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Statistical Thinking to Improve Quality
This blog examines the use of data analyses and statistical tools in a framework of statistical thinking to improve quality. The following principles form the basis for statistical thinking:

* All work occurs in a system of interconnected processes,
* Variation exists in all processes, and
* Understanding and reducing variation are keys to success.

Statistical thinking significantly improves the effectiveness of data analyses and statistical tools.
Service Time Flowchart
Posted by: Gordon Clark on November 3, 2009 at 7:15PM CST

This post starts a series of posts to present the use of Statistical Thinking Tools in applying Statistical Thinking.   The Statistical Thinking Tool illustrated by this example is a flowchart.   We can have flowcharts for processes having service time objectives as well as processes processes producing a physical product.  Jeffries and Sells (2004) present this example and describe the use of “statistical tools” to meet company service time objectives.   We regard their use of statistical tools as an application of Statistical Thinking.

A Midwest manufacturing firm processes orders for its 6 manufacturing plants and 12 warehouses.   Originally, each plant and warehouse had its own order processing service staffed by a total of 36 customer service representatives.  To improve customer service and reduce costs, the company president directed a team to develop a centralized customer service center located at corporate headquarters.   The president made this decision after the team surveyed customers and found that they were adamant that they did not want to wait for a customer service representative to answer a phone call and they were not very interested in personalized service provided by a plant or warehouse representative.

The team established a goal where 95% of incoming calls would wait less than 2 minutes for a customer service representative.   The team acquired an Automatic Call Distribution (ACD) system to route customer calls to customer service representatives.  The call center would operate from 7:00 am to 7:30 pm Central Time.   The following figure gives a flowchart specifying the process of answering incoming customer calls.

The team collected data giving the distributions of incoming calls by time of day and the service times of the customer service representatives to answer the calls.  Recording and analyzing data for individual steps in the process flow chart is an example of disaggregation.   Classifying and analyzing data by a factor such as time of the day is an example of stratification.

The customer service center staffing levels by hour of the day is a crucial system design parameter.   Wait times will be long without adequate staff.  On two occasions in the past two months, I have had to wait more than an hour for technical service support personnel to answer my calls.   I know that this happens because the companies involved have allocated inadequate staffing to handle the incoming calls.

The team developed staffing levels throughout the day using a simulation of the process represented by the figure above.   Constructing a simulation requires a flowchart.  Refer to Jeffries and Sells (2004) for additional details.

The next post will illustrate the use of a flowchart for a process producing a physical product.

References

  1. Jeffries, R. D. and P. R. Sells (2004). Managing Customer Service Using Statistical Tools: A Case Study. Annual Quality Congress Proceedings.
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