Furthering Service 4.0: Harnessing Intelligent Immersive Environments and Systems

Abstract

With the increasing complexity of service operations in different industries and more advanced uses of specialized equipment and procedures, the great current challenge for companies is to increase employees' expertise and their ability to maintain and improve service quality. In this regard, Service 4.0 aims to support and promote innovation in service operations using emergent technology. Current technological innovations present a significant opportunity to provide on-site, real-time support for field service professionals in many areas. It should be no surprise, then, that intelligent immersive environments have the potential to enhance service operations by improving customer service and increasing overall efficiency. Intelligent immersive systems combine immersive technologies with computational intelligence mechanisms to produce adaptive, context-aware environments for advanced decisionmaking support. Such technologies, e.g., mixed reality (MR) and augmented reality (AR), can potentially enhance working environments, optimizing resources by reducing time and location restrictions, leading to much faster knowledge transfer and a deeper understanding of different processes. These methods can also promote faster response times to provide field service technicians with on-the-job assistance in unfamiliar situations.

Towards augmented reality for corporate training

“…On the other hand, the remaining 60, identify at least one challenge. In general, the following issues have been reported on using AR as a training tool: adapting nontech savvy users to AR technology (Wright et al, 2017), training engagement (Rogado et al, 2017), field of vision (Wang et al, 2018), visual occlusion limitations (Herron, 2016), ergonomics (Stone et al, 2017), environment interference (Bifulco et al, 2014), dependence on internet connection (Ullo et al, 2019), dependence on batteries, fear of changes (Pena-Rios et al, 2018), handling perspective and depth (Abhari et al, 2014), management engagement (Li et al, 2018), high cost of customization (Longo et al, 2017), synchronizing reality and virtuality (Hou et al, 2017), choosing the training scope (Ramírez et al, 2015), resource costs when updating AR training content (Ullo et al, 2019), acquiring trainers for interpersonal interaction (Mourtzis et al, 2018), camera focus (Hořejší, 2015), software issues (Ullo et al, 2019) and ergonomic issues of wearables when used continuously (Jetter et al, 2018).…”

“…From examining the 60 articles we conclude that AR, although with different descriptions, provides the following advantages: reduced training costs (Ullo et al, 2019), facilitated customization (Uva et al, 2018), raised effectiveness (Doshi et al, 2017b), low-risk when exercising critical safety issues (Rogado et al, 2017), attractive to specific groups (Wright et al, 2017), flexible information display (Pena-Rios et al, 2018), improved worker confidence (Torres-Jiménez et al, 2018), fast access to information (Sebillo et al, 2016), support to decision-making (Kobayashi et al, 2018), improved skill transfer process (Pena-Rios et al, 2018), real-time interaction (Sebillo et al, 2016), empowering operators (Syberfeldt et al, 2016), displaying immersive environments (Li et al, 2018), familiarisation with the work routine (Abhari et al, 2014), allowing non-specialised staff to perform specific tasks (Ullo et al, 2019), manpower savings (Hou et al, 2017), decreased training time (Wang et al, 2016), decreased perceived distances (Hou et al, 2017), decreased error rates (Leitritz et al, 2014), reduced cognitive workload (Okazaki and Takaseki, 2017), easy to store and transport (Perdikakis et al, 2015), decreased set-up time (Quandt et al, 2018), welcome by users (Mitsuhara et al, 2017), increased motivation (Bacca et al, 2018), friendly remote assistance and better long term retention of information (Kobayashi et al, 2018) among others. In this subsection, we will discuss how the previously mentioned benefits are documented throughout the articles and analyse how these affect the perceived suitability of AR to training in the working environment.…”

A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations

“…x ∈ F and y ∈ A j if rel(x, y) ≥ 0.7} (6) and rel(x, y) is a relatedness value expressing a partial matching between feature and attribute, i.e., a similarity value (e.g., Wikipedia Linked Measure [17]). The partial match allows us to represent a greater set of situations that may happen in the real scenario, a more generalized AKG.…”

“…Moreover, in the area of service operations, the term Service 4.0 summarizes the idea that the current technological innovations could provide a great opportunity to define on-site, real-time supporting solutions for field service professionals in many areas. Thus, the main challenge for companies is to increase employees' expertise and their ability to maintain and improve service quality [5] [6].…”

Responsible Service Logic

“…Technical experts and governance authorities, are confronted today with many interdependencies between technology, politics, funding, businesses, and social development. They high-velocity, heterogeneous, evolving domain data and the use of advanced techniques and models to store, retrieve, manage, process and analyze the captured information" [22], [23], relying on powerful computational algorithms and "artificial intelligence", able to augment and foster human cognition process [24], [25].…”