Organizations experience many challenges in supply chains, and it is easy to lose a lot of money. Demand forecasting enables organizations to reduce supply chain costs and improve their financial planning, capacity planning, risk assessment decisions, and consequently boost their profit margins. According to studies, demand forecasting is the most popular machine learning application in supply chain management. Around 45% of global companies are already using demand forecasting technology, while 43% are currently shifting to using ai forecasting.

What happens is, machine learning algorithms enhance forecasting techniques in accuracy and boost replenishment processes. With such advancements, organizations can reduce the costs of cash in stock and out-of-stock cases. This guide helps you understand more about demand forecasting and its benefits.

What is demand forecasting?

Demand forecasting estimates the forecast of customer demand through analyzing the historical data. Companies use demand forecasting techniques to avoid and minimize the inefficiencies brought by the misalignment of supply and demand across business operations. By using demand forecasting methods, organizations can enhance their decision-making processes regarding risk assessment, cash flow, capacity, and workforce planning.

The advantages of demand forecasting

• It improves accuracy by time- machine learning algorithms use existing data to learn and make more accurate predictions over time.
• It boosts customer satisfaction- having enough stock at any given time improves customer satisfaction because they get products whenever they order them without delays. On the other hand, stock-outs lead to customer frustration.
• It boosts discount optimization- many businesses or organizations face the challenge of having cash in stock whereby specific products stay unsold for prolonged periods, interfering with cash flow. That leads to higher than predicted inventory costs and increases the risk of such products becoming obsolete, and the company can lose so much money. Demand forecasting reduces such scenarios.
• It enhances manpower planning- demand forecasting can support HR departments by making efficient tradeoffs between full and part-time employee mix to enhance HR effectiveness and optimize costs.
• Overall organizational efficiency- solid demand forecasting allows teams to focus on strategic issues instead of firefighting to increase or reduce stocks and headcount to manage unprecedented demand fluctuations.

AI demand forecasting

According to a study, ai demand forecasting can help companies reduce the supply chain networks' errors by 30-50%. With improved accuracy, sales that get lost due to inventory out of stock issues reduce by 65%, and warehouse costs also reduce by 10-40%. Ai demand forecasting links relevant internal and external data to ensure highly accurate demand forecasting.

The traditional forecasting models are getting obsolete because of the increased data derived from external sources and businesses. With the integration of machine learning into an organization's supply chain management, the organization can enhance forecast results' accuracy.

Artificial intelligence takes demand forecasting to the next higher level. It facilitates forecasts based on real-time data using internal and external data sources such as demographics, social media, online reviews, and weather. In new products that do not have historical data, ai demand forecasting identifies clusters of similar prior products and uses those data sets to make accurate predictions.

The bottom line

Demand forecasting enables organizations to optimize their sales, supply chain, and marketing operations to avoid having excessive products in stock and out-of-stock scenarios.