Taking Advantage of Data Generated by Products: Trends, Opportunities and Challenges

Abstract

Now that all kinds of products are increasingly getting connected to the Internet, it is expected that it will become easier to collect data on how they are actually used during the middle-of-life stage of their product lifecycles. At the same time, a growing number of data analytics technologies offers opportunities to transform this data into actionable knowledge. Over the years, such knowledge extracted from usage data has already become a reliable input for managing maintenance and related services, but other uses such as feedback to design — where product data management systems have started to offer support for data collection practices — and providing advice to end users are now also being considered. Most data from sensors and other product-embedded information devices are collected in batches and analyzed retrospectively. In order for companies to further benefit from data collection in terms of efficacy and acceptance in society, two key challenges are (i) finding ways to effectively use data analytics techniques — which currently do not seem to be used to their full potential, and (ii) finding a good trade-off between respecting privacy and yet producing useful knowledge.

Cyber-Empathic Design: A Data-Driven Framework for Product Design

“…The timing of this work aligns with the recent emergence of the internet of things where information can now be acquired and utilized directly from product operation [49][50][51]. However, acquiring and utilizing data generated by products is restricted to capital intensive products such as automobiles, aircrafts, etc.…”

“…However, acquiring and utilizing data generated by products is restricted to capital intensive products such as automobiles, aircrafts, etc. [51]. Also, use of such data are restricted to predictive maintenance [51][52][53][54], marketing [49][50][51], and environmental impact assessment [51,55].…”

“…[51]. Also, use of such data are restricted to predictive maintenance [51][52][53][54], marketing [49][50][51], and environmental impact assessment [51,55]. In addition, there is a lack of research on utilizing data generated by products as feedback to a design process [51,56].…”

Information chain modeling from product to stakeholder in the use phase – Application to diagnoses in railway transportation

“…A product lifecycle is generally divided into three phases: Beginning-of-Life (BOL), Middle-of-Life (MOL) and End-of-Life (EOL). Even if Product Lifecycle Management (PLM) is considered to cover all the phases of the product lifecycle [2], it often focuses on the BOL and so data in other phases are not sufficiently considered [1,3] resulting in losses in the value chain [4]. Bridging the need for data from the phases after the BOL becomes very critical for the product provider (and stakeholders in general) to improve the product, optimize costs, and be competitive.…”