In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms.

This literature review examines the increasing use of artificial intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. AI is essential for managing the complexities in modern manufacturing, including machine failures, variable orders, and unpredictable work arrivals. This study, conducted using Scopus and Web of Science databases and bibliometric tools, has two main objectives. First, it identifies trends in AI-based scheduling solutions and the most common AI techniques. Second, it assesses the real impact of AI on production scheduling in real industrial settings. This study shows that particle swarm optimization, neural networks, and reinforcement learning are the most widely used techniques to solve scheduling problems. AI solutions have reduced production costs, increased energy efficiency, and improved scheduling in practical applications. AI is increasingly critical in addressing the evolving challenges in contemporary manufacturing environments.

Purpose: – The purpose of this paper is to uncover the significance of quick changeovers in the packaging line of a pharmaceutical company. Using an integration of different lean practices, the study aims to reduce the batch change and changeover time up to 50 per cent, increasing overall equipment effectiveness by 25 per cent. Design/methodology/approach: – The paper gives setup instructions and guidelines for preparing the standardized setup procedure without ignoring the actual constraints in a pharmaceutical company. It uses a case study to generate an integrated setup reduction approach, utilizing single-minute exchange of die tools (SMED) in combination with suppliers, inputs, process, outputs and customers (SIPOC), Kanban, 5S techniques and Total Productive Maintenance (TPM) indexes to achieve faster setups. Findings: – The SMED approach helps the pharmaceutical company to eliminate unwanted activities and to externalize and reduce the internal activities by simplification or standardization. The application of other tools, such as 5S and Kanban tool-kits, allowed the company to optimize the process and reduce the standard deviation of the changeover times. Good manufacturing practice (GMP) procedures of the pharmaceutical sector limit the conversion of internal setup elements to external setup elements. Originality/value: – The paper demonstrates the practical application of SMED, showing how it can bring real breakthroughs in productivity to a pharmaceutical company. Moreover, in this work, we highlight the importance of an integration of different lean practices to reduce variation in the changeover time. In particular, the standardization of setup tasks and the increased reliability in the material supply chain, in addition to reducing the changeover mean time can also reduce the standard deviation of the setup process time.

The transformation from traditional industry to Industry 4.0 can bring many benefits in various spheres, from efficiency to safety. However, this transition involves adopting technologically advanced machinery with a high level of digitization and communication. The costs and time to replace obsolete machines could be unsustainable for many companies while retrofitting the old machinery. To make them ready to the Industry 4.0 context, they may represent an alternative to the replacement. Even if there are many studies related to retrofitting applied to machinery, there are very few studies related to the literature process industry sector. In this work, we propose a case study of a two-phase mixing plant that needed to be enhanced in the safety and maintainability conditions with reasonable times and costs. In this regard, the Digital Twin techniques and Deep Learning algorithms will be tested to predict and detect future faults, not only already visible and existing malfunctions. This approach strength is that, with limited investments and reasonable times, it allows the transformation of an old plant into a smart plant capable of communicating quickly with operators to increase its safety and maintainability.