What: Mix-and-match forecasting combines multiple models and automates their selection to improve supply chain reliability and reduce manual intervention.

Why it is important: Adopting automated, context-aware forecasting models allows retailers to balance cost control with agility, a priority highlighted in recent sector analyses.

Supply chain forecasting in retail faces growing uncertainty due to volatile demand, product diversity, and shifting market conditions. The mix-and-match approach assembles a repertoire of forecasting models (statistical, machine learning, and deep learning), each suited to specific scenarios, then automates selection of the best-performing one for each forecast cycle. This reduces manual adjustments and improves reliability without locking planners into a single method. Models are evaluated by context, data quality, and operational priorities, aligning forecasts more closely with real-world constraints. The framework increases forecast accuracy, simplifies planning processes, and makes the selection logic transparent enough to build team confidence in outputs. Successful implementation requires attention to data quality, performance monitoring, and a gradual start with a defined set of models. In an environment where AI is often discussed in abstract terms, mix-and-match forecasting stands out for its pragmatism and measurable impact on supply chain resilience.

IADS Notes: The Robin Report in March 2026 and September 2025 documented how geopolitical instability and policy shifts are forcing retailers to invest in scenario planning and more adaptive supply chain tools. Retail Touchpoints and BCG, writing in January and February 2026, tracked the sector's move from generic AI toward domain-specific models, with model diversity and structured evaluation now seen as prerequisites for reliable forecasting. BCG in April 2026 and Zebra in October 2025 found that AI deployment across pricing, procurement, and operations is delivering measurable margin gains while reducing manual workload. Retail Touchpoints and BCG in early 2026 further confirmed that models trained on proprietary data outperform generic alternatives in accuracy and adaptability. Journal du Net in January 2026 and Harvard Business Review in March 2026 noted that scalable implementation depends on operational simplicity, modular architecture, and sustained attention to data quality.

"Mix-and-match" forecasting: a strategic approach to improving supply chain reliability