Demand forecasting is one of the primary steps in supply chain planning. It is the process of predicting future demand for any product within a given span of time. When planners get an accurate prediction, they can easily predict raw materials requirements, production capacity, logistics capacity, and marketing strategies. The accuracy of any prediction relies on many factors, and any fluctuation in these factors can affect the precision, limiting the usability of demand forecasting. In this blog post, we will discuss the limitations of demand forecasting that hamper its correctness.
What Are the Limitations of Demand Forecasting?
- Unpredictable EventsThe accuracy of prediction can be greatly impacted by unexpected events, which include natural disasters, economic slowdowns, or any sudden change in consumer buying behavior. These unexpected events can cause a sudden change in demand that cannot be predicted using only historical sales insights.
- Limited Historical DataThe base of any forecast is data, and more data corresponds to higher accuracy. Predicting order requires a lot of high-quality info from different sources such as past year sales, marketing, and finances. In case this is not available, it is not possible to predict demand accurately.
- Changes in Consumer BehaviorIn today’s times, market scenarios change rapidly. Consumers are bombarded with new solutions, products, and services every day. This Impacts their buying behavior quickly, making the past demand forecast invalid and inaccurate.
- Lack of Data on New ProductsWhenever a company launches a new product in the market, it is extremely difficult for analysts to forecast their demand. As the offerings are new in the market, there is limited data available from past sales. Demand forecasting for new products makes it difficult for planners to anticipate how consumers will respond to newly launched merchandise.
- Model LimitationsForecasting models have several limitations, such as lack of accuracy, external factors, time consumption, limited scope, and assumption based. These models can be valuable tools for businesses, but they should be used in conjunction with other sources of information. They should be regularly reviewed and updated to ensure that it is precise.
- Human ErrorOne of the demand forecasting constraints is the lack of expertise in data analysis, data science, market research, and statistical analytics. If the people involved in the process do not have that expertise, it increases the possibility of human error impacting the accuracy of the forecast.
- Data InaccuracyThe demand forecast is as accurate as the data used for analytics. Most companies struggle with data capturing, storage, and maintaining their health. Even if the companies can capture enough info to project customer requirements, ensuring it is high quality and precise is very complex and difficult. And inaccurate data cannot provide correct predictions.
- SeasonalitySeasonality is cyclic fluctuations in demand because of different variables such as weather patterns, holidays, or cultural events. Seasonality can make forecasting more difficult, as historical data may not represent future patterns.
- Market CompetitionWhen there is fierce competition in the market, many competitors offer more alternatives. Because of such a high number of product alternatives, forecasting demand for that product becomes extremely difficult.
- Geopolitical and Economic FactorsGeopolitical and economic changes can have various impacts on forecasting, including changes in trade policies, political instability, natural disasters, climate change, energy policies, and diplomatic relations. Planners must stay current on these changes and adjust their plans accordingly to account for potential impacts on demand.
As the global markets are changing rapidly, the importance of demand forecasting for businesses increases to stay in the competition and enhance their customer experience. This is why they need to find more accurate solutions that ensure higher accuracy of the forecast.
3SC’s demand planning solutions use highly complex AI/ML-based algorithms to analyze historical and real-time data while considering factors such as market conditions, weather, and seasonality to provide accurate predictions.