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  • Writer's pictureAnantaya Pornwichianwong

How can AI Improve Demand Forecasting?

In the fast-paced business world, particularly in the retail and restaurant sectors, dealing with fluctuating demands is a constant challenge. Nowadays, consumer demands can shift rapidly. Utilizing demand forecasting techniques allows businesses to proactively prepare for such changes rather than waiting for them to happen, which is often too late.

Accurate demand forecasting is crucial for the survival of modern businesses. However, it's equally essential for demand forecasting to be flexible and adaptable to real-time changes.

AI technology has stepped in to increase the efficiency of the modern demand forecasting approach. According to McKinsey, implementing AI in demand forecasting for supply chain management can reduce errors by 20-50 percent, which leads to minimizing lost sales opportunities and stockouts by up to 65 percent.

In this article, Sertis will take you to explore how businesses can leverage AI technology to improve demand forecasting and generate increased profits.

What is demand forecasting?

Forecasting is a crucial method in the data world that involves analyzing relevant historical data through predictive analytics to identify patterns that can indicate future possibilities. We can apply this approach to various data types, including sales, inventory, freight routes, and traffic.

Demand forecasting involves predicting customer demand for goods and services, enabling businesses to plan their supply chain systems, manage inventory, prepare raw materials, and allocate employees effectively. By accurately forecasting demand, business owners can better prepare to handle customer needs, improve products and services, or develop new offerings to meet evolving market demands, leading to increased sales, revenue, and overall business success.

Current demand forecasting problems

Traditional demand forecasting that doesn't utilize AI relies on human labor to collect and update data and perform predictive analysis manually, which often leads to the following problems:

1. Inaccurate predictions due to market volatility.

The retail and restaurant industries are highly volatile and subject to frequent changes due to various external factors, such as emerging trends, differing environments at each branch, competitors' improvements, new promotions and marketing strategies, and seasonal demand fluctuations. Relying solely on historical data and manual forecasting is often insufficient and inaccurate. In the restaurant industry, where the freshness of ingredients is critical, and businesses must deal with various raw materials with different lifespans and varying levels of care, accurate forecasting is even more crucial.

2. Poor data quality

Simply having a large amount of data doesn't necessarily result in more accurate forecasts if the data's quality is poor. For instance, there may be enormous data in the retail industry. However, if it isn't properly organized before being analyzed and fed into the demand forecasting system, we may end up getting inaccurate and unreliable results. It can also create an additional workload for staff. According to Gartner, poor quality forecasting results in 30-60 percent of restaurant staff having to review and correct orders, making it difficult to automate operations.

3. Incomplete analysis

To accurately forecast customer demands, it is necessary to analyze all factors that influence the demands for every type of product (SKU) in each store, which can result in an enormous amount of data. It is beyond human capabilities to identify all relevant factors and comprehensively analyze all the data to get an accurate result.

How can AI technology improve demand forecasting efficiency?

All of the above problems can be solved by implementing AI technology to improve demand forecasting in the following aspects:

1. Improve demand forecasting accuracy.

AI technology can improve demand forecasting accuracy by utilizing machine learning models to analyze all relevant data, such as emerging trends, store environments, weather conditions, and sales. With the ability of machine learning to collect data on every SKU-level product category and to process massive amounts of data accurately, we can expect more comprehensive and precise analysis results.

Machine learning models can also automatically input and update new data to keep up with real-time changes. It allows more effective and accurate supply chain management and inventory planning, especially for fresh ingredients with limited lifespans that require special storage.

2. Fully automate demand forecasting.

AI has the ability to assist with nearly all human tasks, from data collection to providing real-time analysis results, making it possible to fully automate demand forecasting and significantly reduce the amount of manual labor required. One of the key advantages of using AI in demand forecasting is its ability to extract and transform data into an appropriate format, ensuring high-quality data for accurate analysis.

Automating demand forecasting also frees up businesses' time and personnel resources, allowing those businesses to allocate those resources to other tasks that can create more value.

3. Facilitate informed strategic decisions based on demand forecasting.

What is more important than accurate demand forecasting is the ability to use those predictions to improve businesses. By utilizing AI-powered demand forecasting, business owners can gain deeper insights that provide necessary information to make more informed, accurate, and timely supply chain planning decisions.

4. Ensure scalability to support business expansion.

As a business expands, such as a restaurant opening new branches, the amount of data that needs to be analyzed increases to the point where manual demand forecasting becomes insufficient and can't keep up with business growth. AI, which can work with enormous amounts of data, is more flexible and adaptable to business expansion and the increase in data. It allows businesses to scale up their operations without running into bottlenecks or performance issues, which is more sustainable in the long run.

Benefits of implementing AI-powered demand forecasting

  1. Facilitate more efficient supply chain and inventory management, resulting in a more accurate daily allocation of raw materials and staff to provide customers with desired products in time, reducing lost sales opportunities and increasing sales and profits.

  2. Reduce costs from inaccurate planning, prevent over-ordering to avoid the cost of overstocking, and save time and personnel resources used in traditional demand forecasting.

  3. Develop strategies that meet customer needs by understanding their preferences and behavior and setting prices and promotions accordingly.

  4. Increase customer satisfaction by ensuring all products are available to meet customer needs and providing faster services to reduce wait time.

We develop intelligent demand forecasting solutions that leverage the power of AI, customized to meet your business's specific needs and requirements. With a team of experts in the data and AI world, we are ready to collaborate with you to enhance your business and drive greater profits.

Learn more about demand forecasting solutions from Sertis at:


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