Customer success

Digital transformation in heating technology: How to predict malfunctions and reduce consumption costs

At a glance

  • Cloud solution enables continuous monitoring of heating data
  • Forecast scenarios ensure more reliable operation and fewer failures
  • Intelligent heating control reduces costs for the consumer

The business situation of our client

An internationally leading manufacturer of heating systems demonstrates what is possible when data is used consistently. As direct customers of the manufacturer, heating engineers have always been able to act only reactively. System defects could only be detected when they occurred. And the necessary spare parts were usually not readily available, which meant a lot of work for the fitter and cold nights for the heating user.

Together with ORAYLIS, those responsible now wanted to tackle the digital transformation of the company and create new values on different levels through continuous monitoring of the extensive heating data. On the one hand, the objective was to enable the heating engineer to take anticipatory measures and optimise maintenance intervals. On the other hand, it was necessary to improve the settings of the heating system and to reduce consumption costs.

The solution for our client

The core of our solution is a flexibly scalable platform in the Microsoft Azure Cloud. A long-term hardware solution in the company’s own data centre would hardly have been calculable due to the high data volume. On the cloud platform, the continuous stream of operating, configuration and status data of the heating systems is collected and compared with existing system data. The findings are first displayed in a real-time dashboard so that the manufacturer can immediately detect irregularities and proactively intervene. At the same time, typical consumption patterns are identified, such as for the use of hot water or the heating system as such. On this basis, activity and rest phases of the systems can be intelligently controlled. Last but not least, we train forecast models with the current and historical data. These not only make increasingly reliable statements about the expected consumption. Likewise, forecasts on malfunctions and individual wear parts, including recommendations for action, are automatically delivered to the service technician’s mobile phone.

How data turns into value

Everyone involved benefits from the many possibilities opened up by our cloud solution:

Consumers

  • The data-based, automated control of the systems saves up to 20 percent in costs.
  • Continuous monitoring of the heating data ensures reliable system operation

Installer

  • Positioning as a modern service provider that ensures smooth operation
  • Massive savings in time and effort through intelligent supply chains

Manufacturer

  • Positioning as a technological innovation driver and industry pioneer in the heating business
  • Installers and consumers increasingly opt for the manufacturer’s products and remain loyal to them


turn your data into value.

At a glance

  • Cloud solution enables continuous monitoring of heating data
  • Forecast scenarios ensure more reliable operation and fewer failures
  • Intelligent heating control reduces costs for the consumer
Jens Kröhnert
turn your data into value

Let’s get started!

Do you also want to seize the opportunities of digitalisation and stand out from your competitors with data-driven services?

Join #teamoraylispeople

Shape the world
of data with us

Customer success

Bakery makes reliable forecasts with anonymous customer data

At a glance

  • AI-supported churn model based solely on anonymised data on purchasing behaviour
  • Controlling can reliably predict customer churn
  • Solution could be built quickly and economically with Azure Cloud services

The business situation of our client

In order to use Artificial Intelligence (AI) to make reliable predictions about the behaviour of your customers, you don’t necessarily need vast amounts of data. Even medium-sized companies with relatively manageable data sets can tap into these exciting possibilities of digitalisation. This is illustrated by our approach to a large German bakery: the company works with anonymous customer cards that primarily offer discount benefits. In fact, the 350,000 active cards are used for about 60 per cent of all purchases. Based on this data on purchasing behaviour alone, we have now trained a so-called churn model that recognises potential customer churn at an early stage makes reliable forecasts.

The solution for our client

To build our AI-supported forecasting model, we first defined the typical “churn”: Customers who have bought from the bakery chain for three months and then not for three months. The model was then trained with this specification and the purchase history from the customer cards.

Our procedure at a glance:

  • We train a model with the Advanced Analytics component of SQL Server in the Azure cloud.
  • Over a longer period of time, we compare the analyses of the model with the purchasing behaviour of the customers.
  • We transfer the analysis results into the existing Business Intelligence system.
  • We make the results available to the controlling department via a self-service application.
  • We continuously refine the classification by means of new data.

How data turns into new values

The solution structure described above makes reliable forecasts of customer behaviour possible in an economical way.

The advantages at a glance:

  • Controlling can reliably predict customer churn tendencies.
  • The analysis of individual customers enables individual marketing measures, e.g. special vouchers.
  • Even small changes in buying behaviour are recognised at an early stage so that quick countermeasures can be taken.
  • Expected sales losses due to cancellations can be analysed by region, branch and time period.
  • Higher-level developments can also be recognised, such as when competition increasingly penetrates a specific region.
  • By using cloud components, the solution can be set up and expanded quickly and cost-effectively.


turn your data into value.

At a glance

  • AI-supported churn model based solely on anonymised data on purchasing behaviour
  • Controlling can reliably predict customer churn
  • Solution could be built quickly and economically with Azure Cloud services
Jens Kröhnert
turn your data into value

Let’s get started!

Do you want to know today what your customers will want tomorrow?

Join #teamoraylispeople

Shape the world
of data with us