The aim of this paper is to describe a new integrated methodology for project control under uncertainty. This proposal is based on Earned Value Methodology and risk analysis and presents several refinements to previous methodologies. More specifically, the approach uses extensive Monte Carlo simulation to obtain information about the expected behavior of the project. This dataset is exploited in several ways using different statistical learning methodologies in a structured fashion. Initially, simulations are used to detect if project deviations are a consequence of the expected variability using Anomaly Detection algorithms. If the project follows this expected variability, probabilities of success in cost and time and expected cost and total duration of the project can be estimated using classification and regression approaches.

Additive manufacturing of spare parts significantly impacts industrial, social, and environmental aspects. However, a literature review shows that: (i) academic papers on the adoption of additive manufacturing have focused mainly on large companies; (ii) the methods required by SMEs to adopt new technologies differ from those employed by large companies; and (iii) recent studies suggest that a suitable way to help small- and medium-sized enterprises (SMEs) to adopt new additive manufacturing technologies from the academic world is by presenting case studies in which SMEs are involved. Given the increasing number of global SMEs (i.e., SMEs that manufacture locally and sell globally), we claim that these companies need to be assisted in adopting spare-parts additive manufacturing for the sake of resource and environmental sustainability. To bridge this gap, the purpose of this article is to present a case study approach that shows how a digital supply chain for spare parts has the potential to bring about changes in business models with significant benefits for both global SMEs (more effective logistic management), customers (response time), and the environment (reduced energy, emissions, raw materials, and waste).

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Purpose: The new competitive environment characterized by innovation and constant change is forcing a new organizational behavior. This requires a digital transformation of SMEs based on collective performance determinants. SMEs have particular characteristics that differentiate them from large companies and a model that allows them to identify, leverage and develop their digital capabilities can help them to advance in digital maturity.Design/methodology/approach: An in-depth review of the existing literature on digital transformation and organizational competence was carried out on Scopus and Web of Science to identify the digital challenges faced by SMEs, and what digital capabilities they have to develop to face these challenges. In order to obtain the necessary information for the refinement of organizational competence for digital transformation model, six experts were interviewed; three of them are academics and the other three are professionals with management responsibilities in SMEs. We used semi-structured interviews, to keep the interviews focused and facilitate cross-data analysis between experts. In addition, it allowed us the possibility of analyzing new relevant aspects that could arise during the interview.Findings: As a result of this study we have developed a refined model of organizational competence for digital transformation that allows SMEs to identify and develop the digital capabilities necessary to advance in the digital transformation, refined with the opinions of six experts consulted. We were able to observe the importance of organizational learning and organizational knowledge to advance the digital transformation of SMEs.Originality/value: The developed model is useful for SME managers to know what the initial starting situation is, what the digital gaps are and to be able to plan the actions to develop the necessary digital capabilities to advance towards digital maturity.