Inventory Cost Reduction
In a previous article, we showed a Glimpse.SX solution that minimized transshipment cost for a statewide multi-echelon supply chain network. Tunable parameters entered in the dashboard visualization enable the user to vary the conditions of the scenario. In this article we’re focusing on inventory cost reduction.
Ordering Policy Components
Each inventory order policy has a unique model. Five key features characterize common policies. For instance:
- Review of inventory levels – Continuous or at fixed intervals?
- Backorders – Are backorders allowed, or are orders lost?
- Order Quantity – Fixed or variable?
- Demand – Constant, seasonal, events, stochastic?
- Lead Time – None, fixed, seasonal, or stochastic?
Inventory Cost Reduction by Varying Holding Costs
In the visualization above, we have selected a scenario with a low holding cost of $0.12 per dollar per year, an ordering cost of $200, and a single organic apples product. For this result, we can see that the slope of the holding cost curve, driving the optimal order quantity out to a size of about 1,530 cases, but an MEOQ value of 4,990 cases per order is selected to cover lead time demand.
So, inventory cost reduction looks like this: with an annual forecast demand of 86,731 cases, we’ll be ordering about 17 times, for an ordering cost of about 17 * $200 = $3,400. In short, this solution saves us 27.95% over the month-ahead order policy that is common with MRP systems.
Inventory Cost Reduction by Varying to Cost of Capital
As an experiment, we vary the cost of capital to see how it affects our economic order quantity and savings. Take a look at the first screenshot below. We see a 2.5% cost of capital and an EOQ and MEOQ of 593 cases. This order size compares to an average month-ahead order size of 615 cases, so there are no actual savings.
Now we increase the cost of capital to 4.5%, as shown in the following figure. The increase reduces our EOQ value down to 556 cases, still producing minuscule savings over month ahead ordering. With a product that has forecast demand of perhaps 70K to 700K cases per year we’d see a lot greater savings and significantly reduce cost of inventory.
So, to conclude, we’ve shown that you can, with the classic application of operations research tools in Glimpse.SX modeling tool, gain substantial inventory cost reductions, as much as 30% or more, depending on your scenario specifics.
Interested in a deeper dive? We cover cost comparison, stocking and reordering policies and optimal inventory policy selection.