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Towards facilitating BI adoption in small and medium sized manufacturing companies
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Informationssystem (IS), Information Systems)ORCID iD: 0000-0001-5435-9535
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This work concerns how to support Small and Medium sized Manufacturing Enterprises(SMMEs) with their Business Intelligence (BI) adoption, with the long term aim of supporting them in making better use of their BI investments and becoming (more)data-driven in their decision-making processes. Current BI research focuses primarily on larger enterprises, despite the fact that the majority of businesses are small or mediumsized. Therefore, this research focuses on the body of knowledge concerning how SMMEs can be more intelligent about their business, and better adopt BI to improve decision-making. Accordingly, the overall research aim is to create an artefact that can support SMMEs to facilitate BI adoption. An understanding of the current situation of BI adoption within SMMEs needs to be attained to achieve this, which is the focus for the first research question: What is the current state-of-practice in relation to BI adoption in SMMEs? The research question adds to current knowledge on how SMMEs are taking advantage of BI and highlights which functions within companies are currently supported by BI. Research question two identifies the main challenges that SMMEs are facing in this context: What are the main challenges for BI adoption in SMMEs? This question adds to knowledge regarding some of the barriers and hindrances SMMEs face in BI adoption. Finally, the third research question addresses how SMMEs can address the challenges in successfully adopting BI: How can the main challenges be addressed? The research question is answered by providing descriptions of work in four participating companies addressing different types of problems. Many of the challenges from literature (and from empirical data from the participating companies) regarding BI adoption are met. The outcome adds to the literature a hands-on approach for companies to address chosen problems in their settings, and addressing many of the factors previously found in the BI adoption literature. An action design research (ADR) method is used to fulfill the overall research aim. The ADR method is used to guide the development of a framework artefact based on previousliterature, and on empirical findings from working with participating companies. Theoretical background was obtained through a literature review of BI adoption and usage. Empirical material was gathered both through interviews and by reviewing documents from the companies. The work that was done in participating companies was supported by previous literature in several ways: through the use of an elicitation activity, through the core concepts of BI, and by focusing on categories presented in a BI maturity model. The principal contribution of the research is in the form of a framework: the Business Intelligence Facilitation Framework (BIFF), which includes four phases. All phases contain activities that support companies in addressing BI adoption challenges from the literature and empirical data, in order to achieve the overall research aim. This research contributes both to research and practice. From a research point of view, the framework provides a way to address many of the factors previously identified in literature that need to be in place to increase the likelihood of successful BI adoption. From a practice perspective, the framework supports practitioners offering guidance in how to improve their BI adoption, providing activities for them to take, and guidance in how to carry out the activities.

Place, publisher, year, edition, pages
Skövde: University of Skovde , 2019. , p. 126
Series
Dissertation Series ; 30 (2019)
National Category
Information Systems
Research subject
Information Systems
Identifiers
URN: urn:nbn:se:his:diva-17819ISBN: 978-91-984918-2-1 (print)OAI: oai:DiVA.org:his-17819DiVA, id: diva2:1366237
Public defence
2019-11-22, G110, University of Skövde, Skövde, 13:00 (English)
Opponent
Supervisors
Available from: 2019-10-30 Created: 2019-10-28 Last updated: 2019-11-08Bibliographically approved
List of papers
1. Analyzing Business Intelligence Maturity
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: 2019-10-28Bibliographically approved
2. On transforming into the data-driven decision-making era: current state of practice in manufacturing smes
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, Vol. 8, 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)000462212700054 ()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: 2019-10-28Bibliographically approved
3. Challenges with BI adoption in SMEs
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: 2019-10-28
4. The SmallBuild+ Business Development Method: Findings from a Longitudinal Study in the Construction Sector
Open this publication in new window or tab >>The SmallBuild+ Business Development Method: Findings from a Longitudinal Study in the Construction Sector
2016 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Norsk Konferanse for Organisasjoners Bruk av IT, 2016
Series
Norsk konferanse for organisasjoners bruk av informasjonsteknologi, ISSN 1894-7719 ; Vol 24, nr 1
National Category
Other Computer and Information Science
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-13143 (URN)
Conference
Norsk konferanse for organisasjoners bruk av IT (NOKOBIT), Bergen, Norway, 28-30 November, 2016
Available from: 2016-11-28 Created: 2016-11-28 Last updated: 2019-10-28Bibliographically approved
5. Towards a Generic Goal Model to Support Continuous Improvement in SME Construction Companies
Open this publication in new window or tab >>Towards a Generic Goal Model to Support Continuous Improvement in SME Construction Companies
2015 (English)In: The Practice of Enterprise Modeling: 8th IFIP WG 8.1. Working Conference, PoEM 2015 Valencia, Spain, November 10–12, 2015 Proceedings / [ed] Jolita Ralyté, Sergio España, Óscar Pastor, Springer, 2015, p. 27-42Conference paper, Published paper (Refereed)
Abstract [en]

Small and medium sized (SME) construction companies are often good at bricks, mortar and carpentry but not at management. However, it is often bad management that hinders companies to become financially sustainable over time and to grow. This paper presents a generic goal model aiming to support SME construction companies to systematically work with continuous improvement towards the overarching goal of becoming thriving businesses.

The goal model has been developed based on the principles of lean, balanced scorecards and the business canvas, as well as on a management consultants experiences from working with this kind of companies for many years.

Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348 ; 235
Keywords
enterprise modeling
National Category
Information Systems
Research subject
Technology; Information Systems
Identifiers
urn:nbn:se:his:diva-11650 (URN)10.1007/978-3-319-25897-3_3 (DOI)000369183600003 ()2-s2.0-84952652787 (Scopus ID)978-3-319-25897-3 (ISBN)978-3-319-25896-6 (ISBN)
Conference
8th IFIP WG 8.1. Working Conference, PoEM 2015 Valencia, Spain, November 10–12, 2015
Available from: 2015-10-30 Created: 2015-10-30 Last updated: 2019-10-28Bibliographically approved
6. Tackling Lack of Motivation in Aspirational Analytics Companies: SME Examples from the Manufacturing Industry
Open this publication in new window or tab >>Tackling Lack of Motivation in Aspirational Analytics Companies: SME Examples from the Manufacturing Industry
2019 (English)In: International Journal of Business Intelligence Research, ISSN 1947-3591, E-ISSN 1947-3605, Vol. 10, no 1Article in journal (Refereed) Published
Abstract [en]

Establishing business intelligence analytics (BIA) in small- and medium-sized manufacturing enterprises is a pervasive problem. SME’s - the majority of businesses - play an important role in creating jobs, but research is primarily focused on large corporations. The authors worked with small manufacturing companies at the aspirational capability level but found that their motivation to introduce BIA was low. They had many business challenges but perceived the obstacles (primarily cost and effort) as too great, and their priorities were with operational issues. A two-phase approach based on a well-known analytics maturity model was devised to help raise company motivation. The article describes three studies in different companies using variations of the approach. Comparative analysis of the cases shows that demonstrating a clear path to improved functional efficiency is key to improving motivation, and that simple, easy to learn tools can provide these insights at little cost.

Place, publisher, year, edition, pages
IGI Global, 2019
Keywords
Business Intelligence, Information systems, Manufacturing, Maturity model, SME
National Category
Information Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-16702 (URN)10.4018/IJBIR.2019010101 (DOI)2-s2.0-85071252646 (Scopus ID)
Projects
BISONMM2
Funder
Knowledge Foundation
Note

EISBN13: 9781522566922

Available from: 2019-03-15 Created: 2019-03-15 Last updated: 2019-10-28Bibliographically approved

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