Surprisingly, although several retailers have automated their ordering process in the last few years, there is almost no academic source examining this topic down to the store level.
Furthermore, while the study of the extent and root-causes of retail out-of-stocks has received substantial interest over the course of the last years, the question as to what extent existing and new practices remedy Out of Stocks, remains largely unanswered.
Quantitative analysis clearly shows that even simple automated replenishment systems (ARS) are able to dramatically reduce the average shelf out-of-stock rate and at the same time, lower overall inventory levels. In addition, a major advantage of ARS over manual ones, is that they show constant results independent of product characteristics. Yet the analysis also shows that poorly parameterized ARS fail to deliver the desired results. The determination of ARS benefits and necessary requirements help to make a cost-benefit analysis.
The major part of retailer costs are personnel costs, and in particular it is the operations in the store that require intensive staff dedication. The major market developments that made retail challenging started in the 1990s and are still prevalent today, namely high cost pressure, shorter innovation cycles, increasing consumer expectations and globalization.
A high availability rate of products on the shelves is of utmost importance for retailers. All the efforts made to improve the supply chain are futile if, in the end, the consumer is unable to buy the product because it is not available on the shelf.
The reaction of customers regarding Out of Stocks differs a great deal. If the customer buys a different brand, we are none the wiser. If he or she does not buy anything at all, then we will see it in our bottom lines. If the customer buys the product in a competitor’s store, that is a catastrophe! 70% of customers change to the competition for good, if they experience repeated Out of Stocks; and that is a ‘complete’ catastrophe.
Profit per square feet follows turnover per square feet
The majority of overstock problems are related to store ordering procedures and store shelving procedures, with ordering being the major factor in store headquarters’ issues, and store forecasting procedures following close behind. Approximately 75% of out of stock problems are related to store practices and the restocking process. Less than 10% are related to items in the store but not put on the shelves.
Overall, out of stocks loose about 4% turnover. As half of all Out of Stocks arise from incorrect ordering and forecasting processes, it is advisable to have a closer look at a store’s replenishment processes and systems.
Some decades ago, there was no alternative to manual store replenishment systems. A planner, for example the store manager, was responsible for deciding the two main parameters of replenishment systems, namely the amount to be ordered, and when to place the order. In order to do this, the planner had to manually check the quantity in stock.
The normal replenishment process has been, until now, store personnel deciding what quantity to order by looking on the shelf. Now, retailers want to let the systems make this decision. Semi-automated systems merely support the planner in his decision; for example, by showing him electronically the inventory and order restrictions. Advanced automated store replenishment systems are IT-based software systems that automatically decide when to order and in what quantity.
The simplest systems just place an order as soon as an article is sold or when a certain minimum stock level is reached. No forecasts are made, and the quantity to be ordered is calculated with a simple algorithm (e.g. fill up to a certain level). Furthermore, such forecasts do not rely only on historical sales. Their sophisticated causal models also consider price, promotion, seasons, holidays and other events when predicting demand.
The storing of huge quantities of point of sale and inventory data became feasible with new data warehouses and storage mediums. Furthermore, not only was it internal data that was more easily accessible; thanks to larger communication bandwidths, it has become possible to access large quantities of external data as well.
During the course of our study of all stores scanning, in 78% profits are up (average increases in profits per store of 2.4%). Of all stores NOT scanning, 85.7% profits were up (averaging profits per store of 1.8%)
I think that’s pretty impressive myself.