This paper provides a literature review on zero defect manufacturing based on the content analysis performed for 280 research articles published from 1987 to 2018 in a variety of academic journals and conference proceedings. The review summarises the state-of-the-art, highlights shortcomings and further directions in research. Accordingly, we investigated how zero defect manufacturing was implemented and evaluated the main research patterns in the sample by analysing key factors. Based on the extensive review of the zero defect manufacturing literature, we identified and highlighted four distinctive strategies based on overarching themes for zero defect manufacturing, i.e. detection, repair, prediction, and prevention. Evaluation of current research and descriptive analysis highlighted six major shortcomings of current research in zero defect manufacturing: (i) focus on a single strategy instead of a holistic approach for global optima; (ii) certain industries are under-researched; (iii) full potential of industry-academia collaboration is not achieved; (iv) not enough focus on the beginning of manufacturing lifecycle; (v) cost–benefit comparative analysis is not evident; (vi) standard and clear definition of terms are missing. Finally, we presented four further directions in which an advance of the topic would stimulate scholarly and practical needs: (i) shift from local to global solutions; (ii) investigate pros and cons; (iii) role of people and human activities in manufacturing; (iv) new business models for zero defect manufacturing.

As a key enabling technology of Industry 4.0, Digital Twin (DT) has been widely applied to various industrial domains covering different lifecycle phases of products and systems. To fully realize the Industry 4.0 vision, it is necessary to integrate multiple relevant DTs of a system according to a specific mission. This requires integrating all available data, information and knowledge related to the system across its entire lifecycle. It is a challenging task due to the high complexity of modern industrial systems. Semantic technologies such as ontology and knowledge graphs provide potential solutions by empowering DTs with augmented cognitive capabilities. The Cognitive Digital Twin (CDT) concept has been recently proposed which reveals a promising evolution of the current DT concept towards a more intelligent, comprehensive, and full lifecycle representation of complex systems. This paper reviews existing studies relevant to the CDT concept, and further explores its definitions and key features. To facilitate CDT development, a reference architecture is proposed based on the RAMI4.0 and some other existing architectures. Moreover, some key enabling technologies and several application scenarios of CDT are introduced. The challenges and opportunities are discussed in the end to boost future studies.

For manufacturing companies, quality management is a key feature for increasing the competitiveness, productivity, profitability, and sustainability of their systems. Quality improvement (QI) methods aim to achieve high-quality parts without reducing performance. The Industry 4.0 framework brought technological developments that cannot be used by traditional QI methods, such as Six Sigma, Lean, Lean Six, the Theory of Constraints, and Total Quality Management, which are widely used in manufacturing companies. The need for higher manufacturing sustainability and market requirements has led to the search for alternative QI methods with superior performance to traditional QI methods such as Zero-Defect Manufacturing (ZDM). The current paper is a position paper with a goal to present the ZDM approach and providing a clear definition about ZDM to align everyone in one common understanding of ZDM. Many researchers and manufactures are skeptical about ZDM, therefore, numerous argumentative questions have been created and answered, to convince them why they should migrate from traditional QI methods to ZDM. The migration to ZDM has already started, to support this statement numerous facts from the literature have been presented. Finally, several directions were identified, demonstrating that there is still plenty of room for research in several domains.

Virtual metrology (VM) involves estimating a product’s quality directly from production process data without physically measuring it. This enables the product quality of each unit of production to be monitored in real time, while preserving the process efficiency. Initially developed for the semiconductor industry, VM has recently been examined for use in other industrial fields. VM is enabled by components such as quality estimators and drift detectors. It enhances various industrial applications such as machine control and sampling decision systems. The literature lacks a comprehensive summary and a systematic review of the state of the art in VM. To fill this gap, this study followed a systematic methodology to conduct a complete and structured literature review of VM. This paper presents a detailed analysis of the 199 papers that we identified based on our search and selection criteria. Following our methodological framework, we assigned these papers to categories and highlighted shortcomings. Finally, we discussed VM’s use in various industrial fields, underlining its potential for every manufacturing industry. This study and the proposed framework are useful for both practitioners and academicians since they raise crucial theoretical and managerial questions as well as provide suggestions for further research on this critical topic.

Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. This paper proposes a knowledge graph modelling approach to construct actionable cognitive twins for capturing specific knowledge related to production planning and demand forecasting in a manufacturing plant. The knowledge graph provides semantic descriptions and contextualisation of the production lines and processes, including data identification and simulation or artificial intelligence algorithms and forecasts used to support them. Such semantics provide ground for inferencing, relating different knowledge types: creative, deductive, definitional, and inductive. To develop the knowledge graph models for describing the use case thoroughly, systems thinking approach is proposed to design and verify the ontology, develop a knowledge graph and build an actionable cognitive twin. Finally, we evaluate our approach in two use cases developed for a European original equipment manufacturer related to the automotive industry as part of the European Horizon 2020 project FACTLOG.

Ontologies provide an opportunity to tackle the interoperability challenge in digital manufacturing. Although ontologies have been used in numerous industrial projects, the problem of semantic interoperability in the industry is still unresolved. One reason for the lack of success of ontologies in the industry is that most industrial ontologies have been developed based on local needs without taking wider applications into account or attempting to learn from earlier ontology initiatives or reuse the existing ontologies. In this position paper, we argue that for widespread adoption and implementation of ontologies, more systematic and coordinated efforts are needed with the participation of industry, academia, and government at an international level. We also hypothesise that a coordinated and systematic ontology development effort will incrementally lead to the creation of an ecosystem of interoperable software applications that support consistent data access and reasoning across the product life cycle. We discuss why efforts such as Industrial Ontology Foundry (IOF) can promote a methodological ontology development process by introducing modular, and reusable ontologies. We draw examples from two domain areas, namely supply chain management and production planning and scheduling, to demonstrate how industrial notions can be analysed ontologically according to IOF procedures and methods.