Demand forecasting, at a glance, is a simple concept. But once you delve into the principles behind it, it becomes a lot more complicated. Despite that complexity, forecasting demand is one of the core fundamentals of inventory management for retailers, distributors, wholesalers, and manufacturers alike.
On top of optimizing your supply chain, forecasting demand can lower overhead costs and increase revenue. But how? That’s what we’re here to discover, so let’s begin.
What is demand forecasting?
Demand forecasting is the process of using data to predict the volume of inventory you’ll go through within a certain period. This time period can range from as little as three months to as long as four years. It’s important to keep in mind, though, that the resulting figure is just an estimate. Your actual inventory levels could dip below and just as easily blitz past the prediction. And whatever the case, it’s important to prepare in advance. Incorporating supply chain optimization tools can further refine these predictions, enhancing accuracy and responsiveness in inventory management
Of course, given how much information businesses have at their disposal there’s different types of demand forecasting. Generally speaking, there are 7 different demand forecasting types you should be aware of.
Passive
Passive demand forecasting is the most straightforward way to forecast demand. Unfortunately, it also requires pre-existing data to work off, making it impractical for start-ups. Passive demand forecasting works off the idea that the current year’s sales will be similar to last year’s. As a result, it caters more to businesses that prioritize stability over expansion.
Active
Active models are much more complicated. They take into account current marketing campaigns, market data, and expansion plans. They also consider external factors, such as economic viability, market growth, and so on. Newer businesses, such as start-ups often use this type of demand forecast, since they don’t have as much historical data to work with. While start-ups make use of active demand forecasting, they’ll also have to rely on some assumptions to complete it. Tread carefully!
Short-term
This is what we were talking about when we mentioned time periods. Short-term demand forecasting attempts to predict sales volume within a period of 3-6 months. Because of the relatively short period of time, businesses usually use short-term models to make decisions regarding just-in-time (JIT) products.
Long-term
As the name suggests, long-term demand forecasting works over a longer period of time, usually 12 to 48 months. And unlike some other methods, it focuses on shaping future growth. As a result, it may be better to think of it as a roadmap or a long-term goal. You can get a pretty good idea of where things will be in four years, but the future is always uncertain. At the same time, business growth sometimes relies on opportunity. Be sure you’re ready to take advantage of it when it shows.
Internal
Internal demand forecasting deals with the internal capacity of a business. Growth is often the overarching goal, but not every business is capable of growth. Can you handle increased demand? Is your operation scalable? Can your cash flow keep up with that scalability? Internal models use all these variables and more to create realistic predictions.
Some businesses also use internal demand forecasting to highlight areas of optimization. If successful, this can increase overall productivity.
External
Rather than calculating internal capacity, external demand forecasting focuses on the surrounding landscape. For example, can the market support another competitor? If so, what market share is feasible? External demand forecasting also considers the acquisition of raw materials and other necessary goods.
Artificial intelligence
The newest one on this list. The term “AI demand forecasting” may seem like a bit of a gimmick, but the truth is AI has a lot to offer when it comes to forecasting demand. While all the prior forecasting methods require humans to collect data, that data still needs to be processed by computers. AI forecasting takes it one step further and uses artificial intelligence to identify common trends in the information. These aren’t just from start-ups, either. IBM, for example, offers an AI demand forecasting system of its own.
The good and the bad of demand forecasting
Correctly forecasting demand brings many benefits, the most obvious of which is optimizing the supply chain. That, in turn, decreases overhead costs and increases profit. How? Simple.
Retailers and manufacturers alike usually purchase goods and materials from other sources. This could be raw materials, individual components, or even entire products. Whatever the case, there’s two immediately obvious costs: purchase price and shipping cost. But once it arrives at the warehouse, it continues to cost your business.
On top of paying to store the product, it’s also taking up space that could hold another product. It could be a worse-selling product, or it could be a better-selling one. If a better-selling one could take its place, you’re effectively losing money. Demand forecasting aims to solve this problem by purchasing enough inventory to avoid selling out altogether. This saves on warehouse costs and leads to an overall revenue increase.
It’s not easy, though. All the different types of demand forecasting we mentioned before work off of data. And a lot of it. Feeding it the wrong data can lead to disastrous results. And sometimes, there’s just not enough data to work off of. This is especially true for new businesses in new industries. It’s why more and more are turning to software and AI. Our software inFlow, for example, can create various reports with the click of a button. We even integrate with Easy Insights for our customers who want to take their data analytics to the next level. Connecting your inFlow account to Easy Insights lets you assemble custom dashboards to include charts, tables, maps, and more!
Different strokes for different folks
Or, in this case, different forecasting methods for different industries. You get the point.
Given just how different some industries are, there are a few different methods used to forecast demand. Here’s some of the important ones.
Sales team estimates
At the end of the day, the sales team has the most direct contact with customers. They see and hear positive feedback and complaints, but most importantly, they see how customers interact with the products. This makes their input valuable. While perhaps not as efficient as a computer, they can also create their own demand forecast.
Trend projections
This method of forecasting works off of previous sales trends. When sales were highest, when they were lowest, when they took place, and so on. While simple and reliable, filtering out outliers where sales were too high or too low is essential.
Market research
Market research is usually based on data from customer surveys. It costs a lot to distribute these surveys and crunch the numbers, but is also very rewarding. It can provide unique insights into things like target demographics and other factors that numbers don’t tell.
Delphi method
The Delphi method is complicated. And, unlike other methods, it relies heavily on human input. Under the Delphi method, a business consults several demand forecasting experts. They then aggregate the responses and use them to formulate another set of questions and repeat this process until they reach a consensus.
Not every business is suited for demand forecasting
At the end of the day, demand forecasting is just one of many tools. And not every tool can be used for every job. Some industries move so quickly that data is too irregular to reveal any patterns. Others are so niche that their sales are always consistent.
On the other hand, if your business can use demand forecasting, you really should. It offers a plethora of benefits over a wide range of areas.
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