Smu senior Design Project: Southwest Airlines
Download 39.48 Kb.
|
- Navigate this page:
- Management Summary
- Background and Description of the Problem
- Technical Description of the Model
- Analysis and Managerial Interpretation
- Recommendations
SMU Senior Design Project: Southwest AirlinesOn Time Performance Spring 2010 EMIS 4395-001
Table of ContentsSMU Senior Design Project: Southwest Airlines 1 Management Summary 3 Background and Description of the Problem 3 Technical Description of the Model 6 Analysis and Managerial Interpretation 14 Recommendations 19 Management SummarySouthwest Airlines asked us to analyze their on-time performance on a line by line basis. Previously, Southwest had only analyzed their data on a leg by leg basis, strictly by departing airports, and by turn time and block time. Turn time includes all activities on the ground between arriving and departing gates, and block time includes all activities in the air between departing and arriving gates. We then proceeded to analyze the data given in hopes to discover the issues with and come up with possible recommendations to improve on-time performance. For this analysis, we collected historical data from Southwest for block time and turn time for the month of January in 2009. After sorting and filtering the data through Excel spreadsheets, we used the AWK programming language to analyze the data. Through AWK, we were able to determine probabilities and statistics of on-time performance at airports around the country on a line by line basis. Our findings point to a few particular problems for Southwest. For example, five of the top ten departure airports have on-time performance probabilities of over 48%. We were also able to find statistics on a line by line basis, showing which elements were problematic and reoccurring in many aircraft lines. The probabilities and statistics that we obtained show percentages of recoveries made in aircraft lines, probabilities of late departures at certain airports, and various other statistics.
Essentially, the purpose of our study was to analyze data on a line by line basis to discover ways to improve on-time performance. We first had to decide what types of methods we should use to approach this analysis. After sorting and filtering the data through Excel spreadsheets, we used the programming language AWK to find probabilities and statistics regarding departing airports, certain aircraft lines, and individual leg performance within these lines. Our goals were to answer the following questions:
Analysis of the Situation We first met with Southwest’s Optimization Team where they described the problem to us. Unlike other airline companies, who use a hub and spoke model, Southwest uses a leg by leg method. This allows them to have minimal idle time, a small number of gates, and allows for each gate to see an average of ten to twelve flights per day. Next, we obtained the historical flight data for January 2009 from our contact within the Optimization Team. We then looked through the headers of the files to create our data dictionary. The data dictionary helped us to understand all of the data available and allowed us to determine which probabilities to find. We approached the problem by sorting and filtering the large volume of data with Excel. Through this process, we were able to organize the data into lines of flying by tail number and the date of the flight, allowing us to view the problem holistically. We then wrote code in the AWK programming language in order to find certain probabilities and statistics within the data. The advantages to using AWK were that we could use one large data set to find many probabilities. We found this to be a more efficient method of analysis, and a helpful tool in seeing the big picture. The first set of statistics that we found determined the probability of on-time performance in relation to late lines (the probability that the current flight and preceding flight are both late), late legs (the probability that the preceding flight is on time but that the current one is late), on time flights (the preceding flight and the current flight are on time), and late leg recoveries (a preceding flight was late, but the current one is on time). Upon investigation, we determined that certain airports had astonishingly high and low probabilities of late departures. Of Southwest’s top ten departure airports (by volume), we noticed that five had over 48% probabilities of late departures, with some even as high as 68%. We created a histogram that showed us those lateness probabilities of 0-36% and 47-68% were outliers, compared to the others, with an average probability of 42%. The final step of our analysis was to put all of the results into a graphical format for the PowerPoint presentation. This allowed us to begin making correlations with the data as well as interpret our data into useful recommendations. Technical Description of the ModelBy using the AWK programming language, we were able to code three different programs to aid our analysis of the historical data. Our objective was to mathematically determine the probabilities regarding on-time performance via airports and aircraft lines. Our sources for the data used are Southwest Airlines and their website. We calculated our expected values from the historical data provided. The first program found the probabilities of late lines, late legs, on time flights, and late leg recoveries. The variables we used are defined as: numlines = total number of aircraft lines olddate = the flight date of the previous leg oldtail = the tail number of the previous leg oldarrivaldelay = the previous leg’s arrival delay currentdate = the flight date of the current leg currenttail = the tail number of the current leg arrivaldelay = the current leg’s arrival delay Source code for Prob1.awk: BEGIN { FS="\t" getline numlines = 0 olddate = $6 oldtail = $7 oldarrivaldelay = $14 } { currentdate = $6 currenttail = $7 arrivaldelay = $14 if (currentdate == olddate && currenttail == oldtail) { if (arrivaldelay > 0 && oldarrivaldelay >0) ++lateline else if (arrivaldelay > 0 && oldarrivaldelay == 0) ++lateleg else if (arrivaldelay == 0 && oldarrivaldelay == 0) ++ontime else if (arrivaldelay == 0 && oldarrivaldelay > 0) ++caughtup } olddate = currentdate oldtail = currenttail oldarrivaldelay = arrivaldelay } END { print "lateline =" lateline print "lateleg =" lateleg print "ontime =" ontime print "caughtup = " caughtup print "total = " lateleg+ontime+lateline+caughtup print "probablity of lateline = P(late|prev late) =" lateline/(lateleg+ontime+lateline+caughtup) print "probability of lateleg = P(late|prev ontime) =" lateleg/(lateleg+ontime+lateline+caughtup) print "probability of ontime = P(ontime|prev ontime) =" ontime/(lateleg+ontime+lateline+caughtup) print "probability of caughtup= P(ontime|prev late) =" caughtup/(lateleg+ontime+lateline+caughtup) } Our second program ranked our worst performing airports by the probability that a departing flight would be late. Our variables are defined as: airport = the name of the departing airport depdelay = the departure delay for a specific flight ontime = the number of on time flights for the departing airport delays = the number of delays at the departing airport Source Code for Prob2.awk: BEGIN { FS="\t" getline }
airport = $9 depdelay = $12 ++linesread if (depdelay>0) {++delays[airport] } if (depdelay==0) {++ontime[airport] } } END { print linesread, "lines read" for(i in delays) { print i, delays[i], ontime[i]+delays[i], delays[i]/(ontime[i]+delays[i])} } The final program allowed us to sort and view the list of aircraft lines organized by tail number and flight date. It also showed the probability of recoveries by aircraft line, as well as the probability that a specific line will be late. The variables are defined as: numlines = number of lines flown in the month of January olddate = the flight date of the previous leg oldtail = the tail number of the previous leg oldarrivaldelay = the arrival delay of the previous leg legsinline = the number of legs in each specific aircraft line currentdate = the flight date of the current leg currenttail = the tail number of the current leg origin = the first leg in the list of lines dest = the subsequent leg in the list of lines arrivaldelay = the arrival delay of the current leg dayofweek = the day of the week corresponding to the flight date Source Code for linelist.awk: BEGIN { FS="\t" getline numlines = 0 olddate = "Initial" oldtail = "Init" oldarrivaldelay = 0 legsinline = 0 } {
currenttail = $7 origin = $9 dest = $10 arrivaldelay = $14 dayofweek = $5 if (currentdate == olddate && currenttail == oldtail) { linename = linename "-" dest if (arrivaldelay > 0) ++numlatelegs ++legsinline if (arrivaldelay > 0 && oldarrivaldelay > 0) ++latelate } else { ++linecount[linename] if (numlatelegs > 0) ++linelate[linename] legslate[linename] += numlatelegs recoveries[linename] += latelate linelength[linename] = legsinline linename = origin legsinline = 1 numlatelegs = 0 ++dayofline[linename,dayofweek] latelate = 0 } olddate = currentdate oldtail = currenttail oldarrivaldelay = arrivaldelay END { Line #observations #latelegs legsInLine #lateLegs #recoveries probrecoveries problinelate for (i in linecount) { if (legslate[i] >0) {precoveries[i] = recoveries[i]/legslate[i]} print i, linecount[i], linelate[i], linelength[i], legslate[i], recoveries[i], precoveries[i], (linelate[i]/linecount[i])} # recovery = ontime after late arrival }
In this section we will provide an analysis of the overall project objectives and the output of our model.
Historically, Southwest analyzes on-time performance by monitoring the turn time stats on a leg by leg basis. An example of a leg is shown above. For this assignment, Southwest asked us to look their data from a broader perspective and analyze on-time performance by line. An example of a line is displayed in the next section.
The data we analyzed was from January 2009. First we sorted the data into two segments, number of lines and number of legs. We then used AWK to determine the probability of lateness by line and leg. Lateness by leg and line were valuable statistics for the following reasons:
We also looked at a lines ability to recover to verify if a late leg had a ripple effect on a line. Below is a chart of this is data. This analysis did reveal a late leg does have a ripple effect on a line as only 10% of lines with a late leg actually recovered from the previous leg’s lateness. Next, we analyzed the frequency of lateness by airport. This data helped us identify which airports were performing well above expectations (as far as low number of late departures) and airports underperforming. (as far as high number of late departures) Here is a snapshot of the probability of late departure by airports. Based on this data we created a box plot and histogram to identify the outlying airports which performed lower or higher than the average range. Below are the charts show the Box Plot and Histogram. The histogram was valuable for the following reasons:
RecommendationsBelow are our recommendations:
Directory: ~barr -> 4395 -> history -> Presentations Presentations -> Senior Design Final Report Presentations -> Casey O’Brien & Julianna LaFerneyTelecom Mergers & Acquisitions: Economical & Technological Effects Download 39.48 Kb. Share with your friends:
|