This research conducts a systematic literature review on Smart Product-Service Systems (SPSS) to identify the current state of research and the gap related to flexibility issues by the views of semantics, requirements, and design methodologies. The review covers studies published between 2000 and 2021 in and finds a lack of systemic approaches on standardized formalization models, interoperability, context-aware systems, and self-adaptation. This gap in knowledge makes it difficult for companies to fully understand and implement a seamless and flexible SPSS development lifecycle. In response to this gap, the research proposes a preliminary approach for the implementation and management of SPSS, offering a possible solution for companies looking to understand and implement SPSS. Theapproach suggests that companies should technically focus on use of Artificial Intelligence (AI) technologies to support decision-making, and on formal stand-ardized models to represent knowledge, helping to enable semantic interoperability across the development lifecycle. The proposed preliminary approach is a starting point for companies and for future research in the field.

The complexity of airplane parts is constantly increasing. For example, the airplane fuselage comprises more than 700.000 parts with different dimensions, geometries, tolerances complexity, and multiple materials. This complexity scenario requires an advanced manufacturing park with conventional and non-conventional machining, rapid manufacturing machining, 3D measuring machines, and others. In parallel, reducing these parts' cost and production time challenges the aerospace industry suppliers, ensuring high manufacturing quality. Therefore, this paper explores a preliminary discussion of the Semantic Web Technologies and Machine Learning application to assist the complex parts manufacturing pricing and manufacturing planning since this process is made manually by a manufacturing engineer. During all quotation processes (3D part analysis, manufacturing planning, and manufacturing price definition), the engineer spends more than one week to quote a part depending on its complexity. This paper contributes to a Computer Integrated Manufacturing (CIM) domain since it discusses the application of Semantic Web Technologies and Machine Learning technologies to automate extracting the information from the part modelled in 3D and plan the manufacturing process to identify the price to manufacture it. Finally, the main research result concerns the identification of the contributions and limitations of the related works in this domain and the research opportunity to cover this research gap.

To reduce costs and, above all, to increase production capacity and respond to market demand in a sustainable way, the Aerospace Industry is increasingly focusing on the design of the entire product life cycle. To meet these challeng-es, a new paradigm is needed to make a disruptive leap forward to ensure competitiveness in the 21st century. One such solution has been the develop-ment and adoption of Ontology-Based Engineering methods, process, and tools. However, its adoption has meant facing new problems associated with modelling, such as managing lifecycles, workflows, or sharing and reusing models. These problems have led the authors to propose the Models for Manu-facturing methodology as a new way of modelling manufacturing systems with collaborative, extensible, and reusable characteristics. These characteristics are typical of the Model Lifecycle Management concept. This article describes the difficulties the Aerospace Industry faces in adopting models based on the entire product lifecycle, the similarity with the adoption of 3D modelling in the past, and how a Model Lifecycle Management system, proposed by the au-thors, can respond to these problems.