Demand forecasting, as the name implies, is the practice of anticipating demand before it occurs. It is a type of predictive analysis in which past sales data is studied to forecast consumer patterns. It is very helpful for businesses as it helps them match the production to the expected demand and avoid wastage. This also gives the business an estimate of the turnover and profit in a given situation. The business can also optimize its inventory based on predicted sales. Demand forecasting may be passive or active in nature.
This is a simpler method of forecasting using past data to predict a given quarter. It uses data from the same quarter for the same category of products, so it is not influenced by changes in other categories of consumption. A simple example of this can be an electronics manufacturer choosing to time the release of a product just before a holiday on which people tend to buy gifts or have shown increased consumption of electronics last year. This method is great if there’s a lot of historical data available and your sector is more or less stable.
This method accounts for changes as they happen and is therefore statistically more complex. It usually takes in external data, such as real-time market fluctuations and marketing campaigns, to predict demand. This is recommended for newer sectors where data is scarce. For example, certain startups furloughed their staff in anticipation of the market crash caused by the pandemic.
Artificial intelligence is a system that learns like a human brain. That is, it has the human-like ability to recognize and correct patterns, which a simple computing device lacks. AI-supported demand forecasting can read and analyze far more data than any team of human beings could at the same time. Further, AI can read data from multiple sources - the consumers, the supply chain, the share markets, and even the weather - to predict the demand for certain items with great precision. A report by McKinsey showed that 53% of executives reported an increase in revenue after introducing AI-based control of their supply chain. AI can incorporate real-time data and correct predictions on a day-to-day basis, and they get better with time.
The amount of customer data available has increased dramatically with the advent of cloud computing and smart home devices. It is expected that available data will double every 12 hours in 2025. Human beings or even traditional computing systems cannot handle such gigantic datasets, whereas AI systems become better with the amount of data available. The following may be the advantages of using AI-based forecasting tools:
For fast-moving products, AI forecasting can predict the demand within a single day, reducing wastage and inventory loss. Such hourly predictions are not possible by traditional techniques.
Automated supply chains can adjust inventory as data is analyzed in real-time. AI and machine-learning models can prune through giant data reserves quickly to show relevant information.
Most products are available via many online and offline modes. AI can predict how fast a product might move via each channel (such as curbside pickup, in-store shopping, online store, etc.) This optimizes product movement and storage.
Since demand is predicted accurately, warehouse space can be saved by having just enough inventory to meet demand.
As the data updates automatically, it saves on errors made by analysts and data scientists, and more fluctuations can be accommodated without human intervention.
AI can predict an increase in demand after a marketing campaign or even predict the demand for a new product based on gaps in the market.
Any anomalous development in buying or spending patterns of the customers can be analyzed by the AI system, and possible causes can be determined. It can also correct the resource allocation based on the analysis of the glitch.
However, AI strategies are fairly new, and research is still ongoing. When using AI for demand forecasting, avoid these pitfalls:
Resource allocation is done after the forecasts are made, and AI techniques can optimize this process. AI-based models lead to more flexible allocations so that no resource is wasted where it is not required. Since AI can compute both internal and external changes and also run what-if scenarios, resource allocation becomes more adaptive to change and more resilient to shocks. Agile allocation is the next challenge overcome with AI models. Such models can also help with the dynamic pricing of goods and services by recognizing the resources most in demand. Many e-commerce platforms even use such dynamic models to reposition their delivery fleets several times a day! That is an example of how fast resource allocation can be corrected by an AI model.
The demand-supply chain is a complicated system spanning many countries, and it is ever-dependent on fresh data. AI approaches can solve these complexities and deliver optimized inventories, better employee efficiency, and seamless operations. Fashinza is an early adopter of these techniques and solves supply chain problems in the fashion industry expertly. Visit fashinza.com to find out more about how to reduce your supply-chain problems.