1. Analysis of Fastenal Quoting Practices
Kadin Browne, Caleb Griesbach, Derek Klein, Jackson Schuette

2. Table of Contents Introduction Data Exploration Classification Models
Problem Overview
Our Approach
Data Exploration
Classification Models
Logistic Regression
Business Value
Future Actions

3. Introduction

4. Our Problem: Quote Success
Searching for better quoting practices
Increase price on "for sure" quotes
Increase quote success rate
Two questions
What are the major drivers?
Can we make a practical, real time, model?
Acquired large dataset, includes
Quote success(invoiced)/failure
Product & product classification
Multitude of prices

5. Our Approach Data Exploration Classification Models Business Value
Find patterns and interesting information about quoting
Classification Models
Classification
Logistic Regression
Business Value
Key drivers
New quote guidance

6. Data Exploration

7. What Drives Success?
Branches are one component to the quoting process.
Average success = 66.7%
Our hypothesis
More customers → More chances of failure
More quotes → More chances of failure

8. Most branches have ≈80% Success Rate

9. Maximum and Minimum
Branch 1032
Branch 1184
Success Percentage = 99.22%
Total Quotes = 384
Number of Customers = 2
Success Percentage = 2.49%
Total Quotes = 1,206
Number of Customers = 21
Does this imply success percentage is related to the number of total quotes or customers?

10. Low # Quotes → Better Success % Low # Customers → Better Success %

11. Branch Success Continued
Number of quotes and customers influences success rate
Average success is 66.7%
Most branches have ≈80% success
What is skewing the average?
Some branches are larger
More quotes
FMI Agreements

12. FMI Agreements
FMI Agreements allow Fastenal to automatically quote a customer when their inventory gets low
No FMI Success Percentage = 66.2%
FMI Success Percentage = 71.39%
FMI Agreements may improve success percent

13. Sale Prices of Quotes Compared Price with Cost
Should get linear relationship
Want to know how cost affects price
Expect Failed price > Success price

14. Successful quote’s sale price > Failed quote’s sale price
Production Cost = Selling Price
Successful quote’s sale price > Failed quote’s sale price

15. Successful Quotes
Failed Quotes
Production Cost = Selling Price

16. Analysis Cost > 40.6 Cost < 40.6
Failed quotes price < Successful quotes price
Unusual
Reminder: High Cost products fail
Cost < 40.6
Failed quotes price > Successful quotes price
Expected

17. Classification Models

18. Classification Method
Classification Idea
Quote A
Quote B
Quote C
Quote D …
Classification Method
Quote A: Y
Quote B: N
Quote C: N
Quote D: Y
Model Assessment

19. Logistic Regression (LR)
Simple & powerful
Original Quotes
LR Outputs
Predict Success
Quote C: 0.89
Quote A
Quote E: 0.73
Quote B LR
Cutoff: 0.673
Quote C
Quote A: 0.61
Predict Failure
Quote D
Quote D: 0.42
Quote E
Quote B: 0.29
True Success
True Failure

20. Actual Classification
Model Assessment
Accuracy
Model Classification
Actual Classification
How good is our model?
Confusion Matrix

21. First Model: Product Centric
Data: One Product
FMI Agreement
Quantity
Unit Price
Logistic Regression
Results
Accuracy
Significance of Variables

22. Results
No significance of variables

23. Summary What went wrong? How can we fix it?
Could only use a few variables:
Quantity, Cost, FMI Agreement
No significance of variables
How can we fix it?

24. Feature Engineering Discount = Wholesale – UnitPrice
TotalDiscount = (Quantity)(Discount)
PercentProfit= UnitPrice−CostOfGoods CostOfGoods
PercentMarkUp= Wholesale−StandardCost StandardCost

25. New Model: Feature Engineering
Data:
Full Set
Original Variables
Feature Engineered Variables
Logistic Regression
Results
Accuracy
Significance of Variables

26. Item Weight, FMI-Agreement, & Total Discount are very significant!
Results
Item Weight, FMI-Agreement, & Total Discount are very significant!

27. Accuracy of New Model
Model Accuracy is greater than the No Information Rate
Majority of Confidence Interval above No Information Rate
Model still isn’t that great

28. Business Value

29. Business Value Drivers of success
Positive coefficients increase probability
Negative coefficients decrease probability
Magnitude is the degree of effect on probability

30. Simulating New Quotes Model Change Variables 100 bolts $5
20% chance it succeeds
Probability
Change Variables

31. Simulating New Quotes Model 100 bolts $2 80% chance it succeeds
Probability

32. Future Work

33. Possible Future Actions
Continue logistic regression (LR) on more data
LR on data by Category ID, Quantity, price intervals
Parameter analysis: 
Linear adjustment to significant variables
Compare new success rate to old success rate
Utilize different methods:
Decision Trees
Random Forests
Boosting

34. Acknowledgements
PIC Math is a program of the Mathematical Association of America (MAA) and the Society for Industrial and Applied Mathematics (SIAM).
Support is provided by the National Science Foundation
NSF grant DMS
Advisors: Dr. Song Chen and Dr. Chad Vidden
University of Wisconsin – La Crosse
Department of Mathematics & Statistics
Industry Partner: Fastenal
Industrial Liaison: Brian Keeling

35. Questions?