Business understanding, challenges and issues of Big Data Analytics for the servitization of a capital equipment manufacturer

One of the most promising areas where Big Data Analytics can be integrated into business-oriented projects-allowing research and development teams to work hand in hand with industry representatives - is the digitalization of manufacturing industry. There are two main driving forces for the interest in this area: the promotion of key strategies such as German Government's Industrie 4.0 or General Electric's Industrial Internet, and the use of servitization strategies to transform manufacturing business models. This paper presents a case study based on a Big Data Analytics applied research project developed for a capital equipment manufacturer. Their current business model is based on selling machinery and storage infrastructure for larger chemical manufacturing companies spread worldwide. The project is developed in the context of a servitization strategy where this capital equipment manufacturer aims at attaching valued-added services to their products, leveraging the use of Big Data Analytics to assist their customers in order to optimize their production process. The paper uses the CRISP-DM (CRoss-Industry Standard Process for Data Mining) methodology as a framework to organize and present our main findings related to the Business Understanding phase. This allow us to provide pragmatic, business-oriented considerations that can be leveraged by research and development teams when exploring opportunities to develop Big Data Analytics projects in the context of manufacturing servitization. Thus, they can face the initial steps of those projects with a better understanding of the specificities of this application field, as well as an a priori identification of problematic situations that may arise and required competencies to be covered by their team members.