1. Improving Automotive Battery Sales Forecast Vinod Bulusu, Haekyun Kim

2. Agenda Company Overview Problem Statement Motivation Methodology Results and Validation Conclusion

3. Company Overview Thesis sponsor is a global technology and industrial leader Global leader in the after market sales of the lead-acid battery market (40% market share) Sales are through automotive retailers

4. Agenda Company Overview Problem Statement Motivation Methodology Results and Validation Conclusion

5. Problem Statement Forecasting of the sponsor company is based on historical sales, without any other variables In 2013 the demand was higher than usual, which resulted in lost sales as manufacturing could not be ramped up Based on Historical salesNot Flexible Higher demand Lost Sales

6. Agenda Company Overview Problem Statement Motivation Methodology Results and Validation Conclusion

7. Motivation -Improve sales forecast Identify whether relationship exists between sales and temperature and thus improve the sales forecast and potentially reduce lost sales Not Flexible Higher demand Lost Sales Current State Based on Historical sales

8. Motivation -Improve sales forecast Identify whether relationship exists between sales and temperature and thus improve the sales forecast and potentially reduce lost sales Based on variables such as temperature Not Flexible Demand Variability but NO Lost Sales Future State

9. Why Temperature -Based on evidence from three orthogonal sources Empirical evidence: Based on the literature review temperature impacted battery failure Preliminary (qualitative) analysis: Based on visualization of sales, we found that sales increased during the time of polar vortex Anecdotal evidence: Based on feedback from SME’s in our sponsor company

10. Agenda Company Overview Problem Statement Motivation Methodology Results and Validation Conclusion

11. Methodology 3-step process Data Collection POS Temperature Data Analysis Visualization SKU Selection City Selection Data Modeling & Validation Regression Validation Step 1: Temperature and sales data collection Step 2: Identifying appropriate SKU’s for modelling Step 3: Regression and model validation

12. Methodology: Step 1, Data collection Sales Data and Temperature information Weekly Point of Sales (number of units) was obtained from thesis sponsor Weekly temperature (Maximum and Minimum) information obtained from NOAA * NOAA (National Oceanic and Atmospheric Administration) POS Data Temperature (Boston)

13. Methodology: Step 2, Data analysis Selection of SKU Point of Sale information consisted of multiple SKU’s Appropriate SKU needed to be identified to reduce confounding with other variables such as demographics, regions, battery types etc. SKU 65 was selected based on volume of sales and geographical prevalence Volume of Sales Geographical prevalence of 65

14. Methodology: Step 2, Data analysis Selection of Cities Selected cities: Los Angeles, Boston, Houston, Washington D.C. and, Chicago Aggregated several zip codes in the selected cities 5 Selected cities Several zip codes in Boston

15. Methodology: Step 3, Modeling of SKU 65 sales with temperature Regression Modeling (Using JMP) Dependent Variables (Y-parameter): -Normalized sales as a continuous parameter Independent variables (X-parameter): -Minimum Temperature, as a continuous parameter -Maximum Temperature, as a continuous parameter -Year, as ordinal -Quarter, as ordinal 6 Models with different combinations of variables were created

16. Agenda Company Overview Problem Statement Motivation Methodology Results and Validation Conclusion

17. Model selection: Balance of Fit and Parsimony 6 Models with different combinations of variables were evaluated for fit and parsimony The selected model included quadratic and linear effect of minimum temperature and quarter ParsimonyFit Interaction of quarter and temperature Cubic and Quadratic effects R2R2 Predictability Over fit

18. Regression Model Maximum temperature and Year were not significant

19. Model validation - Is our approach valid? Validate across time (Model T) Validate across geography (Model G) Model To validate our approach we needed to validate the model over time and geography Two models were created: -Model G: Model with data from LA, Chicago and Houston (Validate the model for Boston and Washington D.C.) -Model T: Model with data from 2011, 2013 and 2014 (Validate the model for 2012)

20. Validating the model across time: Model T Predicted vs. actual values are compared for 2012 Trends are similar and so are absolute values

21. Validating the model across geography: Model G Predicted vs. actual values are compared for Boston and Washington D.C. Trends are similar, but the absolute values are significantly different Boston Washington D.C.

22. Validating the model across geography: Model G, cont. Predicting the % change rather than absolute value The % change is predicted well for both Boston and Washington D.C. Boston Washington D.C.

23. Agenda Company Overview Problem Statement Motivation Methodology Results and Validation Conclusion

24. Conclusion -Use minimum temperature to improve sales forecast There is a linear and quadratic relationship between temperature and battery sales The model predicts absolute sales better over time than geography Prediction of % change is similar across geography but absolute value cannot be determined by Model G -Normalizing the sales data by Vehicles in Operation (VIO) is not enough -Other factors needs to be considered: Demographics, Public transport system, Infrastructure, Initial Value The model can be further refined by -Adding age of the battery as a failure -Additional geographical data -Additional SKUs

25. Acknowledgements Roberto Perez-Franco (Thesis Advisor) Center for Transportation and Logistics (CTL) Thesis sponsor company