**1. Introduction**

By analyzing the relevant scientific and technical literature, the most important challenges in the process of developing paints and coatings were identified. Products should meet the aesthetic criteria, as well as assure the long-term protection of the surface. Increasing requirements for e ffective surface protection, on the one hand, and continuous tightening of environmental regulations, on the other, can be observed [1,2]. An additional focus is achieving new product development and specifications in the shortest possible time. Because of these requirements, rapid development of technology o ffers more and more opportunities for the design of quality products; however, the development of new products becomes increasingly complex [3–6].

Continually evolving demands for greater capability, e fficiency, and functionality of coatings; increasing regulatory complexity; and growing competition in this area account for the need for accelerated development of coatings' raw materials, formulated products, and rapid problem-solving [7]. Because of the many raw material combinations and interactions that a ffect performance properties, additive formulating is resource-intensive and time-consuming for manufacturers [8].

With the requirement for safe, environmentally friendly coating components and the acceleration of material discovery, the need for rapid, reliable testing is becoming more important [9]. A number of accelerated tests have been developed to reduce the testing time and decrease environmental variability. Most importantly, the tests lead to qualitative results, which can be significantly di fferent when various individual evaluators and di fferent test instruments are compared [10].

Coating formulations include di fferent raw materials, such as resins, additives, pigments, fillers, catalysts, solvents, co-solvents, water, etc. Therefore, exploring all possible combinations in search of the best formulation is impossible—especially on the benchtop, which allows for a limited number of options to be explored. To better visualize what that means, the senior manager of R&D Additives (Evonik Resource E fficiency GmbH) presents an example: "If 10 resins, 10 additives, 10 pigments, and 10 catalysts were to be considered for a formulation, 10,000 combinations are possible without considering di fferent quantities for each." It is necessary to explore a large number of possible combinations in order to find the optimal formulation, which is possible only with high-throughput technology [7].

Many papers deal with techniques, such as high-throughput testing [11], statistically planned experiments [12], multiscale modeling, and self-repair, which can make significant contributions to new product development. However, these techniques could be combined in a synergistic manner and thus develop new approaches to self-repair [4]. Data modeling, computer simulation, and visualization tools have also been developed to keep pace with increasingly large datasets. With the aim of the comprehensive evaluation of additives, they sugges<sup>t</sup> the use of high-throughput tools, which facilitate the evaluation of a wider range of samples, usage levels, and formulations [8,13].

Evonik Resource E fficiency GmbH has developed a high-throughput system that automatically doses raw materials, formulates them into coatings, applies the coatings to substrates, and tests them. The system allows the formulation of 120 samples within 24 h. The company emphasizes that the system also completes labor-intensive tasks and thus allows laboratory sta ff to focus on experimental design and analysis [7]. It is clear that high-throughput regression methods will reduce lead optimization times, but such methods are of little help unless suitable high-quality materials are available for screening, and detailed correlative mechanistic studies are needed to subsequently extract the design principles [14,15]. More important than how much time one repetition of a R&D testing process takes is what to test in order to avoid superfluous testing.

The solution could be the use of information technology and digital transformation approaches. Those approaches are widely and successfully used in other industries [16]. It is time to transfer best practices to the paints and coatings industry, too. The 2019 chief information o fficer (CIO) of Gartner's agenda [17] recommends that CIOs "secure a new foundation for digital business." The agenda is based on a Gartner survey of 3102 CIO respondents in 89 countries and across major industries, representing \$15 trillion in revenue and public sector budgets and \$284 billion in information technology (IT) spending. The agenda recommends: (1) a business and operating model transformation, (2) secure consumer-centricity, (3) the use of targeted and capability-building techniques to move from projects to direct product delivery, and (4) the use of business-enabling technologies: machine learning, data analytics (including predictive analytics), cloud processing and storage (including XaaS), digital transformation, and more.

Digitalization is a completely di fferent approach and requires significant changes to take place on many di fferent levels throughout the entire organization and not only for the team responsible for strategy implementation. For the purpose of better serving their customers, organizations must strive to connect all business and managemen<sup>t</sup> processes [18].

Some early signs that digitalization is becoming fact in the coatings area are already present. However, only a few companies have created digitalization strategies and established business groups focused on digitalization. However, most are adopting this approach only, for example, to improve customer experience or business processes, or to introduce new business models. In 2016, a white paper from the Digital Transformation Initiative for the chemistry and advanced materials industry was published. The paper emphasizes that, with digitalization driving operational optimization, one can expect improved e fficiency, productivity, and safety across the whole supply chain of paints and coatings. It is known that digitalization opportunities go beyond and across functional areas, but the question is when they will be enforced. The director of digital and e-commerce says that the answer is dependent on technological maturity, business activities, and the strength and willingness of the company to digitalize, innovate, and change [18].

Some modern tools and technologies already exist, such as "blockchain," "Internet of Things (IoT)," "quantum computing," etc. However, the most important "digital transformation" tools are those that enable data and knowledge managemen<sup>t</sup> along and across both internal functions and external networks, and that allow companies to find specific pieces of information within the huge ocean of data available in a structured way. Those tools can facilitate smart ways to combine systems that use internal product, customer, market, and manufacturing data. Companies that wish to benefit will also have to make some data available to the system. For example, in the marine coatings area, a "big data" service has become available, which helps select the optimal anti-biofouling coating, based on analysis of billions of data points. At the same time, "cloud-based" platform access and shared external and internal data enable estimated arrival times of ships at port, which helps to optimize delivery planning [15]. Also, a case of prediction profilers using historical data has been published. Prediction profilers can be used in combination with 3D data visualization tools to describe important results. The ability to predict performance reduces the number of formulations that need to be physically tested in a laboratory. Consequently, the development process is accelerated [7].

Both of the above examples are positive. However, a thorough and radical reengineering of key business processes could not be found, especially internal and external processes in a value chain at the same time. A business process is defined as "the sequence of activities in an organizational and technical environment with a structure that describes their logical order and dependence, with the main goal to produce the desired result" [19,20]. New technology can significantly improve e ffectiveness and e fficiency, but it can also make the existing process more complex, reducing usability and causing more integration problems. As processes become more complex, problems with locating and correcting are increasing dramatically [21]. Business process adjustment is often carried out through business process redesign projects, which have the same goal: to achieve more e fficient operations. According to Urh, Kern and Roblek [22], top managers are often faced with important questions: What is the level of business process performance e fficiency? Is it necessary or reasonable to adapt the process? What adjustments must be made in business process performance? and How will the projected changes influence the business process performance e fficiency?

These questions are also asked about the paints and coatings development process. The purpose of this research was to investigate whether the process can be dramatically and rationally improved using cloud-based information technology and a "big data" approach. In order to carry out the research, a sample of several companies involved in the production of paints and coatings was selected. The sample included small and medium-sized enterprises that carry out the development process in a classical manner—without the use of information technology that would enable digital transformation. The following sections describe the basis of the methodologies used (Section 2) and present research results (Section 3). This article concludes with a discussion of the research results and the conclusions reached (Section 4).
