Demand forecasting, also known as sales forecasting, refers to the process of making estimates about future customer demand over a certain time period. It uses historical data along with other information.
When demand forecasting is correctly implemented, businesses have valuable information about their potential in the current market as well as other markets so managers can make informed decisions about business growth strategies, pricing, and Market potential.
Failing to use demand forecasting puts businesses at risk for making poor decisions about their target markets and products.
These ill-informed decisions can have far-reaching effects on customer satisfaction, supply chain management, inventory holding cost, and ultimately profitability.
Reasons Why Demand Forecasting Matters
There are many reasons why demand forecasting is an important process for companies:
- With sales forecasting, businesses have information to assist with planning, goal setting, and budgeting. With a good understanding of what your future sales may look like, it’s possible to build and informs procurement strategy to ensure your supply matches customer demand, at the specific product level.
- Organizations can more effectively optimize their inventory levels, increase inventory turnover rate, and reduce holding costs.
- With sales forecasting, businesses are also able to identify and rectify any issues in the sales pipeline ahead of time to keep business performance study throughout the entire period. In terms of inventory management, most e-commerce business owners know that too little or too much inventory can be detrimental to operations.
- Demand forecasting provides insight into your upcoming cash flow so businesses can more accurately budget payments for suppliers and other operational costs, while still investing in the growth of the business.
- Anticipating demand means knowing when to increased staff and allocate other resources to ensure operations run smoothly during peak activity periods.
Demand forecasting is a valuable tool for businesses, but understanding which type to use and how to implement it is key to getting the most from it.
Demand Forecasting Methods
The majority of traditional demand forecasting techniques are in one of these three basic categories:
Qualitative Forecasting
Qualitative forecasting techniques are used when there is not a lot of data available to work with.
This is common for relatively new businesses, or when new products are first introduced to the market. In this case, other information such as market research, comparative analyses, and expert opinions are used to create quantitative estimates regarding demand.
Time Series Analysis
The time series analysis is an option when historical data is available for a product line or a product. When the trends are clear, businesses tend to use the time series analysis approach for demand forecasting.
The time series analysis is useful for identifying seasonal fluctuations in demand, key sales trends, and cyclical patterns.
The time-series approach is most effective for well-established businesses with several years worth of data to work from alongside relatively stable trend patterns.
Causal Models
The causal forecasting model is the most sophisticated and complex forecasting tool. It uses specific information about the relationships between variables affecting demand in the market such as economic forces, competitors, and a variety of other socioeconomic factors.
Historical data is key to creating an accurate causal model forecast.
For instance, an ice cream business could create a causal model forecast by looking at factors such as their marketing budget, promotional activities, new ice cream stores in their local area, competitors prices, historical sales data, overall demand for ice cream in their area, their local unemployment rate, and even the weather.
Types of Demand Forecasting
Passive Demand Forecasting
This type of forecasting is carried out for stable businesses with conservative growth plans.
They use Simple extrapolation of historical data with minimal assumption. This rare type of forecasting is limited to small and local businesses.
Active Demand Forecasting
Active demand forecasting is done for scaling and diversifying businesses that have aggressive growth plans for marketing, product portfolio expansion, and consideration of competitors’ activities alongside the external economic environment.
Short-Term Demand Forecasting
This type of forecasting is carried out for shorter periods of time ranging from 3 to 12 months.
In the short-term, the seasonal pattern of demand and the effect of tactical decisions on customer demand is considered.
Medium to Long-Term Demand Forecasting
With medium to long-term demand forecasting, that it is typically carried out for more than 12 months to 24 months in advance.
Certain businesses may forecast for 36 to 48 months. Long-term forecasting is used to drive business strategy planning, financial planning, capacity planning, sales and marketing planning, capital expenditure, and more.
External Macro Level Demand Forecasting
In external macro-level demand forecasting, you’re dealing with the broader Market movements that are dependent on the macroeconomic environment.
This type of forecasting is used to evaluate the Strategic objectives of a business like product portfolio expansion, entering a new target market, technological disruptions, a paradigm shift in consumer behavior, and risk mitigation strategies.
Internal Business Level Demand Forecasting
Type of forecasting, you focus on the internal operations of a business including the sales division, financial division, manufacturing group, and product category.
This addresses the estimation of the cost of goods sold (COGS), annual sales forecast, net profit margin, cash flow, and more.
Key Sales Forecasting Metrics
Once you have the basis for your sales forecast and please, define and track the following metrics for the entire forecast period.
Product Lead Time
The number of months it takes from placing the purchase order to being ready to sell each product.
Sales Period
How many months of sales are expected from each product
Costs Paid Per Purchase
What percentage of the costs of products are paid when a purchase order is placed
Days Payable
How many days you have to pay the remainder of the unpaid inventory costs.
Stock Levels
The amount of each product you have to keep in stock based on forecasts.
Purchase Costs
The cash needed to make necessary purchases.
Forecasting Seasonality and Other Trends
Seasonality refers to variations in demand that occurred during specific times on a periodic basis like the holiday season.
Trends on the other hand can occur at any time and signal an overall shift in behavior.
Trend projection plays an important role in preparing for what the market demands, and past sales data can help.
When it comes to sales forecasting, it’s crucial to factor in estimates of Trends and seasonality to accurately plan your inventory management strategy, operational processes, and marketing efforts.
That’s why retailers hire additional help during the holidays since they know people will be shopping for gifts.
You need more than the stock for people to buy. You need staff on hand to get the stock on the shelves, out of the warehouse for online orders, and more.
That’s why shipping companies like FedEx and USPS hire additional delivery drivers and package handlers.
Even with the additional staff, the extra volume can present challenges for businesses and consumers alike. Forecasting helps ensure you’re not left without the manpower to handle the demand.
Successful sales forecasting isn’t a one-and-done task. It’s an ongoing process that should involve:
- Actively shaping demand by optimizing your customer experience, sales channels, product offerings, and more
- Working to reduce bias and error over time
- Building an intelligent and agile response to demand with advanced analytics
Demand forecasting is a great way to anticipate what customers want from your business in the future so that you can adequately prepare inventory and resources to meet that demand.
Forecasting demand allows you to cut down on holding costs and other operational expenses when they’re not needed while assuring you have everything you need to handle peak periods as they occur.
Examples of Demand Forecasting
Take a look at major retailer Walmart. They have more than 11,000 stores across 27 countries with an average of 32 billion dollars in inventory. As such, their supply chain is incredibly complex.
Their logistics are known for being precise and technologically-advanced. In 2013 however, they developed a reputation for having a serious in-store out-of-stock problem.
The lack of stock on the shelves was attributed to mismanaged inventory as the stock was available and warehouses, but there wasn’t enough staff available to move it to the shelves.
In this situation, cost cut it cutting measures resulted in a negative customer experience for many, which is something that could have avoided with a proper demand forecast.
A leading car maker takes a look at the last 12 months of actual sales based on its cars’ model, color level, and engine type.
Based on expected growth, they forecast the short-term demand for the next 12 months for purchase, production, and inventory planning purposes.
This demand planning ensures they have enough red cars, enough four-door cars, and enough supercharged engines to meet customer demands for the next year.
Automated Demand Forecasting
Traditional methods of manually manipulating and interpreting data for sales forecasting aren’t practical for businesses that deal with fast-changing markets and customer expectations.
For businesses to be truly agile and have an up-to-date data-informed approach for their decision-making, demand forecasting must occur in real-time, which means you need technology to take care of the work for you.
Using key sales and inventory data from PLANERGY makes it easy to identify patterns and create insights about future demand at your chosen level of granularity.
With the system, you can also trigger automated inventory alerts to recommend reordering quantities based on forecasted sales demand.
You know when to order stock and make data-informed business decisions without having to do any of the forecasting manually which translates to better cost efficiency and time savings – two things any business needs to be successful.
