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Surviving a Patrol Strike - Case Study Example

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The graph below represents the progression of parking revenues and fines in the City Centre Car-Park over the last 23 weeks, or almost six months. The pink line represents each week's revenue, and the yellow line indicates the amount of parking fines that are issued each week.
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Download file to see previous pages However, linear regression requires that one of the two variables be fixed, which is possible in the current scenario. The yellow line (for fines) is fairly easy to deal with. If we eliminate the artificial blip of Christmas week and strike the clear outlier (Week 8) from the data, a trend appears. Smoothing would eliminate the faster spike right before Christmas, as well as the blip for the week when there were no fines, because of no enforcement.
Over time, the car-park's traffic appears to be continuing to rise, even figuring in the seasonal effects of the Christmas shopping rush. This increase is leading to significantly higher revenues for parking fees in the weeks leading up to Christmas. However, both the higher changes in the weeks before Christmas and the lower results after Christmas do not suggest a business in trouble: instead they suggest a business that is in a highly competitive industry, City Centre Car-Park's market share appears to be growing, even if we smooth out the increases associated with the larger number of Christmastime shoppers.
As will happen occasionally in the labor market, the staff who are responsible for operating the car park have threatened to walk off the job after week 23. Because this staff is responsible for making sure the self-pay machines are locked and for patrolling the lot and issuing fines, this could have a significant effect on the car-park's revenues. While it is likely that people would still pay the machines, the question of enforcement and collection would be a sticky one.
It is possible to use time-series analysis to figure out the approximate effect of such a walkout on the revenues of the car-park. The fundamental assumption of time series analysis is that the data being considered contain a systematic pattern, interrupted by error, or random noise, which can make the pattern difficult to find. Successful time-series analysis takes the random noise out of the situation as much as possible.
The majority of time-series patterns consist of one of two basic types: trend and seasonality. Trend refers to a linear or nonlinear component that undergoes change over time and does not repeat within the time utilized by the model. Seasonality is a smaller version of trend, because it represents a cycle that repeats itself within the time utilized by the model. A set of data may contain both trend of seasonality. A common example would be retail sales, which may grow from year to year but may also be easily predicted to spike around the Christmas season within each year (Time Series Analysis 2002).
While there is no established or proven way to find trend components within a set of time-series data, trends are fairly simple to identify, as long as they consistently move in one direction or another. When a set of data is considered to contain a significant amount of error, though, the first step is to try a process called "smoothing."
This consists of averaging data within the set with the goal of canceling out the individual data that do not fit within the existing system. The most common way that smoothing occurs involves moving average smoothing. In this technique, each item of data in a series is replaced by the simple or weighted average of n surrounding pieces of data, where n is defined as the width of the "window" used for ...Download file to see next pagesRead More
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