1. Measurement and Evaluation of Retail Promotion Authors: Asen Kalenderski and Satya Sanivarapu Sponsor/Advisor: ProdCo Advisor: Dr. Chris Caplice, Dr. Francisco Jauffred MIT SCM Research Fest May 21, 2015

2. Agenda MIT SCM ResearchFest May 21, 20152 Importance of Promotions Promotion Performance Cuboid Case Study: Classification of Dataset Key Insights Questions

3. Why are promotions important? MIT SCM ResearchFest May 21, 20153 Retail sales in March 2015 were $441.4 billion Large retailers spend 10 to 20 percent of their sales on promotions Spending helps increase sales by 30 percent Only 18 percent of the promoted brands create increased store profits

4. What makes promotions difficult? MIT SCM ResearchFest May 21, 20154 When? What? Where? Inventory? Stock-outs? Forecast? Sales? How? Why? pricing supply chain collaboration retailers advertising strategy planning production execution capacities replenishments

5. Promotion characteristics Promotion Period Inventory ramp up replenishment

6. Pillars of evaluation MIT SCM ResearchFest May 21, 20156 Days of supplyStock-outsSales accuracy to forecast

7. Days of Supply MIT SCM ResearchFest May 21, 20157 Lower Boundary of Bins for Days of Supply at the End of Promotion

8. Days of Supply Difference MIT SCM ResearchFest May 21, 20158 Lower Boundary of Bins for Days of Supply Difference

9. Days of Supply Difference Percentage MIT SCM ResearchFest May 21, 20159 Lower Boundary of Bins for Days of Supply Difference %

10. Sales accuracy to forecast MIT SCM ResearchFest May 21, 201510 Lower Boundary of Bins Sales accuracy to forecast

11. Promotion Metrics MIT SCM ResearchFest May 21, 201511 Sales accuracy to forecast HighSales = ForecastLow Stock-outs HighLow Days of Supply HighGreen ZoneLow

12. Agenda MIT SCM ResearchFest May 21, 201512 Importance of Promotions Promotion Performance Cuboid Case Study: Classification of Dataset Key Insights Questions

13. Promotion Performance Cuboid MIT SCM ResearchFest May 21, 201513 DoS Difference Low Green Zone High Sales Accuracy Negative Sales=Forecast Positive 0 +20% -20% <-20% >20% 0 > 0 < 0

14. Agenda MIT SCM ResearchFest May 21, 201514 Importance of Promotions Promotion Performance Cuboid Case Study: Classification of Dataset Key Insights Questions

15. Case Study: Supply Chain MIT SCM ResearchFest May 21, 201515

16. Case Study: Classification (Low SO) MIT SCM ResearchFest May 21, 201516 21.03% 4.44% 13.24% 12.93% 7.88% 10.70% DoS Difference Low Green Zone High Sales Accuracy Negative Sales=Forecast Positive 7.10% 7.86% 8.15% 0 +20% -20% <-20% >20% 0 > 0 < 0

17. Case Study: Classification (High SO) MIT SCM ResearchFest May 21, 201517 0.06% 0.01% 0.07% 0.08% 0.07% 2.58% 0.34% 3.44% 0.02% DoS Difference Low Green Zone High Sales Accuracy Negative Sales=Forecast Positive 0 +20% -20% <-20% >20% 0 > 0 < 0

18. Case Study: Classification (Major Cubes) MIT SCM ResearchFest May 21, 201518 A 21.03% B 13.24% C 12.93% D 10.70% DoS Difference Low Green Zone High Sales Accuracy Negative Sales=Forecast Positive 0 +20% -20% <-20% >20% 0 > 0 < 0

19. Case Study: Classification (Major Cubes) MIT SCM ResearchFest May 21, 201519 A 21.03% SKU Sales – Low Replenishments – On time Inventory at store – Low

20. Case Study: Classification (Major Cubes) MIT SCM ResearchFest May 21, 201520 B 13.24% SKU Sales – High Replenishments – On time Inventory at store – Just right

21. Case Study: Classification (Major Cubes) MIT SCM ResearchFest May 21, 201521 C 12.93% SKU Sales – Low Replenishments – On time Inventory at store – Just right

22. Case Study: Classification (Major Cubes) MIT SCM ResearchFest May 21, 201522 D 10.70% SKU Sales – Low Replenishments – On time Inventory at store – High

23. Agenda MIT SCM ResearchFest May 21, 201523 Importance of Promotions Promotion Performance Cuboid Case Study: Classification of Dataset Key Insights Questions

24. Key Insights MIT SCM ResearchFest May 21, 201524 Days of Supply Difference High Look into Replens frequency and quantities Low High Inventories Green Zone High Look into Replens frequency and quantities Low SKU Sales strategy Low High Inventories Low SKU Sales strategy Focus Areas Stock-Out

25. Agenda MIT SCM ResearchFest May 21, 201525 Importance of Promotions Promotion Performance Cuboid Case Study: Classification of Dataset Key Insights Questions

26. MIT SCM ResearchFest May 21, 201526 DoS Difference Low Green Zone High Sales Accuracy Negative Sales=Forecast Positive 0 +20% -20% <-20% >20% 0 > 0 < 0