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Jägstam, Mats
Publications (10 of 19) Show all publications
Liu, Y., Syberfeldt, A., Urenda Moris, M., Jägstam, M., Jenny, E. & Kloo, H. (2016). Evaluating environmental impacts of production process by simulation based life cycle assessment. In: Proceedings of the 7th Swedish Production Symposium: . Paper presented at 7th Swedish Production Symposium, Lund, Sweden, October 25-27, 2016.
Open this publication in new window or tab >>Evaluating environmental impacts of production process by simulation based life cycle assessment
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2016 (English)In: Proceedings of the 7th Swedish Production Symposium, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Historically, the manufacturing industry is one of the main contributors to the environmental issues. With conservation of the environment becoming more and more critical for survival, it is of importance for the manufacturing industry to take responsibility for minimizing their productions’ environmental impacts. Life cycle assessment has been widely used in the product’s development phase within the manufacturing industry. However, the environmental impacts that come from various dynamic manufacturing processes are only estimated with large uncertainty. Some studies have suggested that the combination of life cycle assessment and production flow simulation is an appropriate approach to address the environmental impacts from the manufacturing processes. Nevertheless, these studies are often limiting their concerns to the limited life cycle phases or certain environmental impacts. This study proposes a framework regarding how to develop a method for evaluating and identifying improvements that help reduce the life-cycle environmental impacts of complex production processes. In addition, this work employs a simplified case study to demonstrate the proposed framework. 

National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-13225 (URN)
Conference
7th Swedish Production Symposium, Lund, Sweden, October 25-27, 2016
Funder
Knowledge Foundation
Available from: 2016-12-12 Created: 2016-12-12 Last updated: 2018-01-31Bibliographically approved
Goienetxea Uriarte, A., Urenda Moris, M., Jägstam, M., Allert, A.-L., Tööj, L. & Karlsson, M. (2011). An Innovative Collaboration Between Industry, University and Nonprofit Agency, for a Competitive Industry: A Swedish case. In: I. Candel Torres, L. Gómez Chova, A. López Martínez (Ed.), ICERI 2001: 4th International Conference of Education, Research and Innovation: Conference proceedings. Paper presented at 4th International Conference of Education, Research and Innovations, Madrid, Spain, 14-16 November, 2011 (pp. 4154-4162). International Association of Technology, Education and Development, IATED
Open this publication in new window or tab >>An Innovative Collaboration Between Industry, University and Nonprofit Agency, for a Competitive Industry: A Swedish case
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2011 (English)In: ICERI 2001: 4th International Conference of Education, Research and Innovation: Conference proceedings / [ed] I. Candel Torres, L. Gómez Chova, A. López Martínez, International Association of Technology, Education and Development, IATED , 2011, p. 4154-4162Conference paper, Published paper (Refereed)
Abstract [en]

In a knowledge based economy, manufacturing industry has to continuously improve their operations, processes and develop their employees in order to remain competitive in the market.

In this context, the collaboration between industry and universities becomes of vital importance. Universities and industry have traditionally maintained fairly informal or lose ways of cooperation when it comes to education. This article presents a fruitful cooperation which has been established between the University of Skövde, the Industrial Development Center in the region, IDC West Sweden AB, and the manufacturing industry.

The paper describes the development, lessons learned and the outcome of more than 3 years’ experience of close collaboration between the different stakeholders. It presents a methodology, used by the consortium to help manufacturing industries to improve their competiveness using a well defined process including: a company analysis, applied education and long-term coaching. A special focus is put on a long-term commitment by all partners. This alliance has performed more than 140 company analysis, conducted applied education for more than 2500 employees from more than 120 companies and performed coaching of more than 80 companies on site. The trend is that these figures will increase over time.

The established collaboration has been strengthened over this period of time by a number of shared research projects. One of these projects involves an evaluation of the impact that this presented consortium has had on the region´s industry. Lean Learning Academies is another project that has been funded by the European Union within the Lifelong Learning Program, with the aim to increase the competitiveness of European companies and enhance the employability of students.

Place, publisher, year, edition, pages
International Association of Technology, Education and Development, IATED, 2011
Series
ICERI proceedings, ISSN 2340-1095
Keywords
University-industry collaboration, cooperation benefits, lifelong learning, education in production development and process improvement, Lean Learning Academy
National Category
Engineering and Technology
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-5842 (URN)000317080004022 ()978-84-615-3324-4 (ISBN)
Conference
4th International Conference of Education, Research and Innovations, Madrid, Spain, 14-16 November, 2011
Available from: 2012-05-04 Created: 2012-05-04 Last updated: 2017-11-27Bibliographically approved
Ng, A., Bernedixen, J., Urenda Moris, M. & Jägstam, M. (2011). Factory flow design and analysis using internet-enabled simulation-based optimization and automatic model generation. In: S. Jain, R. Creasey & J. Himmelspach (Ed.), Proceedings of the 2011 Winter Simulation Conference: . Paper presented at 2011 Winter Simulation Conference. Phoenix, US. 11-14 Dec. 2011 (pp. 2176-2188). IEEE conference proceedings
Open this publication in new window or tab >>Factory flow design and analysis using internet-enabled simulation-based optimization and automatic model generation
2011 (English)In: Proceedings of the 2011 Winter Simulation Conference / [ed] S. Jain, R. Creasey & J. Himmelspach, IEEE conference proceedings, 2011, p. 2176-2188Conference paper, Published paper (Refereed)
Abstract [en]

Despite simulation offers tremendous promise for designing and analyzing complex production systems, manufacturing industry has been less successful in using it as a decision support tool, especially in the early conceptual phase of factory flow design. If simulation is used today for system design, it is more often used in later phases when important design decisions have already been made and costs are locked. With an aim to advocate the use of simulation in early phases of factory design and analysis, this paper introduces FACTS Analyzer, a toolset developed based on the concept of integrating model abstraction, automatic model generation and simulation-based optimization under an innovative Internet-based platform. Specifically, it addresses a novel model aggregation and generation method, which when combined together with other system components, like optimization engines, can synthetically enable simulation to become much easier to use and speed up the time-consuming model building, experimentation and optimization processes, in order to support optimal decision making.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2011
National Category
Engineering and Technology
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-5843 (URN)10.1109/WSC.2011.6147930 (DOI)000300520802045 ()2-s2.0-84858015005 (Scopus ID)978-1-4577-2109-0 (ISBN)978-145772108-3 (ISBN)
Conference
2011 Winter Simulation Conference. Phoenix, US. 11-14 Dec. 2011
Available from: 2012-05-04 Created: 2012-05-04 Last updated: 2018-05-14Bibliographically approved
Ng, A., Jägstam, M., Pehrsson, L. & Deb, K. (2011). Improving Factory Productivity and Energy Efficiency Through Holistic Simulation Optimisation. In: The 21st International Conference on Multiple Criteria Decision Making: . Paper presented at The 21st International Conference on Multiple Criteria Decision Making, Jyväskylä, June 13-17, 2011 (pp. 235). University of Jyväskylä
Open this publication in new window or tab >>Improving Factory Productivity and Energy Efficiency Through Holistic Simulation Optimisation
2011 (English)In: The 21st International Conference on Multiple Criteria Decision Making, University of Jyväskylä , 2011, p. 235-Conference paper, Published paper (Refereed)
Abstract [en]

There is an urgent need for the automotive inductry to explore strategies and methods to accelerate the industrial efficiency progress and support decision making in order to regain profitability. At the same time, decision making should not be made strictly from a view of productivity and investment cost. Manufactures worldwide are taking steps towards more sustainable manufacturing. Sustainability, in terms of "Energy Efficiency", "Lean", "Lead Time Efficiency" and other forms of reuse/conservation of resources has become a paramount factor that needs to be considered not only during the operational stage but from the very first day a production system is designed or configured. Therefore, to optimise a manufacturing system today is not only about maximising capacity and minimising costs, it is also about minimising energy use, minimising loss, minimising manufacturing lead time and other sustainability measures. The aim of the presentation is to introduce an innovative simulation-based optimisation and knowledge elicitation methodology for decision-making support within the production systems lifecycle to increase the profitability (increasing cost effectiveness) and simultaneously sustainability (increasing energy efficiency, reducing losses/wastes and shorten Order to Delivery Time) of the Swedish manufacturing industry. The methodology is so called Holistic Simulation Optimisation (HSO) because unlike today's industrial practice that productivity, cost and sustainability are optimised separately, the framework proposed takes into account productivity, cost, and sustainability in a multi-level and multi-objective context. The HSO methodology is realised through the development of a software toolset that synergistically integrates Discrete Event Simulation with the sustainability and cost models that have been developed or extended by industrial companies with state-of-the-art multi-objective optimisation and data mining technologies. The potential benefits of using the HSO methodology on the efficiency of the production systems that are measurable and can be verified quantitatively are: 5-15% increase in productivity; 10-20% reduction in manufacturing lead time; reduction in environmental wastes, in terms of energy use and other forms of losses (10-20%). The paper will present how these estimations are based on the case studies conducted in Swedish automotive industry.

Place, publisher, year, edition, pages
University of Jyväskylä, 2011
Keywords
Sustainability, Multiobjective optimization, Productivity, Simulation
National Category
Engineering and Technology
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-5648 (URN)978-951-39-4310-3 (ISBN)
Conference
The 21st International Conference on Multiple Criteria Decision Making, Jyväskylä, June 13-17, 2011
Available from: 2012-03-27 Created: 2012-03-27 Last updated: 2017-11-27Bibliographically approved
Ng, A., Urenda Moris, M., Jägstam, M. & Svensson, J. (2009). An Internet-Enabled Tool for Multi-Objective Simulation Optimization. In: OPTIMA 2009: VIII Congreso Chileno de Investigacion Operativa. Paper presented at VIII Congreso Chileno de Investigacion Operativa, 7-10 Oct, 2009.
Open this publication in new window or tab >>An Internet-Enabled Tool for Multi-Objective Simulation Optimization
2009 (English)In: OPTIMA 2009: VIII Congreso Chileno de Investigacion Operativa, 2009Conference paper, Published paper (Refereed)
Keywords
Multi-objective optimization, simulation based optimization, production system optimization
National Category
Engineering and Technology
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-3812 (URN)978-956-332-083-1 (ISBN)
Conference
VIII Congreso Chileno de Investigacion Operativa, 7-10 Oct, 2009
Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2018-05-14Bibliographically approved
Linnéusson, G., Jägstam, M. & Kinnander, A. (2009). Bridging a Methodological Gap in Using System Dynamics in Manufacturing. In: B.-G. Rosén (Ed.), Proceedings of The International 3’rd Swedish Production Symposium, SPS’09: . Paper presented at Proceedings of The International 3rd Swedish Production Symposium, SPS'09, Göteborg, Sweden, 2-3 December 2009 (pp. 19-26). The Swedish Production Academy
Open this publication in new window or tab >>Bridging a Methodological Gap in Using System Dynamics in Manufacturing
2009 (English)In: Proceedings of The International 3’rd Swedish Production Symposium, SPS’09 / [ed] B.-G. Rosén, The Swedish Production Academy , 2009, p. 19-26Conference paper, Published paper (Refereed)
Abstract [en]

Development of manufacturing systems is dependent on human decision making. One important factor in the decision making process is the organisational ability to transform available information into useful knowledge. The ability is generally limited by the organisation's level of competence and use of methods. However, real systems are not simple and straightforward but dynamically complex and difficult to interpret in order to perform successful change. One tool for diagnosing and solving complex business problems is system dynamics. It is interesting for its capability to acknowledge dynamic complexity.

This paper presents a framework of guidelines that facilitates implementing a system dynamics project for manufacturing systems development. It is the result of industrial case studies, supporting verification of the framework contents. This is presented in order to improve using system dynamics as a decision support in manufacturing. And it may bridge a gap between academic theory and industrial practice.

Place, publisher, year, edition, pages
The Swedish Production Academy, 2009
Keywords
system dynamics, manufacturing systems development, decision support for change, group model building, methodology development
National Category
Engineering and Technology
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-3814 (URN)978-91-633-6006-0 (ISBN)
Conference
Proceedings of The International 3rd Swedish Production Symposium, SPS'09, Göteborg, Sweden, 2-3 December 2009
Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2017-11-27Bibliographically approved
Linnéusson, G., Jägstam, M. & Näsström, C. (2008). Cutting Tool Management: A Dynamic Assessment of Opportunities for Improvement. In: Leo J. de Vin (Ed.), Proceedings of the 18th International Conference on Flexible Automation and Intelligent Manufacturing: FAIM 2008. Paper presented at 18th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2008, June 30th - July 2nd, 2008, University of Skövde, Sweden (pp. 1084-1091). , 2
Open this publication in new window or tab >>Cutting Tool Management: A Dynamic Assessment of Opportunities for Improvement
2008 (English)In: Proceedings of the 18th International Conference on Flexible Automation and Intelligent Manufacturing: FAIM 2008 / [ed] Leo J. de Vin, 2008, Vol. 2, p. 1084-1091Conference paper, Published paper (Refereed)
Abstract [en]

Lack of time due to daily problems in need of attention restrains proper assessments of improvement opportunities. There is neither proper support at hand to deal with the dynamic complexity of human activity and systems in use. This paper explores if system dynamics simulation can be used to model tooling problems on a management problem level at a manufacturer and evaluates its use. System dynamics is a methodology designed to aid understanding of dynamically complex problems and increases decision making impact. The results focus on the achieved models which prove to have sense behaviour despite lack of thorough data. In conclusion the applied method provides with an analysis of complex problem situations applicable for a decision support, otherwise performed through good guessing. Main characteristics from reality have been included in model and an experimental laboratory to test future policies on achieved.

Keywords
cutting tool management, system dynamics, manufacturing
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:his:diva-13106 (URN)978-91-633-2757-5 (ISBN)
Conference
18th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2008, June 30th - July 2nd, 2008, University of Skövde, Sweden
Available from: 2016-11-16 Created: 2016-11-16 Last updated: 2017-11-27Bibliographically approved
Ng, A., Grimm, H., Lezama, T., Persson, A., Andersson, M. & Jägstam, M. (2008). OPTIMISE: An Internet-Based Platform for Metamodel-Assisted Simulation Optimization. In: Xu Huang, Yuh-Shyan Chen, Sio-Iong Ao (Ed.), Advances in Communication Systems and Electrical Engineering: (pp. 281-296). Springer Science+Business Media B.V.
Open this publication in new window or tab >>OPTIMISE: An Internet-Based Platform for Metamodel-Assisted Simulation Optimization
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2008 (English)In: Advances in Communication Systems and Electrical Engineering / [ed] Xu Huang, Yuh-Shyan Chen, Sio-Iong Ao, Springer Science+Business Media B.V., 2008, p. 281-296Chapter in book (Refereed)
Abstract [en]

Computer simulation has been described as the most effective tool for de-signing and analyzing systems in general and discrete-event systems (e.g., production or logistic systems) in particular (De Vin et al. 2004). Historically, the main disadvantage of simulation is that it was not a real optimization tool. Recently, research efforts have been focused on integrating metaheuristic algorithms, such as genetic algorithms (GA) with simulation software so that “optimal” or close to optimal solutions can be found automatically. An optimal solution here means the setting of a set of controllable design variables (also known as decision variables) that can minimize or maximize an objective function. This approach is called simulation optimization or simulation-based optimization (SBO), which is perhaps the most important new simulation technology in the last few years (Law and McComas 2002). In contrast to other optimization problems, it is assumed that the objective function in an SBO problem cannot be evaluated analytically but have to be estimated through deterministic/ stochastic simulation.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2008
Series
Lecture Notes in Electrical Engineering, ISSN 1876-1100 ; 4
National Category
Computer Sciences
Identifiers
urn:nbn:se:his:diva-2801 (URN)10.1007/978-0-387-74938-9_20 (DOI)2-s2.0-84885011189 (Scopus ID)978-0-387-74937-2 (ISBN)978-0-387-74938-9 (ISBN)
Available from: 2009-03-02 Created: 2009-03-02 Last updated: 2018-01-13Bibliographically approved
Persson, A., Grimm, H., Ng, A. & Jägstam, M. (2007). A Case Study of Using Simulation and Soft Computing Techniques for Optimisation of Manufacturing Systems. In: Proceedings of Swedish Production Symposium 2007, Gothenburg, Sweden, August 28-30, 2007: . Paper presented at Swedish Production Symposium 2007, Gothenburg, Sweden, August 28-30, 2007.
Open this publication in new window or tab >>A Case Study of Using Simulation and Soft Computing Techniques for Optimisation of Manufacturing Systems
2007 (English)In: Proceedings of Swedish Production Symposium 2007, Gothenburg, Sweden, August 28-30, 2007, 2007Conference paper, Published paper (Refereed)
Identifiers
urn:nbn:se:his:diva-7327 (URN)
Conference
Swedish Production Symposium 2007, Gothenburg, Sweden, August 28-30, 2007
Available from: 2013-02-26 Created: 2013-02-26 Last updated: 2017-11-27Bibliographically approved
Ng, A., Grimm, H., Andersson, M. & Jägstam, M. (2007). A Platform for Metamodel-Assisted Parallel Simulation Optimisation using Soft Computing Techniques. In: The 24th annual workshop of the Swedish Artificial Intelligence Society (SAIS2007): . Paper presented at The 24th annual workshop of the Swedish Artificial Intelligence Society (SAIS2007) (pp. 181-184). Borås: University College of Borås
Open this publication in new window or tab >>A Platform for Metamodel-Assisted Parallel Simulation Optimisation using Soft Computing Techniques
2007 (English)In: The 24th annual workshop of the Swedish Artificial Intelligence Society (SAIS2007), Borås: University College of Borås , 2007, p. 181-184Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Borås: University College of Borås, 2007
Identifiers
urn:nbn:se:his:diva-2077 (URN)
Conference
The 24th annual workshop of the Swedish Artificial Intelligence Society (SAIS2007)
Available from: 2008-05-28 Created: 2008-05-28 Last updated: 2017-11-27Bibliographically approved
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