Efficient supply chain planning enables organizations to optimize the balance between supply and demand. As a critical component of demand planning, forecasting aims to capture business intelligence at various planning horizons.
By leveraging advanced analytics to improve forecast accuracy, businesses have the opportunity to realize economic benefits and operating efficiencies, while providing better visibility into financial and operational performance.
Though forecasting requirements vary by business and industry, there are several key elements that enable organizations to apply advanced analytics as an integral part of their forecasting process. Below is an advanced forecasting analytics initiatives checklist:
Outlier Detection & Management
Detect, identify, estimate, and adjust unusual and influential observations (i.e. outliers). Multiple types of outliers should be considered, including level-shifts (seasonal and non-seasonal), transitory change, and innovative outliers. Avoid common pitfalls , such as assuming outliers represent a single-period event, or treating all outliers as additive.
Identify and incorporate suitable internal and external factors based on domain knowledge and discussions with business managers. These demand-shaping factors aim to incorporate additional layers of intelligence and provide greater intuitive understanding of the forecast data.
Select Best-Fit Algorithm
Run the adjusted demand history and demand-shaping factors into a suite of advanced algorithms, such as multiple variants of exponential smoothing, seasonal-trend, local regression, and Box-Jenkins ARIMA models. Compute forecast error (either relative or absolute), depending on the nature of demand, at the appropriate lag and hierarchy by leveraging a robust backcasting mechanism.
Estimate the degree of forecastability at different hierarchical levels. Segment along different dimensions such as revenue and forecastability to support decisions about processes that drive efficient demand management.
New Product Introduction Forecasts
Implement a forecasting methodology for new items with limited demand history using techniques such as identifying like items or using a forward looking forecast model.
There is no magic click that will make a mathematical forecast model work flawlessly; the forecasting process and algorithms should include the nuances of each particular business. Combining all the intricacies described above is challenging, yet necessary, to implement advanced analytics in an organization’s forecasting process.