A Hybrid, Adaptive Regression PLF Forecasting Model
PLF measures the capacity utilization for airlines and the bettering of this KPI affects the plan and costs of complementary functions such as workforce, fuel, catering and ground services. Obase PLF Optimization solution is built on a hybrid adaptive regression model and forecasts PLF with smallest error margin possible. The model delivers an optimum revenue and enables the business units to cleverly act on price and campaigns.
Passenger Load Factor (PLF) indicates the efficiency of the airline: filling seats and generating revenues. The bettering of this KPI affects the plan and costs of complementary functions such as workforce, fuel, catering and ground services.
While forecasting PLF is difficult as it’s highly affected by seasonality, unpredictable demand and even political & economical issues, Obase PLF Optimization solution has been able to deliver above industry standard results by building airline-specific models to forecast the aggregate passenger traffic in a certain time frame, region or an individual flight.
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Mehmet Zeki GüngörBI Manager
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Mehmet Zeki GüngörBI ManagerAras Kargo
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İbrahim Oral EmülBig Data and BI ManagerTurkish Airlines
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