The power of cloud-based RFID solutions in retail

Refill methods

Tackling the challenge of merchandise refill in retail stores

February 15 2021

It is a common understanding that product availability is key in retail stores. To ensure perfect availability, a significant number of fashion and apparel retailers have already deployed RFID technology or are considering doing so. However, most of these solutions only solve a part of the product availability challenge – the DC-to-store replenishment. That means that the product is now guaranteed to be in the right store, but it is not necessarily on the sales floor and easily accessible for customers to buy. Such an item is commonly called a NOSBOS item – an item that is “not-on-shelf-but-on-stock”.

In this long read, we will shortly describe the existing methods to refill missing items (and reduce NOSBOS) and then discuss three more sophisticated methods to guarantee that stock is not sitting in the back of the house, but is actually out on the sales floor.

Traditional refill methods

1. Manual/visual checks

With the first method, a store employee goes around the store with a piece of paper and a pen, writing down what sizes of which products are missing on the sales floor, and then gets these from the stock room.

2. POS transactions

The second method is based on sales transactions. In regular intervals, a list with all POS transactions is printed and the sold items can be refilled from the stockroom. However, both methods are very time-consuming and flawed. The first method requires that store employees are very accurate in finding what sizes are not or no longer on the sales floor while this is quite a daunting task for even the most experienced and disciplined. Moreover, when the list is finally complete, there is no guarantee that the missing size is actually available in the stock room, to begin with. The item might be out-of-stock, which makes the entire refill process very inefficient and most likely not very satisfying for the store staff.

The second method based on POS transactions does not take into account any refills that have happened throughout the day (e.g. when an item was already refilled from the stock room manually) and items that leave the store in any other way than via the POS (e.g. stolen items) will never get refilled. Just like the first method, this method also does not take into consideration items that are out-of-stock in the stock room, leading to inefficiencies, because store associates search for missing items in the stock room. Because of these flaws and new technological developments, these traditional methods are increasingly being substituted by more sophisticated refill methods. But what are the pros and cons of these new methods?

New refill methods

1. Complete list of differences: sales floor vs. stock room

Using RFID, it is possible to differentiate stock located on the sales floor from stock located in the stock room. Based on the RFID count per sub-location, retailers can easily make a complete list and start refilling. Unfortunately, the reality is a lot more complicated. Why is that? Basically, because there can be a multifold of reasons why an item is not or should not be displayed on the sales floor such as:

Therefore, this ‘naïve’ approach where you simply state that any product should be refilled that has come up from the RFID count as being present in the stock room and missing on the sales floor, is also flawed. After each RFID count, you would get a long list with a lot of irrelevant results...week after week after week. This makes this refill method also very inefficient because store employees need to spend a lot of time to figure out which results on the list are relevant and which are not.

2. Build a planogram

To work around the above-described issue, retailers may work with a planogram that gives every item a pre-assigned position on a specific shelf. All store associates have to do then is to take the planogram and refill the missing spots. What sounds logical at first, is quite a challenge.

The biggest challenge is that a planogram by definition will differ from store to store. Larger stores will carry more varieties than smaller stores, and even within those categories, there might be variations. Different countries will carry different products, but also display different sizes to cater to variations in customer sizes.

To make matters worse, this will differ over time: from week to week, or even day to day – based on the weather. Even if you were able to specify a planogram for each store, then it will be obsolete over time because fashion is time-dependent.

To summarize, maintaining a planogram that is this flexible is an extremely challenging job. The efforts required to maintain a decent planogram that works for a variety of stores outweigh the benefits of using it for a refill.

3. Use algorithms & machine learning

The sub-field of algorithms and machine learning has increasingly gained more popularity in the past couple of years. The foremost reason is that machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought earlier.

But how can machine learning be used to build a refill solution for retail stores? Based on existing and historic stock levels, algorithms can learn what product/size combinations are the most important on the sales floor, and which ones should not be on the sales floor. With these insights, it is possible to produce a priority list per store, which can be matched with the RFID stock data from both the stock room and sales floor. The results can then be presented in a prioritized ‘refill suggestions’ list.

Hence, using algorithms and machine learning, refill becomes a much faster and more effective process, because store associates can be sure that the items on the ‘refill suggestions’ list are items that are a) available in the stock room and b) are (most likely) meant to be out on the sales floor. While this method can boost efficiency and effectiveness, it is important to note that a ‘human check’ is still needed of course, because machines can never take all exceptions into account.

Approach & Results

Based on the above concept of applying machine learning, we built a refill feature that is fully integrated into the iD Cloud app. This app is also used for all other RFID tasks in the store, such as counting, programming new labels, etc.

Immediately after the RFID stock per sub-location, iD Cloud presents a ‘Refill suggestions’ list, which store employees can use to decide on what to refill. That means that store associates now have one single view, which displays both the current state of the sales floor and stock room. To make it even clearer to store employees, also pictures of the products are included, which makes it possible that even a new staff member can perform refill effectively.

This methodology has been tried and tested with various apparel retailers in a significant number of stores. Based on this study, the following results were obtained:

  1. The on-shelf availability for three core sizes improved (on average) from 88% to over 98% in a matter of weeks.
  2. Store employees spent 55% less time on refill, as they directly know what can be refilled and are guaranteed to find the items in stock that are suggested on the refill list.

The above results show that the ‘refill suggestions’ list is an extremely valuable tool for store employees to ensure product availability on the sales floor for their customers. This in turn leads to soft and hard benefits such as:

  1. An increase in on-shelf availability has been proven to result in increased sales.
  2. By removing dull work, store employees are happier and have more time serving customers.


Having perfectly stocked shelves is one of the biggest challenges for retail stores because refilling the right items can be quite a challenge. In this white paper, we have looked at different methods to solve this and concluded that refill based on visual checks, POS transactions, and planograms lead to inefficiencies and far from perfect results.

One would expect a much better result with refill based on RFID, but a complete list of all differences between the sales floor and the stock room only looks great at first sight. In practice, this ‘naïve’ method also leads to an inefficient refill method, because it results in very long refill lists with a lot of irrelevant results.

Here, RFID counts combined with algorithms and machine learning deliver much more meaningful results. Of course, there is always a ‘human factor’ involved. In the end, the store employees still need to put effort into refill. However, the first results prove that working with a prioritized ‘refill suggestions’ list delivers promising results because this refilling process is faster, the on-shelf availability is better and even results in a sales increase due to the improved availability.