The role of digitalization in business development during the COVID-19 pandemic

According to a study conducted by Euler Hermes earlier this year, countries with higher levels of digitalization have proven to be more resilient to the economic shock caused by COVID-19. Not surprisingly, the United States, Germany and Denmark are in the top three of the Enabling Digitalization Index (EDI) for 2020.

Euler Hermes’ digitalization index (EDI) measures the ability and flexibility of countries to stimulate the growth of digital companies and the ability of traditional businesses to recognize digitalization. The study involves 115 countries and the results are based on five indicators: regulatory requirements, knowledge, connectivity, infrastructure and size.

For 2020, the United States is the best in the knowledge ecosystem, competitive market size and favorable regulatory requirements. Germany is a leader in terms of knowledge and commercial infrastructure, but at the same time the quality of connectivity has fallen, following what has been happening around the world. It is interesting what is happening with China – the country is experiencing growth in all indicators, but faces problems in improving the skills of the population in the digital environment.

The main conclusion of the study is that digitalization can alleviate the shock caused by COVID-19. From an economic point of view, countries whose environment is more favorable for business digitalization (good connectivity, market size, regulations, logistics and knowledge) have managed to successfully respond to the crisis and have increased digitalization in many sectors.

One of the key outcomes of the DTAM project is the creation of the “DTAM Training Course” consisting of training units on digital and transversal skills relevant for IT and OT technicians in AM environments that will enhance the ability of industries to deal with the digitalization challenges caused by the COVID-19 crisis.

The role of Internet of Things in Advanced Manufacturing

Internet of Things (IoT) paves the way for savvy manufacturers to redefine the manufacturing process and how products are designed. Advanced manufacturing allows companies to deliver more value to customers resulting in a much stronger partnership. IoT together with Artificial Intelligence and Big Data are among the key enabling technologies of Advanced Manufacturing.

The adoption of the IoT technology in the advanced manufacturing environment, also called Industrial IoT, has many benefits: cost reduction, shorter time-to-market, mass customization and improved safety. Below are some indicative Industrial IoT applications:

  • Predictive maintenance. By connecting IoT-driven devices that are equipped with different types of sensors, to other devices, cloud or legacy systems, technicians and engineers can obtain essential maintenance data allowing to estimate the current condition of machineries, determine warning signs, transmit alerts and activate corresponding repair processes.
  • Remote Production Control. Thanks to the IoT technology, data collected from various field devices like switches, valves, and other indication elements is transmitted to the industrial automation system that ensures an overall control of machinery amid production process.
  • Asset tracking. The use of IoT technology in combination with native web and mobile apps makes it possible to obtain real-time asset information and make reasonable decisions.
  • Logistics management. IoT can reveal supply chain inefficiencies by eliminating blind spots from logistics processes.

Maximizing the impact of Industrial IoT requires innovation through strategic IT/OT partnerships. For instance, just recently, Siemens, IBM, and Red Hat announced a collaboration that would allow Industrial IoT users to use a hybrid cloud solution to maximize their efforts.

The DTAM consortium brings together education and training providers, advanced manufacturing industry representatives, as well as other key actors of the advanced manufacturing ecosystem to co-design, co-develop and test a novel curriculum and IoT labs for the upskilling/reskilling of advanced manufacturing technicians.

We are almost finalizing the first version of the DTAM curriculum and we are going through the first steps towards defining the specifications of the IoT labs which will be used during the pilots to promote problem-solving and experimental learning.

Stay tuned to learn more!

Sources:

[1] https://www.byteant.com/blog/5-best-use-cases-of-iot-in-manufacturing/

[2] https://www.forbes.com/sites/danielnewman/2021/04/19/how-tech-partnerships-are-driving-the-expansion-of-the-industrial-iot/?sh=3557b0c41c8e

Image credit: Technology photo created by rawpixel.com – www.freepik.com

What is Machine Learning & how is it used in Advanced Manufacturing?

Artificial Intelligence (AI) provides several applications for systems to work in an automated environment and Machine Learning (ML) is one of those applications. ML provides the ability for a computer system to learn, adapt and improve from previous accessed data and procedures, without the need to be specially programmed. Firstly, the learning begins with the data collection and observation while using the data to find patterns and improve the decisions of the system in the future. The goal of the system is to learn automatically and readapt its procedures, without the need of human intervention or supervision. Note must be made that the difference in context for the ML and classical algorithms can be easily understood regarding a simple text. Classical algorithms interpret it as a sequence of keywords, while the ML algorithms use semantic analysis and mimic the human ability to understand the content of the text. The most uses ML algorithms are supervised ML algorithms, unsupervised ML algorithm, semi – supervised ML algorithms and reinforcement ML algorithms.

Furthermore, the techniques used in ML can discover valuable patterns in data. This characteristic is very valuable in the manufacturing sector and leads to Advanced Manufacturing (AM) procedures. Because the manufacturing world is ever evolving, there are no universally applicable methods and is important to have a clear understanding of the requirements of each task. ML applications are useful in the manufacturing because:

  1. deal with different data (numerical, nominal, text, and image),
  2. handle noise, outliers, fuzzy data,
  3. real time processing,
  4. deal with large data sets and data of high dimensions,
  5. produce easily understood results,
  6. simple to implement.

Finally, there are several advantages in the ML application for the AM, such are:

  1. the techniques are able to handle NP complete problems for optimization of intelligent manufacturing
  2. ML handles high – dimensional, multivariable data and extracts relationships within large datasets and evolving manufacturing environment
  3. ML increases the understanding of the manufacturing domain
  4. Improves the lifecycle of the manufacturing process
  5. Discovers unknown relationships between the manufacturing entities (Pham & Afify, 2015)

Future workers in advanced manufacturing need to be trained not only in cutting edge technology, but also in creativity, team leading, problem solving, self-learning, adaptability and flexibility in order to be employable and provide services of high quality. DTAM’s training curriculum aims to fulfil this emergent need and provide and integrated programme with the appropriate balance of skills for advanced manufacturing workforce of today and tomorrow.

Thus, one of five Innovative Training Modules of DTAM will be dedicated to Machine Learning as a modular digital training pathway for IT and OT technicians in AM.

Stay tuned to take full advantage of the DTAM training course we are working on.

#DTAMproject #upskillingyourfuture #Industry40 #AdvancedManufacturing #MachineLearning

References:

[1] Pham, D., & Afify, A. (2015). Machine-learning techniques and their applications in manufacturing . Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering, 219(5): 395-412.

[2] Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: advantages, challenges and applications. Production & Manufacturing Research, 4:1, 23-45.

Transversal skills in Advanced Manufacturing

Transversal skills were rated highly in the industry during the 21st century and became of utmost importance since 2020. The critical role of transversal skills is also highlighted by the European Commission, which made the acquisition of skills an integral part of the industrial policy and the recovery plan for Europe. The aim of the EU policy is to create a workforce with sector specific technical skills but also with transversal skills, a combination that can provide passport to global employment. The latter is further supported by the European Foundation for the Improvement of Living and Working Conditions, which defines employability as “a combination of factors, such as job-specific skills and transversal skills, which enable individuals to enter into employment, stay in employment and advance in their careers”[1].

COVID-19 has accelerated automation and digitalization and resulted in an ever-changing career environment. Workers in advanced manufacturing need a balanced combination of skills that will help them to adjust to the post COVID era. According to recent surveys, companies declare skills gaps and this discrepancy revealed a need for transversal skills shifting the job requirements from traditional working to ways of working. Employees are in need of soft skills that will enable them to adapt effectively into the new working environment.

Future workers in advanced manufacturing need to be trained not only in cutting edge technology, but also in creativity, team leading, problem solving, self-learning, adaptability and flexibility in order to be employable and provide services of high quality. DTAM’s training curriculum aims to fulfil this emergent need and provide and integrated programme with the appropriate balance of skills for advanced manufacturing workforce of today and tomorrow.

Stay tuned and learn more about DTAM project!

#DTAMproject #upskillingyourfuture #Industry40 #AdvancedManufacturing

References:

[1] Eurofound. Skills and training. 22 April 2021. Online article. 23 April 2021. <https://www.eurofound.europa.eu/bg/topic/skills-and-training>.

Image credit: Gerd Altmann | Pixabay

Specificities of the Industry 4.0 technologies in the food and beverage sector

Industry 4.0 is a powerful wave bringing lots of innovation and improvements to a great deal of economic sectors. Then how about a sector which concerns pretty much everyone – the Food & Beverage sector? What are the specificities of Industry 4.0 technology used within the Food & Beverage sector? We took this question to Mr. Antonio Cataldi, Area Manager of Siemens SCE and this is what he shared with us:

«From a technical point of view, the technologies specific to Advanced Manufacturing in the Food and beverage are the same used in other production sectors. There are no particular products, but specific solutions designed accordingly to the needs of each company».

Let’s take a wine-producing company: «The same technological product allows to follow the different stages of production: from the vineyard, to the vinification process, to the bottling. In each of these environments, data is collected – soil conditions, temperature, humidity, parasites, etc. – and thanks to this data, production is adjusted. In wine production, it is possible to calculate the reaction of a vine to the weather, control the growth of the grapes or the reactions in the winemaking process. The power of Industry 4.0 enabling technologies is that the data enabling these actions are collected in the same way, whether you are in a vineyard or in a steel factory. The tools are the same, only the environments and the sensors change».

«In short, Big Data is the goal, it is the data from which analyses are carried out and strategies are designed; the Internet of Things is the bridge, the gateway that collects data from the various worlds, while Machine Learning allows the machine to adapt to production requirements on the basis of the data. Cybersecurity, lastly, is everything that allows using these 4.0 technologies safely for the company».

One of project DTAM’s goals is to exploit Industry 4.0 technologies such as Big Data and Machine Learning to help workers and students excel in their Advanced manufacturing career.  Stay tuned to learn more about that in the coming months.

#DTAMproject #upskillingyourfuture #Industry40 #AdvancedManufacturing

Agriculture of the future

We are living in turbulent times. Humanity currently faces crucial challenges on a variety of aspects of life and on a larger scale than ever before. How we deal with these challenges and navigate the coming decades may be pivotal to the long-term survival of the human race.

One of these challenges entails the development of a form of agriculture that is balanced and sustainable in order to successfully feed a world population of an estimated 11 billion in the year 2100. Our current form of agriculture is depleting the earth and polluting our own habitat. It has never been designed with the principles of sustainability in mind, it is based on the false promise of eternal growth.

We need a new way of growing foods and one possible answer to this question is a solution called vertical farming. With the still increasing urbanization, it makes sense to see how we can maximize the production of our foods within the cities themselves. 

Two students of the new education ‘Smart Technology’ at the Da Vinci College in Dordrecht, The Netherlands, are working on a prototype for a smart operated vertical farming solution. Central to their project is the use of sensors in order to regulate the climate of the plants that are to be grown. Temperature and humidity sensors continuously log atmospheric conditions, another sensor measures air quality. Based on all these parameters ventilation is regulated by means of several fans. Watering is automated based on moisture readings from the soil and lighting is switched on and off depending on the needs of the plants. The realization of this vertical farming system is executed in phases, where each group of students extends and improves upon the work of the previous students.

The growing importance of sensors in a wide range of industries stresses the importance of proper training in the application of these sensors. The European funded project Digital Technologies in Advanced Manufacturing (DTAM) aims to create an internationally valid and applicable program in order to provide that training. By developing this training program, the goal is to prepare students for a future that will encompass more and more sensors and actuators operated based on that sensor data.

#DTAMproject #upskillingyourfuture #AdvancedManufacturing #agricultureofthefuture #Advancedsensorica

How much data do we generate

Ever wondered how much data we generate? Well, you better brace yourself, because the numbers are mind-blowing and they are probably growing as you are reading this:

👉 There are more than 208,000 people in Zoom meetings

👉 $1,000,000 are being spend online

👉 Whatsapp users share some 41,666,667 messages

👉 People make 1,388,889 video/voice calls

👉 Facebook users upload 147,000 photos

Did we mention that this is happening every single minute?

Have a look at the 2020 edition of the Data Never Sleeps 8.0 by Domo, Inc. below to find out more:

Project DTAM will include #BigData in its innovative training curriculum and provide a modern learning opportunity via a dedicated #IoT hub. Follow us to take advantage of this and the rest of the interesting developments we are planning on doing.

#projectDTAM #upskillingyourfuture #erasmusplus #upskillingyourfuture #bigdata #IoT

Source: https://www.domo.com/learn/data-never-sleeps-8

Importance of talent development for the advanced manufacturing sector

Advanced manufacturing provides solutions and means of production for the leading sectors in the economy, thereby helping to improve their productivity and competitiveness. The industrial sectors involved in advanced manufacturing require well-prepared talent and qualified workers. Thus, the role of the training centers, both VET and universities is essential.

The markets are changing constantly and technologies are evolving extremely fast. New relevant solutions are being adopted by the industry constantly. Clear examples are the digitalization and industry 4.0 with a wide range of technology enablers that are changing the manufacturing systems and market conditions. These changes mean on the one hand introducing complexity but also improving productivity and competitiveness. Industry 4.0 and the digitalization bring new competences, skills and profiles. Industry in general has to be prepared for the changes and it is essential to maintain good educational systems in order to continue having the talent that companies require.

This is why initiatives such as #projectDTAM (Digital Transformation in Advanced Manufacturing) are very relevant. The project will provide innovative training modules in important fields as #Big Data, #Machine learning, #Sensors, #Cyber-security and #Transversal competencies. Stay tuned to learn more about that in the coming months.

#DTAMproject #upskillingyourfuture #Industry40 #AdvancedManufacturing

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Data Analytics Technologies

Data Analytics, better known as Big Data, is the approach that allows companies to analyse big amounts of data generated in their activity enabling to draw conclusions that affect their business. A proper use of this data may even help them to improve their business turnover. Thus, improve operational efficiency, customer user experience and allows them to improve their business models.

All data generated by companies in their activity is one of the concerns they are facing today, including manufacturing sectors. They must evaluate the relevance of this information, which of it they need to store or even which part of all these data can be monetized.

Data analysis means the translation of information into opportunities for companies to take advantage of all data generated (Schneider,B. 2017). Therefore, “Data Analytics” is also called as a translator or business generator as it allows to explore personalised solutions to carry out more customized projects.

Nowadays, information as a service is a business model that is expanding wherein increasingly more businesses are seeking to monetise the information they get.

Number of services offered by platforms related to data analytics in industrial sectors keeps growing as well as new solutions in terms of storage capacities as well and processing capacity. 

Some of the platforms that currently exist are as follows:

Interested in learning more about Big data? A key milestone of the DTAM project is the training curriculum that will focus on five key areas, one of which will be Big Data. Follow us and stay tuned to learn when the training course will be available.

#projectDTAM #upskillingyourfuture #BigData #AdvancedManufacturing #DataAnalytics

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The evolution of Industry 4.0: Revolutionizing manufacturing processes

Industry 4.0 is a concept that provokes the interest of every modern manufacturer putting it among the most important elements of their everyday agenda. Everyone is talking about Industry 4.0 and how digital technology is changing people’s lives, but what is the real meaning behind this term? Keep reading to find out.

Industry 4.0, or also known as the Fourth Industrial Revolution, is characterized by the use of communication and information technologies in industry. It builds on the developments of Industry 1.0 (mechanisation), Industry 2.0 (production) and Industry 3.0 (automation), and represents an integral part of manufacturing, having the power to create numerous competitive advantages, opportunities for growth and advances in factory environments.

There’s no doubt that Industry 4.0 is a game-changer and has the necessary potential to change entirely the way people work and make it more efficient. The digitalization of the manufacturing environment transforms the way how goods are created and delivered and allows more flexibility. For example, this includes machines that are able to foresee failures in a production chain and start the maintenance process which is required on their own, or autonomous logistics which respond to unexpected changes in manufacturing process.

Though the Fourth Industrial Revolution brings added value for manufacturing enterprises, many of them face difficulties in the full-scale adoption of Industry 4.0 since often their knowledge on business fundamentals and the implementation stages of this process is limited.

Reflecting on this, one of the European Commission’s priorities for the period 2019-2024 is „A Europe fit for the digital age“, meaning that the Commission is taking a number of steps to facilitate the implementation of the EU’s digital strategy in order to transform the way of work for people and businesses, making this Europe’s „Digital Decade“.

One of project DTAM’s goals is to leverage Industry 4.0 technologies such as Big Data and Machine Learning to aid workers and students in their Advanced manufacturing career.  Stay tuned to learn more about that in the coming months.

#DTAMproject #upskillingyourfuture #Industry40 #AdvancedManufacturing

Image credit: Designed by vectorpouch / Freepik