Demand Forecasting: How To Forecast Demand For Your Business


Predicting the future would be a valuable skill in any business, and we even have ways to attempt this today. Demand forecasting is our way of studying the world around us to form a theoretical map of how things, like demand, could fluctuate down the line. While this type of forecasting is valuable, being able to do so in real-time would be even better. Luckily, we have some good news. Real-time demand forecasting is indeed a thing and it has many benefits to bring to supply chains around the world.

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Demand Forecasting

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, it’s the process of predicting your customers’ future desire for your products. Using historical data, it makes predictions on when the peaks and valleys of demand will occur throughout the year. It 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.

So what is real-time demand forecasting? As you can probably imagine, it happens in real time — no delays, no waiting time and no outdated data.

Some Different Types of Demand Forecasting

Before we take a deeper dive it may be helpful to get a handle on some other varieties of forecasting:

  • Passive: Usually limited to small and local businesses. Uses straightforward projections of historical data.
  • Active: Normally used within businesses that are rapidly growing. Actively measures competition, economic environments and the expansion of product portfolios.
  • Short-Term: Carried out between three and 12 months, takes into account seasonal demand patterns and possible decisions that could affect customer demand.
  • Medium- to Long-Term: Carried out between 12 and 24 months normally. This type of forecasting informs strategy planning, sales and marketing planning, financial planning and more.
  • External Macro: Deals with broad-strokes market changes. Aids in evaluating strategic planning and large scale shifts in consumer behaviors.
  • Internal Business Forecasting: Focuses on internal operations and how they could affect keeping up with demand. Analyzes sales divisions, financial divisions and includes annual sales forecasts.

The Difference Between Demand Forecasting and Demand Planning

Before we dive further into the subject, 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 forecasts 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].” While demand forecasting gives you the important numbers to work with, demand planning uses those numbers to take action.

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Why Demand Forecasting Is Important

First and foremost, demand forecasting is critical to formulating many vital strategic planning processes in business. Without demand forecasting things like budgeting, financial and marketing plans, procurement strategies and capacity planning would suffer from a lack of information and would end up unsuccessful. Without the ability to forecast demand properly balancing product levels would be impossible and would land you in hot water with your customer base.

If you can’t get a feeling for how demand trends are fluctuating the agility of your company can suffer greatly. In the event of a rapid unforeseen change in demand you want to make sure your resources are positioned in the proper areas to keep up.

Demand Forecasting Software Tools

Demand forecasting software tools are critical to remaining agile

Demand Forecasting Methods

Knowing how to approach something like this is no small task. Luckily, there are some tried and true strategies that can make the process easier:

  • Market Research: This qualitative method leverages customer surveys to generate an accurate view of demand. Ensure that you are keeping variables like demographic and location in mind when distributing surveys to customers. You want the information gathered to be relevant enough to formulate a strategy with, a random sampling won’t do much to help.
  • Trend Projections: A quantitative method that requires a sales history of about 2 years to use effectively. Sales data from the past is analyzed to create a time series that generates a demand projection based on historical sales of a product.
  • Sales Force Projection: Another qualitative method that relies on the opinions of your sales teams. Each salesperson should analyze their region and pass on the respective demands of their customers. These collections of data are brought together and used to form a realistic projection of demand.
  • The Barometric Method: Unlike trend projections, this quantitative method utilizes data from the immediate present. By analyzing certain economic indicators this data can be used to create demand forecasts.

Now that the definitions and explanations are out of the way, let’s look closer at an exciting trend that is gaining traction.

Real-Time Demand Forecasting Methods

The most common and, oftentimes, the most accurate forecasts come from the use of real-time data. But there are also other methods that many businesses use. The following three methods are the most common today: More often than not, multiple methods are used in tandem.

Expert Predictions

The first and simplest method we will cover is the 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.

It is important to note that experts and forecasting are not mutually exclusive ideas. Even though formulating an expert opinion takes longer than a minute or two, this method still falls within the realm of real-time forecasting.

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Time-Series Forecasting

One of the most accurate techniques is the time-series method. This strategy 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 likely to continue into the future. That said, this method is still quite useful. 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 these features are 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 these tools 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 forecasting capabilities. Supply chain management software has a plethora of features that go beyond these features, 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 forecasts will be accurate for years to come.

The Future of Real-Time Demand Forecasting

If we were to take out a crystal ball and peer into the near future, edge forecasting would appear before us in a mysterious cloud of mist. So, what is edge forecasting and what does it mean for the future?

For starters, edge forecasting involves the internet of things (IoT) and the myriad of internet-connected devices that make it up. Most forecasting tools require data to be sent back to a data warehouse server that would then clean and dissect the information. Important data related to demand would then be transmitted back and used to plan for fluctuations in demand.

Edge analytics and forecasting involve the collection and analysis of data at the touchpoint using sensors and IoT connected devices. Instead of waiting for data warehouses to scrub and identify useful variables, edge analytics processes data the second it is generated.

Benefits of Edge Analytics

As of now, there are two major benefits to this type of analytics and forecasting. Firstly, edge analytics allows users to immediately take action on data-based insights gathered by IoT enabled devices. The immediate turn around of this type of forecasting is immensely attractive for businesses around the world.

Secondly, edge analytics and demand forecasting drastically limit the amount of data being shipped off to the cloud for analysis. If you are looking to cut costs, this benefit should hit home. This method cuts back on bandwidth usage while still handling the same amounts of data.

Until recently, the technology needed to make this type of forecasting was hard to come by, but with the rise of predictive analytics tools, this strategy is no longer a dream. Some attribute the sudden rise of this topic to unnecessary hype, but supply chains around the world are still tuning in to see if this method takes off.

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Final Thoughts

Real-time forecasting is a valuable tool in the world of supply chain management. Demand is constantly fluctuating, and having the tools available to keep your company ahead of it is important in maintaining a successful supply chain.

How has leveraging forecasting taken your supply chain management to the next level? Let us know with a comment below.

Hunter LoweDemand Forecasting: How To Forecast Demand For Your Business


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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.

  • Chris - September 22, 2019 reply

    Exponential smoothing method is a form of time series model. You seem to distinguish it from time series models. The model you mentioned that assigns equal weight to all the demand data is known as the Moving Averages. There is also the trend projection model that uses regression. It will be misleading to think that exponential smoothing is different from time series models.

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