The Energy Worker Profiler from Technologies to Skills to Realize Energy Efficiency in Manufacturing

In recent years, the manufacturing sector has been responsible for nearly 55 percent of total energy consumption, inducing a major impact on the global ecosystem. Although stricter regulations, restrictions on heavy manufacturing and technological advances are increasing its sustainability, zero-emission and fuel-efficient manufacturing is still considered a utopian target. In parallel,companies that have invested in digital innovation now need to align their internal competencies to maximize their return on investment. Moreover, a primary feature of Industry 4.0 is the digitization of production processes, which offers the opportunity to optimize energy consumption. However, given the speed with which innovation manifests itself, tools capable of measuring the impact that technology is having on digital and green professions and skills are still being designed. In light of the above, in this article we present the Worker Profiler, a software designed to map the skills currently possessed by workers, identifying misalignment with those they should ideally possess to meet the renewed demands that digital innovation and environmental preservation impose. The creation of the Worker Profiler consists of two steps: first, the authors inferred the key technologies and skills for the area of interest, isolating those with markedly increasing patent trends and identifying green and digital enabling skills and occupations. Thus, the software was designed and implemented at the user-interface level. The output of the self-assessment is the definition of the missing digital and green skills and the job roles closest to the starting one in terms of current skills; both the results enable the definition of a customized retraining strategy. The tool has shown evidence of being user-friendly, effective in identifying skills gaps and easily adaptable to other contexts

Advantages and Drawbacks: automatization of Extractions and Measurements from Patents

L'analisi automatica del testo (Text Mining) è un processo che consente di estrarre informazione implicita contenuta in testi non strutturati, attraverso l’applicazione di un algoritmo di Mining e il supporto di software specifici. All’interno dell’ampio spettro di possibilità offerte, la classificazione di documenti e parole risulta essere particolarmente rilevante, ed è resa possibile attraverso la Named Entity Recognition (NER), il riconoscimento di specifiche classi di parole nel testo. I Tool di NER, selezionata una determinata classe semantica, mirano ad estrarre tutte le parole che vi appartengono. Un sistema di NER risulta efficace solo attraverso la compresenza di algoritmi di Machine Learning allo stato dell’arte e la conoscenza tecnica del dominio al quale appartengono i documenti di analisi. Nella presente tesi di Laurea è stato analizzato, attraverso un sistema di NER, un database costituito da estrazioni di parole contenute nei brevetti, con lo scopo di estrarre vantaggi e svantaggi delle invenzioni descritte. Il lavoro di ricerca è stato incentrato sulla formulazione di euristiche finalizzate a rendere il processo di analisi meno complesso e più performante. Una prima parte del lavoro ha riguardato la definizione di una Tassonomia in grado di rendere il processo di estrazione il più preciso possibile, garantendo l’eliminazione di risultati non pertinenti. Una volta formalizzato, esso è stato applicato nella fase successiva, consentendo di massimizzare l’utilità e l’utilizzabilità della nuova base di dati, costituita da estrazioni provenienti da brevetti sulle Batterie Ricaricabili al Litio. Nel Caso Studio finale è stata quindi elaborata una procedura di analisi automatizzabile in cui operano sinergicamente l’Analisi Statistica, la Clusterizzazione, e la Trend Analysis, consentendo di individuare informazioni strategicamente significative sia per la Progettazione e Sviluppo che per il Marketing. The Automatic Analysis of Texts (Text Mining Process) consists of the extraction of hidden information contained on unstructured texts, through the application of Mining Algorithms and the use of specific software. In detail, the classification of documents and words proves to be extremely relevant and it is made possible through the Named Entity Recognition (NER), which identifies specific word classes on the text. The NER Tools, selected a particular semantic class, aim at extracting all the words which belong to it. A NER system could be considered efficient only through the coexistence of the technical knowledge of the domain to which the documents belong and the state-of-the-art Machine Learning Algorithms. On this thesis work, a starting database had been analyzed through a NER system; it had been made by words which belong to Patents, with the aim of extracting Advantages and Drawbacks of the technologies described. The research work focused on the wording of Heuristics intended to make the analysis process less complex and more efficient. The first part of my work concerned the definition of a “Taxonomy” which let the following process be as precise as possible; moreover, this phase was fundamental to define some rules that allow me to delete all the unusable extractions. Once it was formalized, it was applied on a new database, made of extractions belonging to Lithium Rechargeable Batteries Patents. During the Final Case Study, the procedure of automatic analysis was worded; the method includes Statistical Analysis, Clustering and Innovative Trend Representation which work synergistically together, let anyone who would use it identify key information for both “Marketing” and “Research and Development” Function

Il Lato Umano della Rivoluzione Digitale: Strumenti di Text Mining per Affrontare il Fenomeno Industria 4.0

L'avvento dei Big Data e il loro progressivo aumento di volume e complessit\ue0 ha permesso lo sviluppo di scenari completamente nuovi per le aziende. In particolare, le tecniche di gestione, analisi e conversione dei dati grezzi in informazioni \u201csexy\u201d sono in continua evoluzione. Allo stesso tempo, il paradigma 4.0 sempre pi\uf9 diffuso svela cambiamenti radicali non solo nella tecnologia ma anche nella struttura e nelle dinamiche del lavoro, modificando competenze e capacit\ue0 richieste dalla manifattura e dai servizi. Inoltre, se le attivit\ue0 di routine sembrano essere suscettibili alla digitalizzazione, d'altra parte il possesso di competenze trasversali (note anche come soft skills) identifica sempre pi\uf9 un collo di bottiglia per l'automazione, e le stesse risultano essere sempre pi\uf9 richieste dal mercato del lavoro. In relazione a ci\uf2, lo scopo del presente lavoro \ue8 studiare gli effetti dell'Industria 4.0 sulla forza lavoro, sviluppando strumenti di Text Mining ad hoc per gestirne adeguatamente l'impatto e fornendo un supporto tangibile in questa fase critica di transizione. Inoltre, questa tesi si concentra sul soddisfare le esigenze di tre principali stakeholder: la gestione delle risorse umane nel recruiting, riqualificazione e miglioramento delle competenze; le Istituzioni per costruire percorsi di apprendimento personalizzati e aggiornare database riconosciuti come standard a livello internazionale; i policy maker per prevedere gli effetti eterogenei della digitalizzazione sui profili professionali, analizzando il mutamento della domanda di competenze sulle filiere in Emilia-Romagna.The advent of Big Data and their progressive increase in volume and complexity has allowed the development of completely new scenarios for companies. Particularly, the techniques of management, analysis and conversion of raw data into \u201csexy\u201d information are constantly evolving. At the same time, the increasingly widespread paradigm 4.0 discloses radical changes not only in technology but also in the structure and dynamics of work, changing skills and abilities required by manufacturing and services. In addition, if routinely tasks seem to be susceptible to digitalization, on the other hand ownership of transversal skills (also known as soft skills) is ever more recognized as a bottleneck for automation and they are ever more required by the labour market. In view of this, the purpose of the present work is to study the effects of Industry 4.0 on the workforce, developing ad hoc Text Mining tools to properly manage its impact, and providing a tangible support in this transitional and critical phase. Moreover, this thesis focuses on satisfying the needs of three main stakeholders: HR management in recruitment, reskilling and upskilling; Institutions to build customized learning paths and to update International recognized databases; Policy makers to foresight the heterogeneous effects of digitalization on job profiles, analysing the change of skill demand on supply chains in Emilia-Romagna

Who rises and who drops? New technologies, workers and skills. The case of a developed region

In this paper, the authors study the evolution of the demand for new professional profiles and new skills in Emilia-Romagna in the decade 2008- 2017, through the analysis of the SILER database (Mandatory No ca ons to the Ministry of Labour). The focus of the analysis is on digital skills. The results, among the few available for Italy, are in line with those offered in the international literature. The proposed methodology provides a measure, built on employment balances, that allows to identify `winners and losers' in a small open economy and is a useful tool to monitor business choices and public policies

Robot, ICT e globalizzazione: gli effetti sui mercati del lavoro locali in Italia. (Robots, ICT and globalization: effects on local labour markets in Italy)

Italy, like other advanced economies, is in the midst of a profound transformation of the production system. At the heart of these processes are two long-term shocks: exposure to competition from emerging and newly industrialized countries and exposure to new digital technologies (ICT and robots). Starting from the works of Acemoglu and Restrepo (2017) for the United States and Dauth, Findeisen, Südekum and Woessner (2017) for Germany, an empirical analysis of the impact of digitalization and globalization on the employment dynamics of the Italian local labour markets (SLL) is proposed. To this end, a database has been built that unifies the structural data on ISTAT SLLs for the period 1991-2011 with data on robots from IFR (International Federation of Robotics). The database integrated the data on investments in ITC technologies, provided by EU-KLEMS, the data relating to the trade flows of WITS (World Integrated Trade Solution, World Bank) and, lastly, COMTRADE (UN) data. The analysis highlights two results. The first result is that, in the recent history of Italian economic development, the fall in manufacturing employment is due to a much greater extent to competition from emerging and newly industrialized countries compared to the digitization process. The second result is that both in relation to digitization and in relation to globalization the effects on the whole country are far from homogeneous. The effects are spread across the territory according to the different exposure and the different characteristics of the local productive system that suffer the two shocks. In this sense, SLLs are the necessary tool to understand who wins and who loses

The worker profiler: Assessing the digital skill gaps for enhancing energy efficiency in manufacturing

<p>In recent years, the manufacturing sector has been responsible for nearly 55 % of total energy consumption, inducing a major impact on the global ecosystem. Although technological advances are increasing its sustainability, zero-emission and fuel-efficient manufacturing is still considered a utopian target. Moreover, a primary feature of Industry 4.0 is the digitization of production processes, which offers the opportunity to optimize energy consumption. However, given the speed and often unpredictability with which innovation manifests itself, tools capable of measuring the impact that technology is having professions are still being designed. In light of the above, in this article we present the Worker Profiler, a software designed to map the skills currently possessed by workers, identifying misalignment with those they should ideally possess to meet the renewed demands that digital innovation and environmental preservation impose. In more detail, the authors inferred the key technologies and skills for the topic, isolating those with markedly increasing patent trends and identifying green and digital enabling skills and occupations. Thus, the software was designed and implemented at the user-interface level. The output of the self-assessment is the definition of the missing digital and green skills that enable the definition of a customized retraining strategy.</p&gt

Here Comes the Robot: Measuring the Risk of Automation of Human Competences through a Quantitative Approach

In recent years, digital technologies shaped all aspects of the current socio-economic scenario. The relation between these new technologies and workers is a classical controversy. If on one hand digitalization allows firms to substitute tasks previously performed by workers, it is doubtless that the use of these digital technologies increases labor productivity and consequently impacts employment. A mismatch exists between skill demand and supply due to the complexity of the problem: technologies are very different from each other, and so are their impact on occupations. In this scenario, understanding and predicting which are the competences impacted by digital technologies is fundamental for preparing workers, firms and policy makers to address this digital wave. The study of the evolution of skills requirements in the labor market is well-established in the literature [1] and initially such phenomenon was mainly linked to routine based activities that can easily be performed by sophisticated algorithms. Recently, also the automation of tasks which have always been considered too complex to be performed by a technology seems like a plausible scenario [2]. Moreover, soft skills are ever more recognized as a bottleneck for computerisation, since machines cannot replicate what is uncodable [3]. Frey and Osborne [4] quantitatively estimate the computerisation susceptibility of job profiles on 702 detailed occupations collected in the O*NET database (https://www.onetonline.org/). Several authors have investigated the results of Frey and Osborne [4] in the past years [5] or propose new methodologies to study the topic [6]. Despite the large literature that exists on the subject, there is still a lack in quantitative measures of the effect that automation will have on what workers do. The most similar study in this direction has been done by Brandes and Wattenhofer [5], that refine Frey & Osborne’s results assigning automation probabilities to tasks. Anyway, our study goes deeper in this direction studying how the risk of automation is linked to skills, abilities and knowledge. Like other authors who deepened Frey & Osborne [4] results, we use their output to switch the focus, from jobs to competences. To fully understand our method it is important to describe how O*NET is structured. O*NET taxonomy, developed by the U.S. Department of Labor, contains abilities, skills and domains of knowledge to perform a job. O*NET distinguishes the competences in 3 macro-categories: (1) abilities that are enduring attributes of the individual that influence performance; (2) skills that are developed capacities that facilitate learning or the more rapid acquisition of knowledge; (3) knowledge that is an organized set of principles and facts applying in general domains. We use the term “competence” to refer either to abilities, skills or knowledge. Each job profile has quantitative information about “importance” and “level” for every owned competence. The “importance” answers the question “How important is a given competence to the performance of a given job?” whereas the “level” answers “What level of a given competence is needed to perform a given job?”. We estimate the probability of computerisation of each competence using the computerisation probability of the occupations and the “importance” and “level” information. Using this approach, we are able to give statistical evidence of different levels of automation probability of different competence groups. We found that the computerisation probability of the macro-class “ability” is 0.50, that is greater than those of “skills” (0.40) and “knowledge” (0.36). This is a reasonable result since the abilities comprehend enduring factors of workers (such as speed of limb movement, control precision, rate control etc.) that are more simple to codify in a sophisticated algorithm than complex concepts to learn and use at the right time (i.e. knowledge) or soft and technical skills, that could be acquired through experience. Among the abilities those at greatest risk of automation are reaction time/speed abilities (0.61). Differently, idea generation (as an ability) has a lower probability of automation, 0.39. The skills more susceptible to automation are the technical skills, such as equipment maintenance (0.63). In contrast, the systems skills i.e. developed capacities used to understand and improve socio-technical systems, have a low level of computerisation (0.35). Finally, among knowledge we could distinguish high automation risk knowledge, such as therapy/counseling (0.20), and knowledge with a low computerisation probability, as for example mechanics (0.53). From an academic point of view, we offer a quantitative approach to measure to what extent competences are minimizing the risk of being substituted by machines, giving statistical evidence of different levels of automation probability of different groups of competence. Moreover, we offer a holistic view: new technologies bring new opportunities (e.g. automation) but also new needs (e.g. managing automation). Most of the state of the art deals with the aggregate employment impact of innovation, and does not disentangle the analysis in terms of competences. Finally, our results can help companies and policy makers to estimate the impact of automation on competences. Given that there is less debate about the positive employment effect of innovation, a quantitative understanding of this phenomena, possibly free from negative or positive biases, can help face the future of training and hiring. References [1] Cedefop, (2019). Online job vacancies and skills analysis: a Cedefop pan-European approach. [2] Colombo, E., Mercorio, F., & Mezzanzanica, M. (2019). AI meets labor market: exploring the link between automation and skills. Information Economics and Policy. [3] Acemoglu, D., & Autor, D., (2010). Skills, tasks and technologies: implications for employment and earnings. doi:10.3386/w16082 [4] Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological forecasting and social change, 114, 254-280. [5] Brandes, P., & Wattenhofer, R. (2016). Opening the Frey/Osborne black box: Which tasks of a job are susceptible to computerization?. arXiv preprint arXiv:1604.08823. [6] Montobbio, F., Staccioli, J., Virgillito, M. E., & Vivarelli, M. (2022). Robots and the origin of their labour-saving impact. Technological Forecasting and Social Change, 174, 121122