Demand forecasts are essential for the success of any business because they enable effective planning of production, storage and resources. How accurate they are, however, depends on the consideration of the correct input factors – and on choosing the most suitable method.
Creating reliable demand forecasts is a complex challenge that is certainly one of the most demanding disciplines in supply chain management. Of course forecasts should never be based on mere guesswork and intuition, but rather on reliability and future viability, extensive data collection and analysis. A wide variety of factors such as historical data, market trends, sales figures and seasonal fluctuations must be taken into account, correlated and interpreted in a meaningful way. The more successful this is, the better you will be able to control your supply chain and thus increase your company’s competitiveness.
Such smart demand forecasting enables you to plan budgets more effectively, optimize inventories, shorten delivery times and use resources more efficiently. For example, identifying which product needs to be available in which quantity at which time and in which location can save costs and, at the same time, improve product and service quality. However, the level of accuracy of the predictions that can be used as a basis for making well-founded business decisions varies considerably depending on the underlying method.
Historical data analyses or causal analyses, for example, are often still based on spreadsheets that can quickly reach their limits, especially with increasing data volumes. They then become very complex and confusing and can be prone to errors, which leads to limits in the representation of information graphically and their predictive power. Because of this many companies also rely on statistical software such as SAS or SPSS and programming languages such as Python to develop specialized algorithms for data analysis and forecasting. But these sometimes fail to capture all relevant data sources, have difficulties processing unstructured data, can sometimes fail to recognize or learn from patterns in historical data and cannot create predictive models.
The greatest success in demand forecasting is achieved using machine learning and AI - fields in which paretos is one of the world’s leading providers with its Decision Intelligence (DI) solution. Our industry leader uses its powerful AI model to automatically analyze the entire data pool, identify the influencing factors for strategic goals and KPIs and link all available data to create a significant overview. Automated AI pipelines are then used to select and train the best forecasting model for customer demand. Based on its method for dynamic demand and inventory forecasting, paretos then derives optimized forecasts taking into account all possible scenarios, on the basis of which solutions can be found for every use case within the business.
AI-powered demand forecasting enables companies to improve their inventory planning by up to 30% by providing maximally accurate and reliable forecasts of future customer demand for both regular sales and short-term promotions. This allows them to always ensure that the correct inventory is available at the right time, minimizing the risk of out-of-stocks and replenishment costs as well as overstocking. Not only does this save money, it also increases customer satisfaction – ensuring the success of the business.
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