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Implementation Case Studies A Six Sigma Case Study – Tutorial for IT Call Center – Part 3 of 6

A Six Sigma Case Study – Tutorial for IT Call Center – Part 3 of 6

This combination “case study and tutorial” tracks the inception of a Six Sigma DMAIC project by an IT support business. The project is aimed at helping the company become more competitive and profitable. Each part follows the project team as it works through another stage of the DMAIC methodology. Click to read the rest of the case study: Part 1, Part 2, Part 4, Part 5 and Part 6.

Having developed a good understanding of the project’s business case and customer requirements (identifying the Y‘s), and the as-is process, the Six Sigma project team of the IT services business began to focus on the Measure phase. The team identified the measures and data collection plan for gathering the right amount of the right data to impel their learning about root causes and drivers that impact the project Y‘s.

The DMAIC (Define, Measure, Analyze, Improve, Control) roadmap called for work in these areas during the Measure phase:

M1. Refine the Project Y‘s: Getting even clearer about how the project’s key outcome measure(s) will be defined, measured and reported.

M2. Define Performance Standards for the Y‘s: Identifying how performance will be measured – usually somewhere on the continuum from capability measures like Cp and Cpk for “variables” data that is normally distributed to percentile or other capability metrics for “attribute” and other data that may be skewed in distribution.

M3. Identify Segmentation Factors for Data Collection Plan: Starting with the natural segmentation of project Y‘s and moving though consideration of prospective driving factors (X‘s), segmentation suggests the packets of data that should be collected in order compare and contrast segments to shed light on Y root causes and drivers.

M4. Apply Measurement Systems Analysis (MSA): In any project, raw data is gathered and then converted into measures. That process comprises a “measurement system” that should be characterized and strengthened in terms of its accuracy and repeatability.

M5. Collect the Data: Gathering data, preserving its meaning and noting any departures from the discipline put in place under MSA.

M6. Describe and Display Variation in Current Performance: Taking an initial look at the data for its distribution, extreme values and patterns that suggest special variation.

M1. Refine the Project Y‘s

During this step the team conisdered exactly how the project Y‘s would be defined and measured:

PrimaryCustomer Satisfaction1. By industry standard monthly survey
2. The project will require additional, more frequent, case-by-case customer satisfaction data. A measurement system that tracks with the industry survey will be devised and validated.
SecondarySupport Cost (Per Call)The staff time connected with each call:
– Call answering and discussion
– Case research
– Callback time
will be loaded with a distribution of benefits and infrastructure costs to compute overall support cost per call.
Related / of InterestDays to CloseTime span from call origination through client indication that the issue is closed to their satisfaction.
Wait TimeAutomatically tracked for calls in queue. Summed for calls encountering multiple transfers.
TransfersAutomatically tracked for each call moved to another extension.
Service TimeAutomatically tracked for time from staff call pickup until hangup or transfer.

M2. Define Performance Standards for the Y(s)

For each project Y, the current baseline and best-estimate target was documented. In some cases, the team found that good baseline data was unavailable. (Unfortunately that is a common occurrence in DMAIC projects.)

 MeasureCurrent BaselineTarget
PrimaryCustomer satisfaction (Per Collins Industry Assessment)90th percentile / 70-80 percent satisfied90th percentile / 85 percent satisfied
SecondarySupport cost per call90th percentile / $4090th percentile / $32
Related / of InterestDays to closeDid not have good baseline data90th percentile / 3 days or less
Wait timeDid not have good baseline data90th percentile / 4 minutes
TransfersDid not have good baseline data90th percentile / 2
Service timeDid not have good baseline dataMean: < 8 minutes
St.Dev.: < 0.5 minutes

M3. Identify Segmentation Factors for Data Collection Plan

The first question was: How is Y naturally segmented? Often Y data is organized by customer type, geographic region, product or service type, etc. Thinking about where the strongest “action” was for the dynamics under study, the team focused its initial data collection effort on the segment(s) that offered the most potential for the team’s learning. This helped conserve the limited resources available for data collection and analysis. Instead of “measuring everywhere,” the team started with a focused subset of all possible data. The data was naturally segmented by call center – with most of the traffic in one center. Data from that site was used for the initial data collection.

The next question was: What factors may be driving the Y‘s? Identifying these factors and gathering data on their behavior may shed light on the root causes and drivers for the Y‘s. This is the planning part of the Six Sigma drill-down that is sometimes called “peeling the onion.” While the fundamental interest is in the Y behavior, the idea is not to truly solve a problem by trying to “fix the Y.” That approach might provide a temporary fix. Understanding the underlying drivers (the x’s) offers the possibility of addressing the root cause, and fixing the problem so that it will stay fixed.

A Y-to-X tree depicts the array of lower level x’s that may be driving a Y. Other tools with a similar thrust – cause-and-effect diagrams and cause-and-effect matrices (illustrated later) – can be helpful in identifying and prioritizing the prospective X‘s for data collection and study.

The team’s Y-to-X trees for support cost and customer satisfaction are shown in Figures 1 and 2.

Figure 1: Y-to-X Tree for Support Cost

Figure 1: Y-to-X Tree for Support Cost

Figure 2: Y-to-X Tree for Customer Satisfaction

Figure 2: Y-to-X Tree for Customer Satisfaction

Input-Output Analysis

Building on the information developed in the SIPOC / COPIS table, the team reviewed process inputs and outputs, classifying each as “controlled” (with process provisions in place to measure and influence that input or output, if necessary) or “uncontrolled” (with no such provisions in place). See Figure 3.

Figure 3: Process Inputs and Outputs Classified as Controlled or Uncontrolled

Figure 3: Process Inputs and Outputs Classified as Controlled or Uncontrolled

Cause-and-Effect Matrix

To further explore potentially influential factors, the team created a cause-and-effect matrix (Figure 4). The high scoring items in this analysis were strongly considered for data collection. The highest, “Available Staff for Transfer,” was included. The next highest scoring, “Staff Experience/Training” was not readily available in the historic database. (There had been reluctance to log personal data as part of the ongoing call logging.)

Figure 4: Cause-and-Effect Matrix

Figure 4: Cause-and-Effect Matrix

M4. Apply Measurement Systems Analysis (MSA)

To prepare for the measures to be collected in the next step, the team reviewed the measurement systems. In transactional processes, any activity that gathers raw data and converts it into counts, classifications, numbers or other forms of measure is a “measurement system.” While the statistical methodology connected with MSA is beyond the scope of this article , Figure 5 depicts the four questions that are usually posed for measurement systems in transactional processes. Viewed simply, the intent of MSA is to strengthen a measurement system so that it is suitably accurate, repeatable, reproducible and stable. A fifth issue, “linearity” (the accuracy of the system over the range of mesaured values), is sometimes considered.

Figure 5: Questions Usually Posed for Measurement Systems

Figure 5: Questions Usually Posed for Measurement Systems

M5. Collect the Data

A plan was formulated to gather data from the past year’s database. This required retrieving call data, as well as tracing call resolution times, staffing levels, call volume levels and relevant follow-up events. For each call sampled, the team rebuilt information about staffing, call type, number of transfers, wait time, etc. (Figure 6)

Figure 6: Format Used to Record Collected Data

Figure 6: Format Used to Record Collected Data

M6. Describe and Display Variation in Current Performance

A first look at the data coming in provided the team insights about extreme values and patterns suggesting problems with the measurement system. With the information, the team began to forecast what the Analyze phase would reveal. The team’s question at this point was: How is the Y distributed? The team looked for the layout of measured values collected – for symmetry and for extreme values. This suggested the kinds of graphical and statistical analysis that would be appropriate (Figure 7).

Figure 7: How Is the Y Distributed?

Figure 7: How Is the Y Distributed?

Data on the call center X measures was graphed and charted a number of ways. Figure 8 shows the variation in customer Wait Times on an Xbar-R control chart. Variation above and below the chart’s control limits suggested that there were “special causes” in play – worth understanding in more detail by the team in the Analyze phase.

Figure 8: Xbar-R Chart of Wait Time

Figure 8: Xbar-R Chart of Wait Time

Part 4 is about the Analyze phase of the project.

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Leave a Comment


Raman Rao

Fig 3 – what is the reason for identifying the ‘Controllable’ and ‘Uncontrollable’?

Fig 4 – An explanation of how the cause and effect matrix was derived would be helpful, at least picking 2 or 3 items as examples.

Fig 5 – Examples using the Case Study data to explain what is said in the table would be helpful


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