The development of early-stage simulation capabilities is a critical step in the quest for ‘frontloading’ early stage PSS design activities, reducing the cost and risk for rework associated with sub-optimal decisions. This paper describes how life cycle simulations, based on a Discrete Event approach, can be applied to support cross-disciplinary decision making in PSS design, facilitating the identification of the most valuable hardware configuration for a given business model. The proposed approach is exemplified in a case study related to the design of a zero-emission asphalt compactor, which is part of a product-oriented and use-oriented PSS offer. Co-located physical meetings and interviews with industrial practitioners highlight the role played by the simulation as an enabler for leveraging tacit knowledge sharing across roles and disciplines in the organization, making possible to explore the design space with more rigor. They further reveal the need to exploit data mining techniques and to develop new constructs able to inform decision makers of maturity and impact of models used in a specific decision scenario.

The notion of ‘value’ has become pivotal in the PSS domain, with a plethora of ‘indicators’, ‘drivers’ and ‘measurements’ proposed to guide the assessment of PSS concepts across the design process. This paper presents the results of a systematic literature review that maps existing contributions dealing with metrics for PSS value in early design. The findings reveal the lack of a common taxonomy to define what PSS value is, as well as differences in terms of granularity of the applied metrics, which span from very generic to highly case-study specific. This mapping aims at validating a proposed classification framework for such metrics, which balances customer and provider value perspectives in early stage PSS concept assessment activities. Its goal is to raise the cross-functional design team awareness on the multiple value types impacted by early stage design decisions when working with MADM matrixes; hence to highlight opportunities for improvement, recombination and refinement. 

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.

To boost revenues and create a lasting competitive advantage in the present global market, an increasing number of manufacturing companies are experimenting with shifting from product-centric offerings to service solutions leveraging digital technologies according to the Industry 4.0 paradigm. This (digital) transformation, known as “Digital Servitization”, aims to provide new (digital) services and/or enhance existing ones. Yet, this transformation is challenging and manufacturing companies frequently have trouble meeting their expectations. To shed light on the current state of the Digital Servitization trend in the global manufacturing sector, researchers involved in the ASAP Service Management Forum and the IFIP WG5.7 Special Interest Group on “Service Systems Design, Engineering and Management” have conducted an international survey targeting manufacturing SME managers. The main survey objectives are twofold: (i) to analyse how manufacturing companies are implementing digital technologies to support their Digital Servitization transformation from traditional business models based on product sales to models focused on service delivery, and (ii) to identify which critical issues and best practices are characterizing the Digital Servitization transformation of manufacturing companies. Survey results have demonstrated a rising trend in the global manufacturing sector towards the use of digital technologies for service delivery, but more mature servitization strategies, data management activities, coordination efforts at the ecosystem level, and supporting tools for conscious decisions in the delivery of (digital) services are still required to succeed in the new Digital Servitization arena.

 This book is a compilation of theoretical and practical contributions aimed at facilitating the servitization of manufacturing companies, specifically focusing on data-driven decision-making within the context of Product-Service Systems (PSS). Providing a comprehensive overview, it discusses the latest breakthroughs in operational, tactical, and strategic decision-making for PSS, leveraging data-driven processes, methodologies, and tools. Therefore, the book significantly contributes to strengthening the knowledge on the use of data-driven decision-making methods and tools all the phases of the PSS lifecycle.

From extended warranties and leasing to pay-per-use and other innovative configurations, manufacturing companies are increasingly adopting servitized business models and PSS to create additional value for their customers and users and create robust relationships with them, ensuring more reliable cash flows. In this setting, effective information management and the utilization of aggregated operational data have become essential for guiding strategic, tactical, and operational decisions.

This book not only consolidates theoretical frameworks but also offers practical insights into data-driven decision-making in PSS, providing deep knowledge on how complex decisions can be taken along the various phases of the PSS lifecycle using data-driven methods and tools. Key areas of focus include: 

1. In the Beginning of Life, introducing new services tailored to customer needs, disclosing new business opportunities in terms of revenues, and extending the PSS Middle of Life.

2. Designing or re-designing assets and/or services, thereby influencing the PSS Beginning of Life.

3. Enhancing daily decisions related to asset management to improve the PSS Middle of Life.

4. Optimizing daily decisions related to service delivery and management, aimed at enhancing the PSS Middle of Life.

5. Facilitating informed decisions on recycling, remanufacturing, refurbishing, and revamping, impacting the PSS End of Life.

This book will be of interest to researchers and managers in industry as it offers insights that bridge the gap between theory and practical application in the evolving landscape of PSS.

Literature and field study research highlight that transiting toward PSS means for many companies to either miss interesting business opportunities or to bet on the wrong solution concept. Early stage assessment activities play a crucial role in this respect, guiding the selection of a value-adding PSS while preventing commitment of resources on poor design alternatives. Emerging from empirical studies in the manufacturing industry, the paper highlights the need for systematic procedures, tools and metrics able to encompass customer satisfaction together with the producer’s value perspective in such assessment. The paper proposes a 2 step assessment method based on the Importance Performance Analysis (IPA) matrix enabling the integration of customer and provider value and supporting early stage PSS design decision making. Verification activities highlight the positive implications of balancing the trade-off between customer and provider value during the PSS assessment phase.

While the provision of PSS is becoming more and more common, the transition toward a servitized business model is still critical. This is particularly true for small and medium-sized enterprises (SMEs) due to their limited internal resources and ability to define a servitization strategy. A crucial aspect during this transition is the identification of the right solutions to be implemented into the business that must render high value capture for providers and create value for customers. While a literature review showed that a large number of evaluation methods are available, these are often complex and require substantial amounts of resources in order to be carried out. This paper aims at supporting companies in taking decisions during early design phases by proposing a method that combines two existing approaches: The EVA method and the ProVa method. The combined method simplifies the existing ones while aiming at an easy implementation and application in SMEs. It is composed of two steps, the first pursuing the identification of valuable concepts from both the customer and the provider perspective; the second pursuing an individual analysis of the components available for use in the concepts selected. The application of the simplified method in a student-case of a startup company seeking to enter the distributed mobility market highlights the ease of use of the method in rendering valuable PSS concepts and in evaluating its specific components. 

Purpose – This 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/approach – The 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.

Findings – The 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/value – The 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.