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.
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:
- Picture outlines were often blurred
- 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.
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:
- Selecting an appropriate performance metric
- Measuring the current performance
- 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 density
Sigma: 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 Variation||Sum of Squares||Degrees of Freedom (df)||Mean Square||F||P-value||F-crit|
|Between groups||Page to page||0.276975||9||0.030775||0.50307||0.867476||2.016598|
|Within groups||Within a page||4.282209||70||0.061174|
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.||Avg||St Dev|
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|
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:
- Daily control chart plotting by a operating team
- 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.
- Line leader’s boss reviews this process once every two weeks
- Senior management reviews process once a month
- 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.
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).
The improvement shown is summarized in Table 4:
|Table 4: Percentage of Dots in Range for October and April|
|No. of Copies Printed||Percentage of Dots in Range|
“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.
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.