Demand forecasting is an area of predictive analytics that seeks to understand & predict customer demand by optimizing supply decisions by corporate supply chains & business management.
But what is demand forecasting method, you might ask? Simply put, it extends to making estimations about future customer demand using historical data and other knowledge. Proper demand forecasting solution gives businesses valuable knowledge about their potential in their current market and other markets, so that managers can make clued decisions about pricing, business growth strategies, and market potential.
Without demand forecasting, businesses risk making poor choices about their products and target markets – and ill-informed decisions. It can have far-reaching weak effects on inventory holding costs, customer satisfaction, supply chain management, and profitability.
There are a number of reasons why demand forecasting methods is an important process for businesses:
- It allows businesses to more energetically optimize inventory, increasing turnover rates and reducing holding costs.
- It provides an insight into upcoming cash flow, meaning businesses can more correctly budget to pay suppliers and other operational costs, and invest in the growth of the business
- Anticipating demand means knowing when to gain staff and other resources to keep operations running smoothly during peak periods.
Types of demand forecasting
Most traditional demand forecasting methods fall into one of three basic categories:
Qualitative forecasting
In this instance, other data such as expert opinions, market research, and comparative analyses used to form quantitative estimates about demand.
This path is often used in areas like technology, where new products may be extraordinary, and customer interest is difficult to gauge ahead of time.
Time series analysis
When historical information is available for a product or product line and trends are clear. Businesses tend to use the time series analysis tends to demand forecasting. A time series analysis is useful for analyzing seasonal fluctuations in demand, cyclical patterns, and key sales trends.
The time series inquire path is most effectively used by well-established businesses. Who have several years’ worth of data to work from and relatively stable trend patterns.
Causal models
The causal model is the most practical and complex forecasting tool for businesses. Because it uses specific data about relationships between variables affecting demand in the market. Such as competitors, economic forces, and other socioeconomic aspect. As with time series analyses, historical data is path 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 information, marketing budget, promotional activities, any new ice cream stores their area, their competitors’ prices. The climate, overall demand for ice cream in their area, and even their local unemployment rate.
Forecasting seasonality and trends together
While seasonality assign to variations in demand that occur during specific times on a periodic basis. Trends can found at any time and signal an global shift in behavior.
When it comes to demand forecasting, you should aspect in estimates of trends and estimates of seasonality to accurately plan your inventory management strategy, marketing efforts, and operational processes.
- Actively building demand by increasing your customer experience, product offering, sales channels, etc.
- Driving an intelligent and quick response to demand by utilize and applying advanced analytics
- Working to decrease bias and error over time
Essentially, demand forecasting is a good way to forecast 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 grabbing costs and other operational outlays when they’re not needed while ensuring you’re equipped to handle peak periods when they happen.
Automated demand forecasting: taking the guesswork out of growth
The traditional process of manually manipulating and interpreting data to forecast demand simply aren’t practical for businesses that are grateful to fast-changing customer expectations and markets. For businesses to have a truly quick and up-to-date data informed approach to decision-making, demand forecasting needs to happen in real time – and that means sharing technology to do the hard work for you.
Planvisage demand forecasting functionality, for example, uses key sales and inventory data to classify patterns and pull out insights about future demand at your called level of granularity: by product, variant, location, etc. The system also switches 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 specifying to any of the forecasting manually. That equals greater price efficiency and time savings – two things that are integral to the success of any business.
