Remo Meier
Carla Märkl
“Life is a puzzle game. Only when we have put all the pieces in the right place can we understand it.” This quote by Gerhard Strobel can also be applied to today’s industrial environment. After all, our Big Data world is made up of many individual puzzle pieces that, when put together correctly, provide better insight into the big picture.
This is exactly where Business Intelligence (BI) comes into play. At its core, business intelligence is the systematic analysis of a company’s own business through data collection. Since the 1990s, software-supported solutions for BI have been used at many different levels of the company. The insights gained are then used to derive appropriate recommendations for action.
the importance of business intelligence for industry 4.0
In the context of Industry 4.0 and the underlying data collection and networking, BI tools are becoming increasingly important in companies. They are used to make data evaluations along the value chain more agile and efficient. In particular, the automation of report generation with the help of BI tools is common practice in many companies. Based on the reports, historical data is prepared in a comprehensible manner to support executives in their decisions with well-founded insights.
Today, analytics-driven applications move at incredible speed, enabling rapid, fact-based planning and decision-making. With the help of BI systems, analyses, reports, monitoring and alerts can be created or even predictable events can be forecast in a very short time. This is often done with the help of artificial intelligence.
By the way, the big players Microsoft, SAP and IBM are among the market leaders in the field of BI and analysis software, with a share of around 30%.
which BI tool is appropriate for my purposes?
BI tools help to bring transparency to your company’s data. Therefore, you should first be clear about what information and KPIs are relevant for whom and what goal you are pursuing. Selecting the right BI tool also depends on how complex the visualization is and whether the use of artificial intelligence is necessary. In addition, it makes sense to already have interfaces to the required data sources to enable seamless data processing.
The following BI tools for analyzing historical data are commonly used because they provide interfaces to various data sources, transform data, process it and visualize it on a dashboard so that automated reporting is possible.
real-time data or long-term analysis?
The data basis has constantly evolved in the sense of Big Data and Industry 4.0. Data from production can no longer be used just to control production, but also to evaluate the day-to-day business. The real-time use of data, in particular, offers a decisive advantage here compared to long-term analysis.
In operational intelligence, also known as operational business intelligence, ad hoc analyses take place parallel to the processing of business data from ongoing processes. These analyses are usually performed in very timely intervals, such as seconds, hours, or days, in order to achieve process improvements, productivity increases, and risk minimization.
The goal is to learn more about the current status of processes and operations based on production data, for example from MES and ERP systems, in order to make continuous improvements. In this context, real-time data evaluations from machines and sensors can support operational decisions for the current state and ensure an overview of performance.
operational intelligence makes the difference in everyday business.
The information gained through BI systems is not only an enrichment for the management level. Employees who are directly involved in the processes, i.e. in production on the shop floor or in logistics in the warehouse, also benefit from comprehensibly prepared data. However, when things have to move quickly and decisions have to be made immediately, Operational Intelligence is preferable to the more common Business Intelligence.
With Operational Intelligence you optimize processes by providing real-time data to create transparency and to be able to intervene directly in running processes. In this way, problems at the point of value creation, such as error messages from machines, can be identified and solved directly. In addition, the results of the measures taken can be visualized immediately. This means that employees have an overview of process changes at all times and can make quick and well-founded decisions to eliminate problems, thus making a lasting contribution to increasing efficiency.
Here you will find some examples of how Operational Intelligence can be implemented in practice:
Both business intelligence and operational intelligence have their justification for existence in the company and offer very different added values. However, if repetitive workflows and processes are to be improved directly at the point of action as well as sustainably, it can be worthwhile to use the often underestimated resource of real-time data. In this way, the full potential of intelligent communication can be exploited!
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How to use visual management to make processes and goals in the company transparent.
