Barriers and Practical Challenges for Data-driven Decision-making in Circular Economy SMEs

Abstract

The circular economy (CE) model is seen as an alternative model to the linear economy models, which seem to be reaching their physical limits. The CE business model aims to reuse materials and decrease the need for virgin materials. This requires the implementation of a reverse supply chain, close collaboration between actors, as well as well-organized logistics. For this reason, the CE companies have typically high demand for digitalized processes and the utilization of data on both operational and business development dimensions. Also the utilization of big data collected from the companies’ business environment can provide new opportunities for business development in CE. Despite the fact that utilization of data collected from the business environment and operations enables data-driven approaches for various decision-making functions in companies, many companies still struggle to figure out how to use analytics to take advantage of their data. In the small- and medium-sized enterprises (SMEs), in particular, the managers are facing difficulties with ever-increasing amounts of data and sophisticated analytics. Indeed, prior research identified several kinds of barriers to the effective utilization of data in SMEs. Still, research on data-driven decision-making remains scarce in CE context. This chapter presents a case study consisting of seven cases, all representing SMEs operating in the field of CE in Finland. In the case study, the barriers and practical challenges for data-driven decision-making in CE SMEs are investigated. Based on the case study results, this chapter proposes that utilization of data, lack of resources, lack of capabilities, and regulation are the main barriers to data-driven decision-making in CE SMEs.