Post on Tuesday, August 28th, 2018 in Accounting
Demand forecasting is basically looking into the future and predicting how your products will sell by using previous sales data as well as other related information. It helps retail businesses to make informed decisions about pricing, growth strategies and market potential.
It is also important for companies that need to solve the following issues:
This is what we’ll focus on in this article – solving these issues, with particular attention paid to demand and inventory forecasting strategies.
Typically a demand forecasting process involves the following steps:
Now let’s take a look at some common demand forecasting methods. The choice of a method depends on availability of historical data, because such data is not often available for new products or companies.
If you forecast demand for newly released products, you do not have any historical data yet, so the demand forecasting methods for such products are not based on past sales – you have to find a new way to gauge your demand.
Here are some of our favorite ways to accomplish this:
The evolutionary method assumes that a new product is an improvement over an existing one. According to this method, the demand is forecasted based on the demand for that old product. The method is appropriate only when forecasters or marketers are sure that customers would accept the new product as the improved version of the older one. It is perfect when forecasting demand for new versions and upgrades of technology-related products, such as electronic devices or software.
This forecast technique assumes that the new product is a substitute for a previous product, and that it meets the same expectations of the customers as the previous product did.
The potential consumer approach is based on analyzing the potential target audience for a product in cases when a product is unique and has no existing competitors. Estimating the size of a potential target market is also a part of this method. It’s difficult to be accurate with potential consumer analysis, but it does have value in that it will be easier to predict sales volume for your product.
This method is based on marketing a product in a specific geographic region or to a specific demographic group in order to see how it sells before introducing it to the wider market. The market segments should have a population that is demographically similar to your target market. Testing using market segments is good when a product is introduced in a specific country for the first time. However, it takes a long time to gain accurate data from market segments.
These methods are typically based on historical data from product sales, however, there are a few different ways to go about it, some less scientific than others. We’ll get into them here:
The consumer survey method consists of contacting consumers directly and asking them about their purchase plans. Specifically, a company can ask potential buyers whether they are going to buy any product within a certain time period. This method is a good fit when forecasting demands for industrial products.
This approach assumes that salespeople are closest to your customers and each of them can estimate expected sales in their respective segments. Such individual forecasts are then combined into a total company forecast.
This forecast technique involves sending out several rounds of polls to a panel of experts. After each round, the anonymous responses are collected and shared. The rounds are repeated until the experts arrive at a single consensus.
The econometric method analyzes historical sales data to find a relationship between sales volume and economic indicators, such as price, income, advertising, etc.
This method uses historical data patterns to analyze a past trend and try to project it into the future.
Time series analysis includes the following components: cycle (reaction of sales to events such as fluctuations of economic activities), unpredictable events (strikes or other unforeseeable events), trends (non-repeated sales patterns that the customers follow during a specific time period), and seasons (repeated increases in sales volumes during specific seasons, such as Christmas).
This is one of the most complex demand forecasting tools. It uses information about relationships between variables that affect the demand, such as competitors, economic forces, weather, unemployment rates, and other socioeconomic factors that are specific for your business. The causal method includes all relevant sub-methods such as consumption level, chain ratio, leading indicator, and end use.
This technique uses artificial intelligence and machine learning to collect data for forecasting and demand management. It can combine all of the methods mentioned above.
Demand forecasting is a crucial aspect of any inventory management system. Businesses refill their inventory based on demand forecasts. Therefore, inventory forecasting is directly related to demand forecasting.
It is crucial to define how much inventory is needed to satisfy the customer demand. Inventory forecasting refers to determining the optimal inventory quantities to order as well as calculating reorder points. It helps to prevent the issue of too much inventory, which can increase storage costs, or too little inventory, which can lead to customer dissatisfaction.
To forecast inventory, you will need the following figures:
Now you know what demand forecasting is and which methods you can use to predict the demand for your products as well as define how much product you should keep in stock.
Dynamic Inventory offers businesses a suite of comprehensive forecasting tools that will help you to streamline this process and manage your inventory more efficiently. Feel free to browse our website and see what we can offer, or contact us directly for more information.
Adam is the Assistant Director of Operations at Dynamic Inventory. He has experience working with retailers in various industries including sporting goods, automotive parts, outdoor equipment, and more. His background is in e-commerce internet marketing and he has helped design the requirements for many features in Dynamic Inventory based on his expertise managing and marketing products online.
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