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

2. 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

3. 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

4. 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

5. Data Overview
11/7/2018
MIT SCM ResearchFest 2014

6. 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

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

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

9. 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

10. 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

11. 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

12. 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

13. 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

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

15. 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

16. 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

17. 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