Robots inspired by human behavior

When we are talking about smart manufacturing, robots cannot be missed. Industrial robots have considerably increased productivity and relieve humans of heavy tasks since they were first incorporated into manufacturing lines. As the use of robots raises many ethical issues regarding the employability of humans, it is important to mention the words of the professor Robert Radwin: “We envision a workplace where workers won’t be replaced by robots, but rather where robots will assist workers in their jobs. That’s our goal” [1].

Robot learning combines several machine learning techniques with robotics-related content i.e., in robot learning and behavior. Robot learning places more focus on producing actions as the output than traditional machine learning does on monitoring the environment as the input. For instance, whereas reinforcement learning offers formalizations for machine behavior, deep learning assists the robot in handling unstructured settings. Animal behavior is similar to this type of scenario.

 

Figure 1: Human behaviour and machine behaviour [2]

Humans use their sense organs, such as their eyes, nose and hands, which are connected to the brain through the neurological system, to perceive their surroundings. For instance, light-sensitive cells on the retina detect environmental light before sending bio-electricity signals to the cerebral cortex. To create motion, however, the cerebral cortex also communicates with the relevant muscles. Close-loop formation is created by this pipeline, same as in automatic control. For instance, a human will use his eyes to detect the glass of milk and watch his hand as it to reach it, take it to his lips, drink from it, and then put the glass back. Similarly, behaviors are shared by robotic devices in the closed-loop, as seen in the Figure 1. Cameras are used by the robot to examine its surroundings, while computers are used to analyze perceptual data. The robotic manipulator is then driven by algorithms sending commands to the robot controller [2].

Lastly, some of the benefits of operating robots in industry include, reducing programming time and automation costs, making it easier for workers in the industry. Stephen Hawking, a theoretical physicist, believed that humans would be replaced by robots, and robotic technology has advanced more quickly than expected [3]. Thus, this robot-human matter needs more research and development so that worker’s jobs are not threatened.

References

[1] L. Liu et al., “Human Robot Collaboration for Enhancing Work Activities,” Hum. Factors, 2022, doi: 10.1177/00187208221077722.

[2] Z. Liu, Q. Liu, W. Xu, L. Wang, and Z. Zhou, “Robot learning towards smart robotic manufacturing: A review,” Robot. Comput. Integr. Manuf., vol. 77, p. 102360, Oct. 2022, doi: 10.1016/J.RCIM.2022.102360.

[3] How robots are stealing human jobs and threatening our future.” https://www.trtworld.com/life/how-robots-are-stealing-human-jobs-and-threatening-our-future-28285 (accessed Jan. 04, 2023).

Featured image: Freepik/macrovector

Decoding Industry 4.0: Big Data Analytics Unleashing Manufacturing Potential

In the era of Industry 4.0, where advanced technologies converge to create smart, interconnected systems, the role of big data analytics has become paramount in unlocking the potential of vast datasets. This article explores the transformative impact of big data analytics in the context of Industry 4.0, specifically within the manufacturing sector. It delves into how organizations leverage big data to process and derive meaningful insights from the massive volume of data generated, facilitating informed decision-making and driving innovation.

 

Processing Large Datasets

The manufacturing landscape in Industry 4.0 is characterized by an unprecedented influx of data from various sources—sensors, machines, production lines, and even supply chain interactions. Big data analytics serves as the backbone for processing these large datasets. With the ability to handle massive volumes of structured and unstructured data, analytics tools provide manufacturers with the capability to extract valuable information in real-time. This processing power is instrumental in identifying patterns, trends, and anomalies within the data, laying the foundation for data-driven decision-making.

 

Real-Time Monitoring and Optimization

One of the key advantages of big data analytics in Industry 4.0 manufacturing is its ability to enable real-time monitoring of production processes. Sensors and connected devices generate a continuous stream of data, and analytics tools process this information instantaneously. Manufacturers can monitor machine performance, track production efficiency, and identify potential issues in real-time. This facilitates proactive decision-making, allowing for on-the-fly adjustments and optimizations to enhance overall operational efficiency.

 

Predictive Maintenance for Enhanced Reliability

Big data analytics empowers manufacturers to move from reactive to predictive maintenance strategies. By analyzing historical data and monitoring real-time performance, analytics tools can predict when equipment is likely to fail. This proactive approach to maintenance minimizes downtime, extends the lifespan of machinery, and reduces overall maintenance costs. Manufacturers can schedule maintenance activities precisely when needed, ensuring equipment reliability without unnecessary disruptions.

 

Quality Assurance and Defect Prevention

In a data-rich environment, big data analytics plays a crucial role in quality assurance. Manufacturers can analyze data from production processes to detect patterns related to product defects or variations. By identifying potential issues early in the manufacturing process, analytics tools contribute to defect prevention and quality improvement. This not only reduces waste but also enhances customer satisfaction by delivering products that meet or exceed quality standards.

 

Supply Chain Optimization

Industry 4.0 emphasizes the integration and connectivity of the entire supply chain. Big data analytics facilitates the optimization of supply chain processes by analyzing data related to inventory levels, demand forecasts, and logistics. Manufacturers can make data-driven decisions to streamline supply chain operations, minimize lead times, and respond quickly to changes in market demand. This level of optimization contributes to increased efficiency and responsiveness in the supply chain.

 

Challenges and Considerations

While big data analytics presents significant opportunities, its implementation in Industry 4.0 comes with challenges. Managing and securing large volumes of data, ensuring data privacy, and addressing interoperability issues among various systems are critical considerations. Additionally, organizations need skilled professionals who can interpret and derive actionable insights from the data, emphasizing the importance of cultivating data literacy within the workforce.

 

Big data analytics stands as a cornerstone of Industry 4.0, particularly in the realm of manufacturing. By harnessing the power of analytics to process large datasets, organizations can unlock valuable insights that drive efficiency, enhance reliability, and foster innovation. The ability to monitor operations in real-time, predict maintenance needs, ensure quality, optimize the supply chain, and make informed decisions positions big data analytics as a transformative force in the evolving landscape of Industry 4.0 manufacturing. As technology continues to advance, the marriage of big data analytics and Industry 4.0 is poised to redefine how manufacturers operate, innovate, and thrive in the digital age.

 

Featured image credit: Freepik/storyset

Connectivity Unleashed: IoT and Advanced Sensors Revolutionize Manufacturing Processes

In the dynamic landscape of advanced manufacturing, the fusion of Internet of Things (IoT) technologies and advanced sensors has ushered in a new era of interconnected, data-driven, and highly efficient production processes. This article explores the profound impact of IoT and advanced sensors on manufacturing, highlighting how these technologies enhance efficiency, connectivity, and data-driven decision-making in the realm of advanced manufacturing.

Efficiency Through Real-Time Monitoring

One of the transformative aspects of IoT in advanced manufacturing is real-time monitoring facilitated by a network of interconnected devices. Advanced sensors embedded in machinery and production lines collect and transmit data continuously. This real-time monitoring allows manufacturers to gain insights into production processes, identify bottlenecks, and optimize workflows for maximum efficiency. From machine performance to inventory levels, IoT-driven real-time monitoring provides a comprehensive view of the manufacturing ecosystem.

Connectivity Across the Manufacturing Ecosystem

IoT creates a seamless and interconnected manufacturing ecosystem by enabling devices to communicate and share information. This connectivity extends beyond the shop floor, encompassing supply chains, logistics, and even customer interactions. Manufacturers can track raw materials in real-time, optimize supply chain logistics, and respond swiftly to changes in demand. The result is a more agile and responsive manufacturing ecosystem that can adapt to dynamic market conditions.

Data-Driven Decision-Making

The marriage of IoT and advanced sensors empowers manufacturers with a wealth of data. This data becomes a valuable asset for informed decision-making. Through analytics and machine learning algorithms, manufacturers can extract meaningful insights from the vast datasets generated by IoT devices. From predictive maintenance to demand forecasting, data-driven decision-making enhances the accuracy and effectiveness of strategic choices, fostering a proactive approach to challenges.

Predictive Maintenance for Increased Reliability

IoT-enabled sensors play a pivotal role in predictive maintenance, revolutionizing how manufacturers approach equipment upkeep. These sensors monitor the condition of machinery in real-time, detecting anomalies and potential issues before they escalate. Predictive maintenance allows for timely interventions, reducing downtime, extending equipment lifespan, and optimizing maintenance schedules. The result is increased reliability and cost savings for manufacturers.

Quality Assurance and Continuous Improvement

Advanced sensors enhance quality assurance processes by providing detailed insights into production parameters. Through continuous monitoring and data analysis, manufacturers can detect variations in product quality and address issues promptly. This proactive approach to quality control not only ensures that products meet or exceed standards but also facilitates continuous improvement. Manufacturers can iterate on processes based on real-time feedback, driving innovation and quality excellence.

Security and Scalability Challenges

While the integration of IoT and advanced sensors brings unprecedented benefits to manufacturing, it also introduces challenges. Security concerns, including data privacy and the vulnerability of interconnected systems to cyber threats, must be addressed. Manufacturers need robust cybersecurity measures to safeguard sensitive data and protect against potential breaches. Additionally, scalability issues may arise as the number of IoT devices increases. Adequate infrastructure and a strategic approach to scalability are crucial for ensuring the seamless operation of IoT-enabled manufacturing processes.

The marriage of IoT technologies and advanced sensors is reshaping the landscape of advanced manufacturing, unlocking unprecedented levels of efficiency, connectivity, and data-driven decision-making. As manufacturers embrace these transformative technologies, they gain the ability to monitor and optimize production processes in real-time, enhance connectivity across the manufacturing ecosystem, and make informed decisions based on comprehensive data analysis. While challenges exist, the potential benefits far outweigh the drawbacks, positioning IoT and advanced sensors as indispensable tools in the pursuit of a more agile, efficient, and innovative future for manufacturing.

Featured image credit: Freepik

Navigating Tomorrow: Unveiling Transversal Skills for the Future Workforce

As we stand on the cusp of the fourth industrial revolution, marked by advanced manufacturing and digital transformation, the landscape of the workforce is undergoing a profound shift. Beyond specialized technical skills, the future demands a versatile set of transversal skills—attributes that cut across disciplines and industries. This article delves into the identification and analysis of essential transversal skills crucial for individuals to thrive in the dynamic and rapidly evolving realm of advanced manufacturing.

Adaptability and Flexibility

In the face of constant technological advancements and evolving job roles, adaptability and flexibility stand as cornerstone transversal skills. The ability to embrace change, pivot when necessary, and quickly acclimate to new tools and technologies is vital for individuals navigating the dynamic landscape of advanced manufacturing. Adaptable professionals are better equipped to respond to industry shifts, ensuring their continued relevance and success.

Critical Thinking and Problem Solving

With the integration of advanced technologies comes a surge in complexity. Critical thinking and problem-solving skills are indispensable for individuals working in advanced manufacturing. The capacity to analyze situations, evaluate information, and devise innovative solutions is crucial for addressing the intricate challenges presented by digital transformation. Professionals adept in critical thinking become invaluable assets, contributing to effective decision-making and problem resolution.

Effective Communication and Collaboration

In the interconnected world of advanced manufacturing, effective communication and collaboration are paramount. Transversal skills in communication involve the ability to convey complex ideas clearly, while collaboration skills facilitate seamless teamwork across diverse disciplines. As projects become increasingly interdisciplinary, individuals who can communicate ideas and collaborate across teams contribute significantly to the success of advanced manufacturing endeavors.

Creativity and Innovation

Innovation is the lifeblood of progress, and transversal skills related to creativity and innovation are essential for pushing the boundaries of advanced manufacturing. Individuals who can think outside the box, generate novel ideas, and contribute to the development of groundbreaking solutions are well-positioned to thrive in an environment characterized by continuous innovation and technological advancement.

Digital Literacy

In the age of digital transformation, digital literacy is not just a technical skill but a transversal competency. Beyond merely operating digital tools, individuals need to understand the broader implications of digital technologies on their work and industry. This includes data literacy, the ability to interpret and derive insights from data, and a foundational understanding of emerging technologies like artificial intelligence and the Internet of Things.

Emotional Intelligence

As automation and technology reshape the workforce, the importance of emotional intelligence becomes more pronounced. Transversal skills related to emotional intelligence, such as empathy, self-awareness, and interpersonal skills, are crucial for navigating complex human interactions. Professionals who can understand and navigate the emotional landscape of the workplace are better positioned to lead teams, resolve conflicts, and foster positive working relationships.

Continuous Learning and Adaptation

In a rapidly evolving landscape, the capacity for continuous learning and adaptation is paramount. Transversal skills encompassing a growth mindset, curiosity, and a commitment to lifelong learning empower individuals to stay abreast of industry developments. This not only enhances individual career trajectories but also contributes to the overall agility and resilience of the workforce in the face of change.

As advanced manufacturing and digital transformation reshape the future of work, the significance of transversal skills cannot be overstated. Individuals equipped with adaptability, critical thinking, effective communication, creativity, digital literacy, emotional intelligence, and a commitment to continuous learning are poised to thrive in the dynamic and interconnected world of advanced manufacturing. Nurturing these transversal skills not only prepares individuals for the challenges of tomorrow but also contributes to the resilience and innovation of the workforce as a whole.

Featured image credit: Freepik/mdjaff

Navigating the Future: Digital Transformation in Vocational Education and Training

In the ever-evolving landscape of education, digital transformation has emerged as a powerful force, reshaping traditional approaches and revolutionizing learning methodologies. Nowhere is this transformation more evident than in Vocational Education and Training (VET) programs. This article explores the dynamic integration of digital technologies, including online platforms, virtual reality, and other digital tools, and their profound impact on the landscape of vocational education.

 

Integration of Online Platforms

 

Accessibility and Flexibility

Digital transformation has democratized education, breaking down geographical barriers and providing learners with unprecedented access to vocational training programs. Online platforms offer flexibility in scheduling, allowing learners to balance work and education seamlessly.

Interactive Learning Modules

VET programs leverage online platforms to create interactive learning modules that engage learners effectively. Rich multimedia content, real-world simulations, and collaborative tools enhance the learning experience, catering to diverse learning styles and preferences.

Self-Paced Learning:

Digital platforms empower learners to progress at their own pace. Self-paced modules accommodate varied learning speeds, ensuring a personalized educational experience that fosters deeper understanding and mastery of vocational skills.

Integration of Virtual Reality (VR)

Immersive Practical Training

Virtual Reality brings a transformative dimension to vocational education by offering immersive practical training experiences. Learners can simulate real-world scenarios, honing their skills in a risk-free environment before entering actual workplaces.

Hands-On Simulations

VR allows for hands-on simulations that closely mimic the complexities of vocational tasks. From welding to healthcare procedures, learners can practice and refine their skills under realistic conditions, boosting confidence and competence.

Collaborative Learning in VR

Virtual reality facilitates collaborative learning experiences, even when learners are physically distant. Students can engage in group projects, problem-solving scenarios, and interactive simulations, fostering teamwork and communication skills vital for the workplace.

 

Integration of Other Digital Tools

 

Gamification for Engagement

Digital transformation introduces gamification elements to VET programs, making learning more engaging and enjoyable. Points, badges, and leaderboard systems motivate learners, turning education into a dynamic and rewarding experience.

Real-Time Feedback

Digital tools enable real-time assessment and feedback mechanisms. Immediate feedback on assessments allows learners to identify areas of improvement promptly, facilitating a continuous learning process.

Data-Driven Insights

Educational institutions can harness data analytics to gain insights into learner performance and program effectiveness. This data-driven approach enables institutions to tailor VET programs to meet the evolving needs of industries and learners alike.

The digital transformation in vocational education and training heralds a new era of accessibility, interactivity, and innovation. Online platforms, virtual reality, and a myriad of digital tools are not merely augmenting traditional approaches; they are fundamentally redefining how vocational skills are acquired and honed. As educators, institutions, and industries collaborate to harness the potential of these technologies, the landscape of vocational education becomes more dynamic, responsive, and aligned with the demands of the modern workforce. Embracing the opportunities afforded by digital transformation, vocational education and training are poised to empower learners and bridge the skills gap in unprecedented ways.

Featured image credit: Freepik

Are you taking advantage of the Digital transformation?

Nowadays, the exponential growth of technological advances is causing disruptions and continuous changes at a global level. The impact these changes are having on industry is also affecting on the strategies of managers, those responsible for R&D&I departments, etc.

The digital transformation we are undergoing is having an impact on the processes, strategy and culture of companies. In this regard, some conclusions drawn from a survey conducted by KPMG in 2017 stated that:

  • Companies do not know how to implement and deploy digital transformation in their own processes.
  • Digitalisation is seen as a phenomenon that generates tactical benefits, such as cost reduction, etc., but they are not aware of its actual benefits.
  • There is a lack of knowledge about what kind of technology a company can use to cope with digital transformation.
  • Organisations are showing increased interest in cognitive automation (also known under the phenomenon of Machine Learning / Artificial Intelligence).
  • The main barriers to these changes are often related to a lack of strategic vision, uncertainty about where to start the process towards digitalisation, lack of technology profiles or digital skills among the workforce.

In the absence of organisational capacity and/or culture to understand the impact these technologies are having on businesses, it becomes even more important to understand some of the wide range of existing disruptive technologies out there. For instance, the Internet of Things and Data Analytics are already being exploited to bridge the gap needed to achieve productivity goals and improve customer experience. The use of Artificial Intelligence and robotic automation software to manage transactions is making it possible to predict customer needs.

In this sense, we understand the relevance of preparing companies for the digital transformation and how to face the challenges posed by the technological disruption we are experiencing. Therefore, it is necessary to educate and train new generations of students in these areas, hence the importance of the DTAM project, which aims to do exactly that. 

Our training course on Digital Transformation Technologies is on its way to being pilot tested. Stay in the loop as we are going to be sharing more exciting news very soon.

Featured image credit: Freepik/vectorjuice

Unleashing Potential: Machine Learning Applications in Advanced Manufacturing

In the era of advanced manufacturing, the integration of cutting-edge technologies is revolutionizing traditional production processes. One such transformative force is machine learning, a subset of artificial intelligence that empowers machines to learn from data and make intelligent decisions. This article delves into the pivotal role of machine learning algorithms in optimizing production processes, predictive maintenance, and quality control, reshaping the landscape of advanced manufacturing.

 

Optimizing Production Processes

 

Demand Forecasting

Machine learning algorithms analyze historical data to predict future demand accurately. This enables manufacturers to optimize production schedules, reduce lead times, and minimize inventory costs by aligning production with actual market needs.

Production Planning and Scheduling

Advanced manufacturing environments involve complex production schedules. Machine learning algorithms optimize scheduling by considering various factors, such as machine availability, resource constraints, and order priorities. This results in streamlined workflows and enhanced overall operational efficiency.

Energy Consumption Optimization

Machine learning algorithms can monitor and analyze real-time data to optimize energy consumption in manufacturing processes. By identifying patterns and correlations, these algorithms suggest adjustments to machine settings or production schedules, contributing to both cost savings and environmental sustainability.

 

Predictive Maintenance

 

Equipment Health Monitoring

Machine learning algorithms play a crucial role in predictive maintenance by continuously monitoring the health of machinery. By analyzing sensor data, these algorithms can predict equipment failures before they occur, allowing for timely maintenance interventions and minimizing unplanned downtime.

Anomaly Detection

Anomalies in machine behavior can be indicative of potential issues. Machine learning models, through continuous analysis of sensor data, can detect deviations from normal patterns, signaling the need for maintenance or inspection. This proactive approach extends the lifespan of machinery and reduces maintenance costs.

Reducing Downtime

Predictive maintenance based on machine learning insights helps manufacturers transition from reactive to proactive maintenance strategies. By addressing potential issues before they escalate, downtime is significantly reduced, enhancing overall equipment effectiveness (OEE) and productivity.

 

Quality Control

 

Defect Detection

Machine learning algorithms excel at image recognition and pattern detection. In quality control, these algorithms can analyze images or sensor data to identify defects or irregularities in products with a level of precision that surpasses traditional methods. This ensures that only high-quality products reach the market.

Process Optimization for Quality

By continuously analyzing production data, machine learning algorithms can identify correlations between process parameters and product quality. Manufacturers can then optimize production settings to consistently achieve desired quality standards, reducing variations and improving overall product quality.

Machine learning applications in advanced manufacturing are catalyzing a paradigm shift in how production processes are optimized, maintenance is managed, and product quality is ensured. The ability of machine learning algorithms to learn from vast datasets and adapt to dynamic environments positions them as invaluable tools for manufacturers seeking efficiency, cost-effectiveness, and competitiveness in the evolving landscape of advanced manufacturing. As industries embrace these technologies, the synergy between machine learning and advanced manufacturing is expected to propel the sector into new realms of innovation and sustainability.

Featured image credit: Freepik/vecstock

Collaboration between Industry and the Academic world in the Cybersecurity Field

Within the digitalization strategy of Politeknika Txorierri, Jokin Goioaga, the Electricity and Electronics department coordinator, accompanied the cluster formed by Cybasque (group Gaia) and Basque Cybersecurity Center in a trip to Cardiff (Wales), alongside a group of professors and ICT managers of several Hetel centers.

The aim of the trip was to get to know the cybersecurity ecosystem formed by the education centers, institutions and specialized companies. During the visit they met with the regional government representatives, the University of Cardiff representatives and the managers of the companies Thales and Purecyber. Moreover, he had the opportunity to attend the launching of a partnership event between Airbus and the University of Cardiff about excellence in socio-technical cybersecurity.

Currently, Wales is considered one of the most important innovation poles in cybersecurity. In this aspect, it is worth mentioning the collaboration between the public and private sectors, and the proximity between the companies of the sector and the academic institutions, as this assures outstanding professionals, according to the necessities of the industry, that help develop this sector in Wales.

Overcoming the Manufacturing Skills Gap

If you have indeed being following our project development for a while, you already know we are very dedicated on addressing the Manufacturing Skills Gap. DTAM is meant to attend to this problem, by designing and delivering a strategic integral online methodology, offering a flexible multidisciplinary modular training with access for learners to a network of remote IoT labs linked to various industrial sectoral processes (thanks to differing regional industrial focus offered within the partnership).

Yes, it’s a complicated issue as there are many reasons creating and enlarging this Manufacturing Skills Gap. Sectoral associations, companies, regional and EU policymakers and educational institutions are aware of the need for initial and adaptive training for both operational and ICT technicians to face the emerging technological and digital transformation inherent in evolved manufacturing processes.

There are many reasons contributing to the Manufacturing Skills Gap and while we can easily blame the technological advancements we are witnessing like AI and Robot based automation, there are also clear mismatch in the perceptions of both businesses and their employees like false job expectations and even “simple” things like retiring professionals. According to industry leading product value management company Propel, the skills gap issue “revolves around the labor market being unable to find workers who have the manual, operational, and highly technical skills, knowledge, or expertise to take the open positions”. The article also specifically stresses out the importance of upskilling current workers as one of the ways of fixing the skills gap (read the whole article here) and that is indeed very true.

According to CEDEFOP 2 in 5 EU workers find their skills not fully used at work. The European skills and jobs survey (ESJ) also discovered a more troubling trend where “around 26% of EU adult employees have significant skill deficits (their  skills are much lower compared to those an average worker needs to be fully proficient in their job) leaving much scope to improve skills and  productivity. According to the same ESJ survey, “more than one in five adult employees in the EU have not developed their skills since starting their job”.

Manufacturing companies, which make up 8,9% of the EU economy (EUROSTAT May 2019), recognize that the emerging reality not only requires specialized engineers but also a trained pool of skilled operative technicians capable of understanding, installing, configuring, transferring data and securely maintaining the high end digital technology which connects and controls manufacturing – now called Advanced Manufacturing.

The above is especially true when it comes to SMEs, as 99% of EU companies are SMEs and are key to ensuring economic growth, innovation, job creation, and social integration in the EU (Source – NACE Sectoral Analysis of Manufacturing 2016).

Source: Unleashing the full potential of European SMEs

Manufacturing SMEs are forecast to increasingly need adequately trained OT technicians with digital competence. New initial VET training is required, as well as reskilling and charted self-learning pathways.

Industry and VET providers need to meet this clearly identified skills gap for which the design of a quality curricular solution based on, and validated according to EU standard accreditation guidelines, is pressing. VET/HVET providers require a flexible, well-designed curriculum in digital competence at the right skills level that will reinforce regional industrial competitiveness in the global market and their own educational offer, enabling both sectors to generate knowledge and employment and to contribute to the welfare and prosperity of regions, in line with territorial, European and international agendas.

The Digital Transformation in Advanced Manufacturing – DTAM project aims to create and provide innovative curricular training (reskilling and upskilling opportunities) in digital transformation competences for the advanced manufacturing sector (AM), for mid-high level IT and OT technicians at EQF Levels 4-5 +. The DTAM partnership will design and deliver an innovative curriculum in key enabling technologies and transversal competences for AM. The integral DTAM curriculum will prepare:

  • ICT technicians to approach and understand digital technology in relation to AM processes and machinery (how to install, configure and monitor cyber physical intelligence and tools in AM environments)
  • Robotics and Automation (or other OT) technicians with the ability to understand and manage digitalisation tools and the most advanced AM technologies for secure deployment and maintenance.

It’s now been two years since we’ve launched our project and we are almost done with our key milestone i.e. the DTAM training course. Interested in the checking it out? Sign up for our newsletter to make sure you don’t skip on this or the rest of good stuff coming up in the next months.

Featured image credit: Freepik/storyset

Boosting the technical and non-technical skills and competences of Smart Cities technicians and engineers

According to estimations, till 2050, two thirds of the world population will live in towns, consuming more than 70% of energy and emitting just as much greenhouse gases. As the population of cities grows, the demand for services but also pressure on resources will grow. This demand puts a strain on energy, water, waste, and any other services that are major for the prosperity and sustainability of a city.

A Smart City is an innovative city that uses ICTs and other means to improve quality of life, efficiency of urban operation and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental as well as cultural aspects. The global market of Smart Cities is expected to grow from $410,8 billion in 2020 to $820,7 billion by 2025 with 14,8% compound annual growth rate. This growth is driven by the increasing demand for public safety, rising urban population, and growing government initiatives. Smart Cities contribute to the EU objectives towards social fairness and prosperity, empowerment of people through digital technologies, as well as the objectives of the “European Green Deal”.

Smart cities utilize data and deploy services using advanced technologies, such as Cloud Computing, Artificial Intelligence, and Internet of Things to offer new and enhance existing services, as well as, to provide context-aware views on city operations. Their development is highly complex and challenging and requires technicians and engineers from the public sector and industry equipped with skills and competences that are currently in short supply. Thus, given the dynamic nature of Smart Cities, their workforce need to be reskilled/upskilled by acquiring new and transferable skills and knowledge.

The SMACITE project aims to address the Smart Cities skills gap by designing and testing a vocational education and training program. The program will use a novel and multi-disciplinary curriculum combining digital skills on Smart Cities enabling technologies with soft, entrepreneurial, and green skills.

The expected project results are:

  • A Smart Cities competences map and ESCO-compliant Smart Cities job profiles.
  • A Smart Cities curriculum combining both technical and non-technical skills and competences and promoting personalized learning pathways.
  • Learning resources for Smart Cities enabling technologies and for building the soft, entrepreneurship and green skills of Smart Cities technicians and Engineers.
  • A diagnostic tool to identify personalized learning pathways.
  • A MOOC for Smart Cities enabling technologies.
  • Virtual Worlds for building the soft, green and entrepreneurship skills of Smart Cities technicians and engineers.

The main project beneficiaries are Smart Cities technicians and engineers, either from the public sector (i.e. municipalities) or enterprises providing Smart Cities solutions, as well as HEI and VET students interested in Smart Cities.

The SMACITE consortium brings together 12 organizations from Greece, Bulgaria, Italy, Spain, and Belgium that represent different stakeholders that share a common vision: Higher Education Institutions, Vocational Education and Training providers, Associations of IT and Technology Enterprises, Public Sector Organizations, and a Certification Body.

SMACITE is a 3-years project (01/06/2022 – 31/05/2025) coordinated by the University of Patras (Greece) and co-funded by the European Union (Project Number: 101052513).

Do you want to learn more about the project? Visit www.smacite.eu and follow the project on Facebook, LinkedIn and Twitter!