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DGCA published data is unreliable and misleading

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DGCA published data pertaining to an airline’s performance, commonly quoted by the media, such as Load Factors and OTP, is unreliable and misleading.

The data errors can only be recognized in single fleet airlines and/or airlines that have only recently started operations. In both cases, simplicity allows for cross verification of data.

Investigation into the data errors was suggested by a senior officer of a full service Indian airline.

Load Factors

The most interesting of all airline performance indicators is load factors. Load factors are often looked upon as indicators of successful commercial operations at an airline.

DGCA publishes certain airline related data based on an ICAO (International Civil Aviation Organisation, a UN body) ATR (Air Transport) ‘FORM A’. This form is filled and submitted by airlines to the DGCA, which the DGCA then uses to report load factors airline wise.

The manner in which the DGCA computes load factors is by dividing Passenger-Kilometers (PK) by Available seat Kilometers (ASK). PK is a product of total passengers flown and the total kilometres flown by the airline in a particular month. ASK is the number of seats on all flights multiplied by the total kilometres flown by the airline in that particular month. Dividing PK by ASK simplifies to the ratio of Passengers Flown by Available Seats, which is the definition of load factor.

Another way of computing load factors is to determine the available seats using data not reported in ICAO ATR FORM A. This is the number of seats on every flight. FORM A mentions the number of departures in a month. In single fleet airlines such as IndiGo, Go Air, AirAsia and Vistara, the number of seats on every aircraft is uniform fleet-wide. This means that every flight on each of the above mentioned airlines flies 180, 180, 180 and 148 seats, respectively.

Multiplying the number of flights by the number of seats per aircraft will result in the number of seats flown in that month. Dividing the number of passengers flown by the number of seats gives us load factors for the month.

The first and second method should result in the same numbers. However, this is not the case. Below is the reported load factors versus the computed load factors for IndiGo since it started operations. The two methods agree with each other till December 2008. From January 2009, when the DGCA changed its format of reporting data, the errors have been present, and have been unacceptably large and inconsistent.

Load Factors IndiGo Computed Reported Error Difference

The data shows that, according to computations, domestic load factors at IndiGo never crossed 90%, and that load factors crossed 80% only on 7 occasions in 9 years. Average domestic load factors at the airline, across 9 years, is recomputed as just 71.5%, with the highest at 83.3% in the month of May 2015. Of course, this arguably assumes that the number of departures and the number of passengers reported by the DGCA are correct.

Similarly, AirAsia India’s and Vistara’s load factors are not always representative of the actual load factors. In the case of these two airlines, the error is small. However, every 1% error in load factor corresponds to a monthly revenue of INR 56 lakhs for an airline the size of AirAsia India, and INR 16 crore for an airline the size of IndiGo.

Load Factors AirAsia India Computed Reported Error Difference

Vistara’s load factors have never crossed 70%.

Load Factors Vistara Computed Reported Error Difference

Below is that of Go air, for 9 months only:

Load Factors GoAir Computed Reported Error Difference

Considering that the data is derived from what airlines have published, it may be that part of the onus for the error rests on airlines. It is difficult to compute the error in load factors of airlines such as SpiceJet, Jet Airways, Air India, and Air Costa.

Faith in our method of computation is based on cross checking certain computed load factors with the information revealed by a senior airline official.

On Time Performance

Airline on time performance is another parameter met with much enthusiasm. For example, for the month of April of 2015, DGCA reported that AirAsia India had an on time performance (OTP) of 100.0%. DGCA mentions the OTP as observed at only four airports: Bengaluru, Hyderabad, Mumbai and Delhi. Back then, AirAsia India was based only out of Bengaluru.

However, Bengaluru International airport, in its On Time Performance (OPT) report for April, clearly mentions AirAsia India’s arrival OTP as 89% and departure OTP as 98%. This averages to 93.5% OTP, which made headlines as 100%. (Click here for an NDTV piece on this)

Similarly, Go Air’s OTP for Bengaluru was reported by the DGCA as 88.9%, while the airport stated that the airline had an arrival OTP of 73% and a departure OTP of 86%. The DGCA’s OTP for Go Air at Bengaluru was impossibly higher than the higher of the two OTP for the airline for that month.

IndiGo’s OTP at Bengaluru was reported as 77.2%, while the airport stated that the airline had an arrival OTP of 73% and a departure OTP of 81%. In this case, the average of the departure and arrival worked out to 77%, which is acceptable.

In the case of SpiceJet, OTP at Bengaluru was reported as 68.2%, while the airport stated that the airline had an arrival OTP of 78% and a departure OTP of 78%. In this case, the reported OTP is lower than the actual OTP of 78%.

Conclusion

Data reported by the DGCA is very informative. The data is used by analysts and major industry bodies for studies, reports, and analysis. However, no matter how good the analysis, junk data in results in junk data out, with misleading facts and figures about the industry and the performance of airlines.

Poor data standards may give airlines a way to falsely drive up their performance figures, which may be for many reasons, such as driving up investor sentiment.

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3 thoughts on “DGCA published data is unreliable and misleading”

  1. Interesting analysis. The difference between the two ways of computing load factors is that the RPK/ASK method puts more weight to longer flights as RPKs and ASKs are a function of distance flown. Dividing number of passengers by number of seats does not take into account the route length. Looking at the graphs above I don’t see any errors in DGCA reporting, as quite often longer flights have higher loads than shorter. And if the airline changes their operating pattern so that in a given month there are more longer or shorter flights than before, then there will be more variation in the results. If the airfares vary depending on the length of trip, so that longer flights generate higher revenue per passenger, then the RPK/ASK model gives a better picture on the airline’s revenue as it gives more weight to these longer routes. One reason why analysts and investors prefer the RPK/ASK model is that they also look at the airlines’ unit costs (expressed as costs per available seat kilometer or CASK). With the RPK/ASK method it is also easier to make comparisons between airlines that have different network structure. Most international airlines I know nowadays use RASK model also for internal route or citypair level reporting and analysis.

    As for the discrepancy in OTP figures, it is difficult to say why the gap exists. Perhaps the airlines and airports use different criteria on what constitutes a delay. Or maybe the airlines – as they provide the numbers to DGCA themselves – deliberately discount some delays which may be outside their own control. Would be interesting to know the real reason.

  2. Interesting observations. Pls note the following:
    1. LF: Actually the gap is due to double counting of pax on via (same-plane) and connecting flights. While the pax numbers are reported as headcount, LF calculation will double count a single pax (headcount) as twice on a sample flight lets say HYD-DEL-VNS. The difference calculated by you broadly corresponds to the volume of via/connecting pax in domestic market.
    2. OTP: Indian regulator measures only departure OTP and not arrival OTP.

    Hope it helps.

  3. Interesting observations. Pls note the following.
    1. LF: Actually the gap is due to double counting of pax on via (same-plane) and connecting flights. While the pax numbers are reported as headcount, LF calculation will double count a single pax as twice on a sample flight lets say HYD-DEL-VNS. The difference shown by you broadly corresponds to the volume of via/connecting pax in domestic market.
    2. OTP: Indian regulator measures only departure OTP and not arrival OTP.

    Hope it helps.

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