New business models based on data

Although the importance of data can no longer be ignored, some take it a step further and develop new business models based on data. Data as business model is on the up but how does the transformation of raw data into valuable intelligent actionable data work exactly?

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Data as business model

The following figure shows an overview of the different steps and on how they interact with one another. These steps are necessary to transform raw data into valuable information that can support different business models. In this way, companies can implement new business models based on data that will gradually generate turnover.

Setting up a new business model is a major step and can initially have a disruptive impact on a company. Everyone in the company must make the transition and often new sales profiles will be needed to sell the new offer. In this context, we expect that the ‘traditional’ CAPEX (= investment-based) business models will be replaced by OPEX business models (= based on variable operating costs).

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Actionable (intelligent) data generate USP's

We’re about to enter an era in which a new competition focussing on actionable (intelligent) data will be unleashed. Previously, companies distinguished themselves by their physical product, the hardware as it were. Today, we can see that, following the use of data, the unique competitive edge is shifting from the physical product to the software and the optimal use of data. Products that adapt themselves to the user and to changing ambient factors, thereby becoming ever better/more useful, will gradually start to dominate the market.

Data from products and production

Collecting the right data is not as obvious as you may think. Both the type of data and the frequency of saving them are important to be able to properly understand your data. Also the data structures that you will use are crucial for conducting analyses or making comparisons at a later stage.

Collecting data from a product/machine is done through several control loops. We will need both fast in-depth control loops and slower overall control loops to be able to build a complete picture. Also the speed and frequency at which data are retrieved, will vary from very rapidly to slow, depending on the loop. By monitoring sensor values, you can easily and rapidly adapt suboptimal product performances. Furthermore, you will be able to predict when your machine will fail and schedule preventative maintenance to prevent this from happening.

Compared to products, collecting data in a production environment will be performed on a higher level and at a lower speed and frequency. By collecting and analysing production data, you will be able to significantly increase your production efficiency and, ideally, achieve a zero-defect production process. You will also be able to measure where in the production line efficiency gains are still possible, for instance by supporting operators with a robot or cobot.

Production environments are typified by several machines that can each have their own individual defects and that, in some cases, are linked. The failure of 1 of these machines may shut down the whole production system. To prevent this worst-case scenario, you will have to collect data to detect which machine or machine part within the chain is causing problems. By measuring this Overall Equipment Effectiveness (OEE), you can read the performances of a product or production line in 1 figure. By thoroughly analysing this figure, you can draw conclusions on what should be changed to increase your productivity. The uniform definition of OEE also allows to compare figures from different departments within and beyond your company and check them against the benchmarks in your industry.

The success of a business depends on various factors. The optimal collection and use of data is a new factor that should not be underestimated. The efficient use of data even has the potential to unlock new business models. Although these business models may lead to better performances, they will inevitably create new challenges as well. In future, success will depend on how fast companies will be able to pick up the very latest trends and cope with the resulting challenges.
 

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Dirk Torfs - CEO
Auteur

Dirk Torfs - CEO

Dirk Torfs is CEO of Flanders Make since 2014. Dirk is a Civil, Mechanical and Electrical Engineer as well as a Doctor in Applied Sciences (KU Leuven). He has over 20 years of experience in management positions in the Flemish industry and is Professor of Quantitative Decision-Making for the Executive MBA programme of the Flanders Business School.

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