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Why the Average Based Calculations Fail?

Author Zilvinas Lapacinskas  Zilvinas
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– The average reading time for this post is 3 minutes and 30 seconds –

Imagine a physician in the hospital, who is trying to optimize his working time and therefore visits his patients only when the average body temperature of all patients together he has exceeds 36.6°C (97.9°F). At first glance, it may look like a brilliant idea: if the average temperature is 36.6°C, probably all patients are ok? But if some patients have fever while others are dead… the average temperature could also be 36.6°C!!!

That is a crazy thought, you would say, wouldn’t you? So why are the averages in the inventory management used to analyze historical sales/consumption data in order to forecast future demand? In fact, average based calculations are used even in the most popular inventory management software.

As in our case with the physician, when managing your inventory, it is better to stick to the peaks rather than the averages.

Let’s analyze one example. Assume, that we need to find out the inventory demand for 14 days period, and of course, we would like to achieve near 100% service level without overstocking.

For our example, we will run this task for 3 different items A, B, and C with completely different sales and consumption data patterns. Depending on sales characteristics, there possibly are thousands (if not millions) different sales patterns, but to make it simpler we shall divide them into 3 groups:

  • Smooth Flow – when sales are daily and quantities are similar each day, or changing in proportion with some trend;
  • Flow with Peaks – most of days have sales similar to flow but from time to time some unordinary quantities are sold.
  • Rare Sales – when period between each sale is more than one day (it could be few days, weeks or even months) and length of this period changes in wide range; also the sales quantities are different.

Item A: Smooth Flow
item-a
It is clear, that in case of Smooth Flow the peak calculations are very close to the average calculations. In fact, such items can be managed with average based calculations with high accuracy. The problem is that usually there are only about 5% of items with such pattern of sales.

Item B: Flow with Peaks
item-b
Difference between average and peak calculation tells us that if the order is calculated according to the average, there is a high probability of stock out if peak appears. Stock out leads to lost sales and dissatisfied customers. This fact forces inventory or purchase manager to add some reserve quantities to average calculations (usually it is realized in the algorithm code or setup, by adding some percentage from calculated quantity as a reserve); the higher the peaks, the more reserve is needed to compensate. Because average calculations do not allow recognizing sales pattern type, the same reserve will be added for items with flow or near flow patterns; this creates overstock, decreases inventory turnover ratio, and consumes precious working capital. Even more, quantity of items with various peak patterns may create up to 80% of total assortment.

Item C: Rare Sales
item-c
Planning of rarely sold product quantities is a huge challenge for companies that use average-based algorithms. Besides a large difference between the average and peak calculations, it is also hard to recognize the trend of sales levels.

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In addition, it is necessary to say, that the shorter period, for which inventory demand is calculated, the more different peak and average calculations will be (you can verify this statement yourself on our previously described sample patterns or using some of your assortment data).

Nowadays, distribution companies try to increase inventory turnover ratios while keeping high service level. In most cases, recipe number one is to shorten replenishment cycles. Unfortunately, the more frequent replenishment and the shorter demand calculation period company has, the less accurate average based calculations will be.

Modern inventory calculation methods, such as Dynamic Buffer, allow calculating inventory demand and reacting to trends even with very complex sales patterns, at the same time filtering out unnecessary peaks (sales campaigns, sellouts, tenders and similar). Using software systems that work according Dynamic Buffer such as Soft4Inventory, companies can decrease overstock, increase service level, and achieve high automation level for inventory management tasks.

 

SOFT4, as a provider of software solution for efficient inventory management, is constantly releasing blog articles about how to meet and cope with the trends, tendencies, and challenges, and give suggestions as to how to react to the changing environment in order to stay competitive.

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