Högskolan i Skövde

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.

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.

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.

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 consultant’s experiences from working with this kind of companies for many years.

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.