Inventories are dead capital and cost money. Manufacturers as well as retailers invest an additional 19 to 30% of their average inventory value annually in inventory costs (Fig.1). Consequently, profit can be increased by up to 30% of the reduced inventory value when inventories fall and can free up to 100% of liquid funds. The best starting point for an inventory reduction is a strategic inventory analyses by means of logistic simulation and, based on this, improved sales forecasts, and the correct setting of planning and scheduling parameters in the ERP-system. This is confirmed by the rolling project evaluation of Abels & Kemmner.
Fig. 1: The level of inventory costs is determined by a whole range of parameters.
Although all companies, including SMEs, today use ERP-systems, the level of inventory employed is still quite high.
Although the ERP-systems try to support the planners, they rarely offer sufficient analysis functions and tools for the optimization of strategic inventory problems. In addition, the required analytical effort can often not be provided by the companies themselves, as daily business does not allow time for this.
Our project experiences prove time and again that a large part of the inventories and the related costs can be reduced with relatively little effort while at the same time ensuring product availability.
Our Analyses show significant potentials for savings
To identify potential for improvement, we structure materials according to importance and predictability in a so-called situation analysis. The criteria used are, for example, the share of sales, the inventory level, the service level, but also article characteristics like trends or seasonality. This information then flows into an ABC-XYZ analysis and allows the articles to be structured or grouped accordingly. For each article group, suitable planning and replenishment strategies as well as precisely adjusted planning and scheduling parameters are determined by means of simulation and automatic optimization. The required stocks are then calculated by simulation.
On average, our analyses show at least 15% savings potential in 82% of the companies examined. In 32% of the analyses, even more than 25% of inventory can be saved. The largest cluster with 43% of the companies can reduce inventory between 20 and 25% without affecting product availability (Fig. 2).
Fig. 2: In 75% of the companies analyzed at least 20% of inventory can be reduced
What Are the Main Challenges in Inventory Management?
The main reasons for the existing optimization potential are that companies rely on the MRP parameters once set during article creation, do not have the necessary know-how to adjust the parameters correctly, underestimate the time required for manual maintenance of the MRP parameters or are not willing to invest in strategic optimization of inventory management.
An analysis of the existing conditions often reveals deficiencies in the following specific areas:
- when new ERP systems were introduced, preset parameters were transferred from the old system without verification;
- if disposition parameters are deliberately set, they are often based on outdated data;
- the forecasting and replenishment methods in use are often not suitable for the consumption pattern of the items concerned;
- still many ERP systems offer only simple, inadequate inventory planning procedures;
- important factors of uncertainty in material planning (excess consumption, delivery delay or underdelivery) are not sufficiently taken into account.
A large part of the calculation methods commonly used for inventory planning (mean value, exponential smoothing, etc.) assume normally distributed item consumption without regarding the actual distribution. In practice, however, consumption patterns show different statistical distributions. In our repeatedly performed analyses only a minority of items show a Gaussian (normal) distribution. A relatively large proportion of the observed statistical consumption pattern, approx. 25%, cannot be assigned at all to any of the theoretical distribution types we usually examine (Fig. 2).
Fig. 3: Example of analysis of the frequency of different forms of demand distribution in a product portfolio.
Conclusion of the Inventory Management Analysis 2021
Applying a company-specific forecasting and replenishment strategy, supported by a correct setting of planning and replenishment parameters in ERP-systems allows to increase product availability and thus ultimately sales and competitiveness, with reduced inventory and inventory carrying costs. This also allows for increased profits due to a streamlined product portfolio.
According to our experience an empirical simulation is the best approach to identify the necessary need for action and to determine the associated potentials. We apply such empirical simulations regularly with very good success.
Human Actions in Cooperation With Simulation and Artificial Intelligence Optimize the Inventory Managment
Decades of experience with the described challenges have been incorporated into the APS and ERP optimization system DISKOVER SCO. As an add-on system DISKOVER supports the operative and the strategic material management.
Distribution-free calculation methods for demand forecasting and for safety stock calculation, supplemented by modern AI methods efficiently overcome the obstacles described above. An automatic item-specific empirical simulation identifies the optimal procedures and parameters for each item.
What are your experiences?