Data-driven decision making for the Circular Economy

Data science and the circular economy are two key issues for future business competitiveness. Data-driven decision making (DDDM) provides new insights into consumer behaviour and material flows across the supply chain thereby enabling circularity strategies such as end-of-life collection, refurbishment, remanufacturing and recycling. However, companies are currently seeking for strategies to unlock the full potential of data-driven decision making for circular strategies. This is mostly because empirical research that integrates the development of circular strategies with the use of data from a business perspective remains almost inexistent. This paper fills this gap by looking into how product and service related data informs the decision making process to build circular business strategies in the Dutch industrial sector. To this end, a multiple case study approach is adopted where we study a sample of 10 manufacturing companies. The Netherlands manufacturing industry is particularly interesting as it is accelerating its transition towards a circular economy pushed by a governmental programme with the ambition to achieve full circularity by 2050 and a 50% reduction in the use of primary raw materials (minerals, fossil and metals) by 2030. The data collection for this research is structured in three steps: first, a literature review and desktop research set the basis for the analysis framework; second, a list of manufacturing companies to be studied is analysed to select the ones that already have a data-driven decision making process established; third, semi-structured interviews are conducted with representants from the selected manufacturing companies who are experienced with data-driven decision making. Next, we compare the cases studied to understand how manufacturing companies vary in their data collection, data analysis and decision making process for circular strategies. We expect that the data-driven decision making process will depend on product-specific characteristics, such as the phase in the product life cycle, a product’s position within the value chain, as well as sectoral characteristics such as customers’ behaviour. The key findings and insights from this research will be summarized as an implementation strategy of DDDM for circular business models. This paper makes three major contributions, namely: (i) it develops a new analytical framework bridging the literature on DDDM and circular economy which is useful for strategy development in both fields; (ii) it offers new empirical evidence on how companies may use DDDM to foster the transition towards a circular economy; (iii) it sheds new light into policy mechanisms capable of optimising the interface between DDDM and circular economy in the manufacturing sector.