John does think that their digression into statistical decision theory has rein-forced the need to identify customers for the decision process and to better translate their needs into activities that are included in his decision process management program. He is pretty sure that a standard customer-supplier relationship (Berk and Berk 1993) is what he needs to deal with the first need and that quality function deployment will serve well to answer his sec-ond concern.
He and Charlotte consider the decision process of the operator as he or she decides to make a process adjustment or to let the process continue run-ning without adjustment. The customers for this process must first be iden-tified. But this identification process might not be as easy for decisions as for manufactured products. This decision affects the product quality itself.
Therefore it seems reasonable that the customers of the decision are identi-cal to the customers of the product. In this example, these customers are probably other manufacturers who receive the final products. For definite-ness assume that there is one customer for the material processed on line 2 and that this customer is Plastic Toys.
Clearly in the case of an external customer there should be a contract with a detailed set of specifications for the product. This set of specifica-tions will naturally include the target characteristics of the delivered prod-uct, such as length, weight, plasticity, and surface integrity. These specifications may include explicit consideration of the statistical variabil-ity of the products with respect to these characteristics. Hence there may be a requirement that 95% of the product meet the target specifications for each characteristic. There are very likely to be conditions on delivery dates and packaging as well. If the customer is internal, the same kind of require-ments must be identified and understood by both customer and supplier.
Some kind of feedback must be established to guide corrections if commit-ments are broken on either side of this relationship.
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In the case of the cutter adjustment decision process John envisions that a set of simplified customer requirements might read like this:
1. Product weight must be within 90 g to 100 g.
2. Product length must be within 99 cm to 101 cm.
3. If more than 2% of any daily shipment is out of tolerances, it will be returned at cost +25%.
He keeps the list short for now but realizes that in real applications the actual requirements can be intricate and difficult to achieve.
Once the customer is identified and the customer-supplier relationship created, the supplier can take the next step of tracing back from customer needs to operational elements. Ideally one does not want to waste effort on anything that is not directly contributing to the customer delight. Of course, this ideal situation is rarely met in real manufacturing concerns and should not be expected to be satisfied completely for a decision process application either, but it serves as guiding principle. A systematic approach to this process of translating customer needs into supplier activities is embodied in the quality function deployment (QFD) tool (Berk and Berk 1993). The basis of QFD is to draw an array that specifies customer requirements on one axis, say the vertical, and supplier activities on the other axis. A rela-tionship between the two axes is specified by a mark in the correct inter-secting cell. It is important that there be at least one marked cell for every need and for every activity. Otherwise some needs are not being addressed or some activities are being wasted.
For the cutter adjustment decision process, John and Charlotte con-struct a QFD array that looks like Table 5.1, in which they assume that they have only a partial list of customer requirements.
In this example all actions are related to at least one of the customer requirements and all requirements are being supported by at least one activity.
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Table 5.1 The quality function deployment array for the cutter adjustment decision.
90g < weight 99 cm < length <2% out of Activity/needs < 100 g < 101 cm tolerance
Use of scale Yes Yes
Graph of weight Yes Yes
Measure length Yes Yes
Graph of weight Yes Yes
Stop process Yes Yes Yes
Scrap product Yes Yes Yes
As an example of an unnecessary activity, the inspection for aspect is not required for any of the simplified needs. Similarly, if there is a need to guar-antee plasticity of the material, no activity or measurement being performed supports this need.
It is reasonably straightforward to apply these quality techniques to the situation in which the quality of the decision is directly related to product quality. The decision can be treated almost as a complementary process step or machine setting. There are only two choices in this simple case, and both lead directly to measurable results. Many decision processes are amenable to treatment in this fashion, but there are others in which the connections might not be so apparent.
To explore a different kind of situation, John and Charlotte return to the process improvement decision. First, it is important to define the customers and their contracts with the supplier. Second, one can construct a QFD analysis to translate the customer requirements into supplier activities.
Customers are those who pay for the product and ultimately derive ben-efits from its quality. In the example at hand, the customers are the company managers who are involved in funding the improvement projects and main-taining the business with the external customers. They probably include plant managers and at least one higher level of managers at a corporate or divisional level. There are other individuals, often called stakeholders, who are affected by the decision as well. These include the shop personnel such as operators, technicians, and maintenance workers. Other stakeholders might include other shop managers who process the materials just before or after the shop in question. Stakeholders may also include central quality and technical support personnel who are interested in leveraging any improve-ments.
Restricting their scope to include only those members of the manage-ment staff who are directly in relation to John’s shop, the pair imagines this customer-supplier requirement:
1. < 0.1% chance of catastrophic result meaning < $1 million loss.
2. < 15% overrun on budgeted amount.
3. Reduce cost by >10%.
4. Reduce cycle time by >5%.
5. >80% consensus on all decisions.
When the requirements are listed in this fashion, it is readily apparent that they are no different from requirements that might be placed on a man-ufactured good. The trouble lies in the fact that these requirements are usu-ally not so explicitly declared for decision processes. This seems to be another deficiency that has developed from management’s reluctance to Critical Factors Affecting Decision Process Quality 53
systematically improve decision processes. The establishment of a cus-tomer-supplier contract is the remedy for this deficiency. It opens the lines of communication and forces both sides to take the time to understand their responsibilities and to seek remedies to any existing issues. For example, John might use the contract discussions to gain guarantees on engineering resources availability or to gain permission to hire external consultants.
With the customer needs specified, it is appropriate to do a quality function deployment check on the alignment of activities with customer requirements. The array in Table 5.2 describes this result for the line improvement decision process.
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Table 5.2 QFD analysis of the line improvement decision process.
Activities/ < 0.1% <15% 10% 5% >80%
needs > $1 million overage cost time consensus
Operator input Yes
Technical input Yes Yes Yes
Steering team Yes
Peer calls Yes Yes
Consultant Yes Yes
Read books Yes Yes
Have review Yes Yes
Track budget Yes
Track returns Yes
Track cycles Yes
In this case all needs are connected to at least one activity and, likewise, each activity is in support of at least one need. This QFD analysis is a check for sufficiency and necessity of actions from a broad view. Any obvious hole should be addressed, but there is no guarantee that adequate resources will be targeted to the right needs.
John thinks these preliminary analyses are critical to establishing a decision quality process management program for company decisions.
They set the stage for further efforts to analyze and characterize the statisti-cal behavior of the elements that contribute to and control the quality of decisions. For example, since the probability of the success of the projects was deemed critical, it might be one of the first elements of the decision process that undergo additional study via a decision process capability study. Or the existence of rushed decisions might entail a lean manufactur-ing study of the component of the decision process to cut the preparation and delivery time. Or since the shop manager’s decision quality depends on the vagaries of budgeting and review, maybe the company can create a set of contingent decisions that depend on the actual amount of money or
resources available for the review. In this way the process may be adjusted more or less automatically for the variations that are induced by the input processes that feed the decision process. John can anticipate that a version of algorithmic statistical process control (ASPC) can be effectively applied to decision processes.
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