What is to blame for having too much of the stuff you don’t need and not enough of the stuff you do need?  Demand and supply variability are often blamed.  These problems are significant and seems impossible to overcome leaving many organizations to simply accept misallocated stock as a cost of doing business.  However, the real problem it isn’t simply late supplier deliveries and unpredictable demand.  These are supply chain planning “facts of life” and it’s how your company addresses them that counts.  Watch Greg Hartunian’s vlog to hear his thoughts and what you can do about it.

Smart Inventory Planning and Optimization automatically calculates the stocking policy that yields the best return for your business considering holding costs, ordering costs, and stock outs.  To see it in action, register below to watch a 12 minute demonstration.

Many companies adopt a philosophy of “it’s better to have it and not need it, then to need it and not have it.” Planning initiatives such as implementing inventory optimization software in order to optimize reorder points, safety stocks, and order quantities are often seen as narrowly focused on reducing inventory and not pursued. Stock-out costs may very well be extremely high. However, resources are finite. The opportunity cost of keeping too much of one product means less space, cash, and resources for another product. Overstocking on one item reduces the ability to provide adequate levels of service on other items. Justifying overstocks by stating it is good for the customer is a poor excuse at best that hurts the customer and ignores what inventory optimization is really about – properly reallocating inventory investments.

Diminishing Returns and Inventory

Each additional unit of inventory that you carry buys proportionally less service. Inventory optimization software can help you understand the exact stock out risk given a certain level of stock. For example, say your stock-out risk with 20 units of inventory is 10%. If you add another 10 units and carry 30 units, the stock out risk might get cut in half to 5%. If you then add an additional 10 for a total of 40 units, the stock-out risk may only drop to 4%. At some point, the additional inventory just isn’t worth the extra service it buys. This is especially so if the cash used to buy that extra 10 units to get a small service level bump on one item could have been spent on another equally important item for a larger increase in service.

Carrying more than you need means you aren’t efficiently managing assets, which costs money, which means you can’t offer the best price to your customer, which hurts your ability to beat the competition. It also means there is less money for investment in other items. This results in the common adage “We have too much of the stuff we don’t need and not enough of the stuff we do.”

Inventory Optimization is about reallocation

The example presented in the blog’s main image highlights the benefits of reallocating inventory.  We used probability forecasting to estimate the service levels and inventory costs that would result from the current stocking policy. We then conducted a “what-if” scenario by modifying the policy. In the benchmark shown in the first column, the current stock levels were forecasted to yield a 84.78% service level and required $1.67 Million in inventory. Nearly 12% of the items numbers had reached their point of diminishing return and were forecasted to achieve a 100% service level. By imposing a maximum service level of 99% and a minimum service level of 80%, we reallocated inventory.  As a result, the inventory investment dropped to $1.5 Million and service level increased by 3%!

The exact point of diminishing returns will differ depending on the item, the customers involved, and the company making the stocking decision. It is important to understand the inherent levels of stock-out risk that result from current inventory policies and how changes to current policies will impact risk and costs. This enables the reshaping of inventory so that service can be maximized at the minimum possible cost.

Download Smart Inventory Optimization product sheet here: https://smartcorp.com/inventory-optimization/

Managing inventory requires executives to balance competing goals: high product availability versus low investment in inventory. Executives strike this balance by stating availability targets and budget constraints. Then supply chain professionals translate these “commander’s intentions” into detailed specifications about reorder points and order quantities.

A High-Stakes Race Between Supply and Demand

Let’s focus on reorder points (also known as mins). They work as follows. As on-hand inventory decreases in response to demand, it eventually drops down to or below a trigger value, the reorder point or min. At that point, it’s like a gun goes off to start a race between supply and demand. A replenishment order is sent to restock the item, but there is a replenishment lead time, so the restocking is not instantaneous. While your system waits for resupply, demand continues to whittle away at the stock on hand. It is bad news if demand wins the race, because then you won’t be in position to provide what somebody is demanding. Then they either get it from a competitor or get back-ordered and unhappy: either way, stocking out is a bad outcome for you and your customer.

The risk of stocking is controlled by your staff’s choice of reorder points. If they are set too high, stock-outs are rare but inventory is bloated. Set them too low and stock-outs abound. So how should reorder points be set?

Avoiding Foolish Follow-Through

Several factors govern stock-out risk. Each item in your inventory has its own demand history and lead time. Together with your chosen availability targets, these factors determine the best choice of reorder point. But the relationships are statistical and require good analysis to work out. Inventory Optimization Software can compute the proper reorder point for each of tens of thousands of items. But instead of relying on proper analysis, many companies fall back on simple rules of thumb or just “doing what we always do”.

In place of using the right math, companies often rely on rules of thumb that serve them poorly. Here are some examples in order of most common to least common.

1) Multiples of Average Demand

Setting reorder points at some (arbitrary) multiple of average demand starts to rely on actual facts. But it ignores the key demand attribute that drives stock-out risk: demand variability. Two items with the same average demand but very different levels of variability will require very different reorder points to insure the same low risk of stock-out. (See Figure 1)

2) Gut feel

Some companies have self-styled supply chain gurus. Even if they actually are Jedi masters, it’s impossible to keep up with tens of thousands of items whose reorder points should be reviewed frequently.  And if the logic that drives decision making is buried in a hard to use spreadsheet that only they know how to use, the company risks not being able to execute the inventory plan without that one individual –a risky proposition.

3) Average Demand + some multiple of Demand Variability

This approach is taught in many “Inventory 101” courses. But it implicitly assumes some facts about demand that are very often not true: that demand has a Normal (“bell-shaped”) distribution and that demand in one period does not relate to demand in the previous time period(s).  Assumptions of independence and reliance on normal distribution models just don’t cut it.

4) Nursery rhymes

Not at all the norm, hence being last on the list, but we heard of one company that used one simple rule for all items: “If it’s down to four, order more”. It’s crazy to believe that one rule applies to all items at all times. But at least it rhymes.

Your people can do better than to rely on any of these approaches. Do you know whether your company is using any one of them?

Getting It Right

The right way to set reorder points uses the tools of probability theory. The details depend on whether you are selling finished goods or spare parts. Spare parts are usually more difficult to manage because they have quirky demand patterns: high intermittency (lots of zero demands), high skewness (lots of small demands but with some whoppers too), and auto-correlation (“feast or famine” behavior). Modern Reorder Point Software takes these quirks into account to set reorder points that insure the desired level of item availability. Importantly, they also let your people see explicit trade-off curves, so they can strike the balance you want — at the item by location level – between stock-out risk and inventory investment.

Inventory is a major item on the balance sheet and needs high-level attention. At many manufacturers, service parts can represent up to half of revenue. Modern software lets the C-Suite move beyond, incomplete math and other inadequate approaches to managing inventory.

Figure 1:  Two equally important items with the same average demand get assigned the same stocking policy that determines the Min (reorder point) as 2 x average lead time demand.  Despite the “same” stocking policy service performance varies significantly with the stable Item A experiencing overstocks and the volatile Item B experiencing stock outs.