International audiencePurpose Servitization of manufacturing is characterized by very complex decision processes within strongly unstable and uncertain decision contexts. Decision-makers are face situations of lack of internal and external information. This paper intends to develop a decision aid approach to support the management of servitization decision-making processes. Design/methodology/approach The scientific orientation of this research consists in working at improving the efficiency of the servitization decision-making process, by identifying factors of non-reliability, in order to propose remediation actions for the whole process. Improving the final decisions taken by the managers is considered as a consequence of the improvement of the decision-making process reliability. The method, based on modeling and evaluation, requires the specification of a decision process model for servitization, used as a basis to assess decision process reliability and diagnose the enterprise’s servitization decision system. Improving the final decisions made by the managers is considered as a consequence of the improvement of the decision-making process reliability. Findings Key added values: (i) to formalize a servitization decision-making reference model, (ii) to specify a reliability assessment applied to the decision system and (iii) to define a decision pocess reliability diagnosis procedure for servitization, illustrated in a case study. Research limitations/implications A direct perspective is to complete the focus on procedural reliability, by taking into consideration the subjective rationality of decision-makers in the reliability assessment procedure. Additionally, this reliability assessment method and diagnosis could become the basis of a larger risk management approach for servitization. Practical implications The diagnosis procedure proposed in the paper is dedicated to generating practical results for enterprise decision-makers, consisting in recommendations for decision process improvements, in the context of servitization. The approach is illustrated through an industrial SME case study. The practical implications are highly contextualized. Originality/value The key originality of this research is to tackle servitization complexity with a decision system modeling and diagnosis orientation, including the formalization of the notion of ‘decision process reliability’, and the specification and implementation of a quantitative assessment procedure

Part 2: Product-Service EcosystemsInternational audienceIn this paper, we attempt to explain that Product Service Systems (PSS) are not based solely on technical and functional aspects, but that organizational and collaborative aspects are also involved. Thus, we propose a servitization analyzing approach that highlights the need to manage the complexity of the iconic functional and decisional areas related to this strategy. We propose a transition process conceptual model adapted from a modeling framework for business decision-making processes (GRAI framework and tools)

PurposeThe paper proposes an innovative systemic method helping decision-makers to control servitization transition process, through decision process risk diagnosis.Design/methodology/approachThe proposed method is based on the modeling of decision processes and risk identification and analysis. This method was based on an action-research approach, in close relationship with two companies (SMEs). The paper develops the feasibility experiment at Automelec company.FindingsThe method was successfully implemented and delivered concrete diagnosis results.Research limitations/implicationsThe generalization of the applicability of the method needs to be tested on several different cases.Practical implicationsThe first practical implication is related to the efficiency of the method to help decision-makers in a servitization context to limit uncertainty and get a global view of the weaknesses of their decision-making process, it raises their awareness about servitization transition for their companies. Furthermore, the method also helps to explain the strategy of a servitization transition. It enhances the level of maturity of the decision process of the company, and can be used as a training/learning tool for managers.Social implicationsThe results brought by the research contribute to give the decision-making boards for organization living a servitization transition and especially SMEs a better control over the servitization decision process and related risks, which will increase the economic stability of the company and its vision over long, medium and short horizons. This will bring positive impact on the overall economic and social environment and networks of the servitized SME, and enhance the confidence of coworkers, subcontractors and clients.Originality/valueThe first originality of the paper is related to the new way of considering risk, not only as an analysis criterion but as the central driver in steering a strategic transition for the company, such as servitization. The second originality of the study is about assessing risk occurrence over a decision-making process through decision reliability and decision confidence.

International audienceServitization decision process is characterized by a high degree of complexity and uncertainty. We propose a diagnosis approach applied to servitization decision making process in industrial companies. We introduce a decision model to formalize the servitization process and a decision reliability assessment approach. This reliability assessment is interpreted from three viewpoints, to help decision-makers in managing the servitization process. The paper presents this diagnosis method and illustrates it using an industrial SME case study

This paper explores the role that Artificial Intelligence (AI) can play in building resilient project schedules. Based on a literature review and brainstorming sessions, we introduce a conceptual framework that details how AI-enabled predictive and prescriptive analytics can be leveraged to improve project schedule resilience. The latter specifies the potential of AI to make use of historical and real-time data to better contain the effect of disruptions on project schedules.