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nesses can be statistically grouped. 2 Signi cant variables can be identi ed that aid in the prediction of annual solid waste generation rates. 3 Solid waste generation performance parameters can be established.

Reduce to 22 waste groups based on solid waste stream compositions Variables Investigated Number of employees Working days per year Recycling percentage ISO 9000 certification ISO 14001 certification Disposal cost per ton Location

Determine significant variables that influence solid waste generation for each waste group through stepwise technique (resulted in the reduction of waste groups from 22 to 20.2 of the Waste Groups did not have a significant relationships to predict annual waste generation at the 95 percent confidence level)

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Customer critical requirements Project goals and objectives Team roles and responsibilities Project scope and resources Process map and supplier, Input, Process, Output, and Customer (SIPOC) Process performance baseline

Develop regression equations to quantify solid waste generation for each waste group. Regression models can predict the solid waste generated by an individual company based on the significant variables. The models also aided in the identification of waste generation trends

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Establish performance parameters for individual companies to evaluate solid waste generation. This assisted in identifying superior or inferior waste management procedures Companies may use these to evaluate and improve waste generation performance

The rst hypothesis was successfully validated and proven by research results. U.S. companies and government agencies were rationally grouped by characterizing waste material composition percentages. This was completed by clustering 65 SIC code groups into 20 waste groups by applying multivariate cluster analysis t and associated statistical validation tests. These groups were validated at the 95 percent con dence level, using F-tests and ANOVA.

The second hypothesis was successfully validated and proven by research results. This was accomplished by applying the stepwise regression method, t tests, and ANOVA. From the 20 solid waste groups established, solid waste quantities were objectively

In understanding the customer requirements, one can learn from Noritaki Kano s quality approach. Kano s approach sorts the customer requirements into three categories: assumed, specified, and expected requirements. An assumed requirement is one that s taken as a given. Someone who is buying a car, for example, never checks to be sure that the car will include four wheels. When an implicit requirement is not met, customers are extremely dissatisfied. Assumed requirements, then, are dissatisfiers, where ignorance is the best outcome and loss of the customer is the worst outcome. The specified requirements are a customer s explicitly communicated requirements. To meet the requirements is to satisfy the customer. But if an organization has not done anything beyond what customers have specifically asked for, customers will be very open to try competitive products or services. The expected requirements include requirements beyond what the customer explicitly communicates. These are the customers real, unstated expectations of what they would love to receive from suppliers. For a supplier to fulfill these expectations, the supplier must really understand the customers needs for products or services. The supplier must also anticipate the customers future needs, thus demonstrating a desire for ongoing relationships with customers, i.e., showing superior customer service. Once the requirements are understood, they flow down to the operation level, where project goals and objectives are set. Some of the techniques used in the define phase include the following:

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predicted and characterized using statistical techniques to develop a mathematical model. This was completed using multivariable regression analysis for the 20 waste groups and associated statistical validation tests. The number of employees, land ll disposal costs, and ISO 14001 certi cation were statistically signi cant at the 95 percent con dence level in predicting annual solid waste generation tonnages.

The third hypothesis was successfully validated and proved by research results. This was accomplished by developing con dence intervals based on the regression models and testing the outputs with two case studies. Individual company solid waste generation rates were evaluated in a standardized manner by integrating statistical quality control concepts to monitor and control solid waste generation. This was completed by integrating con dence intervals mathematics with the waste prediction models for the 20 waste groups.

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