6 Seasonal Index Mistakes You Don't Want to Make

We all know snow shovels aren’t going to sell in the summer and beach towels will flop in December. Most of us can identify general seasonality and spot a really bad seasonal index by applying simple common sense. But do you frontload your key seasons or reduce orders because your Replenishment System doesn’t quite get the job done? You might be running into these common seasonality issues that cause retailers big headaches. The irony of seasonal index errors is they are one of the few things where an Excel file can actually solve the problem; yes shocking, we all thought Excel was a report tool.

The key point to remember – a seasonal index is just a multiplier to run against the base forecast. Some software solutions will lose you with a discussion using words like multiplicative and additive. While the discussion has merit, these terms only describe seasonality math, not how it needs to be applied in replenishment or even what numbers are needed in the seasonal index math. A seasonal index is multiplicative and a market force is additive against the base forecast…but then I have gotten ahead of myself.

Promotions and clearance:

Your demand forecasting or replenishment system doesn’t separate off-price sales from regular-price sales. This causes your system to see false seasonality due to ads or overstock markdowns. If you don’t repeat the promotion, you will over-buy.

Lost Sales:

The demand forecast does not include lost sales in the calculation. This lowers the seasonal index or moves the sales to different weeks.

Moving holidays:

Your Seasonal Indexes rely on sales occurring in the same week every year. When Easter shifts several weeks from year to year, the index becomes averaged over several years or tries to match last year. Either way, you end up with product coming in too early or too late.

Changing weather patterns:

Snow shovels and ice melt only sell when it starts to snow. If your system is relying on last year’s sales, you may miss the early snowstorm or load up for snow that never comes.

Forecast smoothing:

Some demand forecasting systems try to smooth forecasts into pretty seasonal curves. Smoothing takes some of the in-season sales and redistributes them to out-of-season weeks. This becomes especially troublesome for micro-season items that sell for only a few weeks every year. You end up with too much on hand out of season and not enough on hand during the selling season.

Seasonal indexes applied too broadly:

The same seasonal index is applied to many locations and products. Unfortunately, this leaves Texas and Wisconsin stores with snow shovels at the same time.

Snow Shovels in Texas is a Bad Idea:

Fixing even one of these issues could increase your GMROI, lower your average inventory and make your customers happier. Unfortunately, many demand forecasting systems make correcting these issues difficult. At Data Profits we can help you fix your seasonal indexes without all the headaches. Our iKIS system has a user-friendly interface, easy-to-read graphs and customizable forecast alerts for easier demand forecasting. If you are ready to “Tighten the Links in Your Chain™”, contact us today!

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Stuart D

Stuart Dunkin is a strategic visionary who leads by calling on his broad range of experience as a former retail executive as well as a consultant for top retailers and for the E3/JDA software company. He founded Data Profits to solve a real-world problem plaguing retailers today – the disjointed relationship between supply chain data, people and business goals. He has experienced first-hand the challenges and frustrations that occur when decisions are made based on wrong data, which often leads to the wrong actions being taken with crippling results like overstock, out-of-stock, and increased working capital.
Stuart graduated from Auburn University, with bachelor of science in business administration.