Demand driven operations management in motion picture exhibition
Bibliographic record
Abstract
New opportunities for operational efficiency in movie exhibition exist as a result of recent developments in the industry, such as the mass-scale conversion to digital cinema, the explosion of customer data sources, and the availability of new channels for watching movies. This dissertation begins with an industry overview and discussion of these trends. A review of existing research on forecasting and scheduling problems in movie exhibition is utilized to identify eight factors for decision support systems for operations management in motion picture exhibition. A prototype decision support system (DSS) is constructed using customer loyalty and point of sale data from Cineplex Entertainment. The DSS that is built considers six out of the eight decision support factors that are identified through four modules. The first module projects audience composition for new release movies using loyalty data and identifies a set of the most important movie attributes for each age group. The second module leverages output from the first module with additional data to forecast ticket sales for individual theatre locations. In constructing the second model three different methods (gradient boosted regression, random forest and traditional multiple regression) are tested and the best performing method is utilized in the DSS. The third module applies the attendance forecasts to make labour recommendations. The fourth module uses output from the second module and extends the micro forecast with a concession food demand forecast which is applied to labour recommendations. The DSS is tested empirically in two different ways; firstly forecasted box office sales are compared to actual box office sales to demonstrate that the forecasts being produced are reasonably accurate. Secondly, the labour recommendations from the DSS are compared to the recommendations from the existing DSS at Cineplex and against the theatre manager’s schedules. The DSS performs better than the current schedule and the theatre manager schedule. The labour recommendations with and without the fourth module are compared to demonstrate the incremental value of the concession food module. The study concludes by highlighting further opportunities to extend the system as well as context for practical applications in the motion picture industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".