Proper inventory management is the key to success for any enterprise. Satisfying the preferences of the buyer, minimizing purchasing costs, organizing the storage and transportation of goods, and maintaining optimal stock levels are the main challenges you will face in maximizing inventory management, especially for low-demand products (slow-movers).

There are different approaches to calculating proper stock levels at different stores. This is most apparent at stores that receive direct deliveries or use a **Distribution center (DC)**. Most companies improve their accounting systems by choosing the optimal calculation method to automate orders. However, some still do the process manually.

Choosing the correct method to manage a company's stock level can be challenging, especially when moving from manual control to automation. Most often, the company chooses the **same method for all its products**. Sometimes this works, other times it doesn’t.

## Theory of constraints

One of the most popular methods for managing inventory levels at stores is managing the stock buffer, which is maintained according to the theory of constraints. A buffer is an indicative stock level, which is divided into three zones: red (low stock), yellow (optimal), and green (surplus). A change of over 33% (up or down) results in the buffer shifting zones.

Using the theory of constraints to manage the buffer works best when high-frequency sales do not characterize a product. A good example is the food segment where goods occupy the lion's share with relatively even demand.

DCs play an important role, especially if the supplier has a long replenishment cycle. As a rule, the DC delivers goods to its stores at least once a week, even if they are low-demand products (slow-movers).

Companies in non-food segments face more acute issues. Their replenishment cycle ranges from four weeks to up to six months. With high-variability goods, calculating the demand for DC orders can be impossible without a sales forecast.

## Stock level factors

Calculating your stock level depends on demand and the frequency of deliveries. That is why any method you use comes down to order frequency and analytics. Demand also needs to be analyzed at each storage point.

**The following metrics are most important:**

- Order frequency.
- Fixed schedule orders.
- Ordering at minimum goods levels.

Imagine you have a fixed delivery schedule and you are able to calculate how you need to maintain stock (from delivery to delivery). The more often you receive goods and the smaller the minimum lots, the more accurate the forecast of your required level of stock will be.

Under these conditions, it is easier to **adjust to the constant fluctuations in demand**: do not pile up goods in your warehouse for a long period, but rather buy low demand products in smaller batches and more often, to be able to restore the goods in case of shortage risk quickly.

After all, a lot can be sold for much longer and the return of the goods is not always possible. Moreover, fixed schedules serve as the basis for automating the ordering process and the reporting needed to evaluate a supplier's reliability.

For the second method to work correctly, you must regularly review the minimum level of goods that may be incorrectly determined or become irrelevant due to changes in demand. Therefore there is a risk of lost sales or, conversely, excess stock.

➥ In terms of demand

When determining the best period to analyze demand, most companies use a fixed period to calculate the average sale, **for example**, the last 90 days. Since the company's product range contains products with different sales stability, it is important to structure it by the following criteria:

➥ Sales frequency variability (from one replenishment cycle to another). Variability of sales.

## What is product variability?

Both criteria are determined on the basis of comparative data accrued over several cycles of replenishment. The total variability of the product is a combination of two of these types of variability. Use XYZ analysis, based on the calculation of the coefficient of sales variation, to find your answer. The coefficient of variation is the ratio of the standard deviation of a quantity to its expected (average) value where:

**σ**– standard deviation of sales;**х***– average (arithmetic mean) sale;**xi**– Sales in the i period;**n**– the number of considered sale periods;

The greater the ratio, the less is the uniform demand. When using this method, you must take into account the availability of goods as long breakdowns in the supply chain can significantly affect the result. For a more detailed analysis of the goods falling in group Z, you can make an additional calculation excluding periods with no sales with a balance. This will allow you to select products that **can be sold at almost the same volume**, but with a large variation in the frequency of sales.

Let’s check the relevance of this method of calculating the average sale for a fixed period, for goods of different types, according to the above-mentioned criteria. Input data: 30 days replenishment cycle, an account of the average sale of storages for 90 days.

➥ Slow-moving product frequencies and the number of sales are non-variable.

Slow mover – a product with low demand. The particularity of slow moving is difficult to average sales and demand forecasting calculation - one of the reasons is demand variability.

**For example**, for the first 30 days, a product was sold on average 2 units per day, the following days - 2.17 units, and in the last replenishment cycle, there was an increase in sales of up to 2.5 units per day.

This trend will be almost leveled when calculating the average sale for 90 days. So it is more relevant to calculate the stock based on demand for the last replenishment cycle or use the calculation of the average weighted sale, assigning coefficients (weight) to each period to take into account the trend in demand.

To calculate the average or average weighted sale is essential to consider the speed of your sales. Therefore, days with a balance at the close of business (if sold to zero) are taken into account. Otherwise, the calculation is likely to be understated with a long absence of slow movers goods.

Use the shelf space or warehouse place for a keeping reserve to avoid the lost of sales.

➥ Products with variable sales frequency.

**Example:**

The product is usually sold at a rate of 4 pieces per cycle every 1-3 cycles. Accordingly, it may not be included in the calculation for the last 30 days, but this does not mean that there is no demand for the product and that it does not need to be ordered.

However, if this product is ordered every 90 days your average sale rate could be underestimated.

## A way out

In this case, you should divide your goods into mandatory (primary/high margin) and optional (secondary/low margin) categories. You should use a mix of ABCD and VEN analysis.

**For example**, if a product is significant in one’s product range it makes sense to maintain a large supply even if it remains on the shelf for some time. You can calculate percentiles to exclude explicit sales emissions, but not the usual average of sales, or set up a search for maximum sales during the period. It is this value that should be used as a necessary stock level. If the product is not important, then it is advisable to calculate the average (arithmetic mean) sales rate. In both cases, a calculation for a fixed number of replenishment cycles is appropriate in order to capture several periods.

➥ Products with high sales variability.

If the product is sold every replenishment cycle but with a large variation in the number of sales, then the calculation for 90 days (3 cycles of replenishment) is suitable. Please note that if the replenishment cycle is not too long, and coincides with the number of days required to calculate the average sale this method might not be suitable.

**Example:**

A company uses a 30-day calculation period and its replenishment cycle is 30 days. Data is used only for the last replenishment period, and with a large variation in sales from cycle to cycle, this can lead to surplus or lost sales. To avoid this situation, you can determine the calculation period using a fixed number of replenishment cycles.

➥ Products with high-frequency sales variability.

This type of product is the most difficult to calculate. Knowing how often a product is out of stock is very important. For products that do not have days with the balance and/or sale in the calculation, the minimum you can do is **set the automatic order for one unit** (items, packaging) or use the previous value. However, it is better to set up a notification about positions for less than n% of days the balance/sales fell into account.

We found that it is better to use fixed delivery schedules to determine the frequency of orders. So what should you do with demand analysis to determine the average low-demand product (slow-movers) sales?

Based on the above examples, you should not use the same frequency (periods for accounting) for all types of goods. But there is a solution:

1) Use a fixed number of replenishment cycles to calculate the average sale of goods of uneven demand. In this case, if the goods have very short/long cycles of replenishment, you must specify the minimum and the maximum number of days.

** For example**, if the replenishment cycle is 90 days, and the calculation is made in 3 cycles, then the demand analysis period will be 270 days. In this case, you may limit the calculated values to six months.

2) Use a fixed number of replenishment cycles to define the weighted average sales or the last replenishment cycle to calculate the arithmetic average for products according to uniform demand.

We covered the methods to deal with products with different frequencies and a number of sales variabilities in case standard practices do not work for you. Low-demand products (slow-movers) need not be a challenge for your business; indeed, any product or line of products can be effectively managed with a high-quality system.

**Maryna Makarchuk**Head of Implementation