PURPOSE:

The present paper investigates the real performances of Software-Based Shielding (SBS) in two different stores of fashion retailers located in Northern Italy.

DESIGN/METHODOLOGY/APPROACH:

The study is based on a double case study analysis. Six different factors have been chosen, with two levels each. Namely, we investigated two different (i) stores; (ii) reader models; (iii) power levels; (iv) classification methods; (v) training data sets and (vi) settings of reference tags. The results have been evaluated in terms of overall and specific accuracies, and in percentage of false front (i.e., tags wrongly located in the sales floor area).

FINDINGS:

SBS proves to be a sound method for classifying tags’ location during normal operations in real-life stores, with overall accuracy up to 0.95. Of the two readers, reader A shows better results in terms of both overall and front accuracy, whereas reader B performs better in terms of rear accuracy and percentage of false front. With respect to the classification method, the combination of Method 2 with reads from reader A provides the best results. With respect to the training data, the use of front and back reads for training performs mostly better than the training with sole front data.

ORIGINALITY:

We are not aware of any other study that performed and reported results of SBS testing under normal operations in real stores. To the best of our knowledge, this study is the first one to report such results.

RESEARCH LIMITATIONS:

Main limitations of our research are the limited set of factors and levels, and the specific classification methods that we used, labelled as Method 1 and Method 2. Also, we did not consider tags disposition and density in our study.

PRACTICAL IMPLICATIONS:

We prove that SBS is a feasible option that could replace physical shielding in retail stores. We call to action for further research on this topic, and for retailers to test it in different store locations.