Britz et al (1996, p4), Britz et al (2000, p7), and Hoerl and Snee (2002, p3) all describe the same example of an actual application of Statistical Thinking at a large corporation.   The corporation wanted to decrease the average monthly billing cycle time of about 17 days to the corporation target of 10 days.   A shorter time would improve the corporation’s cash flow and satisfy customer needs to more rapidly close monthly books.  Customers had complained that other competing corporations were not as tardy in submitting bills.

The initial review of the process revealed that three separate departments constituted the billing process.   Each department worked independently to perform their billing functions.   No one understood the entire billing process flow.   The corporation did not have a standard bill process operating procedure.  Members of three departments claimed that the billing time delays were due to the other departments. 

The initial step to improve the billing system was to develop both a systems map and a flow chart.   The systems map identified responsible groups and the information flow among the groups.   The flow allowed the construction of a production schedule for the monthly billing cycle.   The schedule:

·        listed the specific activities that had to be performed

·        identified the responsible group for each activity

·        specified due dates for each activity

The next step involved creating cross-functional teams to improve the performance of individual activities.   They recorded cycle times, and these cycle times highlighted problem areas. They developed solutions to minimize variations in the problem areas.  They documented the entire process and the procedures.   The documented process procedures helped reduce variation in the problem areas.  The documentation also helped in training new employees.

The corporation assigned a process owner to insure that they continued to realize the performance improvements.     

The result was a reduction in billing cycle time in a 5 month period from 17 day to 9-10 days.   That was almost a 50% reduction.   This result satisfied customers and generated annual savings of $2.5 million.

This example illustrates key features of Statistical Thinking.

·        Regarding the system as a process

·        Reducing variation

·        Using data to determine improvements for the system

References

1. Britz, G., D. Emerling,  et al. (1996). Statistical Thinking, ASQ Statistics Division Special Publication
2. Britz, G. C., D. W. Emerling, et al. (2000). Improving Performance Through Statistical Thinking. Milwaukee, WI, ASQ Quality Press.
3. Hoerl, R. and R. D. Snee (2002). Statistical Thinking - Improving Business Performance. Pacific Grove, CA, Duxburry.
   

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.
   

This post illustrates the Statistical Thinking tool, the flowchart or process map, using an example taken from the author’s consulting experience.   A flowchart of a process is sometimes referred to as a process map.   A manufacturer produced automotive door frames, depicted in the following figure.   The door frame consists of four parts which were joined by a welding operation.  The shape and finished product dimensions were important quality characteristics of the finished product.  However, they had a problem meeting dimensional specifications on the assembled final product.   As a result they did considerable rework to insure satisfactory quality for the finished product.  

The manufacturer formed a team to recommend corrective action to reduce rework costs and the time to meet shipment schedules.   Shop floor personnel thought that variations in incoming raw material caused the quality problems.   An analysis showed that the header was the primary quality problem.

The following figure gives the flowchart or process map for producing a header.  The roll mill takes sheet metal, cuts the input material to the proper length, forms the two parts for a header, and spot welds them together.   The bender bends the header to the proper shape punches two holes which will be used to position the part in subsequent operations.   The saw forms the proper angles at the two ends of the header.    The data on the flow chart below each operation specify important quality characteristics.   The symbols h₁, h₂, g, D1, D2, D3 and SC 4 through SC20 specify dimensions.

The manufacturer collected data for the team for relating the quality characteristics on the flowchart to finished part dimensions.   Collecting and analyzing data for individual steps in the flowchart is an example of disaggregation.  A regression analysis resulted in the following conclusions:

1. Variation in material characteristics has little effect on quality characteristics.
2. D1, D2 and D3 have considerable variation and affect finished product quality
3. The left and right headers have significantly different variation for D2 and D3.

The above conclusions motivated corrective action, and the manufacturer eliminated the need for rework.    This example reinforces the conclusion that data-driven decision making gives Statistical Thinking a significant advantage over expert opinion.