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Strand, Mattias
Publications (10 of 23) Show all publications
Liu, Y., Syberfeldt, A. & Strand, M. (2018). A Review of Simulation Based Life Cycle Assessment in Manufacturing Industry. In: Peter Thorvald, Keith Case (Ed.), Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11 – 13, 2018, Skövde, Sweden. Paper presented at 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11 – 13, 2018, Skövde, Sweden (pp. 381-386). Amsterdam, Berlin, Washington,DC: IOS Press, 8
Open this publication in new window or tab >>A Review of Simulation Based Life Cycle Assessment in Manufacturing Industry
2018 (English)In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11 – 13, 2018, Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam, Berlin, Washington,DC: IOS Press, 2018, Vol. 8, p. 381-386Conference paper, Published paper (Refereed)
Abstract [en]

The manufacturing industry has a duty to minimize their environmental impact and more and more legislations include environmental impact evaluations from a life cycle perspective to avoid burden shift. Current manufacturing industry increase their use of computer-based simulations for optimizing production processes. In recent years, a number of studies have been published, combining simulations with life cycle assessments (LCA), to evaluate and minimize the environmental impact of production activities, as part of improving the production processes. Still, current knowledge concerning simulations for LCA is rather scattered. Therefore, this paper reviews relevant literature covering simulation based LCA for production development. The results of the review and cross comparison of papers are structured following the 6 categories in line with the ISO standard definition of LCA (goal formulation, scope definition, environmental impact assessment, data quality, level of modelling details, and model validation) and report the strengths and constraints of the reviewed studies. 

Place, publisher, year, edition, pages
Amsterdam, Berlin, Washington,DC: IOS Press, 2018
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 8
Keywords
life cycle assessment, production process, simulation
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics Environmental Analysis and Construction Information Technology
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16342 (URN)10.3233/978-1-61499-902-7-381 (DOI)2-s2.0-85057394386 (Scopus ID)978-1-61499-901-0 (ISBN)978-1-61499-902-7 (ISBN)
Conference
16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11 – 13, 2018, Skövde, Sweden
Available from: 2018-10-26 Created: 2018-10-26 Last updated: 2019-02-06Bibliographically approved
Gudfinnsson, K. & Strand, M. (2018). On transforming into the data-driven decision-making era: current state of practice in manufacturing smes. In: Peter Thorvald, Keith Case (Ed.), Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden. Paper presented at 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden (pp. 337-342). Amsterdam: IOS Press
Open this publication in new window or tab >>On transforming into the data-driven decision-making era: current state of practice in manufacturing smes
2018 (English)In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, p. 337-342Conference paper, Published paper (Refereed)
Abstract [en]

Current research lacks details on how SMMEs are able to capitalize on how their IT-solutions supports data-driven decision-making. Such details are important for being able to support further development of SMMEs and assuring their sustainability and competitive edge. Prosperous SMMEs are vital due to their economical and societal importance. To alleviate the lack of details, this paper presents the results of four case studies towards SMMEs partly aimed at investigating their current state of data-driven decision-making. The findings reveal that IT-solutions in some areas are either underdeveloped or unexplored. Instead, the SMMEs tend to focus on traditional manufacturing techniques, continuous improvements in the manufacturing process, and manual support routines and thereby neglects opportunities offered in relation to e.g. incident management, product quality monitoring, and the usage of KPIs not directly linked to manufacturing.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2018
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 8
Keywords
Decision making, Manufacture, Metadata, Competitive edges, Continuous improvements, Data driven decision, Incident Management, Manufacturing process, Product quality monitoring, State of practice, Traditional manufacturing, Industrial research
National Category
Production Engineering, Human Work Science and Ergonomics Other Mechanical Engineering
Research subject
Information Systems; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16494 (URN)10.3233/978-1-61499-902-7-337 (DOI)2-s2.0-85057361916 (Scopus ID)978-1-61499-901-0 (ISBN)978-1-61499-902-7 (ISBN)
Conference
16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden
Available from: 2018-12-13 Created: 2018-12-13 Last updated: 2019-02-08Bibliographically approved
Strand, M., Syberfeldt, A. & Geertsen, A. (2017). A Decision Support System for Sustainable Waste Collection. International Journal of Decision Support System Technology, 9(4), 49-65, Article ID 4.
Open this publication in new window or tab >>A Decision Support System for Sustainable Waste Collection
2017 (English)In: International Journal of Decision Support System Technology, ISSN 1941-6296, E-ISSN 1941-630X, Vol. 9, no 4, p. 49-65, article id 4Article in journal (Refereed) Published
Abstract [en]

This paper presents a decision support system (DSS) for making the waste collection process more sustainable. Currently, waste collection schedules and routes are created manually in most waste management organizations. Thisis both very time consuming and likely to result in poorsolutions, as the task is extremely difficult due to the large number of bins combined with the many parametersto be considered simultaneously. With a sophisticated DSS, it becomes possible to addressthe complexities of optimal waste collection and improve sustainability—not least from the environmental perspective. The DSS proposed here is designed to be used on the operational level in the waste management organization and supports daily operations and activities. System evaluation indicatesthat it can reduce truck operating time by approximately 25%, corresponding to a saving of approximately 21,300 kg of carbon dioxide and 187 kg of nitrogen oxides per year and truck.

Keywords
Decision Support System, Simulation-Based Optimization, Sustainability, Waste Collection
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13924 (URN)10.4018/IJDSST.2017100104 (DOI)000418547100005 ()2-s2.0-85028708937 (Scopus ID)
Available from: 2017-07-15 Created: 2017-07-15 Last updated: 2019-01-24Bibliographically approved
Gudfinnsson, K. & Strand, M. (2017). Challenges with BI adoption in SMEs. In: Proceedings of the 8th International Conference on Information, Intelligence, Systems & Applications (IISA): . Paper presented at The 8th International Conference on Information Intelligence Systems Applications 2017, Larnaca, Cyprus, August 27-30, 2017 (pp. 172-177). IEEE
Open this publication in new window or tab >>Challenges with BI adoption in SMEs
2017 (English)In: Proceedings of the 8th International Conference on Information, Intelligence, Systems & Applications (IISA), IEEE, 2017, , p. 6p. 172-177Conference paper, Published paper (Refereed)
Abstract [en]

Business intelligence (BI) has become a well-known umbrella term both amongst academics and practitioners. Researchers have studied how companies can take advantage of BI and what challenges companies are facing when working with BI. However, research is mostly focused on large companies, despite the importance of small- and medium sized companies (SMEs) in both society and economically. This paper presents results of an in-depth qualitative case study on challenges faced by SMEs when adopting BI. The challenges are categorized according to a BI maturity model adopted as unit of assessment. The contribution of the results presented is two-folded; 1) It increases current literature regarding challenges when adopting BI in SMEs, and 2) It serves as guidance for SMEs on common pitfalls that ought to be avoided.

Place, publisher, year, edition, pages
IEEE, 2017. p. 6
Series
International Conference on Information, Intelligence, Systems & Applications (IISA), ISSN 2379-3732
Keywords
Business Intelligence, Business Analytics, BI Maturity, BI Challenges, SMEs
National Category
Information Systems
Research subject
Information Systems; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14976 (URN)10.1109/IISA.2017.8316407 (DOI)000454859600031 ()2-s2.0-85047850236 (Scopus ID)978-1-5386-3731-9 (ISBN)978-1-5386-3732-6 (ISBN)
Conference
The 8th International Conference on Information Intelligence Systems Applications 2017, Larnaca, Cyprus, August 27-30, 2017
Projects
MMC2
Available from: 2018-03-22 Created: 2018-03-22 Last updated: 2019-03-04
Larsson, C., Strand, M., Persson, A. & Syberfeldt, A. (2017). Communicating continuous improvement in manufacturing companies: Divergencies between current practice and theory. In: : . Paper presented at PMAA - Performance Measurement Association Australasia 1-3 march 2017, Dunedin.
Open this publication in new window or tab >>Communicating continuous improvement in manufacturing companies: Divergencies between current practice and theory
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Succeeding with continuous improvement is important for manufacturing companies to increase the competitive edge. In order to succeed with continuous improvement, literature shows that communication of improvement indicators need to be integrated with communication of control indicators. This paper identifies divergencies between current practice and theory in the communication of CI, which can be a reason for why manufacturing companies fail in their CI implementation. An integration of control indicators and improvement indicators could improve continuous improvement results, increasing business performance.

Keywords
Performance measurement, Communication, Continuous improvement, Manufacturing
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Information Systems; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-13469 (URN)
Conference
PMAA - Performance Measurement Association Australasia 1-3 march 2017, Dunedin
Available from: 2017-04-03 Created: 2017-04-03 Last updated: 2017-11-27Bibliographically approved
Gudfinnsson, K., Strand, M. & Berndtsson, M. (2015). Analyzing Business Intelligence Maturity. Journal of Decision Systems, 24(1), 37-54
Open this publication in new window or tab >>Analyzing Business Intelligence Maturity
2015 (English)In: Journal of Decision Systems, ISSN 1246-0125, E-ISSN 2116-7052, Vol. 24, no 1, p. 37-54Article in journal (Refereed) Published
Abstract [en]

Business intelligence has fundamentally changed how companiesconduct their business. In literature, the focus has been on volume-operationcompanies that provide services to millions of customers. In contrast, complexsystemscompanies have fewer customers and pursue customer needs byproviding more customized products and services. This paper presents the resultsof a case study conducted at a complex-systems company, with the overall aim toidentify how complex-systems companies may take advantage of businessintelligence. A framework was used to measure business intelligence maturity ofthe company. In addition, we also explain the current maturity level of the casecompany,based on critical factors for success adopted from the literature. Indoing so, we also contribute on important details regarding factors that must beconsidered by organizations, in order to leverage their analytical capability.Finally, we also propose topics that need to be further investigated, in order toincrease current knowledge regarding BI usage and maturity in complex-systemscompanies.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2015
Keywords
Business Intelligence, Business Analytics, BI Maturity, Complex-Systems companies
National Category
Information Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-10933 (URN)10.1080/12460125.2015.994287 (DOI)000212817900004 ()2-s2.0-84924251992 (Scopus ID)
Available from: 2015-05-13 Created: 2015-05-13 Last updated: 2019-03-04Bibliographically approved
Berndtsson, M., Admyre, M. & Strand, M. (2014). A Fleet Management System Based on Complex Event Processing. In: Gloria Phillips-Wren, Sven Carlsson, Ana Respício, Patrick Brézillon (Ed.), DSS 2.0 – Supporting Decision Making with New Technologies: . Paper presented at IFIP WG 8.3 and SIGDSS Open Conference “DSS 2.0 – Supporting decision making with new technologies”, 2-5 June 2014, University Pierre and Marie Curie (UPMC), Campus Jussieu, Paris, France (pp. 241-252). IOS Press
Open this publication in new window or tab >>A Fleet Management System Based on Complex Event Processing
2014 (English)In: DSS 2.0 – Supporting Decision Making with New Technologies / [ed] Gloria Phillips-Wren, Sven Carlsson, Ana Respício, Patrick Brézillon, IOS Press, 2014, p. 241-252Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IOS Press, 2014
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389 ; 261
National Category
Information Systems Other Computer and Information Science
Research subject
Technology; Information Systems
Identifiers
urn:nbn:se:his:diva-9403 (URN)10.3233/978-1-61499-399-5-241 (DOI)000350220100022 ()2-s2.0-84902262524 (Scopus ID)978-1-61499-398-8 (ISBN)978-1-61499-399-5 (ISBN)
Conference
IFIP WG 8.3 and SIGDSS Open Conference “DSS 2.0 – Supporting decision making with new technologies”, 2-5 June 2014, University Pierre and Marie Curie (UPMC), Campus Jussieu, Paris, France
Available from: 2014-06-10 Created: 2014-06-10 Last updated: 2019-03-04Bibliographically approved
Gudfinnsson, K., Berndtsson, M. & Strand, M. (2014). Taking Advantage of Business Intelligence in a Complex-Systems Environment. In: Gloria Phillips-Wren, Sven Carlsson, Ana Respício & Patrick Brézillon (Ed.), DSS 2.0 – Supporting Decision Making with New Technologies: . Paper presented at IFIP TC8/Working Group 8.3 conference, Université Pierre et Marie Curie, Paris, France, June 2014 (pp. 265-276). IOS Press
Open this publication in new window or tab >>Taking Advantage of Business Intelligence in a Complex-Systems Environment
2014 (English)In: DSS 2.0 – Supporting Decision Making with New Technologies / [ed] Gloria Phillips-Wren, Sven Carlsson, Ana Respício & Patrick Brézillon, IOS Press, 2014, p. 265-276Conference paper, Published paper (Refereed)
Abstract [en]

Business intelligence (BI) has fundamentally changed how many companies conduct their business. In literature, focus has been on volume-operation companies that provide services to millions of customers. In contrast, complex-systems companies have fewer customers and pursue customer needs by providing more customized products and services. This paper presents the results at a case of a complex-systems company with the overall aim to see how a complex-systems company has taken advantage of BI. In addition, a framework was used to measure the BI maturity of the company. Literature also emphasis that complex-system companies may benefit from adopting BI applications from volume-operations companies, but the results indicate that there may be a difference in the importance of BI tools, which in turn may negatively influence such cross-category adoptions.

Place, publisher, year, edition, pages
IOS Press, 2014
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389 ; 261
Keywords
Business Intelligence, Business Analytics, complex-systems, volume-operations, analytic maturity
National Category
Information Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-10135 (URN)10.3233/978-1-61499-399-5-265 (DOI)000350220100024 ()2-s2.0-84902288789 (Scopus ID)978-1-61499-398-8 (ISBN)978-1-61499-399-5 (ISBN)
Conference
IFIP TC8/Working Group 8.3 conference, Université Pierre et Marie Curie, Paris, France, June 2014
Available from: 2014-10-27 Created: 2014-10-27 Last updated: 2019-03-05Bibliographically approved
Strand, M. & Carlsson, S. A. (2008). Provision of External Data for DSS, BI, and DW by Syndicate Data Suppliers. In: Collaborative Decision Making: Perspectives and Challenges (pp. 245-256). IOS Press
Open this publication in new window or tab >>Provision of External Data for DSS, BI, and DW by Syndicate Data Suppliers
2008 (English)In: Collaborative Decision Making: Perspectives and Challenges, IOS Press, 2008, p. 245-256Chapter in book (Other academic)
Abstract [en]

In order to improve business performance and competitiveness it is important for firms to use data from their external environment. More and more attention is directed towards data originating external to the organization, i.e., external data. A firm can either collect this data or cooperate with an external data provider. We address the latter case and focus syndicate data suppliers (SDSs). They are the most common sources when incorporating external data into business intelligence, DSS, and DW solutions. SDSs are specialized in collecting, compiling, refining, and selling data. We provide a detailed description regarding the business idea of syndicate data suppliers and how they conduct their business, as well as a description of the industry of syndicate data suppliers. As such, the paper increases the understanding for external data incorporation and the possibility for firms to cooperate with syndicat data suppliers.

Place, publisher, year, edition, pages
IOS Press, 2008
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389 ; 176
Keywords
Syndicate data suppliers, data warehousing, external data, DSS, BI
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-2428 (URN)000284917300021 ()978-1-58603-881-6 (ISBN)
Available from: 2008-12-04 Created: 2008-12-04 Last updated: 2017-11-27
Strand, M. (2008). Refining the Concept of Syndicate Data: Categories and Characteristics, Definitions, and a View Ahead. In: Information Systems Engineering: From Data Analysis to Process Networks (pp. 292-307). IGI publications
Open this publication in new window or tab >>Refining the Concept of Syndicate Data: Categories and Characteristics, Definitions, and a View Ahead
2008 (English)In: Information Systems Engineering: From Data Analysis to Process Networks, IGI publications , 2008, p. 292-307Chapter in book (Other academic)
Abstract [en]

This chapter introduces the concepts of external data and syndicate data. It contributes with a conceptual discussion regarding different categories of syndicate data, as well as definitions and applications thereof. In addition, the chapter also gives a view ahead for syndicate data, with respect to organizational, as well as technological challenges and trends. Thereby, it increases the understanding for syndicate data as a vital component in business intelligence initiatives and explains why external data in general and syndicate data in particular has become prerequisites in modern information systems. Furthermore, the author hopes that the categorization and view ahead will not only contribute to researchers through the conceptual discussion and the definitions of concepts, but also to practitioners spending resources on data originating from outside the own organization, by increasing the understanding of the concepts and the actors within the industry.

Place, publisher, year, edition, pages
IGI publications, 2008
Keywords
Data integration, data quality, data retrieval, business intelligence, data warehousing, data collection, information retrieval, information system, strategic information, decision support system
National Category
Computer Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-2883 (URN)978-1-59904-567-2 (ISBN)
Available from: 2009-03-23 Created: 2009-03-23 Last updated: 2018-01-13
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