5 Demand Forecasting Best Practices for Smarter Predictions and Better Results

Better understand what’s coming with these demand forecasting best practices.

Demand forecasting is a tough job with a lot of errors.

The people who attempt to tell the future of your customers’ buying decisions are usually wrong.

But sometimes they’re right.

And in those instances, they’ve saved you from buying too much or buying too little  – leading to obsolete stock, and from buying too little – leading to stockouts.

Their job is necessary but very difficult.

To help you do the job of forecasting demand better, we’ll go over a few demand forecasting best practices so you can increase your chances of forecasting demand correctly – increasing profit margins and decreasing costs of inventory.

Before we do that, let’s define demand forecasting.

What is Demand Forecasting?

Demand forecasting is a process of predicting what your customers will buy, how much they’ll buy, and when they’ll buy it.

You can use informal methods (i.e. guessing) or quantitative methods, such as analyzing past sales data.

From production planning to inventory management to entering a new market, demand forecasting will help you make better decisions for managing and growing your business.

But to make demand forecasting as accurate as possible, you’ll need to follow demand forecasting best practices.

Read on to discover these best practices.

Demand Forecasting Best Practices

Demand forecasting is an imprecise science, but that doesn’t mean you can’t improve the process.

Here are a few tips to help you forecast demand effectively:

Create a Repeatable Monthly Process

An increase in demand forecasting accuracy requires a consistent, monthly process that systematically analyzes previous forecasts and compares them to actual market results.

Through this process, you’ll have data on when your predictions were right or wrong, and what market demand has been.

Then, you can sort those “deviations” (when you were right or wrong) from highest to lowest and evaluate the top 20{cb377218d5687e54e8ee9149518f87201a393a7c1db5e8076e9d750029ec0dc3} to determine why you were wrong and how to be right next time.

By following a monthly process and evaluating your past successes and failures, you can minimize future errors.

Determine What to Measure and How Often

You can measure virtually anything in your business, but to accurately forecast demand, you should focus on the most relevant data points.

Here are a few data points you should consider measuring:

• Competitors sales data
• POS data
• Amount of obsolete stock
• Frequency of stockouts
• Shipments
• Orders

Feel free to add any more relevant data points to that list. Then, depending on your industry and rate of inventory turnover, choose whether to measure those data points on a weekly or monthly basis.

Integrate Data From All of Your Sales Channels

If you have multiple sales channels – like an omnichannel ecommerce strategy – then you should aggregate all the data from every sales channel for each individual product into a single data set.

Once you’ve done this for all of your SKUs, you’ll be able to see which channels offer the highest ROI for each product, and what your shipping and order requirements will be – helping you make smarter decisions.

Measure Forecast Accuracy at the SKU, Location, and Customer Planning Level

According to Gartner, only 17{cb377218d5687e54e8ee9149518f87201a393a7c1db5e8076e9d750029ec0dc3} of respondents to their study indicated that they forecast demand at the SKU, location, and customer planning level.

This is unfortunate because a primary driver of demand volatility is increased customer requirements.

Mr. Steutermann, the research vice president at Gartner said, “Customer or sales forecast accuracy should be measured for continuous improvement and accountability. The appropriate place to measure for continuous improvement is in the sales and operations planning (S&OP) review process.”

If you measure demand error down to the customer level, you’ll be able to better understand the source of the error – allowing you to improve your process.

Maintain Real-Time, Up-To-Date Data

You can’t accurately forecast demand if you don’t have accurate data.

Demand forecasting best practices revolve around up-to-date inventory data, sales data, raw materials data, finished goods data, etc.

To make smart forecasts, you’re going to need that data as close to real-time as possible so you don’t calculate demand with any missing data points, and so you can continually forecast demand on a weekly or monthly basis with fresh information.

So how can you track your POS, financial, and inventory data all at once within the same platform?

By using a cloud-based inventory management tool that integrates with all of your business apps.

In other words, DEAR Inventory.