Case Study: North London Airport Hub

Your line manager has presented you the passenger data from $202022$ shown in Table $1.$

Currently the airport is forecasting the passenger numbers using $2$Month Moving Average

method. Your line manager is concerned that this may not be the most appropriate and accurate

method of forecasting for the company. She is proposing that the airport moves to $2$Month

Weighted Moving Average method of forecasting with the weight of $0\mathrm{.}7$ for the most recent

period and $0\mathrm{.}3$ for the older period. She is also interested in finding out which method of

forecasting you would select for the company $[$N$.$B$.$ You must select one of the forecasting

methods covered in the module$].$

She wants you to analyse the available data $($shown in Table $1)$ and produce a short report $(2,000$

words $\backslash$pm $10\%$ excluding figures, tables and references$)$ that include relevant tables and figures to

fully address the issues listed below. Your report needs to consider three different methods of

forecasting; current model, model proposed by your line manager and a model you have selected.

Your aim is to identify a forecasting method that is most appropriate for the North London Airport

Hub.

Include the following in your report:

$1.$ Analyse the data given to you. How does the data behave? What patterns are present in

the demand? How do you know? How is this relevant to selecting a forecasting model?

Support your analysis with appropriate tables and figures.

MGT$2221$ & MGT$2222$ Operations Management; $202324$

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$2.$ Generate the forecast for $202022$ using the current model. Analyse the results. Is

appropriate forecasting model being applied? Why$/$why not? Discuss. Support your

analysis with appropriate formulas, tables and figures.

$3.$ Generate the forecast for $202022$ using the model propose by your line manager. Analyse

the results. Is this appropriate forecasting model for the company? Why$/$why not? Discuss.

Support your analysis with appropriate formulas, tables and figures.

$4.$ What forecasting model would you use? Justify the model you have selected. Why do you

think this is the best forecasting method for the company to use? Generate the forecast for

$202022$ using the model you have proposed. Analyse the results. Support your analysis

with appropriate formulas, tables and figures. $[$N$.$B$.$ You must select one of the forecasting

methods covered in the module$]$

$5.$ Determine the forecast error for the current model, the model proposed by your line

manager and the model you selected. Explain and justify the method you have used to

calculate the forecast error. Analyse the results and compare the three forecasting

methods. What are your key observations? How could you use this information when

selecting the method of forecasting? Support your analysis with appropriate formulas and

tables$/$figures$.$

$6.$ Provide two key recommendations for the company, i$.$e$.$ what action should they take and

why. You must deliver your recommendations from the analysis you have conducted in this

report. Justify your recommendations.

$7.$ Discuss the role of forecasting and the implications of forecasting accuracy to operations of

the airport. Why is forecasting important and how might passenger forecast be used by the

airport? Consider some of the operational issues covered in this module.

$8.$ Cover page, table of content, introduction, conclusions and list of references.

Table $1$: Passenger number in North London Airport Hub $202022$

Year Month Passengers Year Month Passengers Year Month Passengers

$2020$ January $13,441,7182021$ January $13,970,0772022$ January $14,470,077$

$2020$ February $11,942,2212021$ February $12,230,9632022$ February $12,730,963$

$2020$ March $14,670,9962021$ March $15,447,4352022$ March $15,947,435$

$2020$ April $14,286,8442021$ April $14,507,0382022$ April $15,007,038$

$2020$ May $14,537,3142021$ May $15,516,0632022$ May $16,016,063$

$2020$ June $15,906,1012021$ June $16,487,7022022$ June $16,987,702$

$2020$ July $17,362,5862021$ July $17,954,9102022$ July $18,454,910$

$2020$ August $16,969,5282021$ August $17,786,3572022$ August $18,286,357$

$2020$ September $14,010,9202021$ September $14,408,8172022$ September $14,908,817$

$2020$ October $13,599,0302021$ October $14,374,2542022$ October $14,874,254$

$2020$ November $12,919,7462021$ November $13,258,1042022$ November $13,758,104$

$2020$ December $14,289,1052021$ December $15,182,6162022$ December $15,682,616$