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Cost engineering capabilities are becoming increasingly important for the competitiveness of industrial firms, especially for engineer to order products (ETOPs). Despite this relevance, the literature on the use of advanced data-driven methodologies, such as machine learning (ML), for early cost estimation (CE) of ETOPs is quite sparse. Furthermore, ML has still seen little use in real industrial applications due to several challenges. Accordingly, the objective of this paper is threefold: (a) to develop a solid early CE approach for ETOPs, including feature selection; (b) to investigate the benefits of adopting ML for ETOPs' CE; (c) to identify how ML can be introduced into real industrial context with little knowledge on ML. Long action research has been carried out with a large industrial company that produces Oil & Gas ETOPs. We observed how ML facilitates the exploration of the relationships between the choices of early design stages and the CE. ML algorithms also allowed to both capture the high variability of the data and test different combinations of cost drivers in very effective ways. The project resulted in an accurate CE framework with an iterative feature selection process and an approach for introducing ML into a real industrial context. [ABSTRACT FROM AUTHOR]

In the face of global environmental challenges and their significant impact, manufacturing firms have been recognized as major contributors to environmental degradation and emissions. Additionally, these firms encounter various challenges arising from evolving economies, emerging competitors, shifting consumer preferences, and rapidly advancing digital technologies. To address these challenges and foster innovation, manufacturers are turning to servitization as a strategic approach. Within servitization, the pay-per-use service model emerges as a promising solution. Furthermore, integrating circular economy principles into this model presents an opportunity for sustainable business practices. This paper examines successful examples of companies that have adopted servitization and effectively implemented pay-per-use models while incorporating circular economy principles. Drawing upon these lessons, we propose the “Wheel of Servitization” framework, which enables manufacturers to approach servitization holistically, ensuring that all critical dimensions are addressed to maximize the likelihood of success. By embracing servitization and pay-per-use models within a circular economy context, manufacturing companies can embark on a pathway towards sustainability, addressing environmental concerns and simultaneously meeting the demands of a rapidly changing business landscape.

This research sought to uncover the role played by intellectual capital (IC) in the interplay between strategic alliances of small‐ and medium‐sized enterprises (SMEs) and servitisation strategies. An in‐depth investigation of a single case represented by an SME consortium in the elevator industry served as the empirical context. Multiple sets of data were examined by integrating the analysis of documents with the analysis of data gathered through semi‐structured interviews. We found that a strategic alliance among SMEs constitutes a form of relational capital and creates/improves other components of IC that support the formulation of the servitisation strategy and enable its implementation. The development of IC triggered by the formation of a strategic alliance appears to function as a driver of the servitisation strategy and to influence the innovation process. The value of this research lies in the analysis of IC in a new context—the servitisation of SMEs through strategic alliances—that focusses on interrelations among multiple forms of capital. [ABSTRACT FROM AUTHOR]

The provision of advanced services becomes a relevant differentiation for manufacturing companies, in particular for SMEs (small and medium-sized enterprises). These services, also referred to as smart services, require the collection and processing of data from equipment, customers, and processes, as well as the development of analytics models and the interpretation of their results for improved service value propositions. These steps require significant engagement of the firms in terms of technical and human resources, skills, and new types of value creation processes, which is a major hurdle especially for SMEs. As the value that can be achieved when leveraging the information inherent in the data is not known a priori, the enterprises are not sufficiently informed for taking the decision to engage. Consequently, they are missing out on relevant business opportunities due to a lack of quantitative models for assessing the value of data. In this paper, we discuss the existing literature on data valuation models and explore the state of practice through an interview-based field study. We develop a model for the utility-based valuation of data that helps companies expand their fund of knowledge and skills about the value of their data and thus make better-informed investment decisions. A simulation-based model is developed to support companies in this assessment by providing quantitative insights in the value potential of the data in various use cases. This model opens a series of new research questions for the further elaboration of the data valuation models.

With the rise of digital technologies, the possibility to retrieve a large amount of data from connected sources can become a key source of value creation in the context of service provision. Nevertheless, product service offerings that can be built on data retrieved from connected products, that we will call data-driven product service system, remains poorly covered from both the academic and the practitioners' perspectives. Notably, a univocal definition of the characteristics of data-driven product service system is still missing. Therefore, this paper represents a first attempt in the systematic definition of data-driven services types and their features as a fundamental step to support their understanding and development. To accomplish this aim, the paper proposes a holistic view of data-driven product service system characteristics, employing a two-level hierarchical conceptual framework for business to business applications. The framework is constructed with a theory-building approach involving multiple industrial companies. The paper eventually reports a case study to demonstrate the applicability of the framework. [ABSTRACT FROM AUTHOR]

The role of digital technologies in service business transformation is under-investigated. This paper contributes to filling this gap by addressing how the Internet of things (IoT), cloud computing (CC) and predictive analytics (PA) facilitate service transformation in industrial companies. Through the Data-Information-Knowledge-Wisdom (DIKW) model, we discuss how the abovementioned technologies transform low-level entities such as data into information and knowledge to support the service transformation of manufacturers. We propose a set of digital capabilities, based on the extant literature and the findings from four case studies. Then, we discuss how these capabilities support the service transformation trajectories of manufacturers. We find that IoT is foundational to any service transformation, although it is mostly needed to become an availability provider. PA is essential for moving to the performance provider profile. Besides providing scalability in all profiles, CC is specifically used to implement an industrialiser strategy, therefore leading to standardised, repeatable and productised offerings. [ABSTRACT FROM AUTHOR]