Navigating the World of Forecasting: A Deep Dive into Various Approaches

Selecting the optimal forecasting model for a product depends on various factors, such as its life cycle stage, seasonality, and historical sales data. A one-size-fits-all approach is not feasible. However, the following general guidelines can be considered:

Approach 1: Leveraging Historical Data

Products like pharmaceuticals, with lifespans of 15-20 years, offer ample historical data for reliable forecasting. Time series analysis, which accounts for trends and seasonal patterns, is well-suited for these products. For instance, Motilium, which experiences increased demand in South Asian countries during November and December due to seasonal illnesses, can benefit from this approach.

Approach 2: Balancing Historical Data and Expert Judgment

Industries like furniture, with shorter product lifecycles of 12-24 months, present a challenge for purely data-driven forecasting. A blend of historical data and expert judgment is often necessary. One approach involves combining growth or decline factors from recent months with those from the same period in the previous year. However, this baseline forecast requires significant input from sales and marketing teams to account for various market dynamics.

Approach 3: B2B Forecasting

B2B companies, such as machine manufacturers or steel producers, often rely on demand profiles at the account or business level. Sales managers, with direct customer relationships, play a crucial role in forecasting. Historical data and past consumption trends serve as a baseline for validating and refining these forecasts, rather than forming the sole basis.

Approach 4: Project-Based Forecasting

Large-scale machine manufacturers often treat each project as a unique endeavor. Past performance may not be a reliable indicator of future success due to the competitive nature of bidding processes. In such cases, probabilistic forecasting methods, which consider various scenarios and uncertainties, are more appropriate.

Ultimately, the choice of forecasting method depends on a company's specific operational model, market position, product characteristics, and strategic goals. A tailored approach, combining data-driven techniques and expert insights, is essential for accurate demand forecasting.