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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.

There is limited scientific and grey literature studying the phenomenon of how the current job profiles are being affected by Industry 4.0 technologies at the operational level. This paper aims to answer the following question: how can the evolution of Workforce 4.0 job profiles be analyzed from a job-task perspective concerning the adoption of smart and digital technologies in manufacturing companies? To this end, it presents a task classification framework addressing three task classification dimensions, namely: (i) routine/nonroutine tasks, (ii) physical/cognitive tasks, and (iii) individual/social tasks, and a job-task analysis method to analyze the evolution of job profiles due to smart or digital technology adoption at the task level. Both artifacts were created using a state-of-the-art review to ground their conceptualization in the most recent knowledge available on work design and job-task analysis methods and were later evaluated and refined using an action-research approach to increase their applicability and usefulness for academic researchers and practitioners. The applicability of the proposed framework and method was demonstrated in an industrial case study discussing the theoretical and managerial contributions of these two artifacts for the development of Workforce 4.0 job profiles. It was concluded that the proposed framework and method are valuable artifacts that contribute to the limited universe of tools available in the literature to first analyze how operators’ tasks and roles change concerning the adoption of new Industry 4.0 technologies and then identify the requirements of new skills and competencies for the evolving and emerging job profiles on the shop floor.

Simulation models have always been an aid in epidemiology for understanding the spread of epidemics and evaluating their containment policies. This paper illustrates how hybrid simulation can support companies in assessing COVID-19 containment measures in indoor environments. In particular, a Hybrid Simulation (HS) is presented. The HS model consists of an Agent-Based Simulation (ABS) to simulate the virus contagion model and a Discrete Event Simulation (DES) model to simulate the interactions between flows of people in an indoor environment. Compared with previous works in the field of simulation and COVID-19, this study provides the possibility to model the specific behaviors of individuals moving in time and space and the proposed HS model could be adapted to several epidemiological conditions (just setting different parameters in the agent-based model) and different kinds of facilities. The HS approach has been developed and then successfully tested with a real case study related to a university campus in northern Italy. The case study highlights the potentials of hybrid simulation in assessing the effectiveness of the containment measures adopted during the period under examination in the pandemic context. From a managerial perspective, this study, exploiting the complementarity of the ABM and DES approaches in a HS model, provides a complete and usable tool to support decision-makers in evaluating different contagion containment measures.

Objectives To assess changes in working patterns and education experienced by radiology residents in Northwest Italy during the COVID-19 pandemic. Methods An online questionnaire was sent to residents of 9 postgraduate schools in Lombardy and Piedmont, investigating demographics, changes in radiological workload, involvement in COVID-19-related activities, research, distance learning, COVID-19 contacts and infection, changes in training profile, and impact on psychological wellbeing. Descriptive and χ 2 statistics were used. Results Among 373 residents invited, 300 (80%) participated. Between March and April 2020, 44% (133/300) of respondents dedicated their full time to radiology; 41% (124/300) engaged in COVID-19-related activities, 73% (90/124) of whom working in COVID-19 wards; 40% (121/300) dedicated > 25% of time to distance learning; and 66% (199/300) were more involved in research activities than before the pandemic. Over half of residents (57%, 171/300) had contacts with COVID-19-positive subjects, 5% (14/300) were infected, and 8% (23/300) lost a loved one due to COVID-19. Only 1% (3/300) of residents stated that, given the implications of this pandemic scenario, they would not have chosen radiology as their specialty, whereas 7% (22/300) would change their subspecialty. The most common concerns were spreading the infection to their loved ones (30%, 91/300), and becoming sick (7%, 21/300). Positive changes were also noted, such as being more willing to cooperate with other colleagues (36%, 109/300). Conclusions The COVID-19 pandemic changed radiology residents’ training programmes, with distance learning, engaging in COVID-19-related activities, and a greater involvement in research becoming part of their everyday practice. Key Points • Of 300 participants, 44% were fully dedicated to radiological activity and 41% devoted time to COVID-19-related activities, 73% of whom to COVID-19 wards. • Distance learning was substantial for 40% of residents, and 66% were involved in research activities more than before the COVID-19 pandemic. • Over half of residents were exposed to COVID-19 contacts and less than one in twenty was infected.

This paper presents a model, based on Analytic Hierarchy Process, to support a maintenance manager with a suitable tool for focusing on the most relevant choices which need to be prioritized. The paper provides an insight on how structural and infra-structural decision elements, traditionally conceived for assessing the manufacturing strategy of a company, could be adopted as criteria for configuring a maintenance system. A model based on Analytic Hierarchy Process has been developed and tested in two industrial case studies in order to demonstrate how it can guide a maintenance manager in keeping the strategic decisions coherently with the overall company’s manufacturing strategy. Main beneficiaries are mainly maintenance managers who have to tackle relevant strategic decisions in managing their maintenance systems. Given the increasing role of maintenance within the operations strategy of a company, the heterogeneity of actors involved, with the relevant risk of assuming conflicting decisions, it is of utmost importance to lever on adequate and shared decision support systems rather than relying on a mere empirical knowledge. The model proposed in this paper, based on the Analytic Hierarchy Process, fills this gap since it provides a structured support in the decision making process by comparing and prioritising the relevant strategic decisions pertaining to the configuration of a maintenance system.

Purpose - The purpose of the paper is to overcome the limitations of the current models available in the literature in terms of relation and consistency between business strategy, service chain configuration and performance measurement systems, and on the alignment between strategic, tactical and operational levels of after-sales decision-making processes.Design methodology approach - The paper draws on a literature review of after-sales performance measurement systems and provides a first validation of the proposed integrated model through industrial case studies related to the provision of durable consumer goods in a business to consumer scenario.Findings - The paper aims to contribute to a better understanding of the factors which influence the performance of after-sales, in order to allow enterprises to consistently design their corporate after-sales service strategic performances with those required at operational levels within a service chain.Research limitations implications - Further development must be carried out in order to: enlarge the sample of companies and cases where the model can be applied, with a specific extension on a business-to-business industry; extend the model to the whole supply and service chain; enrich the framework in order to consider other aspects, as empathy; and develop a full integration with the SCOR model, including the definition of best practices.Originality value - The integration of the strategic and operational views subsumed by the framework would allow enterprises in relating more consistently their corporate After-sales strategic and operational performance within a service chain and to assess the cause and effect relationship between operational drivers and financial and competitive results.

The paper reports the main research activities currently carried out for designing and developing a test-bench service. This service would act as the main reference point for establishing benchmarks on which research results can be compared. These benchmarks will be made available through web technology. The paper, after a first outline of the main features of the project and its overall vision, is particularly focused both on the design principles related to the construction of good benchmark cases and on the technological issues related to the provision of a web-based simulation environment for supporting interactivity between remote scheduling and control systems and a locally resident simulation system. [PUBLICATION ABSTRACT]