Warehouse optimization with ERP and AI: your path to leaner inventories

In most production companies , warehouse management is a balancing act. On the one hand, all necessary raw materials and components should be available at all times. On the other hand, stock levels must not get out of hand, otherwise costs will get out of hand.

The ideal situation is therefore a warehouse that is as reduced as possible and yet always ready for delivery. However, you can only achieve this goal if you use state-of-the-art technologies. An ERP system that thinks along with you is particularly important for warehouse optimization.

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Why is it important to optimize stock levels?

Companies without an ERP system generally rely on high stock levels to guarantee their constant ability to deliver. However, this practice is quite risky, because:

• Too much stock ties up capital.
  This reduces the company’s liquidity. The organization becomes sluggish and is unable to react quickly to new developments. This in turn damages competitiveness.
• A high stock level is not a good investment.
  Depending on how long they are stored, components or products can become obsolete and therefore lose value. Some materials rust or lose their properties when stored for long periods.

High stock levels are therefore a real business risk. Production companies therefore have a legitimate interest in keeping their inventories as lean as possible.

Warehouse optimization: why an ERP system is indispensable

If you want to reduce your stock levels in the long term, you need more efficient purchasing processes. The purchasing department must not stockpile goods too early or in too large quantities in order to avoid excessively long storage periods. Ideally, the department should only procure the necessary material when there is a concrete need for it.

You can achieve this goal in two or three ways: through forward-looking procurement, order-based production or a combination of both. A modern ERP solution is the basic prerequisite for all three methods.

1. forward-looking procurement requires good data

With forward-looking inventory planning, your company creates forecasts of expected sales. It plans the production and ordering processes in line with this.

The efficiency of this approach depends directly on the quality of the business data. After all, the purchasing department can only plan orders adequately in advance if it knows exactly what to do,

• which or how many orders are expected at what time,
• how the cost of goods sold will develop by then,
• what production capacities are available,
• how capacity is utilized in the factory hall and
• how high the stock of long-running components is.

The more precise and complete this data is, the more efficiently the purchasing department can work. The required level of quality goes far beyond the possibilities of manual calculations. Therefore, for optimal warehouse management, you need an ERP system that performs the complex calculations automatically in the background using hundreds of variables.

2. order-related production for lean warehouse optimization with ERP

In order-related production – also known as demand-synchronous production – materials are only ordered and delivered when they are needed to fulfill a specific customer order. Long storage is therefore not necessary.

Order-related production is based on a complex network of communication and logistics that requires automated processing . ERP software also plays an important role in this method. It enables:

Seamless order processing

When an order is received, not only is a production order created in the manufacturer’s ERP system. The suppliers of all subcomponents also automatically receive an order with only a short time delay. This in turn serves as a trigger for the necessary logistics and production processes.
This process would not be possiblewithout ERP-based warehouse management. After all, the purchasing department cannot pick up the phone for every order and contact each supplier individually. Even with a small order volume, the effort involved would be enormous.

A reliable exchange of information

In order-related production, it must be clear at all times which components are available and when. After all, your own delivery capability and adherence to delivery dates depend on this. If an important component is not currently available, customers should ideally be informed of this before placing an order.
However, this requires a comprehensive exchange of information along the supply chain. And this cannot be achieved without digitized warehouse management.

3. ERP warehouse optimization through a combination of predictive procurement and order-related production

Predictive procurement and just-in-time production are not opposites. JIT producers often also have warehouses that they stock in advance – for example with long-running components that take too long to produce. If these parts were not already in the warehouse when the order was received, production would come to a standstill.

It can also make more economic sense to procure and store certain components in larger quantities. This is the case, for example, when the material value is low and missing parts have a fatal impact on production. Typical material types in this category are, for example, screws and fittings.

How does AI make warehouse optimization smarter?

AI warehouse optimization makes more accurate demand forecasts

Intensive master data maintenance is essential for automated material requirements planning. However, this is very time-consuming and requires the expertise of experienced employees. As a result, master data maintenance is often neglected, which leads to disrupted supply chains. An AI module for warehouse optimization solves this problem.

Artificial intelligence can create forecasts that make it possible to predict demand very precisely . To do this, it not only analyzes historical sales figures and production data. It also takes into account external factors such as seasonal conditions and current market trends.

You can recognize these forecasts,

• which components you should order in good time,
• which material requires a higher stock level,
• which parts only consume unnecessary storage space and
• which components are particularly often not available.

One example:

Let’s assume that an air conditioning manufacturer uses an ERP system that enables warehouse planning with AI. Based on historical internal data, the software recognizes an increased demand for air conditioning systems in the summer months. It also uses interfaces to incorporate external data such as the industry’s economic index and sales trends into its analysis.

For example, if the AI module registers an increased demand for energy-efficient appliances, it automatically adjusts the order quantities. In this way, the company reacts to changes at an early stage and reduces both excess stock and supply bottlenecks.

AI warehouse optimization optimizes procurement processes

Due to raw material shortages or geopolitical events, delivery times can sometimes be longer than average or even unpredictable . Not good conditions for forward-looking procurement. Support from AI inventory optimization is therefore a good way to bring security to uncertain times.

For example, the technology makes statements about the minimum stock levels required – tailored to the month in question. It also analyzes inventory data, delivery and transport times and the reliability of suppliers. If delays are likely, the software can suggest alternative suppliers or dynamically adjust order quantities. It is even possible to run through several scenarios in order to select the best strategy at the end.

One example:

An automotive supplier notices that an important delivery of semiconductors is delayed due to a geopolitical event. The system immediately recognizes the risk of a production stop. It automatically reschedules production and informs the purchasing department about alternative suppliers. This allows production to continue without interruption while capital commitment remains low.

Existing customer examples show this: Our innovative AI is able to reduce stock levels by up to 25 percent, depending on the requirements.

Werner Hießl, Asseco Solutions

Successful use case:
Suer optimizes warehousing with AI module

Suer Nutzfahrzeugtechnik GmbH, a wholesaler of vehicle components, stocks over 20,000 items. Stock management used to be a major challenge for the company, as planning was based on manual Excel evaluations.

With the introduction of APplus, the entire warehouse logic was initially transferred to the ERP system. This enabled Suer to achieve initial efficiency gains and improved data quality.

The costs of global procurement have risen dramatically. We therefore benefit greatly from AI warehousing.

Jan Feldmann, Head of IT at Suer

Since 2021, the company has also been using the AI module from APplus for warehouse optimization. By analyzing seasonal factors and fluctuations in demand, the system is now able to calculate optimal reorder levels and replenishment times in just a few seconds.

The result:

• Improving stock availability
• Reduction of unnecessary stock levels
• Flexible adjustment to volatile demand possible

Read full case study

Conclusion: ERP and AI for intelligent warehousing

If you want to reduce your storage costs and improve your delivery capability at the same time, there is no way around planning via an ERP system. Inventory optimization is based on the analysis of daily goods movements, production processes, delivery times and sales forecasts. Without a modern ERP solution in the background, you have no chance of carrying out all these calculations quickly and efficiently.

ERP modules for AI warehouse optimization expand the possibilities by intelligently analysing historical data and external factors. This enables more precise forecasts and smart inventory management. This allows you to remain flexible and capable of acting even in uncertain times.