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Recent economic transformations have forced companies to redefine their value propositions, increasing traditional product offerings with supplementary services—the so-called Product-Service System (PSS). Among them, the adoption of Industry 4.0 technologies is very common. However, the directions that companies are undertaking to offer new value to their customers in the Industry 4.0 have not yet been investigated in detail. Based on a focus group, this paper contributes to this understanding by identifying the main trajectories that would shape a future scenario in which PSS and Industry 4.0 would merge. In addition, future research directions addressing (a) the transformation of the PSS value chain into a PSS ecosystem, (b) the transformation inside a single company towards becoming a PSS provider, and (c) the digital transformation of the traditional PSS business model are identified.

Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service delivery. One of the services that are expected to benefit most from data availability is maintenance. Through the analysis of the asset health status, service providers can make informed and timely decisions to prevent failures. Despite this, the offering of data-based maintenance service is not trivial, and requires providers to structure themselves to collect, analyze and use historical and real-time data properly (e.g., introducing suitable information flows, methods and competencies). The paper aims to investigate how a manufacturing company can re-engineer its maintenance service delivery process in a data-driven fashion. Thus, the paper presents a case study where, based on the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M), an Italian manufacturing company reengineered its maintenance service delivery process in a data-driven fashion. The case study highlights the benefits and barriers coming with this transformation and aims at helping manufacturing companies in understanding how to address it.

Knowledge extraction and reuse are critical topics for manufacturing companies willing to strengthen their Product-Service Systems (PSS) offerings. In manufacturing's maintenance processes, effectiveness and efficiency depend on the ability to learn from past field interventions. Dealing with unstructured descriptions of maintenance activities has prevented manufacturing companies from analyzing them, causing the loss of useful information. Natural Language Processing (NLP) demonstrated high potential, allowing simplified text knowledge extraction and summarization. Besides, the literature presents only a few applications of topic modeling for maintenance improvement in the manufacturing domain. Using a case study, the paper demonstrates the potentialities of NLP adoption to improve not only the maintenance management and execution but also the asset design and management, impacting the whole PSS. In other words, implications will have effects on the operational (e.g. maintenance execution), managerial (e.g. maintenance management), and business levels (e.g. PSS offering definition) of manufacturing firms.

Digital servitization is a business model transformation process enabled by the use of digital technologies to create or improve industrial services and product‐service offerings by creating value and competitive advantage increasing customer satisfaction and loyalty as well as company revenue streams. 5G networks can enable digital servitization of manufacturing by providing faster, more secure, and more reliable communications between machines, devices, and humans. This paper explores the impact of adopting 5G technologies on servitization and identifies the services that can benefit most from 5G networks. The research consists of two parts: a literature review of the technologies currently used in the design and provision of industrial services that could benefit from 5G networks and an exploratory survey involving manufacturing companies that have started the digital servitization journey. The main results emerging from the research suggest that 5G can profoundly impact services supported by Augmented Reality, Cloud computing, and Cyber‐physical systems, mainly concerning maintenance, workforce training, machine diagnosis and monitoring.

PurposeThis paper presents a model aiming to identify the factors influencing the adoption of augmented reality (AR) for industrial services.Design/methodology/approachThe study combines a literature analysis with an empirical study conducted exploring how five large industrial companies are introducing AR for supporting the provision of technical assistance and industrial services to their installed base.FindingsThe authors identify four categories (task, workforce, context and technology) that combine 18 factors that manufacturing companies should consider when introducing AR technology to support industrial services.Originality/valueThis paper systematises the fragmented literature on technology adoption and in particular those works related to the factors affecting the adoption of AR in industrial services. Based on literature and empirical evidence, the authors propose a novel framework that can help companies in the selection of AR solution based on their specific applications and situations. This study therefore contributes also to the existing literature on the adoption of I4.0 and digital technologies in industrial services.

As is known, sustainability issues represent one of the main challenges companies have to face. Among all, the fashion industry is considered one of the most impactful, both in terms of resource utilization and pollution. Fashion renting is a recent business model for companies to reduce their environmental footprint, following a circular economy approach. The study aims to develop and discuss the proposed hybrid approach to effectively support fashion companies in designing new business models, taking into account both the customer and the company perspective. On the one hand, agent-based modeling (ABM) allow us to represent customers’ behaviour and interaction. On the other hand, discrete event simulation (DES) paradigm is used to model fashion renting processes. Because customers’ attitude to that service reflects its successful implementation, motivators and barriers have been investigated to be included in the model. The practical implication is defining a model to support fashion companies in designing rental business models before implementing them. From a theoretical point of view, it overcomes the literature gap about the definition of a unique model for fashion renting, including processes, customers and interactions between agents. Follow-up research will include the presentation of simulation results.

The combination of servitization and digitalization is increasingly changing the economy and society at the global level towards sustainability goals. Companies are shifting their business models, typically oriented to selling products, towards providing bundles of products and services and integrating them with technologies enabling data collection and analysis, resulting in the so-called smart Product Service Systems (PSS). Different approaches and techniques have been put forth to design PSS and, more recently, smart PSS, but they continue to primarily concentrate on establishing value propositions and ignore the question of what sort of operational data can be gathered and used to deliver the PSS solution. Therefore, manufacturing companies willing to expand their portfolio with new advanced services nowadays still face multiple challenges. To address this gap, this study proposes the Service Engineering Methodology for the engineering of smart PSS (SEEM-Smart), which takes into account the trade-off between customer satisfaction and internal efficiency with a focus on data gathering and information flow. The methodology is then applied in a real-world setting. The case study shows the application of the SEEM-Smart for engineering a new data-driven service offering enabled by a cloud-based platform supporting the service provision.

PurposeThis paper aims to present a dual-perspective framework for maintenance service delivery that should be used by manufacturing companies to structure and manage their maintenance service delivery process, using aggregated historical and real-time data to improve operational decision-making. The framework, built for continuous improvement, allows the exploitation of maintenance data to improve the knowledge of service processes and machines.Design/methodology/approachThe Dual-perspective, data-based decision-making process for maintenance delivery (D3M) framework development and test followed a qualitative approach based on literature reviews and semi-structured interviews. The pool of companies interviewed was expanded from the development to the test stage to increase its applicability and present additional perspectives.FindingsThe interviews confirmed that manufacturing companies are interested in exploiting the data generated in the use phase to improve operational decision-making in maintenance service delivery. Feedback to improve the framework methods and tools was collected, as well as suggestions for the introduction of new ones according to the companies' necessities.Originality/valueThe paper presents a novel framework addressing the data-based decision-making process for maintenance service delivery. The D3M framework can be used by manufacturing companies to structure their maintenance service delivery process and improve their knowledge of machines and service processes.

PurposeNowadays manufacturers companies are increasingly compelled to navigate towards servitization. Different methods and approaches were proposed in literature to support them to switch from traditional product-based business model to product service systems (PSSs). However, new knowledge, capabilities and skills were needed to consistently develop PSSs, since they need a joint focus on both customer’s perspective and company’s internal performance and at the same time a proper support for the integration of product and service design. The purpose of this paper is to propose the Product Service System Lean Design Methodology (PSSLDM), a structured methodology to develop PSSs along their entire lifecycle.Design/methodology/approachRetrieving concepts from interpretative, interactive and system development research traditions, and strongly reminding the design research methodology framework, the adopted research methodology is composed of three main phases (observation and conceptualization, theory building and tool development, validation) and involved three heterogeneous companies.FindingsThis paper provides an overview of the PSSLDM, explaining how the different methods supporting its conduction should contribute to properly design an integrated PSS. Moreover, companies highlighted several benefits in the different stages along the PSS lifecycle deriving by the adoption of the PSSLDM.Research limitations/implicationsThe development of a platform based on the PSSLDM methodology raises a discussion on the possible changes needed by current Product Lifecycle Management (PLM) models and systems when they have to do with PSSs.Originality/valueThe PSSLDM enriches the already proposed SErvice Engineering Methodology, introducing new several components linked by lean rules in each of its phases (starting from customer analysis, going through solution concept and detailed design, until the offering analysis) and better supprting the deatil design of both prodcut and service components.

Although the move to more service‐oriented business can be beneficial even to smaller firms, servitization in SMEs remains a largely unexplored topic. The authors contribute to fill this gap exploring how SMEs can overcome the knowledge gaps of servitization faced by companies in the early‐stages of this journey. By combining systematic literature review and expert panel methodology, the authors identify three knowledge gaps that hinder servitization initiatives in SMEs and propose a set of managerial recommendations to tackle with these gaps. In particular, the authors suggest a structured plan of recommendations, and point out how each stakeholder can contribute to fill the mentioned gaps. The proposed actions are specifically suggested for SMEs and focus on greater engagement of internal and external stakeholders. In addition to contributing to the domain scientific research on servitization, the authors therefore respond to the call for application‐oriented research. The authors address the knowledge gaps that hinder servitization initiatives in small and medium enterprises and propose a set of managerial recommendations in order to tackle with these gaps. These results were obtained by combining systematic literature review and expert panel methodology.