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>85%

WEEKLY FORECAST ACCURACY

Volume Forecasting for Smart Mobility

Global Travel Organization — Operations & BPO · 2024

[THE CHALLENGE]

Planning in the Dark.

A global travel organization supporting operations across major international routes faced a critical workforce planning problem: unpredictable support volume. Historic patterns were disrupted by route changes, seasonal variance, marketing campaigns, and external events — making manual headcount planning unreliable.

Overstaffing during quiet periods was burning budget. Understaffing during peaks was burning SLAs and creating customer experience failures. The operations team was planning 2 weeks ahead with accuracy that barely exceeded a random guess — then reacting to reality when it diverged.

The cost wasn't just financial. Every emergency headcount scramble eroded team confidence, vendor relationships, and management credibility. A scalable, automated forecasting capability was no longer a nice-to-have.

Build a forecasting system that gives operations 2+ weeks of accurate demand visibility — and that runs itself.

[OUR APPROACH]

Five Phases.
One Model.

PHASE 01

Historical Volume Analysis

Ingested 18+ months of historical support volume data across channels, regions, and product lines. Identified key variance drivers: day-of-week patterns, holiday effects, route launch calendars, and campaign uplift windows. Baseline accuracy of existing planning process benchmarked at ~54%.

Data AnalysisPattern RecognitionBaseline Benchmarking

PHASE 02

Feature Engineering

Engineered 40+ features: day-of-week dummies, rolling lag features (7, 14, 28-day), public holiday flags by region, route launch indicators, marketing campaign flags with lead/lag windows, and contact reason distribution shifts. Feature importance analysis used to prune redundant signals.

Feature EngineeringSHAP AnalysisPython (Pandas)

PHASE 03

Ensemble Model Development

Developed an XGBoost + Prophet ensemble: XGBoost captures short-term patterns and operational features; Prophet handles trend decomposition and seasonality. Ensemble voting via weighted averaging, calibrated against 6-month held-out validation set. LSTM explored as a tertiary component — included where validation improvement exceeded 2%.

XGBoostProphetLSTM (Keras)Ensemble Methods

PHASE 04

SageMaker Deployment

Containerized model deployed on AWS SageMaker with a daily automated retraining pipeline via Apache Airflow. Forecast outputs written to S3 and surfaced through a Power BI planning dashboard. Scheduling and headcount recommendation logic overlaid on raw forecasts using operational capacity rules.

AWS SageMakerApache AirflowPower BI

PHASE 05

Monitoring & Accuracy Governance

Weekly accuracy reports distributed to operations leadership. Automated retraining triggered when rolling 4-week MAPE exceeds 12%. Drift detection monitors feature distribution shifts. Model performance history logged in MLflow for auditability and trend analysis.

MLflowMAPE TrackingDrift Detection

[KEY OUTCOMES]

The Results.

>85%

Weekly Forecast Accuracy

MAPE <15% sustained over 6+ months post-deployment across all regions.

18%

Overstaffing Reduction

Annualized headcount cost savings through right-sized daily staffing plans.

22%

SLA Adherence Improvement

Reduced understaffing peaks eliminated the primary driver of SLA breaches.

Reactive Scrambles

Emergency headcount requests eliminated within 3 months of deployment.

Forecast confidence increased from ~54% baseline (manual planning) to >85% ML-powered accuracy. Operations leadership gained a reliable 14-day planning horizon for the first time.

[TECHNOLOGY USED]

Stack.

ML MODELS

XGBoostFacebook ProphetLSTM (Keras)Scikit-learn

PLATFORM & ORCHESTRATION

AWS SageMakerApache AirflowAmazon S3AWS Lambda

MONITORING & VISUALISATION

MLflowPower BIPython (Pandas, NumPy)

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