For many years business models in automation have been very similar. However, their foundation is based on assumptions that IIot will probably make invalid. Schneider Electric sees that with the digitisation of plants and processes, there will be a growth in new business models with data as a currency.
Machine automation uses specific data to provide information and coordination between systems. But much of the available data is either unused or simply not collected. Schneider Electric argues that data specialists will interpret relevant data and monetise them. It will bring high-value efficiency and reliability information of machines, devices and processes.
Combining this with contextual data from Smart Machines, offers new opportunities for innovation and development. It will result in new innovative products, services and corresponding business models. Combining operation, process, environmental, diagnostic, historian and current usage data opens the door towards many new opportunities. One recent example is using data for smart preventative maintenance and reducing downtime.
Data as a currency
For automation producer’s, their business is selling devices, accessories, software licenses and supporting services. Turning raw data into informed actions on machines is a specialist operation that is not yet been fully harvested. Markedly, a new business around data management is growing as a niche. This niche is accessible to non-automation players, innovative start-ups or even big IT players. Future competition will come not only from makers of PLCs and drives but from new players. With fewer hurdles to entry, many are already accessing these markets with new technologies and services.
For the data handling companies, data manipulation is their entire roll. For automation suppliers however, it is high value usage data that helps them rank future developments. A move in this direction means automation makers losing access to clients’ data services. Also important is the loss of access to user data from OEM and manufacturers. This data is essential to extend knowledge about product usage. It is from this that makers innovate in hardware and software and which services customers may need.
New business models
The value of data as a currency seems to be clear, but how to turn it into revenue is less so. It is important to accept that new business models do not necessarily mean new cashflow but may add value differently. One example is data combining machine usage, user data, environmental, process or energy data to refine energy use.
Schneider also envisages using data to develop customer loyalty and services by sharing insights to improve their businesses. Furthermore, shared ideas in innovation processes or expertise about optimization of the operation help create the right services and products. Then in return the customers can transfer the need and development costs.
The experience from using more data is that we will find new uses for it, providing deeper insights on product use. Better information on product use aids development of new business model, services and products. In turn this will provide greater planning reliability. Very often achieving customer value for products comes by smartly adding a few major unique value propositions. All these compensate costs either by increasing loyalty or reducing manufacturers costs.
Finding new values
There are also many opportunities for new business models to increase the attractiveness of the complete offer. As the importance of data increases there will be a blurring of boundaries between products and systems and services. These will also change throughout a machine’s lifecycle.
Smart Machines connected to IT will drive OT to expand their IT competencies. But new OT business models should not be solely to monetarize new technologies or concepts. Expanding our view properly, opens new business opportunities. Technologies will become services that speak and listen to other services, and smart devices and digitally augmentation will improve the user experience.