AI / ML · Supply Chain · Analytics
MEM Executive Challenge, NC State · Aug 2024 to Dec 2024
Role
Product Manager & Business Analyst
Type
Academic Competition
Client
AutoTech Motors (case study)
Outcome
Competed, did not place
The Challenge
The MEM Executive Challenge is a competition where teams are given a complex enterprise case study and asked to develop a strategic recommendation. Our team was assigned AutoTech Motors, a fictional automotive manufacturer dealing with serious supply chain inefficiencies after COVID-era disruptions exposed how brittle manual forecasting really is.
The ask: design an AI-powered supply chain transformation strategy, including solution architecture, implementation roadmap, and projected ROI. I led the business analysis, solution framing, and final presentation.
The Problem
Forecast method
Manual
spreadsheet-based, no ML
Supplier visibility
Reactive
delays found after the fact
Inventory approach
Historical
stockouts during demand spikes
AutoTech was losing revenue to stockouts during holiday demand spikes while simultaneously tying up capital in excess inventory during slow periods. The root cause: no predictive capability and no real-time supplier visibility. Every decision was made looking backward.
Our Recommendation
Rather than proposing a single silver-bullet tool, we recommended a layered architecture connecting demand forecasting, supplier visibility, data integration, and inventory optimization.
01
Random Forest Demand Forecasting
DemandML model trained on historical sales, seasonality, and market data to predict demand with 80-85% accuracy, replacing manual spreadsheet forecasting.
02
SAP Real-Time Supply Chain Visibility
VisibilitySAP integration providing end-to-end visibility across global supplier networks with proactive disruption alerts instead of reactive scrambling.
03
Multi-Source Data Pipeline
InfrastructureContinuous aggregation from suppliers, logistics partners, and market feeds feeding all AI models with clean, validated real-time data.
04
AI-Driven Inventory Optimization
InventoryDynamic safety stock and reorder point calculations balancing carrying costs against service level requirements, reducing excess without creating stockouts.
Projected Impact
80-85%
forecast accuracy
20-30%
inventory reduction
$2-3M
projected annual savings
50%
faster disruption response
Implementation Roadmap
Foundation
Form AI team, select vendors, set up infrastructure, run data quality audit.
Integration
SAP integration, ML model training, workforce training, pilot with select product lines.
Security & Governance
Cybersecurity framework, risk management, monitoring dashboards.
Full Rollout
System-wide deployment, advanced analytics, continuous model improvement.
Reflection
We did not place in the competition. Looking back, the strategy was thorough but the presentation lacked the sharpness and decisiveness that a panel of executives would respond to. We over-indexed on comprehensiveness and under-indexed on a clear, confident recommendation.
“Executives do not want every option on the table. They want you to tell them what to do and why. That is the lesson.”
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