Post-automation: report from an international workshop

The purpose of this report is to share lessons from an international research workshop dedicated to post- automation. Twenty-seven researchers from eleven different countries in Africa, Asia, Latin America and Europe, met at the Science Policy Research Unit at Sussex University on 11-13 September 2019, where we discussed empirical research papers and explored post-automation in group activities. We write this report primarily for researchers, but also for activists and policy advisors looking for more imaginative approaches to governing technology, work and sustainability in society, compared to those dominant agendas adapting automatically to the interests behind automation. The report is structured as follows. Section two introduces the workshop topic and papers presented, and which leads into two related areas that became a focus for discussion. First, some challenges in the foundations of automation theory (section three). And second, post-automation as a more constructive proposition to the challenges of automation, and that is happening right now (section four). Section five summarises some key points arising from the workshop, based on empirical observations from the margins of digital technology development, and that give both a flavour of the workshop and help elaborate the post-automation proposition. Some analytical and strategic themes are discussed in section six. We conclude in section seven with proposals for a post-automation agenda

Industrial internet of things: What does it mean for the bioprocess industries?

Industrial Internet of Things (IIoT) is a system of interconnected devices that, via the use of various technologies, such as soft sensors, cloud computing, data analytics, machine learning and artificial intelligence, provides real-time insight into the operations of any industrial process from product conceptualisation, process optimisation and manufacturing to the supply chain. IIoT enables wide-scope data collection and utilisation, and reduces errors, increases efficiency, and provides an improved understanding of the process in return. While this novel solution is the pillar of Industry 4.0, the inherent operational complexity of bioprocessing arising from the involvement of living systems or their components in manufacturing renders the sector a challenging one for the implementation of IIoT. A large segment of the industry comprises the manufacturing of biopharmaceuticals and advanced therapies, some of the most valuable biotechnological products available, which undergo tight regulatory evaluations and scrutinization from product conceptualisation to patient delivery. Extensive process understanding is what biopharmaceutical industry strives for, however, the complexity of transition into a new mode of operation, potential misalignment of priorities, the need for substantial investments to facilitate transition, the limitations imposed by the downtime required for transition and the essentiality of regulatory support, render it challenging for the industry to adopt IIoT solutions to integrate with biomanufacturing operations. There is currently a need for universal solutions that would streamline the implementation of IIoT and overcome the widespread reluctance observed in the sector, which will recommend accessible implementation strategies, effective employee training and offer valuable insights in return to advance any processing and manufacturing operation within their respective regulatory frameworks

Identifying and Assessing the Required I4.0 Skills for Manufacturing Companies’ Workforce

Nowadays, the diffusion of digital and industry 4.0 (I4.0) technologies is affecting the manufacturing sector with a twofold effect. While on one side it represents the boost fastening the competitive advantage of companies, on the other hand it is often accompanied by several challenges that companies need to face. Among all, companies are required to invest in technologies to empower their production activities on the shopfloor without lagging behind their workforce in order to undertake a linear, aware, and structured path toward digitization. The extant literature presents some research conducted to support companies toward digitization, and they usually rely on maturity models in this intention. Nevertheless, few studies included the assessment of workforce skills and competencies in the overall assessment, and in this case, they provide a high level perspective of the investigation, mainly based on check lists which may limit the objectivity of the assessment, and usually they do not customize the assessment based on companies’ requirements. Therefore, considering the importance to balance investments in technologies with those in the workforce to move toward the same direction, this contribution aims to develop a structured, customizable, and objective skill assessment model. With this intention, it has been first clarified the set of job profiles required in I4.0, together with the needed related skills based on the extant literature findings; second, it has been identified the set of key criteria to be considered while performing the assessment of the workforce; third, it has been defined the method to be integrated in the maturity model to enable the initial setting of the weights of the criteria identified according to the company needs; and fourth, based on these findings, it has been developed the assessment model. The developed model facilitates the elaboration of the proper workforce improvement plans to be put in practice to support the improvement of the skills of the whole workforce based on company’s needs

Skills for the Fourth Industrial Revolution - A response to Industry 4.0 challenges

Many countries are now entering the stage of the Fourth Industrial Revolution, also referred to as Industry 4.0, in which technological advances enable significant changes in industry. Industry 4.0 will not only increase resource and time efficiency, it will also change the way people work. The Universities of the Future (UoF) project aims to address the educational needs arising from Industry 4.0 in Europe by creating educational offerings in collaboration between industry, universities, and public bodies. To this end, the project takes two approaches: the identifying of skills required for succeeding in the Industry 4.0 environment, and a report on Industry 4.0 challenges and education focusing on Finland, Poland and Portugal. This thesis serves as part of that report. One of the most important challenges is skilled labour scarcity, which has forced companies and countries to find novel ways to attract or create talent. For every professional, a good understanding of their own discipline is the basis for job performance, but it is also necessary to have the curiosity and motivation to continue lifelong learning, and to have a wider vision that allows them to understand complex problems or situations. Mastering the scientific process and developing creative thinking helps develop problem-solving skills. In addition, everyday life requires working effectively and communicating with people from different backgrounds, and the possibility to learn from our peers. Particularly when developing technology for human use, the synergy of work with people from different disciplines and backgrounds is key. Human work in the fourth industrial revolution is not meant to be discarded, but its role must transform in order to thrive and find new solutions to increasingly complex challenges

South American Expert Roundtable : increasing adaptive governance capacity for coping with unintended side effects of digital transformation

This paper presents the main messages of a South American expert roundtable (ERT) on the unintended side effects (unseens) of digital transformation. The input of the ERT comprised 39 propositions from 20 experts representing 11 different perspectives. The two-day ERT discussed the main drivers and challenges as well as vulnerabilities or unseens and provided suggestions for: (i) the mechanisms underlying major unseens; (ii) understanding possible ways in which rebound effects of digital transformation may become the subject of overarching research in three main categories of impact: development factors, society, and individuals; and (iii) a set of potential action domains for transdisciplinary follow-up processes, including a case study in Brazil. A content analysis of the propositions and related mechanisms provided insights in the genesis of unseens by identifying 15 interrelated causal mechanisms related to critical issues/concerns. Additionally, a cluster analysis (CLA) was applied to structure the challenges and critical developments in South America. The discussion elaborated the genesis, dynamics, and impacts of (groups of) unseens such as the digital divide (that affects most countries that are not included in the development of digital business, management, production, etc. tools) or the challenge of restructuring small- and medium-sized enterprises (whose service is digitally substituted by digital devices). We identify specific issues and effects (for most South American countries) such as lack of governmental structure, challenging geographical structures (e.g., inclusion in high-performance transmission power), or the digital readiness of (wide parts) of society. One scientific contribution of the paper is related to the presented methodology that provides insights into the phenomena, the causal chains underlying “wanted/positive” and “unwanted/negative” effects, and the processes and mechanisms of societal changes caused by digitalization

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Applications of Emerging Smart Technologies in Farming Systems: A Review

The future of farming systems depends mainly on adopting innovative intelligent and smart technologies The agricultural sector s growth and progress are more critical to human survival than any other industry Extensive multidisciplinary research is happening worldwide for adopting intelligent technologies in farming systems Nevertheless when it comes to handling realistic challenges in making autonomous decisions and predictive solutions in farming applications of Information Communications Technologies ICT need to be utilized more Information derived from data worked best on year-to-year outcomes disease risk market patterns prices or customer needs and ultimately facilitated farmers in decision-making to increase crop and livestock production Innovative technologies allow the analysis and correlation of information on seed quality soil types infestation agents weather conditions etc This review analysis highlights the concept methods and applications of various futuristic cognitive innovative technologies along with their critical roles played in different aspects of farming systems like Artificial Intelligence AI IoT Neural Networks utilization of unmanned vehicles UAV Big data analytics Blok chain technology et

An investigation upon Industry 4.0 implementation: the case of small and medium enterprises and Lean organizations

In recent years, industries have undergone several shifts in their operating and management systems. Alongside to the technological innovation, rapid market changes and high competitiveness; growing customer needs are driving industries to focus on producing highly customized products with even less time to market. In this context, Industry 4.0 is a manufacturing paradigm that promises to have a great impact not only on improving productivity but also on developing new products, services and business models. However, the literature review has shown that research on Industry 4.0 implementation is still characterized by some weaknesses and gaps (e.g., topics such as the implementation of Industry 4.0 in SMEs and its integration with Lean Management approach). Motivated by so, this thesis sought to answer four key questions: (RQ1) What are the challenges and opportunities for SMEs in the Industry 4.0 field? (RQ2) What are the resources and capabilities for Industry 4.0 implementation in SMEs? (RQ3) How can these resources and capabilities be acquired and/or developed and (RQ4) How to integrate Industry 4.0 and Lean Management? To deal with the first research question, a semi-systematic literature review in the Industry 4.0 field was conducted. The main goal is to explore the implementation of Industry 4.0 in SMEs in order to identify common challenges and opportunities for SMEs in the Industry 4.0 era. To face with the second and third research questions, a multiple case study research was conducted to pursue two main aims: (1) to identify the resources and capabilities required to implement Industry 4.0 in Portuguese SMEs. Furthermore, based on mainstream theories such as resource-based view (RBV) and dynamic capability theory, it sought empirical evidence on how SMEs use resources and capabilities to gain sustainable competitive advantage; (2) to shed light on how those SMEs acquire and/or develop the Industry 4.0 resources and capabilities. Finally, this thesis employed a semi-systematic literature review methodology to deal with the fourth research question. As such, it explored the synergistic relationship between Industry 4.0 and Lean Management to identify the main trends in this field of research and, ultimately, the best practices. The analysis and discussion of the best practices revealed a set of potential relationships which provided a more clear understanding of the outcomes of an Industry 4.0-LM integration.Nos últimos anos, as indústrias têm passado por várias mudanças tanto nos seus sistemas operacionais, como de gestão. Juntamente com a inovação tecnológica e alta competitividade; as mudanças nas necessidades dos clientes levaram as indústrias a se concentrarem na produção de produtos altamente personalizados e com tempo de lançamento no mercado cade vez menores. Nesse contexto, a Indústria 4.0 é um paradigma de manufatura que promete ter um grande impacto não só na melhoria da produtividade, mas também no desenvolvimento de novos produtos, serviços e modelos de negócios. No entanto, a revisão da literatura mostrou que a investigação sobre a implementação da Indústria 4.0 ainda é caracterizada por algumas lacunas (por exemplo em tópicos como a implementação da Indústria 4.0 em pequenas e médias empresas (PMEs) e sua integração com a filosofia de gestão Lean Management). Diante disso, esta tese procura responder à quatro questões-chave: (RQ1) Quais são os desafios e oportunidades para as PMEs no campo da Indústria 4.0? (RQ2) Quais são os recursos e capacidades necessários para a implementação da Indústria 4.0 nas PMEs? (RQ3) Como esses recursos e capacidades podem ser adquiridos e/ou desenvolvidos e (RQ4) Como integrar os paradigmas de manufatura, Indústria 4.0 e Lean Management? Para responder à primeira questão de investigação, este trabalho empregou uma revisão semi-sistemática da literatura. O objetivo principal foi explorar a implementação da Indústria 4.0 nas PMEs, a fim de identificar quais são os desafios e oportunidades para as PMEs na era da Indústria 4.0. Para fazer face à segunda e terceira questões de investigação, foi realizado um estudo de caso em 5 PMEs localizadas em Portugal a fim de atingir os seguintes objetivos: (1) identificar os recursos e capacidades necessários para implementar a Indústria 4.0 nas PME portuguesas; (2) esclarecer como essas PMEs adquirem e/ou desenvolvem esses recursos e capacidades. Além disso, com base nas teorias resourcebased view (RBV) e dynamic capabilities, buscar evidências empíricas sobre como as PMEs usam recursos e capacidades para obter vantagem competitiva sustentável. Finalmente, para lidar com a quarta questão de investigação, este estudo explorou a relação sinérgica entre a Indústria 4.0 e a filosofia de gestão Lean Management (LM) para identificar as principais tendências neste campo de investigação e promover as melhores práticas. A análise e discussão das melhores práticas revelaram um conjunto de potenciais relações, o que contribuiu para um entendimento mais claro sobre a integração da Indústria 4.0 com LM

Innovation landscape and challenges of smart technologies and systems - a European perspective

Latest developments in smart sensor and actuator technologies are expected to lead to a revolution in future manufacturing systems’ abilities and efficiency, often referred to as Industry 4.0. Smart technologies with higher degrees of autonomy will be essential to achieve the next breakthrough in both agility and productivity. However, the technologies will also bring substantial design and integration challenges and novelty risks to manufacturing businesses. The aim of this paper is to analyse the current landscape and to identify the challenges for introducing smart technologies into manufacturing systems in Europe. Expert knowledge from both industrial and academic practitioners in the field was extracted using an online survey. Feedback from a workshop was used to triangulate and extend the survey results. The findings indicate three main challenges for the ubiquitous implementation of smart technologies in manufacturing are: i) the perceived risk of novel technologies, ii) the complexity of integration, and iii) the consideration of human factors. Recommendations are made based on these findings to transform the landscape for smart manufacturing