Automatic stock replenishment

Store stock replenishment is important and how the replenishment is done impacts the sales directly.

There were a lot of researches carried out on impact of not-on-shelf, out-of-stock(OOS) products on the retail companies' sales on different types of products. The researches were carried out on replenishment of products from DC, from back-rooms of the stores, replenishment modules and most important how customers react when they do not find the product they need.

The reaction of customers (on OOS) differs a great deal. If the customer buys a different brand, we are happy. If she or he does not buy anything at all, then we are not content. And if the customer buys the product on the competitor's store, that is catastrophe! (The Impact of Automatic Store Replenishment on Retail, Alfred Angerer, page 4)

It was calculated for German grocery retail store that around 30% of customers purchase items, they did not find, from another competitor's stores. The most important the frequency of OOS products. In fashion retail, the right-time, the right-quantity is more important. The merchandise sales depend on weather, people mood, trend, day of the week, time of the day, etc, too many variables in the formula. Some general replenishment rules, however, can be applied for never-out-of-stock(NOS) products, like socks, men underwear etc.

The formula to calculate the store stock level is:

E=D+CR-RTM-CW-S&D-S-RTW-TRSF

Where:

D: Delivery to the stores

E: current stock level at the store

CR: Customer returns

RTM:Return to manufacturer

CW: Waste

S&D: Soiled and damaged products

S: Sales

RTW: Returns to Warehouse

TRSF: Transfers between stores

To make the order replenishment: D=CS-E

where CS is planned quantity for the stock level in the store and D the quantity to be delivered to the store. This is the most basic formula. In most retail companies: variables derived from store RPM, store last season sales, promotional activity, distance from the DC(replenishment rate per specific store) is added to the formula making the OOS frequency most minimal.

This formula can be applied on SKU level or can be used as summary. The most important is how this information is entered. Suppose warehouse has created an order for delivery to stores on Thursday according to Monday-Tuesday stock movements. The store stock movements for Wednesday-Thursday are not updated on the company system yet. So the order created does not consist of the merchandise sold on Wednesday-Thursday, two day delay!

CS on a unit base for models consisting of size can be calculated automatically by analyzing the last-season-sales or can be entered by merchandisers manually for a product group. For example: A model consists of 5 sizes: S, M, L, XL, XXL. The merchandiser can let the system calculate the size distribution by the last season sales of the product group the model belongs to or can enter manually entering the percentage per model. For example: S-15%, M-25, L-25%, XL-20%, XXL-15%. After the target is set the only thing is to set how many items can be displayed in the sales floor of the store. The system will distribute the sizes according to the percentages set or calculated by the system. The formula can be extended by entering more variables. Suppose a store is located 1500km away from DC and the RPM rate is considerably high. The merchandiser enters only how many items of a particular model can be displayed at a time so the above formula will not work for a store located 1500km away because there will be only few deliveries in the week. We need to add also a store back-room stock so store personnel can replenish sales from the store's back-room. We need to add an RPM variable to the formula also. Suppose there is a store 1500km away but its RPM is three times lower than the average stores RPM. Do we need a back-room stock for that store? But what if the RPM of the store is 4 times higher than the stores's average RPM and it is 1500km away from DC? The stock collected at WH should be divided into two types of containers: ones for immediate display in sales floor, the other ones are for sales replenishment from the back-room. The containers should have unique numbers on them and every hour stores personnel should get a picking list automatically generated from back-office products to replenish the sales. But if the store is near to DC(50-75km range) no need to add distance and RPM variables to the formula.

The above table is the replenishment ratio per a year. Unlike fmcg goods, it is not important to have 100% replenishment on sales months. The average replenishment ratio is 100% per a year.

In the above graphic, the stock in the store is divided by season. In the beginning of the summer season the stock for summer season merchandise is maximum but as we get closer to autumn the stock for summer merchandise decreases but for autumn merchandise increases. If we dig deeper into the sales/replenishment ratio the replenishment ratio decreases for summer merchandise as we got closer to autumn months. The catastrophic situation if at the beginning of summer season the replenishment ratio for summer season merchandise is low. For NOS products no seasonal approach is not needed.