By Wolfram Keller (corresponding author), Ulf Stalmach, Ralph Wörheide
All industries, including coatings and their periphery, are currently confronted with various challenges, e.g., raw material shortages, Green Deal, new supply chain transparency law, and shortage of skilled labor. Solutions will change coatings companies’ value chains and ecosystems in several ways. Product development, sourcing, formulation, application, the recycling will become much more trackable, traceable, and transparent. There is no way to stay sustainably competitive without appropriate automation and digitalization.
On top to the above mentioned, transparency and reporting requirements regarding supply chain, CO2 footprint, and product safety are increasing, and skilled resources, e.g., lab technicians, data analysts and scientists are scarce. A single company is unlikely to master all these challenges on its own in a timely manner. Joining forces is a valid option. By sharing data, paint companies can establish continuous loops of information from products, services, and applications from the entire value chain. This is required to enable advanced machine learning, models, and simulations.
The concept of the Smart Paint Factory focuses on data integration within and between companies. Stakeholders must be open to exchange information and organize fast and integrated data communication in the entire value chain.
Individual coatings companies cannot master this alone. Number and intensity of cooperations with partners whose competencies and resources complement the paint manufacturers ones will increase sharply. To control the resulting flood of information and create additional value, a Smart Paint Factory is an attractive option, but it presupposes two things.
Firstly, companies must overcome their data mania. Today, internal, cross-functional sharing of data and information is a huge problem in many paint companies. The problem becomes visible when expert paint technicians or chemists are retired and their implicit, never documented knowledge is lost with them. Cross-company exchange of data with suppliers and customers is hardly imaginable. Too much emphasis is placed on know-how as the unique selling point and maintaining person-dependent knowledge despite the readiness of secure IT systems. Usage of Artificial Intelligence, AI, is an attractive method, but its deep learning models need a high volume of reliable data. Very often, this critical amount of data can’t be collected by one company only.
Figure C1: Evolution of increasingly data-based business models during
the coatings companies’ transition from “old economy” to “new economy”
Secondly, coatings companies must be willing to expand their mainly product-centric business model with key elements of digital business models. New economy companies’ business heavily depends on applied information and communication technologies for value creation or revenue generation.
In data-driven business models data are being collected, structured, and analyzed to base any kind of business-related decision, e.g., for optimization of processes, new offerings, or strategic options.
Data-centric business models are defining how new/ different types of data can support the business and create new, primarily digital offerings. Data-centric business models exceed the benefits of the other business models, but they are not applicable to coatings companies with a strong emphasis on physical products.
Each company must find its own business model “hybrid”, depending on how strong its physical core business, i.e., paints and coated surfaces, shall remain and how much it shall be supported by automation and digitalization.
This article is part of our series on sustainable digitalization in the coatings industry, the concept of a Smart Paint Factory and the Smart Paint Factory Alliance, SPFA