SKU Segmentation for a Global Retailer Authors: Brad Gilligan, Huiping Jin Advisor: Edgar Blanco 11/7/2018 MIT SCM ResearchFest 2014

Agenda Project Objective and Challenges Overview of Data Initial Analysis of Supply Chain Process and Relevant Data Introduction to Neural Network Models Model Performance Summary Key Insights 11/7/2018 MIT SCM ResearchFest 2014

The Purpose of Segmenting SKus Identify products that can benefit from different supply chain processes. Commonly used to maximize service levels for high-value items and minimize cost for low-value items. A Typical SKU Segmentation Balanced Responsive Supply Chain Efficient Supply Chain High Value Low Value High Demand Volatility Low 11/7/2018 MIT SCM ResearchFest 2014

Unique Challenges of this Project Traditional Segmentation This Segmentation Product and Supplier Mix: Somewhat consistent mix of products and suppliers. Constantly changing product mix and supplier pool. Many SKUs purchased only one time. Segments determined by: Demand patterns, cost, margin, value-density, etc. Planned sale month, buyer behavior, and historical shipment timing. "Segments": Product types with similar attributes (i.e. innovative products vs. commodity products). SKUs with more or less time to reach market based on PO data and historical timing. Can be grouped by different strategies (expedite, ship normally, or delay). For this company, margin does not drive buying behavior and loss-leader SKUs can be a part of strategy. 11/7/2018 MIT SCM ResearchFest 2014

Data Overview 11/7/2018 MIT SCM ResearchFest 2014

On-Time Performance of Current Process Late Shipment <= -21 days On-time Shipment >-21 days <= 0 days Early Shipment > 0 days On-Time Number of POs Early Late 11/7/2018 MIT SCM ResearchFest 2014 Days of Destination DC Dwell Time

Overview of China Import Process and Lead Time 11/7/2018 MIT SCM ResearchFest 2014

Determining “Flex Time” 11/7/2018 MIT SCM ResearchFest 2014

Looking for Trends Using PO Attributes Each circle represents a PO Dark gray box represents second quartile (25th percentile to median) Light gray box represents third quartile (median to 75th percentile) Whiskers extend to data within 1.5x the interquartile range Destination Lead Time (Days) 11/7/2018 A B C Total MIT SCM ResearchFest 2014

Neural Network Model Introduction Input variables (from PO data): Merchandise type Cargo received date Division Pre-ticked Pre-packed LP month Output variable (model prediction): DC dwell time 11/7/2018 (Note: the model was developed using Matlab software) MIT SCM ResearchFest 2014

Accuracy of model Predictions Predicted Result VS Actual DC Dwell Time (Out-of-Sample Prediction for 100 POs) 11/7/2018 MIT SCM ResearchFest 2014

Model Sensitivity to Planning Horizon Table 1 - Test Results without updating the model (model size=3,000, gap=3,000) How far into the future Fitness Coefficient Bias (days) 100 POs 99.27% 1 4.2 500 POs 98.23% 1.1 4.9 1000 POs 98.30% 5.9 5000 POs 97% 2.9 10000 POs 96.50% 0.98 1.8 30000 POs 92% 0.89 0.69 11/7/2018 MIT SCM ResearchFest 2014

Model Sensitivity to Update Frequency Table 2 - Test Results with updating the model (model size=3,000, gap=3,000) Number of POs updated per run Fitness Coefficient Bias (days) 100 POs 86% 0.92 1.5 500 POs 0.87 1000 POs 85.80% 0.88 1.4 1500 POs 83.70% 0.93 3000 POs 82% 0.82 11/7/2018 MIT SCM ResearchFest 2014

Model Sensitivity to Sample Size Table 3 – Performance sensitivity to different model size 11/7/2018 MIT SCM ResearchFest 2014

Simulated Application of Model Late Shipments (dwell time <= -21 days) On-time Shipment (dwell time >-21 days and <= 0 days) Early Shipment (dwell time > 0 days) 11/7/2018 MIT SCM ResearchFest 2014

Impact of Model on On-time Performance Based on simulation using 100 POs Metric Actual Data Simulated Result Using Model % of POs Late 6% 0% Avg Days Late 21.5 % of POs Early 58% 40% Avg Days Early 36 2.76 11/7/2018 MIT SCM ResearchFest 2014

Key Insights Traditional SKU segmentation may not be appropriate for companies with constantly changing product mix. Analysis of historical data can identify product attributes that are correlated with required supply chain speed. A mathematical model can be used to predict required lead times. 11/7/2018 MIT SCM ResearchFest 2014