Inventory… how much is too much?

Have you ever talked to a materials manager who was content with their inventory holdings? I didn’t think so. Have you ever met one who can tell you why they’re not content? They can probably tell you that they have too much inventory (and still the occasional stock-out) but when you ask them how much is too much, they’re sometimes getting a little fuzzy. 

In my personal opinion there are too many (confusing) inventory KPIs around. For years I’ve been searching for the one KPI which tells the whole story, but that is an ambitious effort which didn’t get me anywhere. After many frustrating attempts to make sense out of turn-overs, averages, days of supply, dead stock and many other measures, I decided to re-think my approach and challenged my mental model about inventory analysis.

Traditionally, inventory is measured by a set of KPIs, which do not all make sense by themselves. Take ‘dead stock’ for example. If you find out that your purchased parts have a dead stock value of US$ 300,000.- over the past 6 months, will you be happy? What if your inventory management system provides a report that says you have an average of 53 days of supply on the 567 finished products you hold in your warehouse? 75% of those are high runners which you should produce to a forecast and the rest is made to a customer order. So what of those 53 days of coverage is right and which part is not? Then you see that you held an average of US$ 1,350,000 on your raw materials. Was that enough to buffer variability? Did the stock-out occur on materials that had low inventory throughout, or did you experience bi-modal inventory swings and had materials that went from too little to too much and around again?

It’s very difficult to derive improvement actions from those KPIs. Some managers then develop strategies out of frustration. In one case, a planner decided that the only way to avoid stock-outs is with excess inventory at all times. What in the beginning was opposed with little skepticism, established itself in the long run. The whole company, including its executives, settled on the mantra that too much inventory is a good thing – it avoids stock-outs – and if anything goes wrong it’s the fault of the supplier. They went as far as – unconsciously – propping up inventory by using static safety stocks. You know, when you use a static safety stock, that quantity is taken out of the plan and becomes invisible. It raises the zero line to the level of the safety stock. It just worked for what their thinking was: more inventory is better.

So here you are,  ending up with inventory you don’t need and still experiencing stock-outs. And the inventory KPIs dead stock, average inventory, turns, days of supply etc. do nothing to help you to know what to do.

How about looking at some action KPIs? But before we do that, we’ll have to lay down some rules. In my mind, the biggest fallacy in planning, is that rules aren’t defined. I have been writing about chance-based and rule-based planning systems before. It still is a mystery to me why people accept that there are rules in traffic, in sports and in every aspect of social life, but when it comes to planning, we slack off and excel in rogue behavior being defined anew every time we have a suboptimal situation.

The rules I am talking about here must certainly not be the ones you are going by. But they are rules that work for me and I use them every time until I find a better way to achieve better results. But until then, those rules stand for me. Specifically, some of these are:

- Out of the sellable products portfolio, those products that exhibit regular sales pattern from period to period and are high movers are made to stock by a forecast and buffer variability to a certain extend with a dynamic safety stock

- Out of the sellable products portfolio, those products that exhibit irregular, unpredictable sales are only made when we have a customer order and we do not hold inventory of those.

- However, some of the ladder is worthwhile stocking a bit (especially when they have a long replenishment lead time), whilst others (expensive with short lead times) we don’t stock at all.

After these rules are defined, I can now measure whether I carry the right amount of inventory. I do so looking at the products from the first rule separately from the products adhering to the second rule. Measuring becomes relatively easy. For product that we make to order, we should not carry inventory. If there is, it’s either because I need to produce to a lot size and have a little left over, or that I falsely maintained a safety stock or have a consumption-based planning method on some products – in other words… I broke the rule and must fix it.

But according to rule three there are some parts that I do hold in stock because they are hard to replenish and are not very expensive. Those should be measured according to the Days of Supply. If I have adequate inventory to just not run out during the lead time, I’m good. I don’t need more than that, so use a matrix that shows which ones of those items have more or less days of supply than their specific replenishment lead time.

For the high movers I compare the total current or average inventory that I am holding to the total average, periodic (weekly or monthly) consumption (Sales). Is my inventory holding more than 3 times as high as its consumption, I know I have more inventory than I need and must fix the policy by which I replenish. To compare the actual inventory you are holding (of your high runners) to an average periodic consumption is a very precise indication of whether you carry too much or too little. Think about it… you don’t want to hold less than what you’re selling, but you also don’t need much more than what you’re selling. The excess over your actual sales is the buffer. So the buffer size is driven by the amount of variability you think you will encounter.

This type of measuring and monitoring makes you think about how you should plan for the future. Most planners are driven by forecast accuracy and therefore hellbent on bringing exactly in what they think how much they need (forecast) and exactly when they need it (forecast). I think that is a fruitless and frustrating behavior. If you measure the buffer you are holding you are driven to think how much buffer you need in the future to absorb variability. So how do you come up with the right buffer size? Instead of predicting the forecast to the tee, you anticipate variability and try to predict how much will come at you. Then you have an inventory target that might be a little high because you are careful not to miss any sales.

But a buffer that ends up being 15% higher than what you predicted as variable is a world better than ending up with 10 times as much inventory as you need. 

The point I’m trying to make here, is that before we can make an educated guess about the right amount of inventory that is held in the warehouse, we must lay down some rules. Simply running some inventory KPIs through your portfolios doesn’t do the trick. I am sure this isn’t a new revelation for most of you, but I do see a lot of inventory systems and I haven’t come across a fully rule-based system yet. Why is that? I believe that it has to do with long held beliefs, mental models, stubbornly holding on to what was always valid. And if the whole company beliefs that the only way to avoid stock-outs is to accumulate huge amounts of inventory, why would anybody dare to plan otherwise.

Time to change the way we think about inventory management.