Benefits of WSSI as a merchandise planning tool – part 2
Can you give an example from your experiences what has been the impact of implementing open-to-buy on inventory turnover and gross margin? (or GMROI)
Anecdotally there is plenty of support for the idea that effective planning optimizes inventory cover and margin, although there is very little quantifiable, empirically based, evidence. In my view this is due to two things – firstly retailers seldom undertake baseline assessments prior to implementation and secondly it is always tricky to attribute improvements over time to the impact of a specific activity or system as there are many external factors which can enhance or diminish the effect.
However, what is clear is that improved planning does, somewhat inevitably, lead to improved performance on the basis that “What gets measured gets done”. If only 1% of sales were added due to the avoidance of stock outs and 1% of sales were reduced from the markdown bill as a result of optimised inventory, then the impact on bottom-line profit would be significant. If you believe that these sorts of incremental improvements would be attainable by improved planning and control then the case for effective Merchandise Planning is clearly made.
It is also important to note that even the best merchandise plan is not going to lead to any improvements if it is not supported by effective downstream systems and processes like allocation and replenishment
What is the appropriate level of detail for open-to-buy?
In my experience most retailers calculate the Open to Buy at a category or class level in the WSSI. Some then go on to perform a similar calculation at a line level (style/colour in fashion, SKU in other areas) for the key lines in the business. Key lines are typically those that are either the main margin drivers or those lines that form a part of the range which is never out of stock.
What are the advantages of doing OTB at retail price vs at cost?
Calculating an OTB using retail valuation allows us to take into account and plan the impact of mark-downs and promotions on sales and margin and thence on stock turn.
Planning at cost is more simple but less intuitive as most merchandisers think of sales at retail value.
Of course we do need to reconcile our merchandise plans with the plans made by finance and, where a company is using cost accounting, this means that we need to convert the retail values from the WSSI to cost anyway, so my answer here is that “it depends”.
How many retailers use open-to-buy?
I would say that nearly all retailers must be using use some form of open to buy calculation, however rudimentary. Without is their purchasing activity would have no sound basis at all. The principles of balancing a stock level against a sales forecast by predicting a cover requirement are fairly widely understood.
What is interesting is the extent to which retailers still rely on spreadsheet systems to support this activity. Whilst spreadsheets are cheap and flexible they have some severe limitations when it comes to the collaborative creation of effective corporate strategic plans. They often end up being just another, relatively inefficient, silo of management information.
What’s your vision, how will the use of open-to-buy change in the times of Big Data and advanced predictive analytics?
I am something of a Luddite here – or maybe that should be a realist. I hear a lot about the potential offered by expert systems and Big Data allowing decisions to be taken at ever more detailed levels, but my experience over the years has been that, for many companies, the Merchandise Planning problems to be resolved remain at a relatively high level.
For these companies the implementation of a relatively simple “best-practice” merchandise planning system would represent both a considerable challenge and a huge leap forward. The idea that these companies should be implementing and putting their faith in predictive analytics and making plans at a SKU / Store level is pure science fiction. We have seen some interesting developments in the area of mark-down optimization, but these systems require a level of input and understanding which are probably beyond most retailers today.
For me the realistic benefits of big data and predictive analytics lie firmly in the transactional areas where black boxed applications, designed by PhD graduates, can churn the data on, for example, allocation or replenishment quantities, with the retailers occasionally tweaking the required parameters. In merchandise planning we need to get the basics right first to support the flair and experience of our merchandisers.