Global Health & Wellness Brand – Building an Enterprise Demand Estimation Platform to Reduce Forecast Error and Strengthen Supply Chain Resilience

Introduction

A global health and wellness brand operating in a multi-level marketing model needed a modern, data-driven demand estimation system. During COVID, the company experienced severe supply chain pressures as demand surged unpredictably across distributors and customers. Leadership needed a forecasting platform that was accurate, explainable, auditable, and suitable for investor-facing projections for a publicly traded organization.

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The Problem (Gut-Based Decisions)

Before the engagement, forecasting was highly manual and inaccurate:

• Demand projections across distributors and end customers were off by nearly 25%.
• The supply chain team struggled to anticipate demand spikes caused by promotions, seasonality, and network behavior.
• Existing forecasting relied on spreadsheets and disconnected reports rather than a unified model.
• Inventory planning was reactive, costly, and often misaligned with true demand.
• The company needed forecasts they could present confidently to investors — requiring transparency, governance, and auditability.

The organization had data, but no reliable way to combine it into a scientifically grounded, explainable forecast.

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The Solution (How Data Changes the Game)

We built a robust, enterprise-grade demand estimation platform using modern cloud and machine learning tools.

1. Redshift Enterprise Data Warehouse

• Served as the centralized repository for all historical sales, distributor behavior, customer activity, and promotional data.
• Designed to support high-performance ML feature pipelines and scalable modeling.

2. Data Ingestion Using Attentive + Informatica

Integrated data from key operational systems, including:

• SAP Hybris (e-commerce transactions)
• Exigo (MLM distributor activity and downline behavior)
• Attentive (engagement, messaging, and customer behavior signals)

All pipelines fed structured, enriched data into Redshift for modeling.

3. Machine Learning in Dataiku

We built a demand estimation model that:

• Utilized 15 distinct features, including:
  • Distributor order behavior
  • Customer order patterns
  • Past promotional events
  • Seasonality and product cycles
  • SKU-level movement
  • Network engagement signals
• Allowed manual overrides for planners when market conditions required it.
• Included model explainability, showing why the forecast predicted certain values — a critical requirement for both internal confidence and investor communication.

4. Tableau Reporting Layer

Delivered clear, visual dashboards for:

• Forecast vs. actual performance
• Feature impact analysis and model explainability
• Inventory planning and SKU-level demand curves
• Executive-level KPI monitoring
• Audit trails and override tracking

This created a transparent forecasting workflow accessible to supply chain, finance, and executive leadership.

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Real-World Example (Specific Client Outcomes)

The impact was immediate and measurable:

• Forecast accuracy improved from ~25% error to just 4% error.
• The company saved millions of dollars in inventory costs.
• Supply chain disruptions were dramatically reduced.
• Leadership gained a forecasting process that was:
  • Explainable
  • Auditable
  • Repeatable
  • Aligned with investor expectations
• Because the business was publicly traded, the system’s transparency and auditability were essential — and the new forecasting workflow became a core component of quarterly projections shared with the market.

The platform elevated demand planning from a reactive function to a strategic, investor-relevant capability.

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Conclusion

By integrating SAP Hybris, Exigo, and Attentive data into Redshift, orchestrating ML models in Dataiku, and delivering explainable insights through Tableau, the company transformed its demand planning process. The forecasting error dropped from 25% to 4%, strengthening supply chain resilience, reducing inventory waste, and enabling confident communication with investors. The result was a modern, auditable demand estimation engine that supported both day-to-day operations and long-term strategic forecasting.