Production structures in companies are becoming increasingly complex, while at the same time the volume of available production data and the complexity of that information are growing rapidly. These developments are pushing simple analysis and decision-making systems in the production controlling departments of small and medium-sized enterprises (SMEs) to their limits.
This is where data analytics comes in. To provide sound support for increasingly complex decisions, various types of analyses are distinguished based on their added value and complexity.
Descriptive analytics evaluate what has happened in the past. This includes, for example, the analysis of historical order lead times. Descriptive analytics provide comparatively little added value because they merely reflect the past actual state of production; however, they are generally easy to implement.
Predictive analytics forecast what will happen in the future, for example in the form of specific delivery time forecasts.
Prescriptive analytics, on the other hand, determine what should happen in the future. They are highly complex but also generate the greatest added value, as they enable forward-looking recommendations for action. One result of a prescriptive analysis, for example, is that inventory levels in production need to be reduced to prevent throughput times and delivery times from exceeding the tolerance limits of strategic objectives in the future.
Data Analytics: Opportunities and Challenges for SMEs
The targeted use of these well-founded analytical methods makes it possible to describe factors influencing the logistical performance of production systems and to utilize this information for production monitoring and controlling. In this way, companies can identify and implement concrete measures to significantly improve performance. This enables them to achieve high logistical performance – such as short delivery times and high on-time delivery rates – while simultaneously keeping logistical costs low.
However, the Institute of Production Systems and Logistics (IFA) has recognized that SMEs face significant hurdles when implementing these advanced analytical methods. Employees often lack the technical expertise, as implementing such models requires in-depth knowledge of programming languages. The extreme shortage of IT specialists, combined with tight budgets, further drastically hinders SMEs from realizing their full digitalization potential.
Potential Through the Use of Low-Code Applications
To overcome these hurdles for the industry, the IFA is focusing on the low-code approach. Low-code is a tool that enables users to develop software and data analyses without in-depth programming knowledge. Instead of writing complicated lines of code, users employ visual drag-and-drop building blocks. They assemble various pre-built analysis components like puzzle pieces to form a complete workflow.
First, users select modules for data import to load the raw production data into the system. They then link standard modules for data preprocessing to, for example, clean or merge the data. Finally, they drag the desired module for the actual logistics analysis – such as a delivery time forecast – into the workflow with a mouse click. Users can then easily apply custom weightings or adjustments via visual menus, without having to modify the underlying program code at all.
This holds enormous potential for SMEs: so-called “citizen developers” – employees from all departments outside of IT – can thus create software applications independently. As a result, companies significantly reduce their development times and costs. The use of low-code enables companies to conduct advanced logistics analyses on their own and thus secure their market position in the long term through measurably improved logistics performance and a more efficient logistics cost structure.
Practical Low-Code Applications for SMEs
Through the LowDA research project, the IFA is translating this theoretical potential directly into industrial practice. To this end, the IFA is developing a freely available library of low-code applications specifically tailored to logistics challenges. In addition, the IFA is developing suitable methods and didactically structured guides to support SMEs in the essential preprocessing and cleaning of large data sets. These data preprocessing steps are absolutely necessary to achieve reliable analysis results. Case studies round out the project and make it easier for companies to get started.
At the conclusion of the research project in the fall of 2027, the IFA will provide industry with an interactive online guide that enables SMEs to independently and reproducibly apply complex analyses in production monitoring and controlling.
