Demand forecasting is the process of predicting future demand for a product. It’s about understanding how many units your customers will want to buy and when so you can make sure you have the right amount of inventory to meet their demands.
Overstocking and understocking can be a nightmare for your business. The last thing you want is to run out of inventory halfway through the day. But equally, you’d rather not have thousands of unsold products costing you a fortune to store in an expensive warehouse.
An accurate demand forecast ensures that you always have the right amount of inventory to serve your customers’ needs. This keeps customers happy and saves you money by allowing you to optimise your supply chain and minimise production costs.
Let’s look at a simple demand forecasting example
Imagine this: You’re running a small business selling sunglasses online. The weather has been poor, so you’ve only managed to sell twenty pairs over the last three weeks. You order another twenty pairs of sunglasses to replenish your stock.
Suddenly, summer arrives. Everyone wants a new pair of sunnies — it’s barbecue weather. There’s a rush at your store. You sell your entire stock within minutes. Bummer.
The following year, you’re determined to be prepared. You diligently check the weather forecasts and review your historical sales data to see how many backlogged orders you had last year. You use this data to order 200 pairs of sunglasses. Summer arrives, and you coast through with promising sales figures and no stockouts. Success.
This is the concept of
demand forecasting,
which uses historical data to predict future sales. It ensures you have the right stock at the right time, helping keep customers happy, avoid stocking issues, and improve your profits.
Why should I use demand forecasting?
Demand forecasting isn’t just a fancy way to predict future sales. It offers dozens of tangible benefits to help you drive major business value. Here are six that are important for you.
1. Demand forecasting and inventory planning
An accurate demand forecast lets you maintain the optimal inventory levels to meet customer demands. It helps you
avoid understocking, which can impact sales (and lead to unhappy customers).
This inventory optimisation also
reduces the risk of overstocking, which can tie up your hard-earned cash in excess storage space. This is especially handy if you deal with perishable goods, like fruit and veg, with a sell-by date.
2. Budget planning and resource allocation
When you understand how much customers will buy and when you know exactly how much money you need to invest (and where you need to invest it) to meet that demand.
Do you need to hire new equipment? Should you employ a new team member? You may need to make a change to shift patterns to account for temporary spikes on certain days.
When you accurately forecast demand, you can budget and make better financial decisions that decrease costs and allow more effective resource allocation
If you’re looking for a more effective way to plan budgets and allocate resources, try Salesforce Sales Planning. Discover how Sales Planning can help your business streamline the sales planning process and optimise strategies to achieve better results.
3. Supply chain optimisation
Demand prediction helps you plan your supply chain down to a tee. You can proactively communicate with suppliers to source stock and optimise inventory levels to avoid potentially costly delays.
You can also use your demand forecast to decide which suppliers to build relationships with. If three of your most in-demand summer products come from the same supplier, it’s a good idea to prepare them for a large order during that period next year.
4. Data-backed business decisions
Demand forecasts encourage informed decision-making. If you see a product underperforming, you can decide whether to invest in advertising, change your positioning, or perhaps pull the product entirely. You may also decide to adjust a pricing strategy based on your knowledge or run a sale to drum up interest.
5. Customer satisfaction
No one likes visiting a website and finding their favourite food item isn’t available. When your highest-quality products are always in stock at the right time and place, this benefits the end customer, making it more likely they’ll come back again. This improves customer retention and gives you a real competitive advantag
You can also use Salesforce Feedback Management to build surveys and reveal insights with AI to gain deeper insights into customer behaviour.
6. Sustainability
Demand forecasting promotes optimal inventory management and better resource allocation, reducing wastage and making your operation more efficient.
This leads to better environmental sustainability — a necessary goal for all businesses today. As a plus, you can also use this as a unique selling point. Win-win.
Demand forecasting approaches
Here are some of the most common strategies you can use to narrow or expand your scope and can help you decide on your overall demand forecasting strategy.
1. Short-term demand forecasting
Looking at demand for a period of
less than 12 months. Short-term forecasting helps assess day-to-day demand that you can use to inform marketing efforts.
2. Long-term demand forecasting
Looking at the demand for
more than 12 months. Long-term forecasting is excellent for identifying seasonal patterns and strategic expansion opportunities.
3. Macro-level forecasting
This overviews broader market and business economic conditions and can highlight opportunities and threats that may affect demand in your industry.
4. Micro-level forecasting
This type of forecasting takes a granular approach. It explores specific details related to a business or demographic and helps with making precise decisions.
5. Active demand forecasting
This hones in on the goods that customers are actively seeking. It guides marketing strategies and avoids stockouts for in-demand items.
6. Passive demand forecasting
Passive forecasting focuses on products that customers are not buying. The primary goal is for businesses
to identify why a product isn’t selling and how they can fix that.
Choosing the right forecasting approach will guide you in selecting one of the many demand forecasting methods, as shown in the examples below.
Demand forecasting methods
Demand forecasting has a straightforward goal. At its core, it’s about predicting
what people want,
how much they want, and
when they want it.
But
how exactly do you go about predicting the future? This is where things can get complex because there is no right or wrong way to forecast demand. It all depends on the type of business, the data you have available, and the tools at your disposal.
Let’s look at the different methods demand forecasters use to predict demand.
To keep things simple, we’ve broken them down into quantitative methods and qualitative methods.
Quantitative methods
Quantitative demand forecasting is based on historical data. It involves using
numbers and
statistics to make future predictions.
Quantitative forecasting can be highly accurate but doesn’t consider unpredictable external factors. For that reason, it’s usually paired with more subjective qualitative forecasts to provide a complete overview.
1. Time series analysis
This is one of the simplest and most common methods of internal business forecasting. It involves using historical data, such as past sales data, to explore patterns over a period of time. It’s a good way to find recurring demand trends, like seasonal fluctuations, that you can use to predict future outcomes.
Time series analysis can be misleading if there are outliers in historical data, so many forecasters use models like moving averages or exponential smoothing to improve accuracy:
- Moving averages involves calculating the average of past data points to eliminate outliers and get a more accurate picture of trends.
- Exponential smoothing is the process of assigning gradually decreasing weights to past data points (i.e., prioritising recent data over data from a long time ago). Here’s author Nicolas Vandeput’s original graph showing this work.
Time series analysis is a good starting point for short-term forecasts for products with relatively stable demand patterns. However, as this relies on the assumption that future sales forecasting will closely mirror past sales data, it isn’t always reliable on its own.
