What is it and why is it interesting for your business?
Demand forecasting is the process of predicting future customer demand for a product or service based on historical data, market trends, and other relevant factors. Demand planning is a crucial part of doing business as it impacts a lot of decisions in your company, like: production planning, inventory management, staffing, or even investment decisions to enter new markets.
Having efficient and accurate demand forecasting can deliver a competitive advantage, increased efficiency and better customer service.
How does it work?
Demand forecasting in retail businesses, particularly in industries which sell in items with limited shelf-live like the food industry, is essential for maintaining optimal inventory levels and maximizing sales while minimizing waste. Achieving this balance requires accurately predicting future demand for products, which is often influenced by various factors such as historical sales data, seasonality, weather conditions, promotions, and external events.
Manual forecasting methods become impractical as the scale of operations increases, leading to inefficiencies and inaccuracies. Implementing AI-driven forecasting solutions allows businesses to leverage machine learning algorithms to analyze large volumes of data and identify complex patterns that human planners may overlook. By considering multiple factors simultaneously, these models can generate more accurate forecasts, enabling businesses to optimize inventory management, reduce waste, and improve overall operational efficiency.
To ensure the effectiveness of demand forecasting, businesses must tailor their approach to account for specific product characteristics and store dynamics. For instance, certain products may exhibit seasonal peaks or fluctuations in demand based on external factors like holidays or promotions. Moreover, each store may have its own unique sales patterns and customer preferences, necessitating a granular approach to forecasting that considers store-specific data. By building predictive models at the product and store level and incorporating a broad set of shared drivers such as past sales, seasonality, holidays, promotions, and weather, businesses can enhance the accuracy and relevance of their forecasts.
Leveraging AI techniques like regression analysis allows for a more nuanced understanding of demand drivers and enables businesses to adapt quickly to changing market conditions. Ultimately, AI-driven demand forecasting empowers businesses to make data-driven decisions, optimize inventory levels, and enhance customer satisfaction, leading to improved profitability and competitiveness.
What do we need to pay attention to?
When conducting demand forecasting, it's essential to remain vigilant of concept drift, i.e., the statistical properties of demand fluctuating over time due to factors like market trends. Transparency in the model's decision-making process and identification of key drivers are crucial for understanding forecasted outcomes. This transparency allows stakeholders to make informed decisions based on a comprehensive understanding of the forecasted outcomes and associated uncertainties.
Additionally, striking the right balance between false positives and false negatives is vital for effective forecasting. While false positives may lead to overestimating demand and unnecessary inventory costs, false negatives can result in stockouts and missed sales opportunities. Achieving an optimal balance between these errors requires careful calibration of the forecasting model, considering factors such as the cost of inventory holding and lost sales.
