At a time when businesses in virtually every industry are facing increasing demand volatility and rapidly-evolving market conditions, sophisticated demand forecasting models are more important than ever.
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But what is demand forecasting, you might ask? Simply put, it refers to making estimations about future customer demand using historical data and other information. Proper demand forecasting gives businesses valuable information about their potential in their current market and other markets, so that managers can make informed decisions about pricing, business growth strategies, and market potential.
Without demand forecasting, businesses risk making poor decisions about their products and target markets – and ill-informed decisions can have far-reaching negative effects on inventory holding costs, customer satisfaction, supply chain management, and profitability.
There are a number of reasons why demand forecasting is an important process for businesses:
Most traditional demand forecasting techniques fall into one of three basic categories:
Qualitative forecasting techniques are used when there isn’t a lot of data available to work with, such as for a relatively new business or when a product is introduced to the market. In this instance, other information such as expert opinions, market research, and comparative analyses are used to form quantitative estimates about demand.
This approach is often used in areas like technology, where new products may be unprecedented, and customer interest is difficult to gauge ahead of time.
Time series analysis
When historical data is available for a product or product line and trends are clear, businesses tend to use the time series analysis approach to demand forecasting. A time series analysis is useful for identifying seasonal fluctuations in demand, cyclical patterns, and key sales trends.
The time series analysis approach is most effectively used by well-established businesses who have several years’ worth of data to work from and relatively stable trend patterns.
The causal model is the most sophisticated and complex forecasting tool for businesses because it uses specific information about relationships between variables affecting demand in the market, such as competitors, economic forces, and other socioeconomic factors. As with time series analyses, historical data is key to creating a causal model forecast.
For example, an ice cream business could create a causal model forecast by looking at factors such as their historical sales data, marketing budget, promotional activities, any new ice cream stores in their area, their competitors’ prices, the weather, overall demand for ice cream in their area, and even their local unemployment rate.
While seasonality refers to variations in demand that occur during specific times on a periodic basis (such as the holiday season), trends can occur at any time and signal an overall shift in behavior (such as a specific product growing in popularity).
When it comes to demand forecasting, you should factor in estimates of trends and estimates of seasonality to accurately plan your inventory management strategy, marketing efforts, and operational processes.
Successful demand forecasting isn’t a one-and-done task. It’s an ongoing process of testing and learning that should involve:
Essentially, demand forecasting is a good way to anticipate what customers are going to want from your business in the future, so you can prepare inventory and resources to meet that demand.
By forecasting demand, you’ll be able to cut down on holding costs and other operational expenses when they’re not needed while ensuring you’re equipped to handle peak periods when they happen.
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. For businesses to have a truly agile and up-to-date data informed approach to decision-making, demand forecasting needs to happen in real time – and that means utilizing technology to do the hard work for you.
TradeGecko’s demand forecasting functionality, for example, uses key sales and inventory data to identify patterns and pull out insights about future demand at your chosen level of granularity: by product, variant, location, etc. The system also triggers automated inventory alerts with recommended reorder quantities based on automatically forecasted sales demand. In other words, you can know when to reorder stock and make data-informed business decisions without needing to do any of the forecasting manually. That equals greater cost efficiency and time savings – two things that are integral to the success of any business.
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