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

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%

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

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

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

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

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

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

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

Our Methodology: Step 3 Utilize regression analysis to predict demand based on enrollment and price Coefficients Intercept 6.895731168 Actual Enrollment 0.438278415 Price 0.008554491

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

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

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

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

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