Abstract
To date, data science and analytics have received much attention from organizations seeking to explore how to use their massive volumes of data to create value and accelerate the adoption of Circular Economy (CE) concepts. The correct utilization of analytics with circular strategies may enable a step change that goes beyond incremental efficiency gains towards a more sustainable and circular economy. However, the adoption of such smart circular strategies by the industry is lagging, and few studies have detailed how to operationalize this potential at scale. Motivated by this, this study seeks to address how organizations can better structure their data understanding and preparation to align with overall business and CE goals. Therefore, based on the literature and a case study the relationship between data science and the CE is explored, and a generic process model is proposed. The proposed process model extends the Cross Industry Standard Process for Data Mining (CRISP-DM) with an additional phase of data validation and integrates the concept of analytic profiles. We demonstrate its application for the case study of a manufacturing company seeking to implement the smart circular strategy - predictive maintenance.
Highlights
In recent years, the concept of Circular Economy (CE) has received significant attention from businesses, policymakers, and researchers as a way to promote sustainable development [25]
The most commonly used is the CRISP-DM process model created by IBM, reporting a use level of 43% followed by 28% of companies using their own methodology [53]
This paper proposed an enhanced CRISP-DM process model and a case study discussing how to structure the data of the analytic profile of predictive maintenance (PdM) for the context of CE
Summary
The concept of Circular Economy (CE) has received significant attention from businesses, policymakers, and researchers as a way to promote sustainable development [25]. The emergence of new technologies as the Internet of Things, Big Data, and Artificial Intelligence - collectively known as Digital Technologies (DTs) - have encouraged a paradigm shift for industrial production, the ‘Fourth Industrial Revolution’ These DTs are seen as one of the key enablers for a wider adoption and accelerated transition to CE [19, 20]. Kiron and Shockley [36], concur and note that organizations have to develop data-oriented management systems both to make sense of the increasing volumes of data and, more importantly, for transforming the insights into business value and a competitive advantage Supporting this transformation, by the use of analytics methods, is the data science process4 [57].
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