During the last 10 years, important contributions about knowledge management (KM) issues in supply chain management (SCM) have been published. The current paper aims to build upon previous literature reviews focused on KM in supply chains (SCs) from an integrative perspective, particularly recognising the studies conducted by Bhosale and Kant (2016. “Metadata Analysis of Knowledge Management in Supply Chain: Investigating the Past and Predicting the Future.” Business Process Management Journal 22 (1): 140–172) and Cerchione and Esposito (2016. “A Systematic Review of Supply Chain Knowledge Management Research: State of the Art and Research Opportunities.” International Journal of Production Economics 182: 276–292) as an effort to discuss the evolution of KM in the SC field. To this end, a systematic literature review including 210 papers is conducted over the period 2008–2017 from three positions previously not addressed jointly within the supply chain (SC) context: research methods employed by the authors; KM processes applied in the business processes across the SC; and intra and inter- organisational performance metrics linked with KM initiatives. Results exhibited that: (i) ‘Survey’ and ‘Case study’ are the two research methodologies mostly employed by authors (ii) the knowledge transfer is discussed in the majority of the studies reviewed, (iii) 114 intra and inter-organisational performance metrics are reported within the SC context from an empirical data approach. Findings concerning previous identified gap analysis and future lines of research are described.

Mainly focused on solving abstraction problems, programming paradigms limit language expressiveness, thus leaving unexplored natural language descriptions that are implicitly expressive. Several authors have developed tools for programming with a natural language subset limited to specific domains to deal with the ambiguity occurring with artificial intelligence technique use. This article presents a review of tools and languages with naturalistic features and highlights the problems that authors have resolved and those they have not addressed, going on to discuss the fact that a “naturalistic” language based on a well-defined model is not reported.

While the use of natural language for software development has been proposed since the 1960s, it was limited by the inherent ambiguity of natural languages, which people resolve using reasoning in a text or conversation. Programming languages are formal general-purpose or domain-specific alternatives based on mathematical formalism and which are at a remove from natural language. Over the years, various authors have presented studies in which they attempted to use a subset of the English language for solving particular problems. Each author approached the problem by covering particular domains, rather than focusing on describing general elements that would help other authors develop general-purpose languages, instead focusing even more on domain-specific languages. The identification of common elements in these studies reveals characteristics that enable the design and implementation of general-purpose naturalistic languages, which requires the establishment of a programming model. This article presents a conceptual model which describes the elements required for designing general-purpose programming languages and which integrates abstraction, temporal elements and indirect references into its grammar. Moreover, as its grammar resembles natural language, thus reducing the gap between problem and solution domains, a naturalistic language prototype is presented, as are three test scenarios which demonstrate its characteristics.