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Is Real-Time Demand Forecasting a Thing?

You may have heard rumors going around of something that many people once thought was just a myth. You’ve probably heard stories that people tell each other over a campfire, but never actually seen it in person. But we’re here to tell you that what you heard is no mere rumor or myth; it’s real. Yes, ladies and gentlemen, real-time demand forecasting is a thing.

What is Demand Forecasting?

The term “demand forecasting” has been around for a while now, as it’s become more prevalent in supply chain management. In essence, demand forecasting is the process of predicting your customers’ future demand for your products. Using historical data, it makes predictions on when the peaks and valleys of demand will occur throughout the year. Demand forecasting plays a crucial role in supply chain planning, as it guides your decision-making processes for everything from your production planning to your inventory control.

The process of demand forecasting also carries the distinction of being the less-mythological twin of real-time demand forecasting. So what is real-time demand forecasting? As you can probably imagine, real-time demand forecasting is simply the process of demand forecasting occurring in real time — no delays, no waiting time and no outdated data.

The Difference Between Demand Forecasting and Demand Planning

Before we dive further into real-time demand forecasting, we need to clear something up. We’ve heard demand forecasting and demand planning used interchangeably far too often, when they’re actually two different (albeit related) processes. Demand forecasting is merely one of several components of demand planning. As explains (and you know they know what they’re talking about based on the name), “Demand planning is defined as using forecasts and experience to estimate demand for various items at various points in the supply chain.”

Demand planning uses the forecasts provided by demand forecasting to adjust the supply chain to accommodate high or low demand. Additionally, demand planning assesses forecast accuracy “through ongoing analysis and tracking of the forecast[s].”

Real-Time Demand Forecasting Methods

Now that the definitions and explanations are out of the way, let’s look closer at real-time demand forecasting. The most common and, oftentimes, the most accurate forecasts come from the use of real-time data. But there are also other methods of real-time demand forecasting that many businesses use. The following three methods are the most common today: More often often than not, multiple methods are used in tandem.

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Expert Predictions

The simplest method of real-time demand forecasting is the old fashioned expert prediction. Although it doesn’t use machine-driven data analysis, this method still has plenty of merit in today’s world. For one thing, when experts make their predictions, they’re not just random guesses. They’re educated guesses based on their education, their experience and current events.

Although expert predictions aren’t as popular as the other methods, it does have one distinct advantage: it takes outside factors into account. Since experts are actual people, rather than programmed machines, they can combine several factors to make their predictions. They can not only look at your previous demand, but also take into account real world events that may affect demand for your products. For example, if they see a cultural shift away from your type of product, they can use that information while creating their predictions.

Time-Series Forecasting

One of the most accurate demand forecasting techniques is the time-series method. This method uses historical data gathered either at particular times or during set periods of time. These forecasts look at the various patterns that occur over these time series, and then use that information to predict future patterns.

Generally speaking, this method is best used when demand has shown consistent patterns that are likely to continue into the future. That said, this method is still quite useful for real-time demand forecasting. As new data rolls in, the forecasts are adjusted to reflect any new patterns. This helps confirm that current demand is either continuing to rise, starting to fall or plateauing, so you can make well-informed decisions on the fly.

Exponential Smoothing

Similarly to the time-series method, exponential smoothing relies on historical data to make its forecasts. However, it weighs each data point differently. In time-series methods, each data point is given equal weight when developing the forecast. But with exponential smoothing, the most recent data is given extra weight. “If there’s a trend in the data, [the exponential smoothing method will] use the recent observations to make up the bulk of the forecast, and the forecast is more likely to reflect the trend,” according to ClickZ.

This is especially important for forecasting in real time. The purpose of real-time demand forecasting is to get a live view of your demand so that you receive the most up-to-date forecasts. Exponential smoothing showcases the importance of the most recent trend, whether that’s a sharp increase or a slight decrease. With this information in hand, you can get ahead of incoming demand so you can ramp up or decrease production at a moment’s notice.

How Can You Take Advantage of Real-Time Demand Forecasting?

So you want to start using real-time demand forecasting in your business? We believe it’s one of the most important supply chain tools available today, so we don’t blame you. To start implementing it in your business, start by gathering some knowledgeable supply chain and demand experts. They can help you by making predictions and helping inform your software search.

After gathering your experts, start a search for supply chain management software that includes real-time demand forecasting capabilities. Supply chain management software has a plethora of features that go beyond demand forecasting, so you can take full control of your supply chain processes. Just make sure that all of your various needs, from forecasting to inventory management to reporting, are met by whichever vendor you choose. This ensures that your software will be viable for a long time, helping ensure that your real-time demand forecasting will be accurate for years to come.

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1 comment

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  • Charu - March 29, 2018 reply

    But this complexity can now be reduced with the help of big data analytics, the only need is to get accuracy during data analysis. Strategies mentioned above are very helpful in making more informed decisions for your business. Thanks.

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