Inventory Forecasting for After-Sales Service Network

The service network operated under a strict three-day repair commitment across geographically distributed centers. Parts availability directly affected contractual obligations and customer satisfaction, while excess stock increased storage cost and capital exposure. Forecast accuracy therefore had immediate financial impact.

Historical repair data was unevenly distributed across materials and regions. A small subset of parts accounted for most repair volume, while long-tail materials showed sparse and irregular demand. Seasonal cycles and regional differences across EU branches introduced variability that could not be captured by aggregate models.

Inventory decisions followed asymmetric business rules, where shortages carried significantly higher penalty than overstock. Standard symmetric error metrics were insufficient for evaluating performance under these constraints.

Repair records contained boundary inconsistencies and non-uniform temporal patterns. Weekly aggregation improved operational relevance but increased sensitivity to outliers and rare demand spikes.

The forecasting framework needed to integrate with existing logistics systems and remain stable under continuous updates, scaling across materials and regions while preserving transparency for business stakeholders.

• Analyzed and normalized historical repair datasets covering 365K+ records, resolving boundary inconsistencies, temporal gaps, and irregular material identifiers
• Designed weekly time-series aggregation logic aligned with operational planning cycles across distributed service centers
• Performed material segmentation based on repair frequency, Pareto distribution, and demand volatility
• Implemented multiple forecasting models including Holt-Winters, SARIMA, Prophet, and baseline naive approaches for comparative evaluation
• Developed sliding-window cross-validation framework for time-series backtesting under rolling forecast scenarios
• Designed asymmetric cost metric incorporating weighted deficit and overstock penalties aligned with business rules
• Built forecast “shift” optimizer to bias predictions toward availability targets under non-symmetric cost exposure
• Evaluated model performance per material cluster rather than applying a uniform global model
• Integrated regional and seasonal signals including holiday calendars and sales correlations into model inputs
• Implemented automated model comparison pipeline with metric aggregation across time windows
• Validated forecast stability under rare-demand and long-tail scenarios
• Designed integration-ready forecasting interface suitable for connection to logistics and shipment planning systems
• Documented model assumptions, performance characteristics, and deployment requirements for operational transition