Supply and demand – they’re what commerce depends on. But how can you predict demand so that you can alter supply to match?
That’s where demand forecasting comes in. The market is always changing, but there are patterns that can be used to predict what changes will occur. Using artificial intelligence, demand forecasting software analyzes past trends, current data, and test markets.
By doing this, it can quantitatively determine patterns of demand and use them to predict future trends, allowing the business to improve efficiency, accuracy, and effectiveness.
Demand predictions form the basis of short- and long-term business decisions, from risk assessment and financial planning to supply chain management and make-to-stock.
Traditionally, businesses have used qualitative methods to predict demand, relying on the advice of economic experts or sales agents who have their fingers on the pulse, or on market research: surveying their clients on a random sampling basis.
But optimization software has allowed executives to predict demand using quantitative data through machine learning data analysis.
Demand forecasting methods
One method is to analyze a time series based on previous sales data, from which cyclical or seasonal patterns and general trends can be extracted.
Demand forecasting software can also analyze historical data through autoregressive integrated moving-average and complex mathematics to determine causal relationships between demand and factors such as advertising/marketing and competitors.
A third type of demand forecasting is to analyze economic indicators in the present to predict upcoming trends, analogous to how a meteorologist analyzes atmospheric conditions to predict the weather.
Demand forecasting results
A sailor needs a map and compass when traveling unfamiliar seas; in the same way, a business leader needs reliable data, or else they may make suboptimal decisions. They may order more inventory than they can sell (or not enough), purchase advertisements when they are not effective, and ultimately lose profit.
The results of demand forecasting can include seasonal variations, such as spikes in the summer, and what direction a product’s popularity will trend in the future.
Understanding this, a business can make informed decisions.
For example, international apparel manufacturer Zara makes its products on a quick turnaround: a two-week cycle. Because of this, the company relies on demand forecasting to make sure it allocates resources in a way that is appropriate to demand.
Demand forecasting is a vital part of business decision-making. Technologies like artificial intelligence allow companies to not just analyze data, but to do it in a do it in the automatic, nimble way the ever-changing economy requires. The result? The ability of decision-makers to make well-informed decisions to further their business.