Data Driven Optimization

Companies collect large amounts of information in digital form in a targeted, but very often also incidental manner. But it is only through the consistent and intelligent use of corporate data that added value and real competitive advantages are created. The use of AI supports management in analysis and decision-making.

Overview

ERP systems such as SAP hold more information on each individual transaction than is viewed and used by default. By using this data in a targeted manner, however, it is possible to gain deep, fact-based insights into the company's processes. This so-called process mining represents a quantum leap in corporate management because it provides an objective basis for precise and effective optimisation measures. Modern tools can extract data "at the touch of a button" and visualise essential core processes in their actual sequences. This quickly reveals strengths and weaknesses, and optimisation measures can be derived in a comprehensible way.

Relevant data from systems with unusual or outdated interfaces as well as from digital documents can also be made usable efficiently and sustainably. When using Robotics Process Automation (RPA), repetitive processes of reading in, collecting and transferring relevant information (e.g. from company-specific databases or from invoice documents) are transferred to software completely and with sustainable quality.

Beyond the extraction and processing of robust data, data-driven optimisation can also support the decision-making process itself. Individual, agilely developed AI algorithms can decipher complex patterns in the data. Against this background, new data can be assessed or forecasts can be made that can be incorporated into a decision or even completely automate sub-processes.

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Questions for our team

Is my company's data suitable for process mining?

The process mining software, which FTI-Andersch uses, can connect to almost all commercially available systems. In this way, results can be achieved quickly, from which initial insights into the lived processes can be gained. Through software adjustments, these can usually be refined quickly.

However, the transparency of deficiencies is also extremely valuable because they show where processes basically need to be clarified to be set up efficiently and to enable real optimisation in the first place.

FTI-Andersch does not stop at the technical IT challenges, but also has the knowledge and methods to advance and change the process landscape itself.

Don't many applications achieve similar effects based on empirical knowledge?

Complete process chains usually have a complex structure and a large number of participants with sometimes very different perceptions. This rarely results in clear empirical knowledge, but often leads to lengthy discussions and resistance when processes are to be changed and optimised. Insights gained through process mining often provide a complete and, above all, fact-based picture for the first time, and decisions become comprehensible for everyone.

Competition is also getting stronger, so it is important to identify and use more subtle approaches in addition to the supposedly obvious ones.

FTI-Andersch masters both the identification and implementation of immediate measures from the turnaround area as well as the implementation of continuous improvement processes from the transformation area.

How does data-driven optimization affect the organization, structure and culture of the company?

The amount of available data and the possibilities to use it have increased rapidly in recent years. Only those who take advantage of this will be able to compete in the future. However, Industry 4.0 is not just a technical issue: it fundamentally influences how we work, discuss, and make decisions.

Thanks to the holistic approach and the broad spectrum of know-how of FTI-Andersch, data-driven optimization can be used as a nucleus to initiate the further digital transformation in the company – from the use of innovative tools such as process mining to the agile introduction of the first use cases of AI in corporate management.

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