The shop manager, who is part of the steering committee for Maxwell’s team’s presentation, is encouraged by what he sees. He asks Victoria, the quality manager, to help him run a similar designed experiment on his own process improvement decision process. Victoria agrees to do this, but since she is not overly familiar with the details of designed experiments she asks Maxwell to participate as an expert consultant. He is happy to do this once his workload is adjusted so he can devote the necessary time to it. Maxwell and Victoria have some one-on-one sessions so that she can come up to speed quickly.
They assemble a team consisting of themselves, the shop manager, the managers of two other shops in the plant, and some financial and informa-tion services representatives. The financial team member has never before been asked to participate on a team and is very pleased. He needs a little extra training on the background, but his unique viewpoint proves to be quite valuable during the discussions. The problem is presented to the team, and background training brings everyone up to a working-knowledge level.
The choice of response is trickier for this problem than for the cutter adjustment decision process. This shop manager has made only 20 or so 100 Chapter Ten
Figure 10.1 Displays of the DOE result on the cutter adjustment process.
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historical decisions of this nature. After lengthy discussions in which the team members struggle to get their heads around this situation, they decide they can use the decision scenario approach that was successfully applied to the decision capability study. It is decided that they will use the differ-ence between the projected gain for each decision that the shop manager made on each decision scenario versus the best outcome that can be gener-ated by the detailed expert approach.
To determine which factors to use, the team revisits some of the brain-storming done for the capability study and decides to build its list of factors for the designed experiment on that original list. Half the team members think this list is sufficient, but the others want two more factors added. Since the designed experiment can hold up to seven factors without additional trials, the team decides to keep both sets of factors in the study. Because of this com-promise the experiment is a little harder to perform, but the whole team can support the results because all interests are covered. The seven factors are:
1. Time of budget 2. Amount of request 3. Requestor expertise 4. Type of improvement 5. Formality of request 6. Cost reduction programs 7. Project accounting report format
With the help of the statistician they conclude that a minimum of eight pseudo-replicates will have to be created for each combination in the designed experiment array. Since Victoria has spent some time already with scenario creation and because the information services representative can automate the process of creating the scenarios, the team recommends 10 scenarios for each trial if the manager and the expert can schedule the time.
The team does not spend too much time on the selection of levels for each of the factors. They readily agree that two settings for each will be a good place to start, and many of those settings are considered obvious. Only the last two factors introduced by the financial representative need any detailed consideration, simply because members are unfamiliar with them.
After a satisfactory explanation the team readily agrees to these settings as well. Despite warnings from the statistician about hidden interactions, the team feels that it can ignore these without compromising the study too much.
The proposed designed experiment conditions are reviewed by the steering committee and agreed to by the shop manager. He hoped that they could simply reuse the scenarios that had been generated in the capability Decision Process Improvement via Designed Experiments 101
study, but he is finally convinced that all new ones must be run because of the two new factors that are added. This effort will add another 20 hours to his workload over the next four weeks but he is used to long hours; if this could improve his decisions he is willing to contribute the time. Table 10.3 shows the finalized designed experiment conditions; factors 1 through 7 are abbreviated in order to reduce the clutter of the array.
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Table 10.3 The design array for the process improvement decision process.
Scenario Date Amount Expertise Type Formality Reductions Report
1 Early <$1000 Low Hard Formal Yes Old
2 Early <$1000 High Soft Formal No New
3 Early >$1000 Low Hard Informal Yes New 4 Early >$1000 High Soft Informal No Old
5 Late <$1000 Low Hard Informal No New
6 Late <$1000 High Soft Informal Yes Old
7 Late >$1000 Low Hard Formal No Old
8 Late >$1000 High Soft Formal Yes New
Ten scenarios are considered replicates in that the expected value of the best decision is equivalent irrespective of different words being used in the problem description. While they are constructing these replicate sce-narios, Maxwell notices that, if done correctly, the scenarios could be used to study the robustness of the decision in the same way that robust engi-neering methods work. He jots this idea down as something he would like to explore later. The scenarios are updated to include the two new factors and are presented to the shop manager in small batches. All eight scenario types are presented two times in a session in a randomized order, and there are five sessions spaced over a four-week period. The results are collected automatically through the software that information services created and then exported to a file that Maxwell and Victoria can use to complete the analysis.
Again Maxwell and Victoria use the analysis of variance technique that is recommended for the designed experiment approach but translate their findings into a form more palatable for the presentation. The response is the difference in dollars between the shop manager’s decision and the expert decision. Because this is the first application of decision process analysis of this type performed at the company, the presentation is well attended by both plant personnel and corporate staff. Even the vice president of the divi-sion is there. Table 10.4 contains the results as presented by Victoria with strong support from Maxwell.
Then in graphical format they present the proposed modifications that produce the best improvement versus the current situation (Figure 10.2).
These differences represent the gaps between the shop manager’s deci-sions and the best decideci-sions based on available information. That is, these are biases built into his decision process. The improvement is to educate the shop manager so that he no longer has these biases. He agrees to pay more attention to this effect and to review his performance more frequently. The third factor can be changed simply by getting rid of the old accounting report formats more quickly. This is in concert with what the financial department wants anyway, so that department is happy to spearhead this effort.
The success of this project strikes a chord with the corporate staff mem-bers. They decide to start a comprehensive corporate program to examine the major decision processes of the company. And since the shop manager John is now considered the expert, he is appointed to head this effort with an accompanying raise and promotion. He asks that Charlotte, Victoria, and Decision Process Improvement via Designed Experiments 103
Table 10.4 The results from the process improvement decision process designed experiment.
Figure 10.2 Display of the DOE results on the line improvement decision process.
Solid line—the results of an average of all current conditions.
Dotted line—the expected impact under the setting of expertise = high, type = hard, and report = new.
Maxwell be appointed to a special support team for this effort answering directly to him. Victoria turns the opportunity down since she has other career plans, and Charlotte is instead asked to take over John’s shop manager job; she gleefully accepts. John and Maxwell turn to their next challenge:
turning their early successes into an unending string of new successes.
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