of this paper is to describe how to facilitate data sharing between data sources and DES models. A test implementation of a simulation data architecture has been realized. A data processing tool, a database and an interface were created, which provide reusable resource event data to pave the way for sustainable resource information in DES projects. The entirety data exchange is provided by standard XML documents following the latest Core Manufacturing Simulation Data recommendations.
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| 27. |
- Chen, Siyuan, 1997, et al.
(author)
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Understanding Stakeholder Requirements for Digital Twins In Manufacturing Maintenance
- 2023
-
In: Proceedings - Winter Simulation Conference. - 0891-7736. ; , s. 2008-2019
-
Conference paper (peer-reviewed)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.
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| 28. |
- Despeisse, Mélanie, 1985, et al.
(author)
-
Battery Production Systems: State of the Art and Future Developments
- 2023
-
In: IFIP Advances in Information and Communication Technology. - 1868-4238 .- 1868-422X. ; 692, s. 521-535
-
Conference paper (peer-reviewed)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.
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| 29. |
- Dettmann, Tobias, 1987, et al.
(author)
-
Startup Methodology for Production Flow Simulation Projects Assessing Environmental Sustainability
- 2013
-
In: Proceedings of the 2013 Winter Simulation Conference. - 9781479920761 ; , s. 1926-1937
-
Conference paper (peer-reviewed)abstract
- Environmental impact assessments for companies and products are important to increase sales and reduceenvironmental impact. To support improvements and detailed analyses, researchers have extended the useof simulation of production flows to include sustainability performance indicators. The research casesperformed until recently lack standardized methodology and thus have comparability issues and an increasenumber of common faults. By using a common methodology and gathering best practice, futurecases can gain a lot. Especially noted by the authors is that the project startup phase is critical for success.This paper proposes a methodology to support the startup phases of simulation projects with sustainabilityaspects in production flows. The methodology is developed and applied in an automotive industry studypresented in this paper. Using a rigid project startup, such as the proposed methodology, reduces iterationsduring modeling and data collection and decreases time spent on modeling.
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| 30. |
- Duan, Xinjie, et al.
(author)
-
A Data Scientific Approach Towards Predictive Maintenance Application in Manufacturing Industry
- 2022
-
In: Advances in Transdisciplinary Engineering. ; 21, s. 292-303
-
Conference paper (peer-reviewed)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.
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| 31. |
- Gong, Liang, 1985, et al.
(author)
-
Improving manufacturing process change by 3D visualization support: A pilot study on truck production
- 2016
-
In: Procedia CIRP. - : Elsevier BV. - 2212-8271. ; 57, s. 298-302
-
Conference paper (peer-reviewed)abstract
- In the global market, customer demands changes rapidly. Manufacturing companies need to meet the demands to keep competitive. Therefore, manufacturing processes constantly need to be changed. The process change is challenging because it involves different actors across the company, especially for international companies that have globally distributed operations.Manufacturing process change is carried out by production engineers and needs to consider the needs and requirement of all actors in the manufacturing system. To this end, guidelines for manufacturing system design and cross-functional team meetings for concept design assessmentare utilized. While the meeting takes place, such meetings require resources to be available in parallel and advanced planning to come to fruition. Thus making the concept design iteration loop considerably longer if inputs are to be collected frequently.This paper presents an approach that utilizes a collaborative tool developed in Unity. It integrates 3D scanned factory data with proposed process changes to provide sufficient context to every stakeholders involved. Thus, to facilitate better understanding and communication of the ongoing changes. It will improve every process change on its design, implementation, and future maintenance. The challenges of implementation and evaluation of the collaborative tool are discussed.
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| 32. |
- Gopalakrishnan, Maheshwaran, 1987, et al.
(author)
-
Buffer Utilization Based Scheduling of Maintenance Activities by a Shifting Priority Approach – A Simulation Study
- 2016
-
In: Proceedings - Winter Simulation Conference. - 0891-7736. - 9781509044863 ; 0, s. 2797-2808
-
Conference paper (peer-reviewed)abstract
- Machine breakdowns and improper maintenance management cause production systems to function inefficiently. Particularly, breakdowns cause rippling effects on other machines in terms of starved and blocked states. Effective planning of maintenance can lead to improved production system efficiency. This paper aims at improving system throughput through prioritization of maintenance work orders by continuously monitoring buffer levels. This paper proposes and tests a new approach to determine the machine priorities for dynamic scheduling of maintenance work orders by identifying buffer utilization. The approach is exemplified in an industrial use-case. The results have shown to increase throughput in comparison to a first-come-first-served approach for executing maintenance work orders. This new approach relies on simple data collection and analysis, which makes it a viable option for industries to implement with minimal effort. The results can suggest that systems view for maintenance prioritization can be a powerful decision support tool for maintenance planning.
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| 33. |
- Gopalakrishnan, Maheshwaran, 1987, et al.
(author)
-
Data Analytics in Maintenance Planning – DAIMP
- 2019
-
Reports (other academic/artistic)abstract
- 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 activities The 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 results WP6 focused on coordination project management In 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.
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| 34. |
- Gopalakrishnan, Maheshwaran, 1987, et al.
(author)
-
Data-driven machine criticality assessment – maintenance decision support for increased productivity
- 2022
-
In: Production Planning and Control. - : Informa UK Limited. - 0953-7287 .- 1366-5871. ; 33:1, s. 1-19
-
Journal article (peer-reviewed)abstract
- 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, seldom updated 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.
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| 35. |
- Gopalakrishnan, Maheshwaran, 1987, et al.
(author)
-
Machine criticality assessment for productivity improvement: Smart maintenance decision support
- 2019
-
In: International Journal of Productivity and Performance Management. - : EMERALD GROUP PUBLISHING LTD. - 1741-0401 .- 1758-6658. ; 68:5, s. 858-878
-
Journal article (peer-reviewed)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.
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| 36. |
- Gopalakrishnan, Maheshwaran, 1987, et al.
(author)
-
Machine criticality based maintenance prioritization: Identifying productivity improvement potential
- 2018
-
In: International Journal of Productivity and Performance Management. - 1741-0401. ; 67:4, s. 654-672
-
Journal article (peer-reviewed)abstract
- Purpose – The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials. Design/methodology/approach – An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential. Findings – The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted. Originality/value – In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.
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| 37. |
- Gopalakrishnan, Maheshwaran, 1987, et al.
(author)
-
Planning of Maintenance Activities – A current state mapping in industry
- 2015
-
In: Procedia CIRP. - : Elsevier BV. - 2212-8271. ; 30, s. 480 - 485
-
Conference paper (peer-reviewed)abstract
- Industrial Product Service System (PSS) thinking can be applied to production system by considering it as a product. Prior studies show that strategic planning of the maintenance activities in manufacturing industries holds great potential to increase productivity. Planning of maintenance activities is therefore an integral decision making aspect for maintenance engineers and it is important to analyze how industries are currently working with planning of maintenance activities and what additional support is needed. This paper aims at mapping the current state of the work procedures for maintenance engineers and planners in the industry and analyzes the gap from current practices to the strategic planning which could increase productivity. The study specifically focuses on how industries work today with finding critical resource, performing criticality analysis, and planning maintenance. A descriptive research approach is followed, where empirical data is collected in Swedish industry through three different data collection methods. The results show the state-of-art industrial practices and the gaps in maintenance planning.
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| 38. |
- Gopalakrishnan, Maheshwaran, 1987, et al.
(author)
-
Simulation-based planning of maintenance activities by a shifting priority method
- 2015
-
In: Proceedings - Winter Simulation Conference. - 0891-7736. ; 2015-January, s. 2168-2179
-
Conference paper (peer-reviewed)abstract
- Machine failures are major causes of direct downtime as well as system losses (blocked and idle times) in production flows. A previous case study shows that prioritizing bottleneck machines over others has the potential to increase the throughput by about 5%. However, the bottleneck machine in a production system is not static throughout the process of production but shifts from time to time. The approach for this paper is to integrate dynamic maintenance strategies into scheduling of reactive maintenance using Discrete Event Simulation. The aim of the paper is to investigate how a shifting priority strategy could be integrated into the scheduling of reactive maintenance. The approach is applied to and evaluated in an automotive case-study, using simulation for decision support. This shows how to shift prioritization by tracking the momentary bottleneck of the system. The effect of shifting priorities for planning maintenance activities and its specific limitations is discussed.
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| 39. |
- Gopalakrishnan, Maheshwaran, 1987, et al.
(author)
-
Simulation-based planning of maintenance activities in the automotive industry
- 2013
-
In: Proceedings of the 2013 Winter Simulation Conference 8 - 11 Dec, 2013, Washington D.C., USA. - 9781479939503 ; , s. 2610-2621
-
Conference paper (peer-reviewed)abstract
- Factories world-wide do not utilize their existing capacity to a satisfactory level. Several studies indicate an average Overall Equipment Efficiency (OEE) of around 55% in manufacturing industry. One major reason is machine downtime leading to substantial system losses culminating in production plans with un-satisfactory robustness. This paper discusses an approach to integrate maintenance strategies into a pro-duction planning approach using discrete event simulation. The aim is to investigate how and where in the planning process maintenance strategies can be integrated and how different maintenance strategies influence production performance and the overall robustness of production plans. The approach is exemplified in an automotive case study, integrating strategies for reactive maintenance in a simulation model to sup-port decision making on how repair orders should be prioritized to increase production performance. The results show that introducing priority-based planning of maintenance activities has a potential to increase productivity by approximately 5%.
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| 40. |
- Gustafsson, Bertil, 1949, et al.
(author)
-
Design and Problem Oriented Education based on the Application of Knowledge – Developing Chalmers' Master’s Programme in Production Engineering
- 2011
-
In: Proceedings of the Swedish Production Symposium 2011. ; , s. 442-449
-
Conference paper (peer-reviewed)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.
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| 41. |
- Ito, Adriana, 1985, et al.
(author)
-
A Collaborative Digital Platform for Root Cause Analysis in a Value Chain
- 2023
-
In: Advances in Transdisciplinary Engineering. ; 35, s. 299-307
-
Conference paper (peer-reviewed)abstract
- Collaboration among actors is necessary to create and develop resilience in value chains. However, collaboration tends to be quite restricted when companies are dealing with disturbances and looking for their root causes. With the objective of increasing collaboration among companies in the same value chain in the process of root cause analysis, this article proposes a high-level design of a digital platform. To develop the high-level design, a design science research approach was taken, with the participation of ten different companies. The proposed design is based on identified problems and requirements regarding root cause analysis in the value chain. This study contributes both practically and theoretically. Practically, the proposed high-level design can be used as a direct input for the actual development of a collaborative digital platform. Theoretically, in this study, problems and requirements regarding root cause analysis at the level of the supply chain are identified, adding to existing knowledge in the field.
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| 42. |
- Ito, Adriana, 1985, et al.
(author)
-
Dealing with resistance to the use of Industry 4.0 technologies in production disturbance management
- 2021
-
In: Journal of Manufacturing Technology Management. - : Emerald Group Holdings Ltd.. - 1741-038X .- 1758-7786. ; 32:9, s. 285-303
-
Journal article (peer-reviewed)abstract
- Purpose: Resistance is expected to emerge with the implementation and use of new technologies in production systems. This work focuses on identifying sources of resistance to the use of Industry 4.0 technologies when managing production disturbances and suitable managerial approaches to deal with them. Design/methodology/approach: A qualitative approach was chosen in this research. The authors conducted a literature review and a series of interviews. Thirty-one papers from the literature review were analysed, and 16 people from five different companies were interviewed. Findings: The authors identified five different sources of resistance and three managerial approaches to dealing with them. The sources of resistance were based on (1) feelings of over-supervision, (2) unclear values, (3) feelings of inadequacy, (4) concerns about loss of power and jobs and (5) work overload. The three approaches to dealing with resistance are (1) communication, (2) participation and (3) training. Originality/value: This work identifies the sources and strategies to deal with resistance to the use of Industry 4.0 technologies in the management of production disturbances. The managerial literature in this area is limited, and to the authors's knowledge, the specific sources for resistance and strategies to deal with that in this topic have not been systematically investigated before.
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| 43. |
- Ito, Adriana, 1985, et al.
(author)
-
Prioritisation of root cause analysis in production disturbance management
- 2022
-
In: International Journal of Quality and Reliability Management. - : Emerald Group Holdings Ltd.. - 0265-671X .- 1758-6682. ; 39:5, s. 1133-1150
-
Journal article (peer-reviewed)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.
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| 44. |
- Ito, Adriana, 1985, et al.
(author)
-
Production disturbances handling: Where are we and where are we heading?
- 2021
-
In: Proceedings of the International Conference on Industrial Engineering and Operations Management. - : IEOM Society. - 2169-8767. ; , s. 12-23
-
Conference paper (peer-reviewed)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.
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| 45. |
- Jain, Sanjay, et al.
(author)
-
Multi-Resolution Modeling for Supply Chain Sustainability Analysis
- 2013
-
In: Proceedings of the 2013 Winter Simulation Conference. - 9781479920778 ; , s. 1996-2007
-
Conference paper (peer-reviewed)abstract
- Consumers are increasingly becoming conscious of the need to reduce environmental impact. This hasmotivated the industry to make efforts to improve the sustainability of their products and supply chains.Such efforts require the ability to analyze the sustainability of supply chains and potential improvements.A systematic approach is needed to evaluate the alternatives that may range from those at the supplychain configuration level to those for improving equipment at a production facility. This paper presents amulti-resolution modeling approach that allows analyzing parts of the supply chain at appropriate level ofdetail. The capability allows studying the supply chain at high level initially and iteratively drilling downto detailed levels in the identified areas of opportunity and evaluating associated improvement alternatives.Multi-resolution modeling directly relates the impact of improvement in one part of the supplychain to overall supply chain performance thus reducing analyst effort and time.
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| 46. |
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| 47. |
- Johansson, Björn, 1975, et al.
(author)
-
Evaluation and Calculation of Dynamics in Environmental Impact Assessment
- 2013
-
In: IFIP Advances in Information and Communication Technology. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1868-4238 .- 1868-422X. - 9783642403514 ; 397:1, s. 135-141
-
Conference paper (peer-reviewed)abstract
- In ten years customers will select products not only based on price and quality but also with strong regard to the product value environmental footprint, including for example the energy consumed. Customers expect transparency in the product realization process, where most products are labeled with their environmental footprint. Vigorous companies see this new product value as an opportunity to be more competitive. In order to effectively label the envi-ronmental impact of a product, it is pertinent for companies to request the envi-ronmental footprint of each component from their suppliers. Hence, companies along the product lifecycle require a tool, not only to facilitate the computing of the environmental footprint, but also help reduce/balance the environmental impact during the lifecycle of the product. This paper proposes to develop a procedure that companies will use to evaluate, improve and externally advertise their product’s environmental footprint to customers.
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| 48. |
- Johansson, Björn, 1975, et al.
(author)
-
Power Level Sampling of Metal Cutting Machines for Data Representation in Discrete Event Simulation
- 2015
-
In: International Journal of Production Research. - : Informa UK Limited. - 0020-7543 .- 1366-588X. ; 53:23, s. 7060-7070
-
Journal article (peer-reviewed)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.
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| 49. |
- Johansson, Marcus, 1992, et al.
(author)
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A TEST IMPLEMENTATION OF THE CORE MANUFACTURING SIMULATION DATA SPECIFICATION
- 2007
-
In: Proceedings of the 2007 Winter Simulation Conference in Washington D.C. USA, December 9-12 2007. ; , s. 1673-1681
-
Conference paper (peer-reviewed)abstract
- This paper describes an effort of testing the Core Manufacturing Simulation Data (CMSD) information model as a neutral data interface for a discrete event simulation model developed using Enterprise Dynamics. The implementation is based upon a model of a paint shop at a Volvo Car Corporation plant in Sweden. The model is built for a Swedish research project (FACTS), which focuses on the work procedure of developing new and modified production systems. FACTS has found standardized simulation data structures to be of high interest to achieve efficient data collection in conceptual stages of production development programs. For the CMSD-development team, implementations serve as an approach to validate the structures in CMSD and to gather requirements for future enhancements. CMSD was originally developed to support job shops, but the results of this implementation indicate a good possibility to extend CMSD to also support flow shops.
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| 50. |
- Joseph, Doyel, et al.
(author)
-
A Predictive Maintenance Application for A Robot Cell using LSTM Model
- 2022
-
In: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963 .- 2405-8963. ; 55:19, s. 115-120
-
Conference paper (peer-reviewed)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.
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