Smart Service Systems in Manufacturing: An Investigation of Theory and Practice

Abstract

The digitalization has put forward numerous devices dubbed as 'smart'. This development can be observed throughout the entire value chain and across industries with fundamental implications on the co-creation of value. In order to structure this phenomenon, the service science discipline conceptualized so-called smart service systems. This article transfers the theoretical conceptualization into the domain of manufacturing. To assess the state of research on smart services in manufacturing, a structured literature review is performed. As the transfer from a general conceptualization into a concrete domain calls for a more practice-oriented approach, we support our literature study through in-depth interviews with a leading automation technology provider. By interpreting the domain as a smart service system, five barriers to adoption in practice are identified and discussed regarding their implications for research.

Predictive Maintenance on the Energy Distribution Grid–Design and Evaluation of a Digital Industrial Platform in the Context of a Smart Service System

“…Predictive Maintenance is a sophisticated maintenance strategy that is based on sensors that detect condition changes and failures with the help of advanced signal processing techniques [31]. Predictive maintenance is already one of the most applied scenarios for smart service systems, especially in manufacturing contexts [32]. The implementation of predictive maintenance avoids downtimes by predicting defects before they occur and also prevents unnecessary equipment replacement [8].…”

“…From a technical perspective, distribution grid operators deploy these assets into their physical distribution grids, i.e., electrical substations. From the perspective of value co-creation, the data gained from these devices is then used to inform value creating activities of customers [32]. More specifically, value is cocreated based on the four capabilities of smart, connected products [19], [75]: monitoring, control, optimization, and autonomy.…”

Digital Factory Transformation from a Servitization Perspective: Fields of Action for Developing Internal Smart Services

“…Companies should, therefore, carefully consider which level of the DIKW hierarchy is possible with the existing data and capabilities of the company. The analysis of large volumes of data and the increasing automation of smart services is often made possible by applying technologies and methods of AI, as described in Section 2.3 [46]. Particularly in the case of smart services that use black-box artificial intelligence methods for decision-making, aspects of traceability and transparency often pose a problem.…”

“…In contrast, other methods, especially deep learning algorithms (e.g., convolutional neural networks), represent a class of machine learning that prioritize predictive accuracy and, thus, the quality of results of the digital service, sacrificing (at least in part) transparency and traceability [47]. By independently linking mathematically defined entities on a multitude of layers, neural networks can increase the quality of the output, but the exact The analysis of large volumes of data and the increasing automation of smart services is often made possible by applying technologies and methods of AI, as described in Section 2.3 [46]. Particularly in the case of smart services that use black-box artificial intelligence methods for decision-making, aspects of traceability and transparency often pose a problem.…”