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Data-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies.

Artificial intelligence is revolutionizing and significantly shaping the work environment. It not only presents an innovative and unique challenge, but it also creates numerous opportunities in various sectors. This study aims to examine a case of artificial intelligence use in the logistics sector, specifically the implementation of text recognition algorithms that use advanced artificial intelligence technology to perform tasks and activities typically carried out by human operators. The implementation under analysis was carried out at an Italian company in Tolentino, in the province of Macerata. The implementation of this tool focuses on the ability to autonomously read shipping notes, with the aim of simplifying the flow of information in the goods receipt phase and reducing the workload of warehouse staff. As illustrated in the following chapters, when artificial intelligence follows several trainings, technical improvements and structural changes in shipping notes, the flow is greatly simplified, significantly facilitating this logistics phase.

The integration of AI technology with digital transformation has profoundly shaped the evolution towards digital triplet architecture, grounded in human-centric methodologies. By infusing human intellectual activities into both physical and cyberspace, innovative links between humans and machines are established. Despite limitations in transitioning from tangible human presence to the digital realm in cyberspace, extensive efforts are underway to harness emotional, visual, and oral responses, thereby enhancing the reasoning and predictive capabilities of digital twins. These advancements aim to elevate real-time human interactions with physical and virtual systems by integrating intelligent AI algorithms and cognitive computing systems into digital twins. This paper meticulously analyses recent trends in digital twins, tracing their evolution from traditional concepts and applications to a nuanced digital triplet hierarchy that incorporates human intuition, knowledge, and creativity within cyberspace. we delve into the hierarchical framework of the digital triplet, resonating with maturity, domination, and volition levels, enhances cognitive and perceptual capabilities in cyberspace. The study provides a systematic overview of the development of ultra-realistic digital models, incorporating real-time data-driven artefacts that integrate intelligent activities with multidomain, multiphysics, and multiscale simulations. The research scope is focused on augmenting the perceptive and heuristic capabilities of the digital triplet framework by utilizing AI in data analytics, retrieving heterogeneous data from virtual entities using semantic artificial intelligence technologies, and amalgamating AI and machine learning with human insight and perceptual knowledge. The proposed digital triplet hierarchy aims to enhance cyberspace's capacity for learning, cognitive skills, and knowledge transfer. It can be a guideline for the researcher to promote cognitive augmentation of the human brain through brain-machine/computer interface, virtual, augmented, and extended reality, fostering a symbiotic relationship between humans and machines in the industrial metaverse and industry 5.0. The paper discusses future directions for research and the challenges involved in developing intelligent digital twins towards the digital triplet paradigm, aiming to embody intelligent activities and cognitive capabilities within the framework of human–machine symbiosis.

Recovering old machinery, once it reaches its end of life, allows companies to be sustainable. Several strategies are available for this purpose, both from the point of view of hardware and software modifications. Especially in the industrial sector, these strategies are classified as revamping, remanufacturing and retrofitting. Machinery revamping, retrofitting and remanufacturing are all used to improve industrial equipment performance, efficiency and sustainability. Each approach has unique benefits and trade-offs, depending on the specific needs and requirements of the equipment and business. Moreover, according to Industry 4.0 principles, it is also possible to talk about smart retrofitting, involving the integration of various technologies such as sensors, automation systems, Digital Twins, artificial intelligence and data analytics software to control and optimise the operation of the machinery. Digital Twins, in particular, have been widely used among smart retrofit solutions and can integrate several innovative aspects of dated systems. However, a literature review needs to clarify their meaning and specific characteristics. For this reason, this paper aims to distinguish different strategies and find a correct definition of smart retrofitting, highlighting its relevance, benefits and sustainability in the industrial sector, focusing more on Digital Twin solutions for smart retrofitting.

Anomaly detection plays a crucial role in preserving industrial plant health. Detecting and identifying anomalies helps prevent any production system from damage and failure. In complex systems, such as oil and gas, many components need to be kept operational. Predicting which parts will break down in a time interval or identifying which ones are working under abnormal conditions can significantly increase their reliability. Moreover, it underlines how the use of artificial intelligence is also emerging in the process industry and not only in manufacturing. In particular, the state-of-the-art analysis reveals a growing interest in the subject and that most identified algorithms are based on neural network approaches in their various forms. In this paper, an approach for fault detection and identification was developed using a Self-Organizing Map algorithm, as the results of the obtained map are intuitive and easy to understand. In order to assign each node in the output map a single class that is unique, the purity of each node is examined. The samples are identified and mapped in a two-dimensional space, clustering all readings into six macro-areas: (i) steady-state area, (ii) water anomaly macro-area, (iii) air-water anomaly area, (iv) tank anomaly area, (v) air anomaly macro-area, (vi) and steady-state transition area. Moreover, through the confusion matrix, it is found that the algorithm achieves an overall accuracy of 90 per cent and can classify and recognize the state of the system. The proposed algorithm was tested on an experimental plant at Università Politecnica delle Marche.

The wider implementation of Industry 4.0 technologies in several sectors is increasing the amount of data regularly collected by companies. Those unstructured data need to be quickly elaborated to make on-time decisions, and the information extracted needs to be clearly visualized to speed up operations. This is strongly perceived in the quality field, where effective management of the trade-off between increasing quality controls to intercept product defects and decreasing them to reduce the delivery time represents a competitive challenge. A framework to improve data analysis and visualization in quality management is proposed, and its applicability is demonstrated with a case study in the fashion industry. A questionnaire assesses its on-field usability. The main findings refer to overcoming the lack in the literature of a decision support framework based on the joint application of association rules mining and augmented reality. The successful implementation in a real scenario has a twofold aim: on the one hand, sample sizes are strategically revised according to the supplier performance per product category and material; on the other hand, the daily quality controls are speeded up through accurate suggestions about the most occurrent defect and location per product characteristics, integrated with extra tips only for trainees.

The current global competitive scenario and the increase in complexity and automation of equipment and systems demand better results from maintenance management in organizations. As maintenance resources are limited, prioritizing maintenance activities is essential to allocate them properly and to meet maintenance management objectives. In the face of these challenges, multicriteria decision-making (MCDM) methods are commonly used in organizations to support decision-making. Nevertheless, selecting a suitable MCDM method for maintenance planning can be complicated given the diversity of methods and their strengths and weaknesses. In this context, this paper proposes a novel knowledge-based method for deciding a multicriteria decision-making (MCDM) method to prioritize maintenance work orders of hydroelectric plants. As the main novel contribution, it translates the intrinsic characteristics of the main MCDM methods into questions related to maintenance planning to guide the recommendation of a suitable MCDM method for organizations through a decision tree diagram. This approach was applied to a maintenance case study of a hydroelectric power plant in order to demonstrate its use and contribute to its understanding. These findings contribute to maintenance management in selecting an MCDM method aligned with the context of its maintenance planning for the prioritization of maintenance work orders.

Despite the extensive use of ejectors in the process industry, it is complex to predict suction and motive fluids mixture characteristics, especially with multiphase flows, even if, in most cases, mixture pressure control is necessary to satisfy process requirements or to avoid performance problems. The realization of an ejector model can allow the operators to overcome these difficulties to have real-time control of the system performance. In this context, this work proposes a framework for developing a Digital Twin of an ejector installed in an experimental plant able to predict the future state of an item and the impact of negative scenarios and faults diagnosis. ANNs have been identified as the most used tool for simulating the multiphase flow ejector. Nevertheless, the complexity in defining their structure and the computational effort to train and use them are not suitable for realizing standalone applications onboard the ejector. The proposed paper shows how Swarm Intelligence algorithms require a low computational complexity and overperform prediction error and computational effort. Specifically, the Grey Wolf optimizer proves to be the best one among those analyzed.

Purpose: This work aims at providing insights to optimise healthcare logistic of the drug management, in order to deal with the healthcare expenditure cut. In this paper the effects of different drug supply chain configurations, on the resulting average stock, service level and Bullwhip effect, of the studied supply chain, is quantitatively assessed. Design/methodology/approach: A case study of an Italian district has been studied, taking into account three echelons: suppliers, central stock, and hospitals. A model of the various supply chain configurations has been created with the use of the simulation. Specifically, 24 supply chain configurations have been examined, stemming from the combination of several supply chain design parameters, namely: transshipment policies (Emergency Lateral Transshipment or Total Inventory Equalization); re-order and inventory management policies (Economic Order Quantity or Economic Order Interval); required service levels (90% or 95%); the number of available vans (one or two). For each configuration, hospital average stock, service level and a “Bullwhip effect” analysis are computed. To know which input variables are statistically significant, a DoE (Design of Experiments) analysis has been executed. Findings: The output of this paper provides useful insights and suggestions to optimize the healthcare logistic and drug supply chain. According to the developed DoE analysis, it can be stated that the introduction of transshipment policies provides important improvement in terms of service and stock levels. To reduce the Bullwhip effect, which results in a service level decreasing, and in a managing stock costs increasing, it is worth to adopt an EOQ re-order policy. Practical implications: This research gives practical recommendations to the studied system, in order to reduce costs and maintain a very satisfactory service level. Originality/value: This paper fulfils an identified need to study which combination of transshipment policies, re-order/inventory management policies and required service levels, can be the best one to reduce costs and maintain a very satisfactory service level, in the specific logistic system.

Power plants are required to supply the electric demand efficiently, and appropriate failure analysis is necessary for ensuring their reliability. This paper proposes a framework to extend the failure analysis: indeed, the outcomes traditionally carried out through techniques such as the Failure Mode and Effects Analysis (FMEA) are elaborated through data-driven methods. In detail, the Association Rule Mining (ARM) is applied in order to define the relationships among failure modes and related characteristics that are likely to occur concurrently. The Social Network Analysis (SNA) is then used to represent and analyze these relationships. The main novelty of this work is represented by support in the maintenance management process based not only on the traditional failure analysis but also on a data-driven approach. Moreover, the visual representation of the results provides valuable support in terms of comprehension of the context to implement appropriate actions. The proposed approach is applied to the case study of a hydroelectric power plant, using real-life data.

Purpose: This work investigates the influence of project managers’ personality on the success of a project in a Multinational Corporation. The methodology proposed for analyzing the project managers’ personality is based on the Myers-Briggs Type Indicator.Design/methodology/approach: Forty projects carried out in 2012 by multinational corporation, concerning new product development (NPD), have been analyzed, comparing the profile of project managers with results obtained in terms of traditional performance indexes (time delay and over-budget of projects) and performance indexes usually used in “Lean Production” sector (waste time and type of “wastes”). A detailed analysis of the most important “wastes” during the project development is carried out using the Value Stream Mapping (VSM) technique.Findings and Originality/value: Relying on the Myers–Briggs personality instrument, results show that extroverted managers (as opposed to introverted managers) carry out projects that show lower delay and lower waste time. Introverted managers often make “Over-processing” and “Defect” types of waste. Moreover, lower delay and over-budget have been shown by perceiving managers.Research limitations: Regarding the limitations of this work it is necessary to highlight that we collected data from project managers in a retrospective way. While we believe that several aspects of our data collection effort helped enhance the accuracy of the results, future research could conduct real-time case study research to get more detailed insights into the proposed relationships and avoid retrospective bias. Moreover we focused on a single respondent, the project manager. This helped us ensure that their interpretations played an important role in product development. But, we cannot examined the opinion of team members that could be different from project managers opinion regarding some questions.Originality/value: This research provides insight useful to develop proactive approaches to prevent project failures, including the creation of more effective messages and methods to help companies to make the best choices during managers´ hiring. Moreover this research would allow organizations to better match the short-term organizational needs with appropriate personality types.