Advanced innovative methods for the digitalization of production processes’ planning and control using integrated information systems and neural networks

In the era of Industry 4.0, there is a pressing need to be increasingly competitive with the players operating in one's target industry by aiming to achieve operational excellence and maximum value for the customer, with an ever-increasing focus on the triple nature of sustainability. The enabling technologies for digitization offered by the Industry 4.0 paradigm represent a lever that is now unavoidable and presents itself as an opportunity for decision-makers to take timely actions based on real-time data processing. In doing so, it is believed to be of paramount importance to combine such a strongly technologydriven paradigm with the central role of the human being, who will be the protagonist in a process of replacing their skills and knowledge. Despite the fact that almost 15 years have passed since the term "Industrie 4.0" was coined, the degree of adoption of these technologies still appears to be low, both for large companies and SMEs. The causes of this inertia can be traced to a lack of a systemic approach to adoption, as well as the actual presence of various barriers that do not facilitate this process-especially for smaller companies. Lack of a long-term vision and blindness to the expected return on the large investments required result in a sub-optimal outcome that does not allow the full potential of the approach to be expressed. The Production Planning and Control (PPC) process-which is strongly conditioned by market, product, and process characteristics-is the perfect framework for approaching digitization, given its centrality in manufacturing realities of any size and being considered as the core responsibility of production management. Two clear underlying needs emerge from the analysis of Industry 4.0's relationship with the business world and scientific research. In addition to the lack of a common definition that also includes the social, economic, and environmental aspects for the Industry 4.0 - strongly technology-centric phenomenon, the need for the elaboration of real case study proposals that have a dual function is outlined: from an academic perspective, to demonstrate the key findings elaborated at the theoretical level; from a business perspective, it is necessary instead to give a justification for the initial investment, especially for realities such as SMEs - which suffer most from the barriers to adoption of unavailability of financial resources and specific skills. It is precisely in the space between these two needs that the development of the case study proposed in this research paper fits in, with the aim of understanding: i. how much the new technologies (I4.0) could impact or influence the production operations, and more in detail the PPC process in a high-tech manufacturing SME? ii. Could a small or medium-sized manufacturing company become more competitive leveraging these disruptive technologies? In order to answer these questions and make a theoretical contribution, the research project set out to outline a theoretical basis for the approach to Industry 4.0. The result is embodied in the proposed definition of a "Smart PPC" process that includes the technological trait, but also dignifies the issues of so-called "strategic fit" and the central role of human beings as benefit users. In addition, extensive insight into the PPC process is provided, with related necessary information systems. The result of the analysis shows a clear need for the adoption of a "Smart PPC" that rests inescapably on an MES system and the ability to minimize the propagation of negative impacts arising from the uncertainties of today's manufacturing environment through improvements in the strategic layer. From a practical point of view, the case study seeks to demonstrate the benefits of implementing two of the main 4.0 enabling technologies - MES and Neural Network (NN) for demand forecasting - in a manufacturing SME that exhibits characters of strong innovation propensity and is eligible to overcome the barriers to adoption traced in the literature. The implementation results clearly show that the MES system turns out to be key and indispensable for the digitization process of a manufacturing company. Moreover, enabling real-time analysis of manufacturing system performancemeasurable from multiple perspectives-generates measurable cross-functional improvements in terms of: (i) cost savings and improvement of first margin, due to better inventory management; (ii) reduction of Production Lead Times (PLT), triggered by a more conscious management of available resources and the ability to immediately identify process bottlenecks and take immediate corrective actions; and (iii) improvement of quality indices. From a strategic perspective, the proposed NN - while improvable in terms of predictive performance - integrates seamlessly with the qualitative Salesforce opinion method, providing a complementary tool for assessing demand and improving forecast accuracy indices, with considerable benefits for the entire planning and manufacturing chain. The main contributions of the research project to the theory can be summarized as follows. The basic hypothesis is that the PPC process is clearly influenced by the attributes that characterize the target market, the product, and the production process. Based on this, it can be said that there is a close relationship between achieving a high degree of smartness for the PPC process itself and the digitization strategy through 4.0 technologies. This study, in addition to providing a systemic definition of "Smart PPC," demonstrates how it is critically important to base the aforementioned strategy on the adoption of an MES 4.0 system, proving its real benefits in a real case and helping to give real-world feedback to what has been theorized in the literature. From a practical point of view, the case study demonstrates how indeed 4.0 technologies, in concert with the centrality of the human being and continuous strategic alignment, turn out to be key to the structuring of a "Smart PPC" process, enabling the entire company in the digitization process and bringing benefits that give justification to the initial investment. The real case thus helps to give perspective for continuous improvementespecially to realities such as SMEs-that hopefully supports the acceleration of the Industry 4.0 adoption process. The current research scheme denotes limitations, prominent among them being the limited number of real cases analyzed and the verticality of the same on a particular MTStype production system, with hybrid production between Job-Shop and Batch production. In addition, the prediction results obtained from the proposed NN have shown accuracy performance with wide room for improvement. Based on these limitations, the study lays the foundation for future research work in other manufacturing contexts, also aimed at improving the performance of the identified solutions. Overall, the present PhD contributes to enhancing the knowledge and abilities concerning the emerging production management domain of PPC process, providing evidence regarding the key role of Industry 4.0 in industrial progress.