Inventory optimization is a phrase much used and abused. But ask a supply chain professional, let alone another senior executive, how close to optimal their inventories are and you will either get bluster (“already optimized, thanks”) or a blank stare. The honest minority will admit that they don’t objectively know what good looks like.

What is going on? And does it matter?

At one level, it is quite easy to know what “good” looks like: don’t have too little and don’t have too much. If shortages and excess and obsolete (E&O) inventory aren’t causing palpitations in your executive board, then life is good. Occasionally there might be a push on reducing inventories or inventory waste, but this is a cyclical concern and business as usual can soon be resumed afterwards. 

“All is well,” especially when the cost of capital is low, may be good enough for many organizations much of the time. But this isn’t inventory optimization.

Defining the Term

Inventory optimization works out the optimal level of inventory to hold, given all available information. It offsets the cost of running out against that of having too much, and finds the sweet spot between them. It doesn’t mean you never run out or never have waste; it just means you’ve got the balance right.

Supply chain professionals understand this as a theoretical concept, but what use is such a precise optimization calculation in real life? Indeed, is it even possible?

Try a thought experiment. What if you knew you could maintain or improve service levels and reduce your inventory levels by 20%-50%? Would you still feel as good about your current levels?

Presumably not. But if you’re a positive thinker, you should quickly realize that even having this insight is tremendously valuable, as long as that knowledge came with enough information to close the gap. You’ll be short of some items, about right for others and overstocked for some.

The trick is to define with sufficient precision what “about right” looks like. Since stock is dynamic — you will have more or less every day — this means you need to understand what the bounds of an optimized stock cycle look like. In particular, you need to know where the top of a cycle is — the maximum you should ever have on hand. Critically, this requires going item by item. Every single item you stock will have an optimal level based on its own particular qualities.

Once you know what the top of a cycle is for every item, you can calculate the delta between that and the stock you actually have. This then gives you a pretty good, if conservative (since you’re comparing with the top of a cycle, not the average) approximation of how close to optimal you are.

Why Isn’t It Done More?

This is all fairly standard theory, but very little applied. There are, of course, several challenges to the exercise, from very practical ones (how to access and manipulate the data required to do this properly) to more technical ones (how precisely to factor in service level, demand, variability and the like).

One reason why this approach isn’t attempted more often is because of the erroneous tendency to think of inventory as primarily, or even purely, a forecast accuracy topic: “If only we knew for sure what demand was going to be then we would have optimal inventories. If we haven’t got optimal inventories now, it’s because our forecast was wrong.” But this way of thinking blinds organizations to their potential for improvement. Most demand is uncertain, and optimized inventory will be calibrated to deal with that uncertainty. The real question is, do you truly understand how uncertain your demand is, and is your inventory management approach optimized for it?

Finding the Right Approach

In many other supply chain disciplines, it’s quite standard to examine the past to learn lessons for the future. If you wanted to improve on-time in-full (OTIF) deliveries to your customers, for example, you would start by looking at current and past performance, then use that analysis to make improvements. You wouldn’t think of saying that “past delivery performance is no indication of future delivery performance.”

This is why inventory optimization approaches that only ever look forward, as part of the planning cycle, are missing the full picture. You can’t use them to drive root cause analysis, and you can’t use them to calculate how close to optimal you are. Each planning cycle is trying to bridge a gap between current inventory and forecast requirements, and is then overwritten in the next cycle.

For organizations with high maturity in inventory optimization, calculating what good looks like is particularly useful as they get closer to optimal. In the absence of an objective measure of how close you are, companies have recourse to unanchored key performance indicators like inventory turns or cover. These are useful for continuous improvement, but they don’t start from a basis of what’s objectively required. 

Instead of a top-down target of 10% improvement each year, a grounded bottom-up calculation will show a much broader range of potential. This is particularly useful for determining where the biggest opportunities are within your network. The site with the highest turns isn’t necessarily the closest to optimal.

Working out how close to optimal your inventories are will not in itself optimize them. But in the absence of that visibility, you’re flying blind. Being able to measure and track the delta between your inventory levels and optimal levels gives you a clear sense of the potential, a basis for driving improvement, and a useful tool for facing the uncertainty ahead.

Matthew Bardell is managing director at nVentic.