Tackling Lack of Motivation in Aspirational Analytics Companies: SME Examples from the Manufacturing Industry

Tackling Lack of Motivation in Aspirational Analytics Companies: SME Examples from the Manufacturing Industry

Kristens Gudfinnsson (School of Informatics, University of Skövde, Skövde, Sweden), Jeremy Rose (School of Informatics, University of Skövde, Skövde, Sweden) and Lena Aggestam (Department of Economics and Informatics, University West, Trollhättan, Sweden)
Copyright: © 2019 |Pages: 18
DOI: 10.4018/IJBIR.2019010101
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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.
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Regardless of size or business domain, companies rely on relevant information to monitor their business activities and to support decision making (Papachristdoulou, Koutsaki, & Kirkos, 2017). Business Intelligence (BI) is used as an umbrella term to cover various technological tools and organizational activities that help decision makers make data-driven decisions and turn business insight into actions (Kumar, Chauhan, & Sehgal, 2012; Lavalle, Hopkins, Lesser, Shockley, & Kruschwitz, 2010; Trieu, 2016). Wixom and Watson (2010) define BI as “a broad category of technologies, applications, and processes for gathering, storing, accessing, and analysing data to help its user make better decisions” (p. 4). BI became established in the 1990’s, and a more recent focus on its key analytical component has become known as Business Analytics (BA), which also encompasses big data and big data analytics. This may be understood as a subfield of BI (Davenport & Harris, 2007) or an advanced discipline in itself (Laursen & Thorlund, 2010). We use the term Business Intelligence and Analytics (BI&A) suggested by Chen, Chiang, & Storey (2012) to indicate our focus on technologies, applications, processes and analytics. Research has addressed different aspects of BI&A, including Cloud BI, mobile BI and various BI applications (Llave, 2017), and reported transformational success stories. However most of these successes involve large companies: Continental Airlines (Anderson-lehman, Watson, & Wixom, 2008), Netflix (Valacich & Schneider, 2010) or Target (Sharda, Delen, & Turban, 2014). When it comes to small- and medium sized enterprises (SME’s) the published work is limited, even though SME’s constitute the backbone of national economies (99% of all European companies are categorized as small or medium sized (Airaksinen, Luomaranta, Alajääskö, & Roodhuijzen, 2015). This research gap has been addressed in literature (Grabova, Darmont, Chauchat, & Zolotaryova, 2010; Llave, 2017; Scholz, Schieder, Kurze, Gluchowski, & Böhringer, 2010), but not substantially addressed even though it has been pointed out that both researchers and practitioners need better understanding on how organizations get value from BI&A (Trieu, 2016). In a comprehensive literature review of BI&A and analytics in SME’s from 2000 to 2016, Llave (2017) showed that popular topics included data warehousing, dashboards, data mining, cloud services and BI&A implementation. However, the relevant research was sparse: nine articles in 2000 focused on BI adoption and three on BI&A benefits for SME’s (Llave, 2017), only three from 2015 and seven from 2016 covered any BI&A topic. Recent interest in big data has refocused research attention on intelligence and analytics, but SME’s are still neglected.

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