1 TQM Case Study: Newspaper Focuses on Customer Service

Niraj Goyal February 26, 2010 4

Quality in the total quality management (TQM) method is defined as customer delight. Customers are delighted when their needs are met or exceeded. The needs of the customer are:

Product quality

Delivery quality

Service quality

Cost value

Improving customer service was the focus of two projects within the deployment of TQM in a mid-sized newspaper in India. This is the second piece in a three-part series of articles featuring case studies from that deployment; Part 1 of the series featured projects leading to improvements in product quality. Part 3 looks at supply-chain improvements.

Reducing Advertisement Processing Time

The newspaper closed its window for booking advertisements at 4 p.m. every day. However, many of the newspapers advertisers expressed that they would be delighted if this limit could be extended to 5 p.m., as they were not able to send ad materials on time for the 4 p.m. deadline.

The TQM leaders formed a team consisting of representatives from each link in the ad-processing chain of work. The team attended a two-day quality-mindset program to expose them to the concepts of TQM and also to open their minds about experimenting with change.

Defining the ProblemIn TQM, problems are defined as Problem = Desire Current status. Therefore, in this case:

Problem = Desired closing time Current closing time = 5 p.m. 4 p.m. = 60 minutesThe 4 p.m. deadline had been instituted because:

Deadline for sending the ad pages to the press was 6:30 p.m.

Standard cycle time for processing ads into pages was 2.5 hours

Achieving a 5 p.m. ad closure deadline meant reducing the standard ad processing time by 40 percent, or one hour. To define the current state, the actual time spent preparing pages to go to press was collected over several days.

Defining the metric: If T = (page processing time page-to-press deadline), then for 99.7 percent on-time delivery, or 3 sigma performance, the average T + 3 standard deviations of T should be less than 0.

Measure the current state: The ad closing deadline could not be delayed by an hour without delaying the dispatch of the newspaper to press by an equivalent amount. Therefore, the current state was calculated by measuring the delay compared to a notional 5:30 p.m. dispatch time rather than the actual deadline of 6:30 p.m. Calculations showed that:

Average T = 72 minutes

Average T + 3 sigma of T = 267 minutes

The problem was defined: reduce 267 minutes to less than 0 minutes.

Analyzing the ProblemThe team monitored the time spent on each activity of the ad process (Table 1).

Table 1: Time Spent on Ad Process

ActivityDeadline

Ad receiving4 p.m.

Dummy dump4:30 p.m.

Pagination complete6:30 p.m.

During the 4 to 4:30 p.m. period, ads received at the last minute were still being processed. At 4:30 p.m., the material was dumped into the layout for pagination, meaning arrangement on the newspaper pages using software and manual corrections. To achieve the objective of a 5 p.m. ad content deadline, the pagination time had to be reduced.

Brainstorming why pagination took two hours produced three possible major reasons:

Error correction

Delayed receipt of ad material for a booked ad

Last-minute updates from advertiser

All this work was carried out after the last ad was submitted. Team members suggested that if ads were released for pagination earlier, removing errors could begin simultaneously with the processing of the last ads in order to reduce cycle time. They agreed to give two early outputs at 3:30 and 4 p.m., before the final dump at 4:30 p.m.

Testing the IdeasTable 2: Problems with New Process

ProblemEffectRoot CauseSolution

Missing material removal15 to 30 min.Material delayed or not receivedOnly feed ads once all materials received

Error file found after last release10 min.Not checking pre dumpCheck for errors pre dump

Special placement instructions not followed10 min.Processing team not aware of special instructionsGive instructions as received

Distorted ads in PDF15 min.Ads not corrected before feedingCorrect before feeding, include in SOP

Ads inserted post pagination completion20 min.Ads accepted after deadlineEnforce deadline

Total time savings possible70 to 85 min.

The process was repeated four times (Table 3).

Table 3: Further Process Observations

ProblemEffectRoot CauseSolution

Observation 2

Repeating old practicesReiterate SOPs

Scanning of materials delayed45 min.Agree on scan turnaround time

PDF conversion problem15 min.Programming problemIT to resolve

Zip error file not scannedZip not required

Observation 3

System failure at peak time75 min.Use back-up system

Observation 4

Add-on section integration delayed25 min.Start integration in pre-dumpsAdd to SOP

Checking the ResultsNine weeks of continuous implementation yielded dramatic improvement. Average processing time was reduced by an hour, from 72 minutes to 12 minutes. However, the level of variability, although 50 percent lower, was still unacceptable. Analysis of the variability showed that it was largely due to slip-ups in implementing the SOPs.

Standardizing ControlsThe team used an x-bar control chart (Figure 1) to monitor and improve performance regularly.

Figure 1: Control Chart of Ad Processing Time

Gradually the performance improved. Two months after implementation, delivery time had progressed from 267 minutes late to 12 minutes early. The deadline for receiving ads could now be relaxed to 5 p.m., delighting the advertisers.

Reducing Customer Complaints

Management indicated that the number of credit notes given to advertisers was too high. Credit notes, issued to rectify errors made in sales invoices, were used to fend off considerable customer annoyance. But this system caused trouble for the paper. Besides increasing non-value-added work, credit notes sometimes resulted in financial loss because customers could use the credit toward ads that had already been booked as sales.

During the previous 12 months, the newspaper had received 80 credit notes per week. The team agreed to try to reduce that number by 50 percent in Phase 1.

Finding the Root CausesAbout 200 credit notes were examined to determine why they had been issued. Categorization of the causes was charted in a Pareto (Figure 2).

Figure 2: Pareto Chart of Complaints Resulting in Credit

Three causes constituted 84 percent of the problem:

1. Wrong billing 46 percent

2. Wrong rate 24 percent

3. Wrong material used 14 percent

Table 4 shows the root causes of a majority of the credits issued, determined using the 5 Whys method, and their corresponding countermeasures.

Table 4: Explanation of Credit Causes and Countermeasures

1st Why?2nd Why?3rd Why?Countermeasure

Wrong billingUnbilled charge picked up; Discount applied incorrectly to all ads in seriesSystem bugRemoved

Wrong rateSales scheme not in sales card; Old scheme continues after updating of sales rate card; Scheme in rate card but not picked up by systemSales cards not updated; Bill system does not pick up entrySOP

Free ads billedSystem does not pick up operator entryModify system to pick up operators entry when prompted, rather than automatically taking billing information from the rate table.

The team tested the ideas, which resulted in an 80 percent reduction in credit notes, from 80 per week to 14 per week. The process was adopted in regular operation, and the results were documented and presented to senior management.

Change in ThinkingTQM often leads to radical changes in employee mindsets. The improvements resulting from the two customer service-related projects helped to create a team environment in which any change idea is easily accepted, tested and if it works implemented.

2 TQM Case Study: Ensuring On-time Newspaper Delivery

Niraj Goyal February 26, 2010 0

Newspapers face pressure from their external customers to minimize the cycle time of their production process at both ends of the supply chain. At the start of the chain, readers want the freshest news, while advertisers want the latest possible closing time for booking ads. At the end of the chain, even a few minutes of delayed delivery can result in unsold market returns.

Editors note:

This article is Part 3 of a series on a TQM deployment at a mid-sized Indian newspaper company.

Part 1 covers improvements made to product quality. Part 2 describes changes made to customer service.

Often, purchase decisions are made in short, fixed windows of time or on the spur of the moment while waiting for the morning bus or train, during the short walk to the office, or when a vendor taps on the car window at a red light. In short, newspaper delivery must be neither late nor early, and it must be completed in a minimum amount of time.

This last article in a three-part series illustrates how TQM was used to make cycle time improvements throughout the newspapers supply chain in order to ensure efficient and on-time delivery.

Defining the Problem

At this particular company, newspapers were not always delivered to sales stalls on time, which was resulting in lost circulation and waste due to market returns. In TQM, the equation to define a problem is:

Problem = Desire Actual statusIn this case, the desire was for the newspapers to arrive at all depots at or before each depots fixed target time every day. At the start of the project, the company had no measured actual status. To gain this data, the team measured truck arrival times at each depot for one week, and calculated the deviation from targets. Negative deviation denoted early arrivals and positive numbers represented late arrivals.

The team determined the following from their analysis:

Deviation from target time for individual depots = d = 11 min.

Average of deviations for all ds for one day = a Average of a over several days = D = 11 min.

Standard deviation, or sigma, of d = s = 33 min.

Sigma of D = S = 17 min.

Delivery delay within three standard deviations for individual arrivals = d + 3s = 110 min.

Daily average delivery delay within three standard deviations = D + 3S = 62 min.

For the daily average to achieve a 3 sigma standard (99.7 percent on-time delivery), the 62 minutes had to be reduced to zero. For individual arrivals to be consistent, 110 minutes also had to be reduced to zero. The team decided to attempt the improvement in two phases: improve by 50 percent in the first phase, and then improve to zero in the second.

Analyzing the Process

The team analyzed the causes of delayed delivery at the end of a supply chain using process mapping. This involved:

1. Identifying the start and end points of the process, the latest cut-off start time and the expected delivery time.

2. Mapping all value-added and non-value-added activities.

3. Determining the standard cycle time and cut-off delivery time of each stage.

4. Timing the actual flow of activities through the process.

5. Identifying the stages where delays occur and the causes.

The newspaper production cycle has two basic work streams: building pages that contain only ads and building pages that contain both ads and editorial material (Figure 1). In the figure below, the cycle for ad pages is shown in green. The cycle for pages that require simultaneous processing of ads and editorial material are shown in the blue and orange boxes. The final activity is shooting the pages to the press for printing, with a deadline time set for each page.

Figure 1: Two Work Streams for Producing Pages

Once the pages are sent to the press, the newspapers are printed and copies are sent to the depots (Figure 2).

Figure 2: Newspaper Printing and Delivery Process

The newspaper chain is geared for designing, producing, delivering and selling a product within hours. This fast pace made improvement seem a daunting task. The team decided to use a segmented approach: Pick the weakest link and strengthen it, then pick the next weakest and so on until the objective is achieved.

Finding the Weakest Link

The team timed the longest chain of printing and dispatching at key points and compared them against targets (Table 1).

Table 1: Printing Process Time Over Six Days

Day 1Day 2Day 3Day 4Day 5Day 6

Line 1 time of start of printing vs. schedule10 minutes ahead10 minutes ahead16 minutes ahead3 minutes ahead5 minutes behind5 minutes ahead

Line 2 time start of printing vs. shedule1 minute ahead9 minutes ahead4 minutes ahead15 minutes behind22 minutes ahead27 minutes ahead

Time printing finished vs. schedule15 minutes ahead36 minutes ahead24 minutes ahead11 minutes ahead30 minutes ahead44 minutes ahead

Manufacturing processes often are blamed for delays. Here, however, despite occasional upstream delays, printing finished each day with a few minutes to spare. Therefore, the team looked elsewhere to find the weakest link.

The next area they investigated was delivery to the depots. For all deliveries to take place on time, 14 trucks needed to reach between 70 and 100 delivery points punctually. This was not the case; delivery times are shown in Table 2.

Table 2: Delivery Time Delays

Truck Departure (D) Time vs. ScheduleTruck Arrival (A) Time vs. Schedule

Average2 minutes behind5 minutes behind

Standard deviation (sigma)21 minutes23 minutes

Average + 3 sigma66 minutes behind64 minutes behind

These numbers indicated that the hold-up was due in part to delayed truck departure, despite on-time printing. After observing 84 deliveries, the team noticed that, 90 percent of the time, the first five trucks left late, while only 6 percent of the last five dispatches left late each day (Table 3).

Table 3: Delivery Time Sample Divided by Truck Group

TotalTrucks 1-5Trucks 6-9Trucks 10-14

On time393828

Delayed4527162

Total84302430

Percent on time46103394

To determine why this was happening, the team examined the process of bundling the two sections of the paper. Section A was pre-printed and kept in machine-counted bundles of 50. Section B also was coming off the line in machine-counted bundles of 50. The paper needed to have Section A inserted in Section B. Each truck had two kinds of bundles:

Count 50: Machine-counted bundles of A and B were strapped and sent separately to be merged at the delivery point.

Odd count (less than 50): Machine-counted stacks were broken, counted, merged and strapped for loading on to the trucks.

The bundling was being done in batch mode:

For the first 20 to 30 minutes after a print run started, workers stacked bundles of 50 for use in creating the odd-count bundles.

After 30 minutes, workers loaded standard bundles of 50 into each truck as per order.

Each truck waited until it had the correct mix of bundles loaded.

The loss of 20 minutes in the beginning meant delays for the earlier trucks until the manual process caught up.

Generating Countermeasures

The printing machine output was a continuous flow. All operations up to trucking were re-designed as a flow as follows:

Bundles built per minute: 6

Fraction of bundles that were non-standard: 0.3

Non-standard bundles built per minute: 1.8

The team identified trucks that had a higher ratio of non-standard bundles, so a second group of workers was brought in for these trucks to ensure flow. The sequence of trucks was adjusted to suit the timing of the trucks at their first drop points.

The organization conducted two weeks of trials for this new process (Table 4).

Table 4: Delays Before and After Flow System Implementation

D BeforeA BeforeD AfterA After

Average2 minutes behind5 minutes behind20 minutes ahead15 minutes ahead

Standard deviation21 minutes23 minutes5 minutes4 minutes

Average + 3 sigma66 minutes behind64 minutes behind5 minutes ahead3 minutes ahead

To standardize the process, the team prepared standard operating procedure documents and trained process owners to run the line in flow, measure and review the average + 3 sigma numbers each week, and kill any new problems that might occur.

3 Newspaper Aims to Improve Printing: A TQM Case Study

Niraj Goyal February 26, 2010 1

Caught in an exploding market, with rapidly improving products, the management of a media organization in India realized that improving the quality of printing of its newspaper was imperative to survival and progress. The organization adopted total quality management (TQM) and has completed several improvements in office processes related to turnaround, which is so vital to a newspaper in bringing the freshest news to its readers.

This three-part series offers case studies of the media companies efforts with TQM. Part 1, below, captures the first foray into improving the quality of shop-floor processes. Part 2 describes changes made to customer service. Part 3 looks at supply-chain improvements.

Examining the Print Process

The organization uses a six-station, web-based printing machine. Each station has four basic colors: cyan, magenta, yellow and black (CMYK). For those unfamiliar with printing, different colors and shades are obtained by superimposing inks of these four colors in carefully set and controlled quantities, one on top of the other. Each station, therefore, has the four inks stored in four separate hoppers. From each hopper, the flow of ink is adjusted through 48 taps.

Most key parameters of printing, such as speed, are set from a central panel. But before the improvement project, the actual quantities of ink to produce the most authentic colors in pictures and advertisements were set manually. Experienced printers visually examined the pictures and set the taps at the start of the print run until they judged the print quality to be satisfactory. Frequent examination of the pictures and adjustment of taps continued throughout the print run.

Competing printers used more expensive modern machines with motorized automated color adjustment mechanisms, but the machines were unaffordable for this organization. Improving the print quality, however, remained a top priority.

Getting Started

The improvement process began with the selection of a cross-functional group: the general manager, two printers, the quality process manager and the printing shop floor managers. The team attended a two-day TQM awareness program to introduce them to the key concepts of the methodology and open their minds to change.

Then the team set about defining the problem they faced. The print quality problem had two distinct identified areas:

1. Picture outlines were often blurred

2. The shade and intensity of colors was not true

Adjusting Blurred Pictures

The team used the Five Why analysis to determine the root cause of the blurred picture outlines:

Why is the picture blurred? The four colors are not getting superimposed exactly one on top of the other.

Why? The printing cylinder plates are not accurately registered on the printing machine rollers.

Why? The locating notches on the plate are not accurate enough.

Why? Plate registration on the notching machine is inaccurate.

Why? The plate-making machine had two stations (Figure 1). In the first station, the print impression was transferred onto the plate while the plate was referenced by the three pins using the base and the left edges of the plate. On Station 2, the same plate was notched using the base and the right edge. This led to a different positioning of the notches with respect to the print on each plate. Because each color had one plate, the images were not superimposing exactly one on top of the other leading to a blurred outline.

Figure 1: Pin Location on Plates

The team generated a countermeasure idea: Shift the roller on Station 2 from right of the plate to left of the plate. They tested the idea, and images with consistently sharp outlines resulted, resolving the problem. From this point, regular production went ahead smoothly and the change was internalized and documented.

Improving Color Accuracy

Next, the team addressed the problem that the printed shades and intensity of the colors was not true to the originals. Resolving the problem required:

1. Selecting an appropriate performance metric

2. Measuring the current performance

3. Defining an improvement target

Process owners explained how print quality was measured: Each of the six printing stations printed four pages. Each station had four printing stages each with one color ink (C, M, Y or K). Color shades were obtained by adjusting the ink flow to each section of each printing roller. The ink flow quantity setting was indicated by eight sets of four dots one for each color on each page. An instrument that measures the intensity of color of the dots was available, and a standard range was specified within which the measurements should fall. However, this system was in disuse because it was too cumbersome.

The team took sample readings of black dots as a special exercise to define the current state of the process. The black (K) dots yielded the following:

Average K: 1.23 print densitySigma: 0.22 print density

In other words, 99.7 percent of the dots fell between +/- 3 sigma (1.01 to 1.45 print density). The desired range was between 1.05 and 1.15 print density, with an average of 1.1 print density. At that time, 90 percent of the dots were outside the desired range. To a team unused to TQM, reaching the desired range seemed impossible. They accepted an initial target to reduce the number of dots outside the range by 50 percent from (90 percent to 45 percent).

The team needed to determine the major source of variation in the black dots, and whether it was within the pages or between the pages. They analyzed sample data using the analysis of variance (Table 1). Variation within the eight dots on a page constituted more than 90 percent of the problem.

Table 1: ANOVA for Black Dots

Source of VariationSum of SquaresDegrees of Freedom (df)Mean SquareFP-value F-crit

Between groupsPage to page0.27697590.0307750.503070.8674762.016598

Within groupsWithin a page4.282209700.061174

Total4.55918579

Further detailed data analysis of 10 sample pages of dots helped the team think of countermeasures more effectively (Table 2).

Table 2: Print Density of Dots for 10 Sample Pages

Dot No.Copy No.AvgSt Dev

12345678910

10.951.020.990.860.990.980.950.981.000.94

20.860.880.870.850.910.890.870.850.860.87

30.840.870.840.800.890.840.820.820.870.840.890.06

41.101.121.121.081.081.091.121.081.141.07

51.101.141.141.131.141.151.101.161.151.20

61.011.041.011.001.101.011.071.061.031.041.090.05

71.411.391.371.431.401.371.411.401.431.41

81.341.331.271.301.331.251.251.331.331.191.340.07

Without red1.080.19

1.050.24

Essentially, the eight black dots over a sample of 10 copies fell into three groups with very different averages, but low standard deviations.

Why was the average within a page varying? The machine was set and controlled not by measurement, but by visually examining a picture and adjusting the 48 taps that controlled ink flow across the page.

To counter this problem, the team decided to set the black dots on the cover page by measurement. To test the idea, they first set the machine using the existing method, and then by measuring the dots and further adjusting the settings. The results are shown in Table 3.

Table 3: Difference in Print Density Between Visual and Measured Settings

GoalVisualMeasured

Avg1.11.051.1

Sigma0.240.03

Although the setting took 30 minutes for one page, the average was at the goal and the sigma was reduced by 85 percent. To help set and maintain the average, the team introduced an X bar control chart for the sample average.

The team instituted a process of measuring the intensity regularly and adjusting the ink flow only if necessary. The improvement occurred in three phases:

Phase 1 After initial skepticism, the measurements were regularized.

Phase 2 The team analyzed the root causes of individual peaks and gradually eliminated them. An example of this is illustrated below:

Why was there a peak? The level of ink in the hopper was low varied.

Why? Addition of ink is manual and the difference between the low and high level is considerable.

Why? It is manual the auto ink dosing level controller does not work.

Why? It has never worked.

The team set out to find and test a suitable level controller. They selected an imported controller, tested it and found it satisfactory. Similar controllers were installed in all stations.

The team detected several similar problems through the control chart and worked to eliminate them. At the end of Phase 2, the sigma had gone from .064 to .033, about a 50 percent difference. With the average set at the standard 1.1 print density, only 13 percent of the points were now outside the standard range significantly better than the target of 45 percent. The team documented the quality improvement story and presented it to management.

Phase 3 After a control period, the sigma reduced another 50 percent from .033 to .016. The team achieved three-sigma quality (99.7 percent of points within range).

Grinding It In

Sustaining improvements often is a problem. Therefore, the team implemented a final step in the problem solving process, based on a simple dictum: If you do not improve, you deteriorate. They instituted the following process for maintaining standards:

1. Daily control chart plotting by a operating team

2. Daily reviews by a small line team to analyze the point with the largest deviation from the average to find the root cause, develop and implement a countermeasure, and check the cycle until it is killed.

3. Line leaders boss reviews this process once every two weeks

4. Senior management reviews process once a month

5. Team reviews the following items:

Frequency of line meetings

Effectiveness of killing problems

Technical inputs

Support from other departments

Customer feedback

6. Efforts are made to resolve any areas from Step 5 deemed lacking.

Grinding in the discipline of a small improvement every day until it becomes a way of life at the line level is essential for sustaining change and is the key role of senior management for any successful change initiative.

Added Bonus

Because the technique of measuring and setting the ink flows was more systematic and nonjudgmental, it led to an unexpected gain the machine achieved set state faster and faster. The team quantified this by plotting a bar chart of the percentage of dots within the standard range against the number of copies printed up to that time (Figure 2).

Figure 2: Increase of Dots in Range Over Time

The improvement shown is summarized in Table 4:

Table 4: Percentage of Dots in Range for October and April

No. of Copies PrintedPercentage of Dots in Range

OctoberApril

5,0004278

10,0004490

15,0004695

20,0005996

25,0005896

30,0005696

35,0005297

40,0004898

50,0004499

60,0004499

Seeing Savings

Quality saves cost is a fundamental axiom of TQM. The monthly percentage of wasted paper was significantly reduced during the three phases of the improvement (Figure 3). The organization achieved a 1.8 percent reduction in waste, which led to $140,000 in annual savings.

Figure 3: Percentage of Paper Wasted During Months of Improvement Project

But the improvement does not end there. The team looks forward to continuing this journey by reducing the waste level 4 percent further and improving the selection and preprocessing of pictures in order to print higher quality images.

4 Fixing Payroll Problems: A TQM Case Study in Human Resources

Niraj Goyal April 21, 2010 0

A large, Indian, fast-moving consumer goods company had completed successful total quality management (TQM) projects to improve its manufacturing efficiency, expedite vendor payments and increase availability of finished products. For its next project, the company wanted to address problems in human resources (HR). By working with HR process owners, a focus for the project emerged the payroll process.

The following case study details the companys experience using the TQM methodologys seven steps of problem solving to address the issue.

Pre-step 1: Select the Problem

After attending an introductory two-day training program in TQM, the project leader asked the companys HR employees to brainstorm key problems in human resources. They also considered the results of each problem (Table 1).

Table 1: Problems in the Payroll Process

ProblemResult 1Result 2

Accuracy of dataDelayErrors

Delayed outputDelay

Functioning of payroll centralization processDelay

Manual data generationDelay

Follow-up on dataDelay

High recruitment turnaround

Lack of standard operating procedures (SOPs)DelayErrors

Communication

Delayed response to employeesDelay

From this list, the group could see that the real problem was that internal customers were facing delays and errors. The group went on to brainstorm and prioritize the major areas of errors and delay within HR (Table 2).

Table 2: Prioritized Areas Where Employees Encounter Errors and Delays

Problem AreaScore

Employee database169

Payroll139

Separation125

Recruitment transfers117

Budget114

Talent development113

Performance management98

Communication90

Training64

Reimbursements63

Discussion revealed that the employee database is not a problem in itself; the team decided to tackle the payroll process instead. HR employees told the group that completing their job each month without delays or errors required a lot of pressure and running around.

A representative group from the finance department, the payroll manager, key payroll personnel and the four regional HR managers were selected for the project team. A leader and secretary were nominated, and the team began meeting every other week.

Step 1 Defining the Problem

In TQM, a Problem = Desire Actual Status; problems also must be measurable. The team faced the challenge of measuring undue pressure on behalf of the payroll employees. They decided that the metric employee overtime could represent this pressure.

The team set out to record how much overtime (OT)each employee was incurring daily and what activities they worked on during that overtime. Measurements during the first month yielded an average of 36 minutes of overtime per person per day.

This average did not appear so bad. In reality, however, the problem was the peaks rather than the average. Employees tend to remember the stressful days when overtime is high. To get a better picture, the team calculated a standard deviation of 18.8 minutes. This meant that on the worst days, overtime was an average of 92 minutes per person (average + 3 standard deviations) and on those days there were two or three employees whose overtime was much higher than 92 minutes.

Therefore, the team decided to work to reduce the average + 3 standard deviation limit to address the problem. They set a Phase 1 target to reduce the average + 3 standard deviation time by 50 percent.

Step 2: Finding the Root Causes

The team mapped overtime activities in a Pareto diagram to ascertain the vital causes (Figure 1). Table 4 shows the top 7 causes accounting for 81 percentof the OT.

Figure 1: Overtime Activities

Table 3: Top Seven Overtime Causes

ProblemOvertime Percent

Recruitment17

Meetings16

Data crunching14

Employee relations14

Master changes in SAP10

Special projects5

Head office formats5

Recruitment necessitated after-hours interviews, while meetings involved other departments not yet trained in TQM. The causes that the team could change were data crunching, master changes in SAP (the enterprise resource planning program) and repeated changes in data formats requested from the head office. These three areas constituted 29 percent of the overtime and were addressed first.

Sixty percent of the overtime in these areas emanated from two regions; another 35 percent came from two employees in the head office. Why? The other region representatives explained that they had put in a special one-time effort to develop data entry and storage formats for the diverse information requested by the head office to reduce future data crunching. They shared this standardized formatting with the two lagging regions to reduce their overtime.

But why were the regions developing formats in the first place? Were the formats not present already? The team mapped the current process steps:

1. Regions enter changes to be made in the SAP personnel master into an Excel sheet

2. Excel sheet sent to head office

3. Head office employees enter data into SAP before the payroll each month. The payroll employees face intense pressure due to gaps and errors in the data entry.

Step 3: Countermeasure Ideas

The team suggested a two-phase process change using just-in-time principles:

Phase 1: Replace batching with flow processing. With this method regions enter and send data weekly, and the head office enters weekly, without waiting until the end of the month.

Phase 2: Eliminate non-value added stages. Eventually, the regions should be able to enter data directly into SAP weekly, and the head office will enter its own entries weekly.

Steps 4 and 5: Testing Ideas and Checking Results

The countermeasure ideas took two months to test. An X-bar control chart was introduced to track the average overtime per person per day. The chart showed a 48 percent reduction in average time + 3 standard deviations, from 92 minutes to 50 minutes.

Step 6: Standardizing Operations

The 3 standard deviation limit was maintained. Simultaneously, however, employees were also experiencing stress and working overtime due to errors or incomplete entries during the payroll run and frantic queries for the correct information. Finding the most frequent errors, their root causes and countermeasures would eliminate this problem.

The team selected the metric errors per query per payroll. There were 65 in the first run. Following is an example of an error, its cause and the countermeasure the team developed to resolve it:

Error: Incorrect deduction of lunch coupons

Number of occurrences: 11 in two months, or 15 percent of total errors

Root cause analysis: All errors occurred in one region. The region with errors gave lunch coupons at the beginning of the month, while other regions gave them at the end of the month, thus making the accounting foolproof.

Countermeasure: Adopt standard process

Check the result: No errors post implementation. Within three months, errors and queries were reduced by 98 percent from 65 per payroll run to 1. Regular progress tracking was introduced (Figure 2).

Figure 2: Errors and Queries Per Payroll Run

Step 7: Maintain Improvements

The team compiled the improvement results and presented them to management. In the future, the payroll manager will meet with the staff after each payroll run to analyze and address any errors that are occurring. The overtime control chart will be plotted every day, and any unusual spikes also will be analyzed and addressed.

The project also led to changes in the mindsets of the employees involved. For instance, after the project, the human resources director remarked how one of the participants made an error in his work and reported it, along with a 5-whys and countermeasure analysis something that would never have happened earlier.

Identifying Six Sigma Projects Using Customer Data An iSixSigma Case Study

Debra Thomas February 26, 2010 0

A successful business knows its customers who they are, what their expectations are and what they think of the products or services. More importantly, a successful business continually improves its processes, reassesses its ability to meet customer needs, and gathers customer data to keep well appraised of changing customer needs and expectations.

There are many different types of customer data and many ways for a business to obtain it. Some customer data is available to virtually any business in the form of complaints, returns and refunds. Additional customer data can be obtained through surveys, focus groups, face-to-face interviews and feedback cards. And all of these can be used to identify Six Sigma projects to improve customer satisfaction.

The following case study illustrates how a fictitious homebuilding company used customer satisfaction survey data to identify the appropriate Six Sigma projects for its business.

In this example, the business had used customer satisfaction surveys to measure performance for several years. Customers were mailed surveys periodically throughout their homebuilding experience, to get customer feedback and perceptions on the various phases of the homebuilding process from the start at contract signing to one year from initial ownership (close). Responses during the last five years showed that while the customer satisfaction scores had been improving steadily from year to year, the overall customer satisfaction for the final survey (one-year from initial ownership) dropped from 86 points in 2002 to 82 points in 2003 (Figure 1).

Figure 1: Customer Satisfaction Scores

The business analyzed the survey results to determine which processes were contributing most to the customer dissatisfaction. They determined that 53 percent of the total customer dissatisfaction at the one-year-from-initial-ownership phase was caused by three processes warranty, lending and workmanship (Figure 2).

Figure 2: Pareto Chart for Customer Dissatisfiers

The customer survey data from each of those processes was analyzed further to identify what about them was causing dissatisfaction. This enabled the business to understand what it needed to work on to improve customer satisfaction, to identify Six Sigma projects and to assign teams of the appropriate people to each of the projects.

The top three items identified on the warranty Pareto chart (inconvenient, more visits and scheduling) covered 74 percent of the dissatisfaction with warranty (Figure 3). The three items were determined to be related closely enough that a single Six Sigma team was formed to improve the warranty process.

Figure 3: Pareto Chart for Warranty DissatisfiersThe top four items identified on the lending process Pareto (long approval time, unexpected closing costs, document errors and attorney/legal issues) covered 72 percent of the dissatisfaction with the lending process (Figure 4). The business determined that these were different enough that several Six Sigma teams were formed to address them. Figure 4: Pareto Chart for Lending Dissatisfiers

The top four items identified on the workmanship Pareto chart (carpet/flooring, cleanliness, exterior paint and ceilings/walls) addressed 72 percent of the workmanship dissatisfiers (Figure 5). The business determined again that several teams would be needed. Since cleanliness seemed to repeatedly be a source of customer dissatisfaction, not only at the final stage of the homebuilding experience but also at various phases throughout the process, the business formed a team that focused on cleanliness throughout the process. Two additional teams were formed. One to work on interior workmanship issues (carpet/flooring and ceilings/walls) and another to work on exterior workmanship issues (exterior paint).

Figure 5: Pareto Chart for Workmanship Dissatisfiers

As the team began addressing the top items on their list of dissatisfiers, synergies were discovered. For example, the first Six Sigma team was formed to improve cycle time (long approval time) and quality (document errors). It also was determined that while streamlining the process to reduce cycle time, this team would be addressing other items lower on the Pareto chart, such as too many people and too many lenders. The second Six Sigma team was formed to improve communication and education to address the unexpected closing costs issue. An ancillary benefit of this team was that the communication and education effort would make the process more understandable to the homebuyer, thus addressing the fifth item on the Pareto chart as well. A third Six Sigma team was formed to address the attorney/legal issues, but it was not immediately clear whether the issues were with the attorney for the business or the homebuyers attorney.

The effort by this homebuilding business was well worthwhile. As with almost all businesses, there is a significant financial advantage to maximizing referrals from prior customers and increasing the likelihood that previous customers will buy again. Referrals bring in new business with little advertising and marketing costs, and less time and effort from the sales staff. That allows the sales staff to concentrate its efforts on other potential customers. Returning customers usually purchase homes that are larger and more expensive and have more upgrades than their prior home. That also results in more revenue for the business with less cost to make the sale. Given these facts, key goals of this homebuilding business wisely are to understand the needs and expectations of its customers, to improve the processes needed to meet and hopefully exceed those needs and expectations, and to improve its customer satisfaction. Achieving these results then translates into more referrals and more repeat customers.

By reviewing and analyzing customer data, any business can identify the top issues causing customer dissatisfaction and identify the appropriate Six Sigma projects needed to address those issues. Assessing the performance of the business and improving its processes leads to increased customer satisfaction which, generally, translates to increased revenue.

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