If you meet delivery deadlines, you become an indispensable partner for your customers. Today, however, adherence to delivery dates can only be achieved by planning production with the help of AI, as shown by the following German examples
Rarely have companies been under as much pressure as they are at present. This is the conclusion of a flash survey of more than 3,000 companies published at the end of August by the Association of German Chambers of Industry and Commerce. It paints a picture of the devastation caused by 18 months of the pandemic, lockdowns in key supply countries and massive delays in international container shipping.
Three out of four companies are currently waiting longer for ordered goods and raw materials than before the crisis. Four out of ten companies are therefore unable to process orders and are losing turnover. To compensate for this, 60 percent of the companies have to invest more time and effort in planning their production.
Adherence to deadlines is considered a sign of quality today
Especially in times of crisis, customers measure the quality of a supplier by whether it meets the deadlines it has promised. At the same time, they are increasingly asking for special and customized products. This makes the situation even more complex for companies. These challenges can only be mastered by companies that manage to deploy their capacities in such a way that they can react flexibly to any order situation.
The German company Vecoplan AG uses intelligent software from INFORM to plan its production processes. The special machine builder from Bad Marienberg in the Westerwald region develops and produces systems and machines that can be used to shred and recycle production, household and commercial waste. The machines are mainly used in sawmills, in the processing of waste wood for pellet production, the recycling of plastics, paper and cardboard. In order to be able to meet the usually very specific technical requirements of their customers, the 450 employees of Vecoplan produce a large number of different variants of their machines. “With the production planning we used to pursue according to the principle of master control, we had reached a point where we could no longer achieve any further improvements in our production efficiency,” reports Michael Grieble, head of work preparation at Vecoplan. The possibilities to further optimise adherence to schedules and the use of resources had been exhausted.
The medium-sized company therefore decided to introduce the Felios production planning software from INFORM. It networks data from all areas of the company that are linked with production. Intelligent algorithms then create the optimal production plan based on the information available to it about capacities on machines as well as the employees and preliminary products available on a given day.
Since the algorithms determine the sequence of orders to be processed on the basis of both the resources required for this and those actually available at Vecoplan at a given time, they calculate reliable delivery dates. The machine manufacturer was therefore able to increase its adherence to delivery dates by a further 10 per cent and adapt its procurement more precisely to the order situation. As a result, the inventory was reduced by 12 per cent and the capital tied up in it by 16 per cent.
Artificial intelligence enables batch size 1
If small batches or one-offs are to be produced, it becomes even more complex. Because with small batch sizes, the number of orders increases per se. Those who do not plan their production digitally will be standing on their own two feet, the mathematician warns. Nevertheless, many companies shy away from planning their production digitally and with the help of artificial intelligence. Seven out of ten companies fear that they do not have the data for this or do not have it with the required accuracy, according to a study by management consultants PricewaterhouseCoopers.
This fear is unfounded. As a rule, the ERP systems introduced in the companies hold 90 to 95 per cent of the information required for digital production planning. The remaining 5 per cent is usually information that is available in the company, such as data on sick leave or employee holidays, but is kept in separate systems and can be integrated from there.
Production planning systems network all data in the company
That is why a digital production planning system networks all this data on one platform. Algorithms can then compare whether all resources are available to complete an order promised on a certain date, or in which order orders can be processed with the shortest lead times. If the AI detects that delivery dates can only be met with overtime and extra shifts, it sounds the alarm in good time. If it registers material bottlenecks, it informs the purchasing department with sufficient advance notice which parts it must procure and by when. Since the algorithms of AI-based production planning can identify patterns and draw conclusions from them, they also recognize when suppliers regularly exceed the deadlines they have promised. Instead of using the date advised by the supplier for a particular order, they then calculate lead times based on this insight.
Digital production planning is the basis of targeted investments
However, the weak points in the processes identified by the algorithms not only help the AI to achieve better planning results. They also show the company for which orders it is worth hiring new employees, whether it is worth investing in additional machines because capacities at the existing ones are always tight, and with which supplier the purchasing department should negotiate because they regularly exceed delivery deadlines.
In order for digital production planning to develop its full potential, however, all departments involved in the production process should be included when it is introduced. After all, departments such as purchasing, design or sales should work hand in hand with production. In order to achieve the best possible results, the foremen and machine operators from production must also be at the table. This is because they often have knowledge and experience that is not mapped in data – for example, about set-up processes and the time required for them. In this way, AI and the expertise of the foremen can be combined to advantage.
Those who take this to heart can massively strengthen their position in the market with AI-based production planning. Because they remain able to deliver even in the current crisis and can promise deadlines that their customers can rely on. In this way, they earn the reputation of being a reliable partner. Their customers are particularly dependent on them in times of high pressure.
About the author
Markus Günther is Head of Sales in the production division at INFORM.