Customer success

Shopping Portal improves its financial reporting with Power BI

At a glance

  • Financial reporting is automated through the use of Power BI
  • Even inexperienced users can work with data and create new value for the business
  • Collaboration platform enables company-wide dissemination of insights gained

The business situation of our client

Being “online” is no longer a competitive advantage. Shopping portals are also facing ever-increasing competitive pressure, which demands ongoing optimisation and quick decisions based on data. This was also evident at a well-known German supplier of branded goods. Until the time of our cooperation, the company’s financial reporting was exclusively created manually and sent by email. Likewise, there were no adequate tools for ad-hoc analyses of acute business issues. Finally, due to a massive backlog of requirements in the company’s own Data Warehouse, many important key figures were not available. Thus, there was an urgent need for a modern solution that would enable the finance department to use the existing data efficiently and flexibly.

The solution for our client

With the help of Power BI – Microsoft’s leading self-service service in the Azure cloud – we were able to quickly set up our own analysis solution for the department. For this, the data from the various sources was imported and merged into an updatable data model that depicts all relevant financial ratios for managing the company. Based on this, standard reports and dashboards can be provided automatically. Power BI also offers business users a wide range of options for carrying out ad-hoc analyses according to their own ideas. Alternatively, the more familiar Microsoft Excel is also available as a tool. In addition, we have established a company-wide collaboration platform in the cloud via the Power BI portal, which also gives other groups of people and areas in the company access to the department’s analysis results.

How data turns into new values

Thanks to the simple data model and the self-service tools used, even inexperienced users can work independently with data to create meaningful reports, dashboards and ad-hoc analyses. Last but not least, the complementary use of Excel ensures high user acceptance. Due to the free access via the collaboration platform, other company departments also benefit from the insights gained. Management and specialist departments can even access all important information on a mobile basis at any time. In addition, key figures can be realised or expanded much faster, so that the reporting of the finance department forms a much more valid basis for decision-making.

However, such isolated analysis solutions only give companies short-term breathing space in the digital race. In the medium to long term, these so-called data silos lead to a proliferation of reports and the loss of uniform key figures. The next step is to expand the existing DWH into a digital platform in the cloud that unites all company divisions and their data. Power BI not only offers the best prerequisites for this approach. The service is harmoniously integrated into the diverse eco-system of Azure Cloud Services. Accordingly, it can be expanded as needed into a modern DWH that serves every conceivable scenario of contemporary data evaluation, from forecast scenarios to digital products.


turn your data into value.

At a glance

  • Financial reporting is automated through the use of Power BI
  • Even inexperienced users can work with data and create new value for the business
  • Collaboration platform enables company-wide dissemination of insights gained
Jens Kröhnert
turn your data into value

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Customer success

How a baker uses data to reduce returns from branches and increase sales

At a glance

  • Digital platform makes all data from operational business available for the first time
  • Real-time analyses enable fast and flexible reactions in day-to-day business
  • Customer satisfaction and sales increase while the return rate decreases

The business situation of our client

The efficient use of data offers bakery chains the opportunity to improve their business processes and service to the consumer in many ways. This is illustrated by the example of a medium-sized bakery business that is one of our clients. The company has set itself the goal of having a complete and fresh range of goods available at all times of the day in its 30 or so branches. At the same time, the return rate should be as low as possible.

In order to ensure the ongoing balancing act between an oversupply and an undersupply of the individual branches, enormous amounts of data are available, for example from checkout processes and logistics. However, our client lacked:

  • a central platform to bring all the data together and enable consistent reports and insights across all business units
  • suitable tools that enable users from the specialist departments to carry out their own targeted analyses
  • Real-time data that allows fast and flexible reactions in day-to-day business.

The solution for our client

To enable our client to effectively transform its data into value for its business, we first built a central database based on Microsoft technologies. Here, all data from the operative business is processed and merged almost in real time. This includes not only receipt and delivery data, but also information on stocks, returns, orders, production, personnel, energy consumption and the weather. The bottom line is that the entire process from production to sales is mapped.

All company departments – whether sales, controlling, production or office work – have access to this integrated data platform. For evaluation, the various user groups have access to suitable analysis tools, such as Power BI, Excel or Reporting Services. In this way, all employees can carry out individual ad-hoc-analyses on the desired data quickly and easily. Standard reports on the relevant key figures are delivered automatically. We have optimised the design of the reporting system with a uniform information design according to IBCS specifications.

How data turns into new values

Through the ongoing analysis of daily updated sales data, our client has achieved its goals:

  • Staff can identify any bottlenecks or surpluses at an early stage
  • Missing goods can be delivered or transferred immediately.
  • Consumers can choose from the full range of products at any time and in any shop
  • Customers are more satisfied and can be retained for longer periods of time
  • Sales potential is optimally unlocked, so that turnover increases
  • The returns rate has been noticeably reduced

In addition, the company benefits from fraud analyses that effectively uncover irregularities in the checkout systems. Last but not least, the diverse analysis options of sales data, buying behaviour, shop development as well as production and delivery quantities provide different departments with a much more well-founded basis for planning. In the process, users no longer have to invest time in data procurement and preparation, as the relevant information is centrally available and easily accessible. Similarly, the automation of processes saves manual effort in many places.


turn your data into value.

At a glance

  • Digital platform makes all data from operational business available for the first time
  • Real-time analyses enable fast and flexible reactions in day-to-day business
  • Customer satisfaction and sales increase while the return rate decreases
Dirk Ohligschläger
turn your data into value

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Customer success

Predictive maintenance: machine builder inspires customers with AI-based forecasts

At a glance

  • Packaging equipment manufacturer enhances its products with data-based services
  • AI-supported forecasts reliably predict machine downtimes and defects
  • Efficiency gains range from warehousing to overall productivity

The business situation of our client

In global competition, efficient production processes are a decisive factor for success. Against this backdrop, digitalisation offers manufacturers of machines and production lines many opportunities to inspire their customers with data-based services from the field of Artificial Intelligence (AI). For example, we implemented prognosis scenarios for predictive maintenance for a globally operating machine manufacturer, which significantly increase the efficiency of the manufactured plants.

The objective of our client was to:

  • Prevent machine downtimes
  • To exclude machine damage
  • Minimise downtimes
  • Identify typical malfunctions
  • Optimise maintenance intervals
  • Improve productivity

The solution for our client

The starting point for our data-based forecasting services is historical as well as current machine data. On this basis, a prognosis model is set up and continuously improved:

  1. We continuously collect the sensor data of the machines
  2. We supplement current measured values with quality-assured master data
  3. We generate diagnostic data on machine problems
  4. We train a machine learning model on the basis of the diagnostic data.
  5. We continuously compare our optimisation algorithms with real-time data.

How data turns into new values

By using our AI-based prognosis model, the machine operators can specifically prevent possible malfunctions and defects. The availability of the machines in operation has improved significantly as a result. But our customer also benefits from the optimisations at its customers: Testing costs and costly recall campaigns are avoided, and warranty costs are significantly reduced. At the same time, customer satisfaction and loyalty have increased.

The improvements for all involved in concrete figures:

  • Machine defects and failures are detected 75% proactively
  • Downtimes have been reduced by 50 to 80%.
  • Maintenance costs have been reduced by 50 to 80%
  • Warranty costs have been reduced by over 50%
  • Inventory costs reduced by 20 to 30%
  • Overtime costs reduced by 20 to 50%.
  • Overall productivity increased by 20 to 30%.


turn your data into value.

At a glance

  • Packaging equipment manufacturer enhances its products with data-based services
  • AI-supported forecasts reliably predict machine downtimes and defects
  • Efficiency gains range from warehousing to overall productivity
Jens Kröhnert
turn your data into value

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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?

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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?

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Shape the world
of data with us

Customer success

Retail: How data can optimise business with perishable goods

At a glance

  • Web app for the ultra-fresh business enables precise planning of order and delivery quantities
  • Uniform planning and analysis interface accelerates processes
  • Sales potential is better utilised and write-off rates are reduced

The business situation of our client

The right use of data can improve processes at any point in your business and lead to significant savings in time and money. Example retail: For a European market leader, we created a solution that optimised the processes around the business with perishable goods – such as fruit, vegetables and cut flowers. Because: too large order and delivery quantities generate high losses, as the goods expire. If, on the other hand, the shelves in the shops are empty, customers migrate to the competition.

Originally, the company used various applications for its different planning and analysis activities. This not only hindered internal processes. Rather, the quality of the findings also suffered because the underlying data was neither complete nor consistent. We have now united all process steps under one user-friendly interface and at the same time ensured a reliable database.

The solution for our client

We developed our integrated planning and analysis interface with ASP.NET and made it available as a web application via the browser. The app uses reporting services to access a Data Warehouse that provides historical data on sales and promotions by region and time as well as in great detail. In addition, we provide continuously updated supplier evaluations, which are carried out throughout the group at warehouse receipt by means of coordinated KPIs.

The combination of Reporting Services and ASP.NET enables data-supported planning with a high degree of user-friendliness. In addition, export options are available in a wide variety of formats, through which analysis results can be seamlessly transferred to further systems such as SAP.

How data turns into new values

The retail group now uses its data effectively for the entire organisation of its ultra-fresh business. In the process, values are created at different levels:

Employees

  • All analyses from quantity to promotion to seasonal planning are covered
  • Fast and efficient work via a fully integrated web interface
  • Suggestions from the system provide well-founded decision support
  • Better supplier selection through quality analyses based on KPIs and thresholds
  • Completed planning can be aggregated and sent directly to downstream systems in report form

Company

  • Cost savings through accelerated processes and lower write-off rates
  • Improved customer satisfaction and loyalty due to better availability of goods
  • Increased turnover due to better use of sales opportunities

Consumers and society

  • Goods are available to consumers when they need them
  • Less food goes straight to waste


turn your data into value.

At a glance

  • Web app for the ultra-fresh business enables precise planning of order and delivery quantities
  • Uniform planning and analysis interface accelerates processes
  • Sales potential is better utilised and write-off rates are reduced
Jens Kröhnert
turn your data into value

Let’s get started!

Would you also like to use your data to optimise the processes around your business?

Join #teamoraylispeople

Shape the world
of data with us