Carrying Cost: Clean up your Inventory Optimization Fuzzy Math

Inventory optimization is based upon two major components: acquisition cost and carrying cost. If either of these factors is inaccurate, then you could be leaving money on the table. An accurate carrying cost calculation can be the difference between a highly profitable inventory optimization program and one that forces you to close your doors.

Carrying Cost Mistakes: Inventory Optimization Killers

So you’ve implemented a highly successful PO tracking program, and you know your acquisition cost for each product and location down to the cent (kudos if you’ve read our latest blog on acquisition cost). Now what?

While many top retailers (or grocers, or wholesalers) may include many of the following factors in their carrying cost calculations, we’ve found that most businesses overly simplify their projections, losing valuable margin in the process. We’ve also found out that calculations often leave out real world restrictions including:

Warehouse Space: While an Order Cycle of 90 days might be the most optimal from a math perspective, do you actually have the warehouse conduits to receive 18 tractor trailers on the same day?
Warehouse Fullness: How full is your warehouse? If it is more than 90% full, then additional inventory may inhibit day-to-day warehouse function. Beware.
DC to Store Shipment Days: An order cycle of 10 days is of no use if you can only make shipments out to your stores every 7 days. Be aware of how often you can ship to your stores when calculating an optimal carrying cost.
DC Processing Time: If shipping from DC to store or DC to sub-DC, then you must calculate a DC Processing Time as well. DC Processing Days is the average number of days between receipt date and ship out date (or date available for pick). Accordingly, DC Processing Days is a critical number across your business. Does your system even track DC processing time?

Have you avoided these pitfalls? Read on and “Tighten the Links in your Chain™.”

Carrying Cost Tips: Profit Makers

Inventory optimization is just a math problem (albeit a complex one). Our advice is simple: abstract out and simplify your cost calculations to come up with a reasonable approximation of your true carrying cost.

There are a series of passive costs that increase with time. Simply, the longer product sits in a warehouse, then the more that these quantifiable costs add up. Those include:

Depreciation
Building Storage
Taxes
Employee Cost
Insurance
Risk of Shrink (Theft)
Risk of Damage in Warehouse (physical, water, fire, etc.)

NOTE: Do you sell perishable goods? If so, calculating an accurate demand forecast will be critical to your ability to avoid product spoilage.

We’ve saved the most important tip for last.

Cost of Money: The Carrying Cost Driving Factor

Cost of money is essentially an opportunity cost, and it’s what you use to gauge the real-world impact of money tied up in stale inventory. Have you leveraged your assets to acquire inventory? Inventory that doesn’t move, or inventory that moves slowly, can literally put you out of business. Even if you’ve paid for your inventory in cash, dollars spent on product X are dollars that cannot be spent on product Y.

Calculating Cost of Money for Inventory Optimization: It’s Complicated

Calculating an accurate cost of money percentage is complicated: we provide a custom cost of money percentage for use in inventory optimization for each of our customers. Different industries (indeed, even different segments or departments of the same industry) often require different percentages. We encourage you to discuss and review your inventory carrying costs semi-annually, at minimum, to identify your opportunities for additional profit.

Closing

We strive to provide our customers with every possible advantage in this low-margin market. We believe that effective inventory optimization can add full percentage points to your bottom line. Want to learn more? Request a demo and we’ll show you how it works at Data Profits.

Author
Recent Posts

Stuart D

Stuart Dunkin is a strategic visionary who leads by calling on his broad range of experience as a former retail executive as well as a consultant for top retailers and for the E3/JDA software company. He founded Data Profits to solve a real-world problem plaguing retailers today – the disjointed relationship between supply chain data, people and business goals. He has experienced first-hand the challenges and frustrations that occur when decisions are made based on wrong data, which often leads to the wrong actions being taken with crippling results like overstock, out-of-stock, and increased working capital.
Stuart graduated from Auburn University, with bachelor of science in business administration.