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Data-Driven Innovation: The Future of R&D in Industry
According to CNI (National Confederation of Industry), investment by industries in innovation and processes grew by 33.4% between 2016 and 2019. At the end of this period, R$16.9 billion had been invested, which represents 69% of the total invested among all different types of Brazilian companies.
This effort to seek more technology and innovation within industries is observed through several actions, such as the implementation of data culture. In the area of Research & Development, this ensures that the industry is up to date with market trends, and that intelligent solutions are incorporated into production.
It is also important to highlight that, among the different industry sectors, the one that invests the most in this area is the pharmaceutical industry . According to the website Talk Science (2023), about the role of P& ;D in this industry, it obtains the world record for investments, totaling US$138 billion. This action allows the search for more efficient treatments, development of new products, new raw material suppliers and improving the bioavailability of medicines.
But it is not the only one, other sectors of the industry are also chasing innovation to improve their products. According to Ministry of Science, Technology and Innovation (MCTI), in the last decade Brazil invested R$24.2 billion in R&D in the food sector. According to Exame magazine (2023), in the article “R&D and the future of food ”, positive advances in this area have many incentives. The main motivation, according to the article, is the search for alternatives to offer the same products, but in a sustainable way.
This is done both for consumers and for the business itself, without losing sight of the likely future shortage of raw materials. Furthermore, according to Instituto Uniemp, The main innovations in the market are focused on the development of inputs, biotechnology, capital goods and packaging.
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An innovative future is focused on data
data culture is the practice of embedding collection and analysis of data in the daily life of industries. This set of habits, when incorporated into the operation, is beneficial in many ways, but mainly in the decision-making process. With this, management is done strategically, always based on data.
It permeates all manufacturing levels, from Thus, it makes it easier to better understand the standards, which has a preventive nature. This prevention feature can be used both to avoid a problem and to identify an opportunity for improvement.
It is with this last aspect in mind, of exploring new opportunities, that the data culture is so widespread in the R&D area. In daily processes, it is possible to analyze and manage data, providing an incentive for innovation.
Within a Pulp and Paper industry, for example, using data science allows for accurate laboratory air conditioning. In this type of industry, it is crucial that there are specific rooms with adjustments for both temperature and humidity, as well as aspects intrinsic to certain types of experiments, such as illuminance. These aspects need to be ensured at all times, not only while the experiment is taking place, but also well in advance, which will ensure that the experiment will not be corrupted.
Due to the countless variables that need to be checked, having a system that collects the necessary information from several pieces of equipment at the same time is a facilitator. Furthermore, having technology that records the history of this data certifies the quality and accuracy of the studies. Discover a real case in which data science allowed us to advance the delivery of results, in addition to making the study robust here.
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The PDCA cycle applied in R&D
The PDCA (Plan – Do – Check – Act) methodology is widely used in Research & Development to optimize operational efficiency, improve processes, and drive innovation. It is carried out in four stages, in which data analysis plays a fundamental role in its proper functioning:
- Planning: at this stage, what you plan to achieve is defined, based on the establishment of specific and measurable goals. Also, from data collection, it is identified which processes need to be improved and how this will be done.
- Execute (Do): This is the execution phase, where actions are done according to the plan. Data recording and adequate training for those involved in this part is also essential.
- Check: Verification stage, in which the results achieved during execution are evaluated. Here the data collected in the previous phase is analyzed, and allows possible deviations and inconsistencies to be identified.
- Act: Last step, where those responsible for the experiment implement changes based on the results of the verification. Depending on what was found, such as unsatisfactory performance, measures must be taken to improve the process. In more positive cases, it is clear that the plan is effective, thus promoting improvement.
The development of prototypes is also an integral part of the routine in this area. This practice allows industries to test their ideas before implementing them on a large scale, using the concept of Minimum Viable Product. In the MVP creation process, it is necessary to clearly define the value proposition, that is, understand what problem the product aims to solve. Afterwards, the construction of the prototype is carried out using only the essential resources, to finally carry out testing and interpretation of the results. In R&D, this resource is used to avoid losses in project development.
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Technology that impacts in different ways
The use of data science in the area of brings numerous advantages, such as:
- Improves supply management, through the use of inputs in the right quantity;
- Error prevention, due to historical data available whenever necessary;
- More assertiveness at work, due to easy access to data for everyone on the team;
- Improve and certify the quality of products, experiments and processes, through uninterrupted data monitoring;
- Reduced rework, resulting in faster processes;
- Strategic decision-making, based on data;
The positive impact goes beyond the procedural, and also affects cultural and professional dynamics. The need to guarantee qualified professionals to work in the areas of R&D, technology and data science led to the creation of several specialization programs.
An example of this is the program “Inova Talentos”, created by the Euvaldo Lodi Institute (IEL) in 2015, which stimulates innovation projects in industries. Those approved are entitled to technological development grants, with the aim of developing research, development and innovation (RD&I) activities. The benefits of this partnership include accelerating results within the organization for up to 3 years.
Innovation and productivity go hand in hand
It has been proven that investing in technology and innovation is always beneficial. According to the article “R&D, innovation and productivity in Brazilian industry” (2015), “innovative companies have a level of productivity around 30% higher than that of non-innovative companies”. It also states that this number tends to increase the more technology-intensive the sector is. This only proves that, increasingly, the relationship between technology and productivity is intrinsic.
It is with this connection in mind that ST-One technology works to transform the industry to a new level of productivity, improving its science with each new challenge. Thus, it makes it possible for the digitalization present in different sectors of the industry – including R&D – to reach the next stage of digitalization and intelligence.
