Riding the Digital Product Life Cycle Waves towards a Circular Economy

Data driven organizations such as Amazon and Uber have raised the capabilities and expectations of customers to a new level by providing faster and cheaper products and services. The reviewed literature documented that 10–15% of the online products are returned and in many cases such products are not shelf-ready due to product obsolescence or slight wear and tear, thereby reducing profits. Many of these products are disposed of in landfills. There were very few publications that documented how integration of digitized product life cycle into the business model improves product returns and the remanufacturing processes. As societies continue on, environmentally responsible, digital journeys with connected devices and people, reverse supply chains and remanufacturing will play increased importance in fulfilling customers expanded expectations. The networks are evolving, wherein, data are collected from all phases of the product lifecycles from design, prototype, manufacturing, usage aftermarket, returns remanufacturing and recycling. The objective of this paper’s authors was to describe how all phases of product life cycles can be digitized to improve global reverse supply chains and remanufacturing. The authors performed a literature review and developed case studies to document current and to predict future transformational waves that will become increasingly used in many industrial sectors. The authors made recommendations about the importance of improved product design, reduced processing costs and increased use of remanufactured products based upon data on returns to manufacturers and service providers. This paper contributes to research by providing a framework of a digitized product life cycle integrated with the business process phases including remanufacturing and supported with real-world case studies for practitioners and academicians. The authors outlined potential future topics for academic researchers and practitioners, for expanding usage of digital tools in real-time predictive analytics to improve remanufacturing system’s efficiency and quality.