1. Forecasting Demand for Printing Course Packets
Brian Lewandowski, Chad Arnold, Sari Levy, Margo Cooper, Shannon Cox Baker April 19, 2006

2. CU Bookstore Only retail source for student course packets
1,500-2,000 individual course packets per semester
Approximate price range: $5-100
Copyright fees, per page printing costs, and customized requests (binding)
Marginal profit: 33%

3. At Issue
Forecasting printing demand for upcoming semester is a challenge
Relationship between sales and actual enrollment is difficult to predict
Annual overproduction losses: $30,000
5% of revenue

4. Our Objective
Minimize financial losses associated with overproduction while ensuring course packet availability can meet 100% of student demand

5. Procedure
Analyze current forecasting procedure and the associated variables
Using a subset of historical course packet purchasing data, determine the optimal forecasting method
Compare our results to current forecasting procedure and prepare conclusions and recommendations

6. Current Forecasting Method
Compare actual enrollment to:
Limited historical purchasing trends
Familiarity with professor
Familiarity with course
Other factors considered:
Price
Undergraduate versus graduate level

7. Limitations of Current Method
Subjective
No documentation of procedure
Historical data is limited
Decision-making process largely dependent on one individual

8. Our Methodology: Step 1 Acquire historical data set
5 semesters of MBA course packet purchases
Determine which variables impact demand
Actual enrollment
Time period between packet request and 1st day of class
Historical sales
Copyright fee
Printing and misc. costs
Selling price

9. Our Methodology: Step 2 Determine correlation between variables
Copyright fee, printing cost, & price are highly correlated to each other
Copyright fees, printing costs and price were highly correlated

10. Our Methodology: Step 3
Utilize regression analysis to predict demand based on enrollment and price
Coefficients
Intercept
Actual Enrollment
Price

11. Our Methodology: Step 4
Apply Crystal Ball to predict optimal printing quantities, explore sensitivity

12. Our Methodology: Step 5
Apply Crystal Ball to a blend of model prediction and Bookstore prediction with actual demand

13. Summary Table Model Service Level Profit
App. 1 CU Printing with Actual Sales 1
$1,535.82
App. 2 CU Printing with Predicted Sales
0.891
$2,053.11
App. 3 Model Prediction Sales with Actual Sales
0.901
$1,838.67
App. 4 Model Prediction with Predicted Sales
0.969
$1,751.10
App. 5 Blend of Model Prediction with Actual Sales
0.975
$1,715.47

14. Results
More revenue can be generated with different ordering practices
Current practices are generally effective, considering a 100% service level goal
More profit can be generated by reducing ending inventory
This will inherently decrease the service level component
By keeping records designed to fit a predictive model, future predictions may be more effective
An effective strategy may be to perform a base case model and make secondary qualitative adjustments – this may increase profits with an acceptable level of decreased service
Students seemed to be generally insensitive to changes in price indicating price inelasticity

15. Recommendations
Continue with current system until 4 semesters of historical data can be accumulated
Create data collection system that includes: enrollment information, size of initial printing run, supplementary printing, copyright fees, packet size including number of pages, shrink wrap cost, total number sold, price
Examine the impact of reduced service levels on other store sales
Explore educational signage that explains pricing strategies to encourage fewer copyright violations
Obtain course packet information in a more timely manner
Create a safety stock