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原文传递 Contingency Workload Demand Forecast Techniques for Cargo and Flying Hours.

题名：	Contingency Workload Demand Forecast Techniques for Cargo and Flying Hours.
作者：	Bradshaw, C. I. J.
关键词：	Airlift operations, Missions, Predictions, Workload, Military capabilities, Military budgets, Time series analysis, Regression analysis, Decomposition, Smoothing (mathematics), Transfer functions, Models, Exponential functions, Annual contingency demand workload forecasting, Amc (air mobility command), Saam (special airlift assignment missions), Twcf (transportation working capital fund), Forecasting techniques, Box jenkins, Ustranscom (united states transportation command), Ustc (ustranscom), Jdpac (joint distribution processing analysis center), Cargo demand forecast, Flying time demand forecast
摘要：	Accurate forecasting of contingency workload demand for USTRANSCOM (USTC) is a herculean effort. Transportation Working Capital Fund (TWCF) managers rely on various subject matters outside and within the combatant command to estimate future workload. Since rates are set annually, when TWCF activities use incorrect or incomplete projections of workload, this leads to erroneous price structures and misaligned customer billing rates. The USTC leadership lacks the ability to accurately forecast workload demand, which is a key driver for service provider rate-setting. As a result, some customers perceive spiked rates and seek service from other competitors, which generates lost revenue, customer dissatisfaction and the inability to maximize workload to meet the readiness goals of the command. Time series forecasting is a technique planners use to model future demand. This paper examines a variety of time-series techniques applied to historical cargo and flying hour workload demand primarily from Air Mobility Commands (AMC) contingency and special airlift assignment missions (SAAM). The goal is to develop a non-prescriptive guide to improve the rate setting process and enable USTC leadership to better manage combat capability. The research introduces a median-based forecast along with an anecdotal guide for anticipating future annual workload to more accurately inform the USTC budget.
报告类型：	科技报告