Demand Shaping & Optimization with AI-powered Driver-based Forecasts
Traditional approaches to Demand Forecasting are trend-extrapolation based, similar to driving a car by looking into the review mirror. This is intuitively dangerous, and now increasingly fatal given polluted history due to pandemic and other disruptions driven by wars and recessionary pressures, etc. What Demand Planners need is a forward-looking engine that can both enable demand forecasting based on relevant driver trends as well as enable an interactive demand shaping or an automated demand optimization powered by AI. Such Demand Shaping & Optimization is critical in the bi-directional orchestration of Demand and Supply in a mature IBP process. Typical IBP solutions follow an archaic sequential S&OP process of Demand Review followed by Supply Review. This approach locks a Demand statement, then tries to identify the least cost based supply response. Flaw with this cost optimization approach is that it misses out on margin optimization opportunities: through modeling changes in pricing, promotions, new product launch schedules, etc. to leverage current supply & capacity to optimize margin.
Demand forecasting is a critical component of supply chain management, helping businesses predict future customer demand and plan accordingly. Among many forecasting methods, trend-based forecasting and driver-based forecasting are widely recognized for their effectiveness. Traditional trend-based forecasting methods rely heavily on historical sales data to predict future demand but often fail to consider the dynamic and complex nature of market forces. However, Driver-based demand forecasting offers a more advanced approach by integrating various external and internal factors or "drivers" that affect demand. Enhancing trend-based optimal forecasting with driver-based forecasting can lead to more accurate and insightful predictions.
Trend-based forecasting involves analyzing historical data to identify underlying patterns or trends. These trends can be linear, exponential, or seasonal, depending on the nature of the data. By extending these identified trends into the future, forecasters can predict future values. Techniques such as time series analysis, moving averages, exponential smoothing and decomposition models are commonly used. This method is simple, data-driven and effective in stable environments, but it assumes past trends will continue unchanged, lacks consideration of external factors and is typically more accurate for short-term predictions. Whereas Driver-based demand forecasting incorporates a wide range of factors that can influence demand. These drivers can be external ( economic indicators, market trends, competitor actions and regulatory changes) or internal (promotional activities, product changes, supply chain factors and internal policies). Identifying the right drivers is crucial for accurate driver-based forecasting. These drivers can vary significantly depending on the industry, market conditions and the specific business context. Nevertheless, it can be complex as it requires extensive data and may introduce subjectivity through driver selection and assumed relationships.
The driver-based forecasting process starts by defining high-level qualitative business goals such as driving revenue growth, increasing profitability or reducing churn. It's essential to keep these goals focused to maintain the simplicity and clarity of the forecasting model ensuring it is understandable and actionable for all business leaders. After setting these objectives, the next step is to determine demand forecasting KPIs that will track progress such as sales volume, order fill rates, stockout rate, inventory turnover, lead time, and forecast accuracy. This alignment ensures the forecasting model is based on measurable outcomes. The next step is to break down these KPIs into specific drivers (factors influencing the KPIs), assumptions about these drivers and the resulting impacts. This results in a detailed dynamic model that links your business goals to actionable insights.
By identifying and quantifying the impact of these drivers on demand businesses can create a more comprehensive and adaptive forecasting model. Systematic analysis of these drivers help businesses to develop more accurate and responsive demand forecasts. For example, understanding the impact of a competitor’s pricing strategy or a new advertising campaign on demand can offer deeper insights than relying solely on historical data. If a retailer wants to forecast demand for a seasonal product, a trend-based method might identify consistent sales increases during specific months. Rather a driver-based method could reveal that these sales spikes are significantly influenced by promotional campaigns and changes in consumer income levels. By integrating these insights the retailer can develop a more accurate forecast that incorporates both historical trends and external drivers.
The integration of driver-based and trend-based forecasting involves combining historical data analysis with the evaluation of key drivers. This hybrid approach leverages the strengths of both methods resulting in a more robust forecasting model. The process begins with data collection, gathering historical sales data along with relevant driver data from sources (internal databases, market research reports, and economic indicators).Then the key drivers that significantly impact demand are identified through statistical analysis, expert judgment or machine learning techniques. Developing the forecasting model involves using advanced statistical methods such as multiple regression or machine learning algorithms which integrate trend analysis and driver impacts. The model is then validated and tested with historical data to ensure accuracy before being implemented in real world scenarios.
Advanced techniques can further enhance this integration. Machine learning algorithms such as neural networks and decision trees can identify complex patterns and relationships between the forecasted variable and its drivers. Scenario analysis involves creating multiple forecast scenarios based on different assumptions about the key drivers by providing a range of possible outcomes. Dynamic regression models such as ARIMAX, merge trend-based and driver-based forecasting by integrating external drivers into time series analysis. As data availability and analytical capabilities continue to improve, the integration of these forecasting methods will become increasingly important for achieving optimal forecasting outcomes.
Integrating driver-based forecasting with trend-based methods offers numerous benefits. One of the most significant is improved accuracy as considering a wider range of factors decreases the chances of stockouts or overstock scenarios. The model's enhanced flexibility allows it to adapt to market changes making it more resilient to unexpected shifts. More accurate forecasts lead to better decision-making, enabling superior strategic planning, inventory management, and resource allocation. Furthermore, businesses that can predict demand more precisely gain a competitive edge as they are better positioned to respond to market changes.
Although the benefits are evident, driver-based demand forecasting poses certain challenges. Ensuring data quality is crucial as inaccurate or incomplete data can lead to flawed forecasts. The complexity of integrating multiple drivers into a forecasting model can be resource-intensive. Additionally, market conditions and drivers can change over time which requires continuous monitoring and model adjustments. Successful implementation often requires collaboration across different departments such as marketing, finance, and operations to ensure all relevant drivers are considered.
In summary, enhancing trend-based optimal forecasting with driver-based methods is not just an option but a necessity for businesses aiming to stay competitive and responsive to market changes. By recognizing the limitations of traditional methods and leveraging the power of driver-based forecasting companies can unlock new levels of accuracy and efficiency in their demand planning processes. Get in touch with the Vyan Team to discover how these demand drivers can revolutionize your demand forecasting process and optimize business performance.
