Revenue Management at SkyLine
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Part one.
Skyline’s expected revenue generated by the remaining 70 seats is obtained by finding the daily HV customers rate and the percentage of daily arrival of HV customers.
Obtaining the number of HV customers that will arrive 10.5 days prior;
(daily HV customer rate 10.5 before flight date) * (% of daily arrivals at a given hour)/4
We are dividing by four since, at every one hour, the maximum number of ticks is 4
The table below shows the revenue created by the Airline from HV customers 10.5 days prior to the flight.
| daily HV rate 10 days before the flight | 3.0853 |
| % daily arrival at a given time slot | 12.7423% |
| number of HV that will arrive after one hour | 0.098282868 |
| number of HV that will arrive | 24.76728271 |
| total revenue | 14376 |
Number of HV customers at a given hour= (daily HV customer rate 10.5 prior to flight date) * (% of daily arrivals at a given hour)/4
=3.0853 * 12.7423%/4 = 0.098282868
To get the number of HV customers, you multiply 0.03213806 by 10.5 * 24 (converting it into hours)
Number of HV customers = 0.098282868*(10.5*24) = 24.7672 which is approximately 24 HV customers.
Total revenue = total HV customers * price
=24 * 599 = $ 14,376
This pricing policy is not viable to the Airline since low fare is only offered up to two weeks prior to the flight. With less people being able to pay for high fare which is imposed may seats that would be otherwise be filled are left empty. 62 seats are left empty this is not a way of maximizing profit.
One recommendation is that the Airlie should keep both low and high fare open all the time to maximize profit.
Part two.
Activating low fare 10.5 days prior to the flight.
This involves the Airline opening up for all type of customers (those who want to pay low fare and those that wat to pay high fare.)
The table below shows the number of low fare customers that will arrive 10.5 days prior to the flight.
| daily low rate 10 days prior to flight | 7.2971 |
| % daily arrival at a given time slot (hour) | 30.2576% |
| number of HV that will arrive after one hour | 0.551982231 |
| number of HV that will arrive | 139.0995221 |
From the table we can see that when low fare is available the number of low fare customers that will arrive within 10.5 days prior to the flight is 139 which is more that the seats available (70 seats)
Hence to get the total revenue received by the company we;
Maximum number of seats available* low fare price
70*249 = 17,430
Revenue from low price activation = $ 17,430.
Activation of both high and low fare within 10.5 days prior to the flight.
From the above calculations we have noticed the number of high fare customers arriving will be 25 and that when both low and high fare are activated only 30% of the high fare customers will still get the tickets at high fare price 599. The remaining 70% will shift to low fare of 249.
Getting high fare customers that will remain in the class;
30% * 24=7.2 approximately 7 customers
Those that will shift to the low fare class;
70%*24 =17 customers.
The assumption is that the 30% of the High fare customers that will still go for High fare ticket will all get the seats provided they arrive on time
Revenue obtained when both are classes of fare are activated will be;
= (7*599) + (63*249) =$ 19,880
These two types of policy classes (low fare and both low fare and high fare activation) will ensure that all the 70 seats available are booked hence utilized leading to increased revenue as compared to high fare activation only which leads too booking of only 24 seats leading to low revenue.
Part Three.
The table below shows sales revenue for the initial HV customers only, LV customers only and the activation of both the low and high face policy and the seats booked by each policy.
| Policy class | Seats booked | Sales revenue |
| HV customers | 24 | $ 14376 |
| LV customers | 70(seats available) | $ 17439 |
| Both LV and HV | 70(7 HV and 63 LV) | $ 19880 |
The two proposed types of policy classes (LV and both LV and HV) will have all the 70 seats booked, ensuring that all seats are utilized, leading to more profit. With the HV customers’ market segment, only 24 seats are reserved hence no maximization of revenue.
The graph below shows both the sales revenue for the three-class policies.
The two proposed policy classes will lead to more sales revenue than segment of the HV customers only since all the 70 seats are utilized.
Part four.
The table below shows the sales revenue for the proposed two policy classes.
| LV customers | $ 17439 |
| Both LV and HV customers | $ 19880 |
From the two policies proposed, both LV and HV customers will lead to more revenue to the business as compared to LV customers only. With bath HV and LV customers, the Airline company will have an extra $ 2441 revenue compared to applying LV customers only. Hence the recommended policy is to have both LV and HV customers.
Some of the challenges in implementing this policy are;
- With both LV and HV, more customers will go for LV since it is cheap, hence reducing revenue.
- For HV, cancellation fees are free; hence revenue that could be obtained from this act is no longer there.
- Projection of revenue is difficulty. This is because, with only one policy such as HV or LV, we can project the revenue easily since there is only one type of ticket being offered. Still, with both HV and LV, sometimes it may be difficult to predict the number of customers on HV or LV since it is only based on probability, which is not always the case.
Part Five
The trade-off graph shows an increase in unit output as a result of the change in the unit input. In this case increase in the number of seats will lead to an increase in revenue realized by the company. For both the two policy classes developed, seats booked are 70, which is the maximum number of seats available compared to the initial 24 seats reserved at HV customers policy. Both proposed two policies have high revenue as compared to only HV customer policy; hence increase in seats leads to an increase in revenue.
References
Professor, Y. (2012). Revenue Management at SkyLine