19. 05. 2021

Reading time: 3 min

Data and manufacturing

For several years, various sectors of manufacturing have been undergoing a major digital transformation. Digitalization is critically important for increasing company efficiency and competitiveness. Part of this process is not only production, but also all related processes, whether internal or external. 

One of the main drivers of production in the Czech Republic is the automotive industry, where this is doubly true. In addition to traditional manufacturing topics such as preventing machine and production line breakdowns and focusing on production efficiency and logistics, there is strong pressure from electromobility, automotive connectivity and ecology. 

As a result, manufacturing companies are investing much more than ever before in information and communication technologies and are beginning to realize that one of the most important enablers of digitalization is data, the "oil of the future". In large manufacturing companies, innovation and digitalization are leading to the emergence of new processes and a change in the approach to business management. 

The individual segments of the company produce a huge amount of data, which is constantly increasing (almost exponentially) as digitization progresses. And it is in this field that new approaches to data processing are beginning to be applied in the form of projects focused on big data, artificial intelligence, optimization, etc. There are entirely new roles are emerging in companies, such as data scientists. 

As time goes on, the range of projects where we can talk about successful digitization using big data or artificial intelligence is expanding very rapidly. Examples include projects aimed at optimizing production capacities and the entire production process in general, optimizing logistics processes (such as container loading), and new eco-friendly digital pallets are making their way around Europe, predictive maintenance of production machines or lines and the elimination of downtime and associated costs, projects focused on communication with customers (chat bots, virtual showrooms...), predicting the need for service of the product delivered to the customer, or projects focused on ecology (such as simulations aimed at reducing CO2 emissions). The Internet of Things comes into play with strong links to infrastructure (e.g. 5G networks), virtual reality (you can walk through a planned factory, try out the assembly of new machinery, hold a virtual trade fair...), etc. 

For all the above examples, data is critical. You can't do predictive maintenance without integrated data from robots and various machines (linking to IoT) and machine learning models built on top of it. Fleet CO2 optimization from a CO2 perspective cannot be done without knowing large amounts of sales, marketing, development, production and financial data. Each car's sensors literally spew out gigabytes of data that can then be used for service improvement. 

Conditions for successful digital transformation

To make all this possible, we need to remove the fundamental barriers to digitization and working with data - and focus on the enablers. 

An open culture and an agile approach to organizing work is important for digitization. When talking about data, a common problem is the vertical organization of the company and the associated data 'silos' and data ownership, which disrupts a single view of entities (e.g. the customer) and thus creates barriers, such as difficult data availability, for analytical projects. For data-driven projects, it is also necessary to have a defined data strategy for the company. 

Digital transformation is usually to some extent an entry into the unknown. Therefore, an agile approach to projects is very important. The project needs to be broken down into smaller parts, ideally starting with a proof of concept phase and gradually implementing the project in relatively small iterations. The project team must include staff from all relevant departments of the company and there should be complete openness within the team. 

At the same time, it is important to recognize the importance of ICT in the company and to strengthen decision-making powers in this direction. The creation of new teams with a focus on innovation and digitalization in general is frequent. 

Not everyone in the organization is always aware of the importance of digital transformation for the enterprise. Therefore, top management needs to be directly involved in evangelizing and actively promoting innovation in the company. Active support includes an emphasis on expanding employee knowledge and teaching the organization how to work with new technologies, active support in identifying opportunities for digitalization and the associated change in mindset, and cultivating an innovation-supportive atmosphere. 

A frequent barrier to digitization is also the availability of infrastructure to implement new projects, for example in the field of the Internet of Things. In this case, the cloud comes into play, which makes it possible to significantly accelerate the development cycle of an innovative project. 

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