Forecasting demand is more complex than forecasting sales because it reflects the sum of sales forecasting with the forecast of missed sales.
1. What is demand forecasting?
Using historical data and other information, demand forecasting is the practise of estimating future customer demand over a predetermined period.
In order for managers to make wise decisions regarding pricing, corporate growth plans, and market potential, effective demand forecasting provides firms with vital information about their potential in both their current market and other markets.
Without demand forecasting, companies run the danger of making bad decisions about their products and target markets. Bad decisions can have significant negative implications on the expenses associated with maintaining inventory, customer satisfaction, supply chain management, and profitability.
Why is demand forecasting important?
There are a number of reasons why demand forecasting is an important process for businesses:
- Sales forecasting helps with business planning, budgeting, and goal setting. Once you have a good understanding of what your future sales could look like, you can begin to develop an informed procurement strategy to make sure your supply matches customer demand.
- It allows businesses to more effectively optimize inventory, increase inventory turnover rates and reduce holding costs.
- It provides an insight into upcoming cash flow, meaning businesses can more accurately budget to pay suppliers and other operational costs, and invest in the growth of the business.
- Through sales forecasting, you can also identify and rectify any kinks in the sales pipeline ahead of time to ensure your business performance remains robust throughout the entire period. When it comes to inventory management, most eCommerce business owners know all too well that too little or too much inventory can be detrimental to operations.
- Anticipating demand means knowing when to increase staff and other resources to keep operations running smoothly during peak periods.
Types of demand forecasting
Most traditional demand forecasting techniques fall into one of three basic categories:
Qualitative forecasting
When there isn’t much data available to work with, such as for a new firm or when a product is presented to the market, qualitative forecasting approaches are utilised. In this case, quantifiable estimations of demand are created using additional data, such as expert opinions, market research, and comparative studies.
This strategy is frequently applied in industries like technology, where novel goods might be introduced and client interest is hard to predict in advance.
Time series analysis
Businesses typically employ the time series analysis technique to demand forecasting when historical data is available for a product or product line and trends are obvious. A time series analysis is helpful for spotting cyclical patterns, seasonal variations in demand, and important sales trends.
The time series analysis method is best useful for well-established companies that have access to data spanning several years and relatively consistent trend patterns.
Causal models
The causal model, which makes use of detailed data regarding links between variables affecting demand in the market, including rivals, economic forces, and other socioeconomic factors, is the most comprehensive and complex forecasting tool for businesses. Similar to time series analysis, a causal model forecast relies heavily on previous data.
For instance, an ice cream shop may use its past sales data, marketing budget, promotional plans, any nearby new ice cream shops, the prices of its rivals, the weather, the region’s total ice cream demand, and even its local unemployment rate to build a causal model projection.
2. Key sales forecast metrics
Once you have the basis for your sales forecast in place, you should define and track the following metrics over the entire forecast period.
1. Product lead time
The number of months it takes from placing a purchase order to being ready to sell each product.
2. Sales period
How many months of sales are expected from each product.
3. Costs paid per purchase
What percentage of the costs of products are paid when a purchase order is placed.
4. Days payable
How many days you have to pay the remainder of the unpaid inventory costs.
5. Stock levels
The amount of each product you need to keep in stock, based on sales forecasts*
6. Purchase costs
The cash needed to make purchases*
3. Forecasting seasonality and other trends
While trends can happen at any time and indicate a general shift in behaviour, seasonality refers to variations in demand that happen during certain times on a periodic basis (such as the Christmas season) (such as a specific product growing in popularity).
For the purpose of effectively planning your inventory management strategy, marketing initiatives, and operational procedures, demand forecasting should take into account estimates of trends and estimates of seasonality.
Demand forecasting that works requires ongoing effort. Testing and learning are ongoing processes that should include:
- Actively shaping demand by optimizing your customer experience, product offering, sales channels, etc.
- Driving an intelligent and agile response to demand by harnessing and applying advanced analytics
- Working to reduce bias and error over time
Fundamentally, demand forecasting is a useful approach to foresee what customers will need from your company in the future so that you can set aside goods and resources to satisfy that demand.
By predicting demand, you can avoid paying holding fees and other operational costs when they are not necessary and make sure you are prepared to handle peak periods when they occur.
4. Forecasting case studies
Once you have the basis for your sales forecast in place, you should define and track the following metrics over the entire forecast period.
IKEA
The point-of-sale (POS) and warehouse management system data used in IKEA’s inventory management strategy are provided through a proprietary inventory system. How much product reaches the shop directly from suppliers and from distribution centres is described in IKEA’s business plan. The logistics manager can use this data to predict sales for the coming days with accuracy and place product orders to satisfy anticipated demand. The manager manually counts the products in stock if the sales data doesn’t match the project turnover for that day.
A human method serves as a safety net to assure total accuracy in this outstanding illustration of forecasting technology assisting business logistics.
Zara
Because of their just-in-time production strategy, Zara can design, produce, distribute, and sell clothing in as little as two weeks. Due to their extensive internal production, they are able to be more flexible in their production cycle and have greater control over the supply chain and manufacturing process than rivals. How do they maintain such a productive cycle, then? Data on sales and client input is immediately forwarded to Zara designers so that changes can be made as soon as possible and in accordance with customer desire. In order to satisfy shifting demand, Zara also always has excess labour capacity, which supports the company’s lean inventory management strategy.
Walmart
Walmart’s supply chain is understandably complicated given that it has more than 11,000 locations across 27 countries and an average of $32 billion in inventory. However, while having accurate and cutting-edge logistics, they also gained a reputation in 2013 for having a significant in-store out-of-stock issue. Mismanaged inventory, or the fact that there wasn’t enough workers to move the merchandise from the warehouses to the shelves, was blamed for Walmart’s shortage of inventory on the shelves. Many customers in this case had a bad experience as a result of cost-cutting initiatives, which could have been avoided by accurately anticipating demand.
Nike
Nike installed demand-planning software in 2001 without conducting enough testing, which resulted to an excess of shoes that didn’t sell well and a shortage of the well-known Air Jordans. Nike lost revenues of $100 million as a result of this. In this instance, Nike suffered because it rushed the implementation of a new system. Demand and forecasting technology is crucial for estimating sales and controlling inventories, but each new system should undergo thorough testing prior to implementation.
5. Automated demand forecasting
The traditional methods of manually manipulating and interpreting data to forecast demand simply aren’t practical for businesses that are beholden to fast-changing customer expectations and markets.
Demand forecasting must take place in real time for firms to actually be agile and informed by the most recent data. This necessitates using technology to handle the labor-intensive tasks.
The demand forecasting feature in QuickBooks Commerce, for instance, combines important sales and inventory data to spot trends and glean information about anticipated demand at the level of detail you select—by product, variant, region, etc. On the basis of automatically predicted sales demand, the system also initiates automated inventory warnings with suggested reorder quantities. In other words, you don’t need to conduct any manual forecasting in order to know when to restock goods and to make data-driven business decisions.
