The aim of this article is to characterize the impacts of Smart Additive Manufacturing (SAM) on industrial production, digital supply chains (DSCs) and corresponding digital value chains (DVCs), logistics and inventory management. The method used consists of a critical review of the literature, enriched by the authors’ field experience. The results show that digital transformation of manufacturing is affecting business models, from resource acquisition to the end user. Smart manufacturing is considered a successful improvement introduced by Industry 4.0. Additive Manufacturing (AM) plays a crucial role in this digital transformation, changing the way manufacturers think about the entire lifecycle of a product. SAM combines AM in a smart factory environment. SAM reduces the complexity of DSCs and contributes to a more flexible approach to logistics and inventory management. It has also spurred the growth and popularization of customized mass production as well as decentralized manufacturing, rapid prototyping, unprecedented flexibility in product design, production and delivery, and resource efficiency and sustainability. SAM technology impacts all five Fletcher’s stages in DVCs. However, the need for clear definitions and regulations on 3D printing of digital files and their reproduction, as well as product health, safety, and integrity issues, cannot be ignored. Furthermore, investment in this technology is still expensive and can be prohibitive for many companies, namely SMEs.

The digital technology transition fosters the flexibility of companies and their ability to respond quickly to market changes. However, Small and Medium-sized Enterprises (SMEs) seem to be particularly challenged by digital transformation. The Alkire-Foster (AF) method was employed to analyze the integration profiles of digital technology in SMEs. Individual and group average partial and composite indicators were computed, as well as group measures of digital technology integration, to benchmark individual companies and groups. Additionally, a linear regression was used to examine how individual overall composite scores of digital technology integration are influenced by dimensions of individual digital technology integration enablers and other contextual variables. The results indicate that most companies possess the digital infrastructure necessary for this integration. However, there is room for improvement in terms of supply and demand of digital skills. This study contributes to research by applying an existing tool, the AF method, in a novel context: measuring digital technology integration in SMEs. The AF method facilitates effective benchmarking at both individual and group levels.