Model Logic has developed a number of inventory models tailored to specific customer requirements ranging from an iterative simulation through to a more formulaic approach.
The purpose of an inventory model is to understand the interplay between lead time, supply interval, minimum order quantity, desired service level and forecast error. This leads in the first instance to the setting of safety stock levels and re order point stock level in order to minimise the risk of a stock out or lost sales. At the same time we wish to avoid overstocking of product as working capital is unnecessarily tied up as well as valuable warehouse space.
We have used the model in a variety of sectors from construction to food, where our clients wished to identify and quantify the inventory savings of stocking SKUs at one central distribution centre versus several regional distribution centres. This has enabled them to classify the stock and identify which products would better be held centrally as opposed to regionally.
It is worth exploring the variables that make up an inventory model in more detail, as most organisations will have data that can be manipulated quite quickly in order to start generating some meaningful results.
1 Lead Time
The period of time between the placement of an order and the visible availability of product for despatch in the warehouse. This is an important variable to consider when sourcing products from the Far East, with a possible sixteen week lead time.
2 Supply Interval
The period of time between the receipts of orders arriving in the warehouse.
3 Minimum Order Quantity
The smallest agreed order quantity that can be placed on the supplier.
4 Desired Service Level
The actual cases supplied divided by cases ordered expressed as a percentage. For example if the Desired Service Level is set at 95%, then for every 100 units ordered, 95 cases supplied would be acceptable.
5 Forecast Error
This is perhaps the most difficult input variable to ascertain.
Model Logic has developed a sub module attached to the inventory model which calculates the Forecast Error also known as Mean Absolute Deviation (MAD)% from a sales history dataset.
We would suggest that, where available, at least a two year sample should be used.
Alternatively, the forecast error is often calculated within a typical sales forecasting system and extreme data or outliers should be filtered out of the analysis.
1 Safety Stock
Also known as buffer stock. This provides the contingency in case the sales forecast underestimates the actual sales.
2 Re-Order Level
This is the stock level at which an order should be placed. Delaying the order any longer will risk depleting the safety stock level.
The models using the above framework work well in industries where shelf life is not an issue. However in the food industry, for example, we often face the added problem of stock write offs – where SKUs have exceeded their sell-by date. So even with efficient stock rotation, parts of the stock have to be thrown away as the actual demand is less than the sales forecast.
This leads to a trade-off between stock write-offs and lost sales, so Model Logic has also developed a Short Shelf Life Model for these applications.