Proper inventory control and management are essential for taking your dollar as far as it can go.
Running out of stock, carrying too much stock, or just not knowing what stock you have and where it is—these are all symptoms of poor inventory control or management, and they all reduce your profitability.
As with any other problem, you’re better off preventing it than you are trying to fix it once it has already occurred. Demand forecasting assists with inventory management by projecting how much of an item you’ll need to order—before you need it. But like the rest of your business, you want to get the best results you can out of your forecasts. To get the best, you have to put in the best. That’s why preparing your data before forecasting is often the most crucial, yet overlooked, step of your inventory management process. Faulty input data will lead to faulty results, even if you’re using the best forecasting models.
To help you get your data to work for you, we’ve come up with 3 tips for data preparation.
For out of stock periods that only last for part of a month, you can simply extrapolate the average sales figures for whichever periods the product was in-stock that month. Use that average number to fill in data points for the stock-out period. Just like that, you’ve patched up the gap!
However, if the product is out of stock for the entire month, then you’ll need to use a similar tactic as with an anomaly. You can fill out the out of stock month with an average of data from the surrounding months, or (if available) you can use data from the previous year.
You’ll want to account for seasonality’s effect on sales rates, too. Using January’s average to cover a stock-out in December will throw off seasonality forecasting for a popular holiday item. Instead, look at data from previous years’ seasonal spike, then adjust with recent growth trends. In the case of a seasonal item, you’ll want to use forecasting models that specifically take seasonality into account when making projections (a topic that we’ll be discussing in a future article).
Depending on the predictive analytics software you are using, adjusting your data can be difficult or simple. A forecasting tool like ForecastRx allows for click-and-type data manipulation, which will let you quickly fix data points and spend more time taking advantage of your forecast results.
To show how convenient ForecastRx makes data manipulation—and the effects data optimization can have on your forecasts—we’re offering a free trial of the program that will allow you to test out all of the tips mentioned here!
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