Diseño de experiencias en dispositivos digitales

Cause-and-effect analysis is used to identify all of the possible contributors (causes) to a given outcome (effect). A diagram called the cause-and effect diagram (or Ishikawa diagram after its originator; or fishbone diagram after its appearance) is used in the analysis. Figure 2.16 is such a diagram showing possible causes for blurred edges around the product logo. As is typical, causes are divided into the categories of manpower, materials, methods, equipment, and environment, though others can be used depending on the problem being studied.

A small team usually conducts cause-and-effect analysis for a given problem with members from different areas and levels of the organization. The team brainstorms to generate as many ideas as possible about causes for the problem. Every idea is considered, no matter how farfetched or ridiculous it might seem at first. As each idea is generated, it is categorized and recorded at the appropriate place on the diagram. To keep things organized, ideas considered as subelements of other ideas are attached at the appropriate places. For example, under the heading Materials, adhesive is shown as a subelement of template.

All ideas listed on the diagram are considered as possible root causes of the problem or as candidates for more detailed scrutiny using Pareto analysis, histograms, flow charts, and so on.

Figure 2.17 Run diagram, two shifts.

In the blurry-edge problem, the team observes the fact that the number of defects seemed to be increasing (Figure 2.12), and that also the defects seem to increase with temperature (Figure 2.14). Let’s say that the workers who applied the paint and inspected the product had been doing the same job for years, which would appear to rule out manpower or methods as potential causes. Unsure about how to proceed, the analysis team decided it needed to collect more data and use a different analysis tool.

Run Diagram

A run diagram shows the results of observations taken at prescribed intervals (for example, every 10th unit, 1 unit every 10 minutes, etc.). Observations are plotted versus time to reveal any excessive or out- of-ordinary results. In the example, suppose the attribute being inspected is paint around the edge of the logo, and results are classified 0 for no blurring, 1 for slight blurring, and 2 for blurring all around. Figure 2.17 shows the results of 100% inspection for 2 shifts, each shift producing 60 units. The diagram indicates no clear pattern, which is often the case when the period of observation is short. Suppose run diagrams with 100% inspection were compiled over a 10-day period and aggregated to give average classification ratings over time. The result, shown in Figure 2.18, suggests a general increase in severity of blurred edges during the day shift and a general decrease during the night shift.

In practice the basic problem solving tools are used in combination, as needed. Returning to the example, the analysis team took the pattern in Figure 2.18 as further evidence that neither manpower nor methods were causing the blurred-edge problem. That left equipment, environment, and materials remaining as likely causes, and environment in particular because the scatter diagram had shown a possible relationship between number of defects and temperature (Figure 2.14).

The team looked again at the painting procedure (Figure 2.15) and cause-and-effect diagram (Figure 2.16), then decided to focus its attention on the materials, particularly the paint and the logo template. Closer investigation revealed the following: the template has a sticky backing to hold it in place as the paint is sprayed on, and when the template is removed, a slight amount of adhesive residue sometimes remains on the product. This was not considered earlier since the residue is difficult to see. The team discovered that the adhesive is temperature sensitive; the higher the temperature of the plant, the more residue that remains on the product. When the product is put into the oven, the residue next to the logo paint slightly melts and causes the paint to bleed before it dries, resulting in a blurred edge around the logo. As plant temperature goes up, so does the amount of adhesive residue on the product and, hence, the number of blurry-edged defects. Since the average temperature inside the plant rises in the summer, so too do the number of defects. Also, plant temperature is slightly higher in the day than at night, explaining the pattern in Figure 2.18.

Figure 2.18 Ten-day average of run diagrams.

Why wasn’t this problem noticed earlier? When the team notified the template supplier, they learned that the supplier had switched to a new adhesive in January. At the time, the temperature in the plant was low enough to cause negligible adhesive residue. Not until warmer weather did the problem show up. The supplier readily agreed to try other adhesives that would leave no residue, regardless of the

temperature.

As this example suggests, finding the root cause of a problem can be, well, sticky. It also shows how the basic problem-solving tools can be a great aid in collecting and analyzing data, determining root causes, and finding solutions.