Ensuring High Delivery Capacity In The Product Portfolio With As Little Capital Tied Up As Possible – A Cost-Effective AI-Supported Methodology Opens Up New Possibilities
High delivery readiness is of decisive importance for a competitive position in the technical trade business. The best strategy would be to always have sufficient delivery capacity for each and every article. Unfortunately, delivery capacity also costs money in the form of high stocks; inventories that not only tie up liquidity but also cause running costs.
According to analyses by Abels & Kemmner, you must reckon with 19% to 30% of the inventory value in annual running costs. Every 100,000€ more inventory then quickly costs 19,000€ to 30,000€ a year in running costs. It is clear: Not only too little delivery capacity costs money, you are also punished for too much.
From a technical point of view, the high inventories result from the fact that the higher the delivery readiness of a product and the more fluctuating its demand, the higher the so-called safety stocks need be. If the articles in question are very expensive, the sum of the safety stocks quickly hurts.
Sustainable strategies are a must
In our projects in technical trade business, we come across two prototypical strategies for securing delivery readiness. On the one hand, safety stocks are set very freely according to the gut feeling of the planning or the sales department. The readiness of an article to be delivered is thus purely random. If customers complain, the stock is simply increased a bit. Since customers never complain about high delivery capacity, stocks are rarely turned down. This strategy is dangerous. It can lead to a situation where, despite unnecessarily high stocks, you do not achieve the delivery readiness that is necessary to keep up with the competition, because the wrong articles have the wrong stocks.
The second prototypical strategy we observe makes much more sense. This strategy starts by calculating, sometimes with great effort, which readiness to deliver leads to which safety stock and resulting total stocks for which articles or article groups. Then the delivery capability is distributed differently across the product portfolio to find a compromise between readiness to deliver and inventory. This procedure is time-consuming and requires some theoretical knowledge. Therefore, once the “operating point” has been reached, it is gladly maintained.
The cost-benefit ratio must be right
Unfortunately, even this approach falls short. Once the desired readiness to deliver has been determined across the product portfolio offered and the safety stocks have been set accordingly, not only must the safety stocks be readjusted monthly, but the readiness to deliver levels should also be reviewed. Changes in the demand pattern of the articles may require other safety stock levels to achieve the previously targeted readiness to deliver. And due to changes in sales volumes, production or purchasing costs of the articles, it regularly happens that the previously targeted readiness to deliver for one group of articles gets expensive, while it tends to become less expensive for another group of articles. For economic reasons, it therefore makes sense not only to readjust the safety stocks, but also to readjust the delivery readiness levels for such groups of articles.
But who actually makes this effort in practice? Very few! Therefore, it is necessary to break new ground to find not only an effective but also an efficient approach to optimising the delivery readiness of a product portfolio. Ideally, it should be possible to readjust the delivery readiness of a product portfolio within a few minutes so that a high delivery readiness is achieved with the lowest possible inventories. This is indeed feasible using artificial intelligence (AI).
Artificial intelligence unties the Gordian knot
The starting point for the use of AI is the specification of a value-weighted total delivery readiness of the product portfolio. This value indicates the percentage of the total value of customer orders for items in stock that the company would like to be able to ship on time, i.e. on the customer’s requested date or alternatively on the agreed delivery date.
Then the entire product portfolio in stock is sensibly divided into e.g. ABC/XYZ classes or any other structure that deems senseful. If specific AI-supported optimisation algorithms are used, such as those provided by the DISKOVER Service Level Booster developed by SCT, the delivery readiness levels for the defined portfolio fields are determined in such a way that the specified value-weighted total delivery readiness can be achieved with the lowest inventory.
The optimisation algorithm can be assigned an optimisation range for each segment of the product portfolio to be considered, so that strategic considerations regarding the minimum and or maximum delivery readiness of certain goods or groups of goods can be considered.
The calculated delivery readiness levels can then be transferred to the ERP system. If the system is not able to calculate reliable safety stocks required for a given target delivery readiness, the safety stock figures can also be transferred directly to the ERP system.
Achieving the same degree of fine-tuning with classical mathematical methods would not be possible because the calculations were so extensive that the calculation required billions of years (no typo) with today’s computing power.
Effective and efficient at the same time
The AI-supported optimisation run of the Service Level Booster, takes a few minutes at most; 90 seconds are usually enough. Thanks to the simple procedure and the short calculation time, it is easy to readjust the target delivery readiness on the portfolio fields monthly, which allows the targeted overall delivery readiness to be achieved sustainably with low inventories. The optimisation potential of the AI-algorithm depends on the initial situation of a company. In applications without previously optimised distribution of delivery readiness in the product portfolio, safety stocks have been reduced by up to 13% on average and total delivery readiness increased by up to 16% at the same time (see Fig. 1). In the case of product portfolios already optimised with the help of an APS (Advance Planning and Scheduling) system, a further average reduction in safety stocks of 3-5% was achieved.
Adjusting the delivery readiness of a product portfolio is only the spearhead of a comprehensive optimisation of the entire planning process. Without a properly sharpened product availability, however, the markets can hardly be hunted down.