Solving the "plan-o-gram compliance" problem with Space planning in retail
Most retailers spend a lot of time defining space plans - both floor plans as well as specific plan-o-grams. These plans are created after a lot of data analysis and are distributed to the stores for execution with expectations of positive impact on sales and margins. A big issue with the execution of these plans is the realization of these plans at the stores. Compliance of execution to these plans typically starts deteriorating almost as soon as the plan-o-grams are executed at the stores. Non-compliance can occur as plans created corporately do not always take into consideration local constraints (Jump shelves, mis-aligned shelving, incorrect fixture information, in accurate floor plan information etc.). Also, non-compliance can occur due to consumer activity at the stores or during re-stocking of shelves. The problem for retailers has always been to determine this degree of compliance to accurately track effectiveness of plans for future correction and analysis.
The task of monitoring compliance is huge given the size and scale of what is to be tracked and measured. Measuring/tracking the position on products across shelves for various product categories will require a lot of investment in man-power, most of which becomes redundant as soon as it is carried out. Some Retailers do rely on such physical compliance measures to sample a portion of the stores/plans to check for compliance. However this is not a very effective means of tracking and monitoring compliance. This is the "plan-o-gram compliance" problem that all retailers face. Getting this solved can provide a very accurate basis for analysis around space planning.
Recent methods that are being tried out leverage image recognition software that can analyze the images on a picture and decompose the same into individual items. So imagine taking a picture/snapshot of a shelf at a store. This image may be quickly taken by a store associate and automatically uploaded into a central database. While uploading details of the plan number and the product category need to be provided. Once uploaded, the software can analyze the picture for the particular category and compare the individual products to the plan-o-gram to create a "real-o-gram". The software will need to resolve individual images into products based on pre-defined images and details for each product. The real-o-gram is then compared to the plan-o-gram to determine spatial position, facings etc. The image comparison could also throw out issues of stock outs and arrangement that corporate planners may not be aware of. This comparison can very quickly generate an audit of compliance and send corrective actions (within tolerance) to store associates to fix.
This activity of taking pictures will require minimal training and is very quick and cost effective. Entire stores and chains may be quickly audited using this method. There is yet to be wide spread adoption of this method, but this can be a great approach to solving the compliance issue. If the compliance variance is known and corrective action is taken, the accuracy of space plans and the measurement of their effectiveness would be significantly enhanced. This could impact the way floor plans are created and also the way in which plan-o-grams are created.
This article has been contributed by Amitabh Mudaliar (Group Engagement Manager - RCL Infosys). You can reach Amitabh at Amitabh_M@infosys.com.