Digitized production.
Three questions for... Markus Günther
Markus Günther, product manager and machine learning expert at software company Inform, explains how artificial intelligence keeps manufacturing companies competitive in times of batch size 1.
Mr. Günther, the trend towards increasingly networked production environments is omnipresent. What role does artificial intelligence (AI) play in this?
AI plays a key role when it comes to producing on time and cost-effectively in the face of an increasingly complex order situation. Algorithm-supported planning tools make it possible to maintain an overview and optimally utilize production capacities, especially in variant-rich single-item and small-batch production. Conventional ERP systems merely manage the data. Intelligent APS systems, on the other hand, are able to make planning and scheduling decisions and provide recommendations for action. In doing so, they take into account all relevant factors and correlations across all areas. This enables them to compare widely branched order networks with the actual available capacities and schedule them dynamically - a valuable feature that traditional ERP systems lack. This provides companies with a robust production plan and enables them to complete their orders on time, even in the event of a disruption. Incidentally, the term AI covers various forms of computer intelligence. At Inform, we rely on a hybrid approach that combines knowledge- and data-driven processes, such as machine learning (ML). However, humans always have the last word.
Where exactly are intelligent algorithms used and what are the specific application scenarios?
Manufacturing companies can benefit from AI at both product and process level. Let's look at ML algorithms, for example: These recognize patterns and correlations in large amounts of data and predict future developments on this basis. For example, they can use past order data to determine replenishment times for purchased parts, predict the duration of work processes or speed up the preparation of quotations by being able to estimate the costs incurred more accurately. This improves process quality and adherence to deadlines. These capabilities also enable machines to evaluate process and sensor data more efficiently, for example by predicting when maintenance is required and thus preventing subsequent breakdowns. The higher detection performance also ensures that machines can detect quality deviations more quickly and identify their cause. This allows product quality to be optimized in a targeted manner.
What do companies that want to digitalize their production planning need to consider?
Many companies only see a small part of what can actually be achieved with AI and ML in particular. It's not just about intelligence at product level. The spectrum of possibilities is much broader and also includes services and processes. There is another misunderstanding regarding the required data pool: companies often worry too much about the supposedly poor quality of their data. However, this is usually completely sufficient to introduce an intelligent planning tool. Data cleansing and maintenance are also.
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