During my almost seven years working with space planning software, I’ve faced almost every question there is to ask around the topic. One, however, seems to come up constantly “Can we use forecast data when making the planograms?”.
The answer used to always be the same; “If you give us the forecast data at the weekly product-location level, then yes”. This is surely the most common answer also today for most planogramming systems – saying one should turn the forecast data into weekly averages and then build the planograms based on that. The problem with this answer is, it doesn’t really make sense.
I always wondered why the conversations about forecast data in planograms never went anywhere, it seemed like a dead end. It was only after I joined RELEX and we began working to bring forecast data into the space planning process that I realised why: When my customer contacts left their meeting with me and sat down with their IT teams and forecasting experts to address my answer about using forecasts in planograms, they quickly realised they were about to face a raft of challenges. How to find a way to combine forecast and historical data? How to manipulate forecasts into averages? Can we be sure the forecasts are accurate enough?
We’ve faced those same challenges at RELEX, so in this blog post I intend to outline them and propose how these challenges can be overcome.
The logic behind using forecast in planograms
A planogram has to represent every trading day in the week, so usually the first port of call would be to turn to one’s sales history and average it out. But retail has moved away from generic planograms towards cluster and store specific planograms because we’ve learned no two stores are the same. By the same token we should do something about the fact that no two days are the same – something everyone who has worked on the front line in retail knows too well. And rather than use past sales in planograms, by using forecast data you can produce forecast-driven planograms that accurately reflect trends and seasonality in demand.
So, as well as having enough bake-at-home pizzas on the shelf for an average day in each and every store, that particular store next to the big auto factory can be ready for an increased demand occurring on payday Friday (if you’re a fan of pizzas)!
The challenge with historical data
When we use historical data to build planograms we are making a crude projection that the next 12 weeks will, on average, be similar to the last 12 weeks. Of course, we all know that’s not the case. By taking demand forecasts in account, we can upgrade these projections and make them more powerful. For example, historical data might not include weather fluctuations, trends, seasonality and so on. Here, forecasts can prove to add significant added value.
So, if we have intelligent forecasts 12, 52 or 104 weeks into the future, that give due weight to historical data, then these are at least as valuable as the historical data we have and likely even more.
Most conversations I had in the past about using forecast data in planograms were focused on using it like it is a new historical metric, the same way we would talk about bringing in consumer loyalty data. The more I work with forecasts the more I realise it would be a foolish exercise to take forecasts built to project the future and then merge or dilute them with historical data. After all the forecasts have already drawn on historic data, and by performing some kind of merge or average, we are taking backward steps.
Why you shouldn’t rely on weekly averages
Planograms have historically made use of weekly sales averages (no doubt owing to the fact that the market leading space planning solutions are entering their 4th decade of life) and that weekly average is then divided by the number of opening days to get the average daily sales. The next step has naturally been to use this average daily figure to work out how many days of coverage there is on the shelf.
I reflected earlier, that nobody in retail can remember ‘an average day’. Why are we then averaging averages and then building the primary interaction point with the customer around these approximations?
Consider this typical example: In the light blue we see the daily demand for a product in a single store, the dark blue shows us the average sales. Note how different almost every day is from the average?
With two deliveries per week it would be common practice to place 3 ‘days of supply’ on the planogram. On average daily sales are 14 units, so our 3 ‘days of supply’ is 42 units. Looking at the example above, if we re-shelve on Friday morning after a delivery the same morning, we would have an empty shelf mid-way through Saturday.
Now – it’s reasonable to consider that we’ve used averaged weekly data in the past to keep data volumes down and smooth out all sorts of peaks and troughs that we wouldn’t want to base our planning on. I’m a big fan of a rough calculations to see if something makes sense, but surely it’s time to start thinking about approaching space planning in a more scientific manner.
Can we be sure the forecasts are accurate enough?
When using historical data in planograms, the most astute retailers will use a time period that reflects the frequency of the category review. So, for a category that is changed once a year, they will use the previous 52 weeks of performance data. For one that lasts 12 weeks, they will use the previous 12 weeks data, and so on.
When using forecast data in planogram production you need to do much the same thing. So, for a seasonal planogram that is to last 12 weeks, you need 12 weeks of forecast data.
I know a lot of retailers would naturally ask “Is the forecast accurate enough to rely on it 12 or 52 weeks into the future?” The only true way to answer this question is to test it empirically, but we can be certain that if we compared 12 weeks of forecasted sales (for weeks 13-25) to actual sales (for weeks 13-25) and then to averaged historical sales (for weeks 1-12) the forecasted sales would be no further away from the actual than the average – and I’d be very confident the forecast would be a lot closer. More about this can be found in our forecast accuracy guide.
Unify your planning and benefit when building planograms
In a world where we have exceptionally powerful technology, and systems that create forecasts and smooth out anomalies, it’s suddenly a lot easier to bring something different and value-adding into your planograms and be thinking ahead. Retail is changing. You run the serious risk that while you miss a trick, your competitors won’t.
Our vision is to unify retail planning and challenge the way the industry has siloed things.
Our vision is to unify retail planning and challenge the way the industry has siloed things like space planning and supply chain planning by breaking down the redundant boundaries between them. If you’d like to read more about what unified retail planning means, have a look at our earlier blog post ‘Unified Retail Planning – Breaking Out of Retail’s Functional Silos’.
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Other blog posts
- Are You Wasting Time and Money on a Poorly Integrated Supply Chain?
- The Importance of Accurate Forecasts in Omnichannel Grocery Retail
- Best Practices in Weather-based Sales Forecasting
- What is a Good Level of Forecast Accuracy?
- Expanding Into New Territories – What Should a Retailer’s Supply Chain Look Like?