Mon Dec 09 2019
by John Hill
Many firms are required to hold inventory of goods for sale, or for use in their day-to-day work. Significant cost savings can be achieved by ensuring that the correct amount of inventory is held, and that the ordering process for new inventory is well-matched to demand.
Why managing inventory is complex
Managing inventory is a complex challenge due to the number of interrelated factors involved in decisions to increase (or decrease) inventory, and the associated time-delays between measuring inventory, and delivery of additional stock. Spreadsheet models are unable to capture these dynamic effects, and often lead to dynamic instabilities - such as oscillations - in the quantity of inventory held.
To understand why this is, we can set up a simple stock-flow simulation in Sheetless, and illustrate the emergence of oscillations in inventory amounts following a demand shock.
By varying the key parameters in this model, such as the length of the delay between placing new orders and their delivery, we can see how the inventory re-ordering system generates significant oscillations in the levels of inventory. These models are able to capture complex effects that simpler predictive solutions, or spreadsheet models, simply can't.
Optimising Inventory Levels
A well-calibrated simulation of the inventory re-ordering process should be able to accurately forecast inventory levels under a wide range of scenarios, including delayed orders, and demand shocks. There is a clear trade-off between efficiency and robustness.
The model can then be used to identify the optimal level of inventory that a firm should hold in order to ensure that it remains robust to unanticipated shocks.