Digital Service: Technological Agency in Service Systems

This paper defines digital service in the context of technologically enhanced value co-creation between service system entities. Progress in digitalization and Artificial Intelligence (AI) is increasing the relative share of technologically enhanced value co-creation between service system entities (e.g., people, companies, nations). Highly automated technical systems increasingly act as autonomous agents, on behalf of service providers, in value co-creation interactions with the system users. Sufficient conceptualization, abstractions and modeling paradigms for research and development of this type of value co-creation are absent from the literature and introduced in this paper. The main contribution of the paper is introduction and definition of digital service and digital service membrane as fundamental concepts in service science and service systems, with directions for future research on the topic

An Experimental Case Study on Edge Computing based Cyber-Physical Digital Service Provisioning with Mobile Robotics

Digitalization of physical interaction and infrastructure intensive industries provides an opportunity for new kind of value co-creation via cyber-physical digital service provisioning. The rapid technological progress is enabling re-distribution of both physical and cognitive tasks and work between people and machines. The paper explores concept of cyber-physical digital service and presents an experimental case study on cyber-physical digital service provisioning for building diagnostics, utilizing a mobile robot as service actor and resource. The case study applies Design Science Research Methodology (DSRM) with an objective to identify insights on design challenges and digital technology infrastructure requirements of cyber-physical digital service provisioning. Based on evaluation of designed, developed and demonstrated trial system, insights on identified design challenges and related requirements for evolution of digital technology infrastructure are provided as result

Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

The current approaches for energy consumption optimisation in buildings are mainly reactive or focus on scheduling of daily/weekly operation modes in heating. Machine Learning (ML)-based advanced control methods have been demonstrated to improve energy efficiency when compared to these traditional methods. However, placing of ML-based models close to the buildings is not straightforward. Firstly, edge-devices typically have lower capabilities in terms of processing power, memory, and storage, which may limit execution of ML-based inference at the edge. Secondly, associated building information should be kept private. Thirdly, network access may be limited for serving a large number of edge devices. The contribution of this paper is an architecture, which enables training of ML-based models for energy consumption prediction in private cloud domain, and transfer of the models to edge nodes for prediction in Kubernetes environment. Additionally, predictors at the edge nodes can be automatically updated without interrupting operation. Performance results with sensor-based devices (Raspberry Pi 4 and Jetson Nano) indicated that a satisfactory prediction latency (~7–9 s) can be achieved within the research context. However, model switching led to an increase in prediction latency (~9–13 s). Partial evaluation of a Reference Architecture for edge computing systems, which was used as a starting point for architecture design, may be considered as an additional contribution of the paper