Top Three Demand Forecasting Methods

Demand forecasting is crucial for analyzing the market demand for a particular product or service. It is essential for business success because demand forecasting methods form the basis for inventory management, capacity planning, and market expansion. As with any other forecasting process, there is no set method or tool for supply and demand forecasting. Leading market players and prudent demand management officials use different demand forecasting methods and combine several processes to make the best decision.

The top three commonly used demand forecasting methods include:

  • Survey-based methods
  • Statistical methods
  • Opinion-polls

Client’s Profile

The client is a farm equipment manufacturing company based out of the United States.

Predicaments Faced

Also, due to the complexities associated with equipment design and development, the farm equipment company could not accommodate such rapid changes in demand. The inaccurate demand forecasting methods adopted by them resulted in recurring cycles of overproduction and underproduction. Such factors along with their inability to meet the demand of their clients jeopardized the client’s position as a global market leader, leading to millions in lost revenue.

Our Approach

The farm equipment manufacturer wanted to leverage Quantzig’s expertise to redesign their demand forecasting process and enhance their ability to predict demand accurately. To determine the strengths and weaknesses of the farm equipment company’s demand forecasting process we conducted a detailed audit of the adopted analytic approaches. This revealed the problems associated with the client’s demand forecasting methods.

To address these opportunities for improvement, Quantzig leveraged its expertise to outline a supply and demand forecasting process by leveraging supply chain analytics and advanced analytic techniques. This involved combining the client’s internal data with relevant external market data, including – pricing data, consumer data, stock exchange data, and others.

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Business Impact

The demand forecasting process recommended by our experts outperformed the accuracy of the farm equipment manufacturer’s previous approach by over 90%. By employing machine learning and supply chain analytics the company benefited from a more fluid demand forecasting model.

Moreover, the devised demand forecasting process turned out to be highly reliable and consistent, delivering high-accuracy supply and demand predictions over time. With such accurate predictions, the farm equipment company was in a better position to predict customer needs and develop effective products and strategic plans to meet them.

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