Cargo vs Passenger Demand Forecasting: A Comparative Analysis

Cargo demand forecasting has historically been overshadowed by passenger demand analysis. However, evolving supply chain practices—such as just-in-time delivery and the growing market for perishables—have renewed its importance. Despite this resurgence, forecasting cargo demand differs significantly from passenger demand for three key reasons:

However, cargo and passenger demand forecasting is different for three reasons.

1. Quality Service Index (QSI) Application

• Passengers prioritize direct flights and convenient departure/arrival times, often paying a premium for these features.
• Cargo, by contrast, tolerates indirect routes and flexible schedules, provided delivery deadlines are met and product integrity is maintained. For perishables, the weakest link in the supply chain determines overall reliability.

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2. Cost of Substitution

• Belly-hold space can be allocated between passenger luggage and cargo, offering flexibility.
• Cabin space, however, remains exclusive to passengers, limiting substitution options.

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3. Supply Chain Dependencies

• Passengers manage their own check-in and transit.
• Cargo requires specialized handling and error-free processes to ensure timely delivery and product quality. A single weak point in the supply chain can compromise the entire route.

Forecasting Methodologies

Two primary approaches underpin demand forecasting:

• Top-Down (Macro-Economic)

  • Passengers: Strong correlation with GDP, income per capita, and population growth. However, this method lacks granularity in explaining cultural or geographic preferences.
  • Cargo: Moderate correlation, influenced by trade advantages (e.g., China’s manufacturing exports, Australia’s agricultural perishables). Seasonality and alternative sourcing markets (e.g., Australia vs. New Zealand vs. USA) must also be considered.

• Bottom-Up (Micro-Economic)

  • Passengers: Builds demand from key travel segments (e.g., VFR, leisure, business). While useful, origin-destination tools provide deeper insights.
  • Cargo: Focuses on major exports/imports (e.g., top 20 commodities). This approach helps estimate demand but may not fully inform airline or route targeting.

Key Insight: Leisure passengers have destination flexibility, influenced by price, trends, and QSI. Cargo flows, however, are dictated by trade economics and product-specific constraints.

Method	Passenger	Cargo	Comments
Top Down (e.g. Correlation with GDP/ income per capita and Population growth)	Good correlation, though this is better at looking at the size of the market, not where (which city) they are going.  It may not explain cultural or geographical factors	Moderate correlation, but also based on unique trade advantages of the source and destination (e.g. China manufactured goods), or Australia natural resource/ agricultural perishables).  Also, seasonality and alternative source markets (e.g. Aus vs NZ vs USA for Agriculture exports) needs to be considered	Leisure – Passenger destination demand also driven by price, trends, QSI of flight offerings and foreign exchange movements – Leisure Passengers have alternatives in their destination, they can go almost anywhere, while Cargo is more based on trade advantages (e.g. where it is cheapest to source the good)
Bottom Up – calculating demand based on each micro-economic variable, or customer segment	We can pick the top 10 reasons for travel, and then build up travel demand (e.g. VFR, School Groups, Family (school holiday travel), business, however this may not give the insights that an origin destination tool (e.g. Airline IS) can	We can pick the top 20 exports/ imports from Australia and use that to bottom up build the cargo demand model – would this really help target airlines or cargo routes?	Business, Premium Leisure strong drivers for direct pax routes, Otherwise indirect routes can work for passengers, subject to minimum QSI requirements.   Cargo can go indirect routes as long as it does not adversely affect the product (e.g. perishables) or takes too long.

Supply-Side Considerations

1. Market Share Allocation:
   For example, should Western Sydney Airport (WSI) capture 75% of chilled meat exports by 2035, given its proximity to abattoirs? Factors include flight frequency and direct vs. indirect connectivity.

2. Infrastructure Quality:
   Cold storage availability and operating hours at WSI vs. Sydney Kingsford Smith (SYD) can significantly impact competitiveness.

3. Transport Reliability:
   Infrequent or cancellable flights (e.g., Newcastle–Denpasar) reduce cargo viability. Alternative routings via Brisbane may offer better reliability.

4. Transit Connectivity:
   Efficient hubs with robust facilities (e.g., Hong Kong for cold storage and onward flights) outperform less reliable options.

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Demand-Side Considerations

Cargo buyers often have multiple sourcing options, influenced by:

• Price and Quality
• Seasonality
• Political Factors (e.g., tariffs affecting trade flows)

For cargo, the end consumer may have a choice of purchasing that product from several suppliers, depending on price, quality, availability (including seasonality) and even political considerations.  For example, the threat of tariffs on Japanese exports to the USA but induce Japanese importers to source their beef from the USA instead of Australia.  This may be true from a top down or macro-economic viewpoint.  On the other hand,  Japanese meat companies may be vertically integrated with Australian cattle farms, and so will retain the export of chilled premium beef from Australia in the short term, though in the longer term, it may divest those farms.

Conclusion

Passenger and cargo demand forecasting share foundational principles but diverge in execution due to differences in service expectations, substitution flexibility, and supply chain complexity. Accurate forecasting requires integrating macro-economic trends with micro-level trade and infrastructure insights.