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Michelle_Michy
EVERYBEANCOUNTS1.docxRunning head: EVERY BEAN COUNTS 1
EVERY BEAN COUNTS
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At Starbucks, while you are in line for a coffee, research conducted shows that one is more than likely going to wait for more than five minutes, which might cause a lot of trepidation.
The research used a reliability engineering project to combine their passions for Starbucks’ coffee and gathering and analyzing data with Kofi Statistical Software.
The research drew on the work experience in crafting studies. Potentially everything can be characterized at Starbucks and can be measured. Once the data to use a tool to draw conclusions and hopefully ameliorate the process was had, real science could begin. Going to Starbucks has become a very common experience, something that everyone can relate to.
It has been observed, when customers go to Starbucks, they are expectant of a consistency in terms of the quality of their beverage and the time needed to receive it. The research team defined meeting a customer’s expectations as receiving their in under 5 minutes. Consequently, to understand whether the national Starbucks would be delivered at arbitrarily selected Starbucks locations, the research team chose two Starbucks stores in different geographies.
One part of the team collected data in one store for three hours, while the other team.. Each team rigged up a laptop and utilized a simple stopwatch application to record customer arrival and wait times in tabulated form. Favourably, the Starbucks “public café” culture simplified the ability to gather data without attracting attention.
After gathering the data, the research team used the Kofi software to analyze it. What they did is subject the frequencies of arrivals to a goodness-of-fit test with regard to the Poisson distribution. Theoretically, the Poisson-distributed arrivals normally go through the Gamma-distributed wait times. Additionally, the team then proceeded to test how well their wait-time data fit the Normal, Gamma, and Weibull distributions, both to substantiate the theoretical supposition and to explain for prospectively confounding of the beverage-making regime.
Once the teams confirmed the wait time distribution, it went on to perform a process capability analysis for each geography, correcting for biased data due to small sample size. Eventually, the team made use of individuals and moving range (I-MR) control charts to make an evaluation as to whether the beverage delivery process remained statistical control.
The process capability analysis for the wait-time measurements gathered from the Location X data had a very low beverage uptake value, which suggests a process that does not have the capacity of meeting the 5 minute upper specification limit. An additional interesting statistic is the PPM value. The analysis of the Location X Analysis of the data stipulates that for every 100 customers entering, 30 will not receive their beverage in less than 5 minutes.
References
Frei, F., & Morriss, A. (2012). Uncommon Service: How to Win by Putting Customers at the Core of Your Business. Boston: Harvard Business Review Press.
