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Following the spread of the Industry 4.0 paradigm, the role of digital technologies in manufacturing, especially in production and industrial logistics processes, has become increasingly pivotal. Although the push towards digitalization and processes interconnection can bring substantial benefits, it may also increase the complexity of processes in terms of integration and management. To fully exploit the potential of technology, companies are required to develop an in-depth knowledge of each operational activity and related human aspects in the contexts where technology solutions can be implemented. Indeed, analyzing the impacts of technology on human work is key to promoting human-centred smart manufacturing and logistics processes. Therefore, this paper aims at increasing and systematizing knowledge about technologies supporting internal logistics working activities The main contribution of this paper is a taxonomy of the technologies that may be implemented in the different internal logistics areas to support a Logistics 4.0 model. Such a contribution is elaborated in accordance with a deductive approach (i.e., reasoning from the particular to the general), and backed up by an analysis of the literature. The taxonomy represents a useful framework to understand the current and possible technological implementations to drive logistics processes towards Logistics 4.0, with specific attention to the relation between human operators and technologies.

Integrating Industry 4.0 technologies in Lean Manufacturing shop floors 1s reshaping job profiles, emphasising increased task variety and the demand for diverse skills among workers. The conventional perception of operators solely performing specialised tasks is transforming into one where they are viewed as flexible production resources capable of managing a spectrum of activities. The accompanying flexibility necessitates the enlargement and enrichment of skills and responsibilities undertaken by workers on \"Digital Lean shop floors\". This research delves into the evolving definitions of \"Job Enrichment\" and \"Job Enlargement\" as discerned by Lean Manufacturing and Industry 4.0 experts through a comprehensive Delphi study. The investigation of these concepts holds theoretical and practical significance, as they serve as pivotal techniques in the (re-)design of job profiles. Understanding their current meanings 1s crucial, given their potential to elevate the motivational levels of workers, enhance job satisfaction, and consequently improve work performance and productivity. This exploration is essential in pursuing socially sustainable factories in the (near-)future, aligning with the transformative goals of Industry 4.0, and emphasising the integral role played by Lean Manufacturing practices in shaping the workforce dynamics of tomorrow.

PurposeGiven the challenges that Industry 4.0 poses, the purpose of this paper is to propose a comprehensive assessment model suitable for evaluating small- and medium-size enterprises’ (SMEs) digital readiness levels, discuss the results of an assessment of 20 manufacturing SMEs using the proposed model and highlight priorities needed to undertake a successful journey towards Industry 4.0.Design/methodology/approachThe research adopts an empirical approach using multiple case studies. Starting with a literature review about maturity and readiness-assessment models for Industry 4.0, the study’s model has been built and validated through two pilot case studies, with the final model used in an extensive case studies research with 20 enterprises.FindingsThe SMEs used in this research present an intermediate readiness level with respect to Industry 4.0. They are aware of the phenomenon, but management is still taking the first steps towards identifying the most appropriate strategy to approach this Fourth Industrial Revolution. Companies need to exploit all opportunities that data availability provides in terms of knowledge creation and decision-making support, in all forms, through investments in people skills and expertise and through an infrastructure that can support data gathering, analysis and sharing.Originality/valueThe Italian industrial landscape comprises mainly SMEs, mostly needing support to understand their path towards Industry 4.0. Therefore, the proposed model specifically focuses on SMEs, given its modularity, ease of understanding and fit to SMEs’ organisational structure. Furthermore, insights from 20 Italian SMEs are examined, and a list of priorities is highlighted.

Digital servitization is a business model transformation process enabled by the use of digital technologies to create or improve industrial services and product‐service offerings by creating value and competitive advantage increasing customer satisfaction and loyalty as well as company revenue streams. 5G networks can enable digital servitization of manufacturing by providing faster, more secure, and more reliable communications between machines, devices, and humans. This paper explores the impact of adopting 5G technologies on servitization and identifies the services that can benefit most from 5G networks. The research consists of two parts: a literature review of the technologies currently used in the design and provision of industrial services that could benefit from 5G networks and an exploratory survey involving manufacturing companies that have started the digital servitization journey. The main results emerging from the research suggest that 5G can profoundly impact services supported by Augmented Reality, Cloud computing, and Cyber‐physical systems, mainly concerning maintenance, workforce training, machine diagnosis and monitoring.

PurposeThis article aims to investigate the organisational implications of adopting Industry 4.0 (I4.0) technologies, giving specific attention to operations. The paper addresses these implications in two directions: organisational prerequisites for, and consequences of, I4.0 technologies.Design/methodology/approachThe research is based on a multiple case study of Italian small and medium enterprises (SMEs) in manufacturing. Ten case studies have been developed through interviews, company visits and secondary data collection.FindingsThe multiple case study results show that: (1) a lean organisational structure supports effective adoption of I4.0 technologies; (2) introducing such technologies is linked to developing a new kind of job profile (i.e. the “Autonomous Operative Job Profile”); and (3) higher levels of technology adoption create a higher need for non-technical competences.Research limitations/implicationsA limitation of this research relates to the highly heterogeneous maturity levels of the sampled companies, due to the relative newness of the I4.0 paradigm. Future research could, therefore, longitudinally analyse the technology integration process within organisations.Practical implicationsThis research provides preliminary evidence about how organisations and technologies co-evolve, thus suggesting that managers should co-design these areas. It also demonstrates the extreme importance of designing a structured process and a clear set of human resource management tools to favour SME organisational development.Originality/valueThe study is built upon a conceptual framework derived from the sociotechnical perspective that analyses the interconnections between technology implementation and organisational change. From the results, three research propositions are derived to be tested on a larger scale.

There is limited scientific and grey literature studying the phenomenon of how the current job profiles are being affected by Industry 4.0 technologies at the operational level. This paper aims to answer the following question: how can the evolution of Workforce 4.0 job profiles be analyzed from a job-task perspective concerning the adoption of smart and digital technologies in manufacturing companies? To this end, it presents a task classification framework addressing three task classification dimensions, namely: (i) routine/nonroutine tasks, (ii) physical/cognitive tasks, and (iii) individual/social tasks, and a job-task analysis method to analyze the evolution of job profiles due to smart or digital technology adoption at the task level. Both artifacts were created using a state-of-the-art review to ground their conceptualization in the most recent knowledge available on work design and job-task analysis methods and were later evaluated and refined using an action-research approach to increase their applicability and usefulness for academic researchers and practitioners. The applicability of the proposed framework and method was demonstrated in an industrial case study discussing the theoretical and managerial contributions of these two artifacts for the development of Workforce 4.0 job profiles. It was concluded that the proposed framework and method are valuable artifacts that contribute to the limited universe of tools available in the literature to first analyze how operators’ tasks and roles change concerning the adoption of new Industry 4.0 technologies and then identify the requirements of new skills and competencies for the evolving and emerging job profiles on the shop floor.

Simulation models have always been an aid in epidemiology for understanding the spread of epidemics and evaluating their containment policies. This paper illustrates how hybrid simulation can support companies in assessing COVID-19 containment measures in indoor environments. In particular, a Hybrid Simulation (HS) is presented. The HS model consists of an Agent-Based Simulation (ABS) to simulate the virus contagion model and a Discrete Event Simulation (DES) model to simulate the interactions between flows of people in an indoor environment. Compared with previous works in the field of simulation and COVID-19, this study provides the possibility to model the specific behaviors of individuals moving in time and space and the proposed HS model could be adapted to several epidemiological conditions (just setting different parameters in the agent-based model) and different kinds of facilities. The HS approach has been developed and then successfully tested with a real case study related to a university campus in northern Italy. The case study highlights the potentials of hybrid simulation in assessing the effectiveness of the containment measures adopted during the period under examination in the pandemic context. From a managerial perspective, this study, exploiting the complementarity of the ABM and DES approaches in a HS model, provides a complete and usable tool to support decision-makers in evaluating different contagion containment measures.

An established yearly survey aimed at monitoring the employment opportunities of Italian graduates, traditionally carried out with Cati methods, has been integrated during the last few years with Cawi. Cawi has become increasingly crucial due to the high number of graduates involved in the survey, which has mandated a reduction in fieldwork duration and unit costs. Although the seven Cawi surveys used here have different substantive and methodological characteristics, preliminary analysis reveals a common trend: the utmost participation is observed during the first few days immediately following initiation of fieldwork and, to a lesser degree, the delivery of follow-up reminders. Web respondents comprise a self-selected subgroup of the target population, having better academic performance and greater computer skills. A Cox regression model estimating response probability (or response time) shows, besides the obvious effects of certain personal and survey design characteristics, that faster response times are expressed by graduates in science or engineering and reporting good computer skills, whereas the fields of medicine/health and defence/security and no computer skills give rise to lower response probability. Ways to use these findings for fine-tuning data collection are discussed.

Per conoscere gli esiti occupazionali oltre i cinque anni dal conseguimento del titolo, il Consorzio ALMALAUREA ha condotto, nell'autunno 2011, un'indagine via web che ha coinvolto un campione di laureati pre-riforma degli anni 2000, 2001 e 2002, intervistati a circa 10 anni dal titolo. Dato l'obiettivo di ricerca, la rilevazione ha dovuto necessariamente coinvolgere solo i laureati preriforma, perché i primi colleghi post-riforma, quelli più rapidi nel terminare gli studi, sono usciti dal sistema universitario solo in anni più recenti. Un collettivo non facile da raggiungere, quello coinvolto nella presente indagine, visto l'ampio intervallo temporale trascorso dalla laurea, ma che nonostante tutto ha permesso di raccogliere circa 13mila interviste. La rilevazione è avvenuta con tecnica CAWI (Computer-Assisted Web Interviewing). Ciò significa che tutti i laureati in possesso di indirizzo di posta elettronica (65mila, il 38% di tutti i laureati del triennio in esame) sono stati contattati via e-mail e invitati a compilare un questionario ospitato sul sito web di ALMALAUREA. Il processo di rilevazione, che ha previsto tre solleciti, ha permesso di raggiungere un tasso di risposta del 20% (rispetto alle e-mail inviate).