Biofuel Production: Utilizing Stakeholders’ Perspectives

Abstract

The use of biofuels as a replacement for fossil fuels is growing in the United States and other countries in part because of economic and environmental concerns. One of the technologies for biofuels production is fast pyrolysis; however, to increase manufacturing of fast pyrolysis units, a better understanding of stakeholders’ requirements and perspectives is needed. This is a complex decision problem. Due to the diversity of perspectives, each group of stakeholders has their own unique requirements, which in total will determine the right manufacturing approach. Previous studies either investigated optimal sizing from a single viewpoint or have combined a subset of perspectives. This study applies multiple tools to develop a more comprehensive view of stakeholders’ perspectives. Individual subject matter experts were asked to review and prioritize a set of requirements that reflected different stakeholders’ perspectives, including economic, environmental, technical, social, and legal. The perspectives were then used to analyze multiple fast pyrolysis units to determine which size was the most effective in meeting the perspectives in total. The analysis indicated that the smallest unit, able to process an average of 50 tons per day, is the best alternative when viewed from the economic, technical, social, and legal perspectives. However, when viewed from the environmental perspective, a medium-sized unit, able to process in the range of 200–500 tons per day, is the best alternative. This work provides the basis for further discussions about the individual perspectives, including the economic and environmental perspectives of biofuel production. Potential avenues for further work in assessment of stakeholders’ requirements are also noted.     

AN AHP FRAMEWORK FOR PRIORITIZING CUSTOMER REQUIREMENTS IN QFD: AN INDUSTRIALIZED HOUSING APPLICATION

Construction of housing in the United States is highly decentralized. There is an increasing use of manufactured components and modules constructed off-site at a manufacturing facility and assembled at die building site. However, there has been little use of modern manufacturing processes and controls. In an effort to develop energy efficient, affordable industrialized housing, a total engineering design approach is needed. This study uses a concurrent engineering approach to examine the production of an essential component in industrialized housing, a manufactured exterior structural wall panel. In particular, we apply Quality Function Deployment to fully integrate the customer's requirements. This paper focuses on the identification and prioritization of those customer requirements. We integrate the Analytic Hierarchy process with QFD to establish a framework for prioritizing customer requirements.     

Team Member Perceptions of Alternative Decision Analysis Approaches

This research evaluated team members’ perceptions of alternative decision analysis approaches when they were asked to analyze a simple or complex data set under a time constraint. There has been significant work done to understand what criteria should be included in this class of problems. However, less work has been done examining the selection of a specific decision analysis approach. This is an area of concern since research has shown that selecting a different decision analysis approach can change the results even when the same data is analyzed. This research effort is a preliminary look at perceptions of alternative decision analysis approaches. A mixed method study analyzing quantitative and qualitative data was completed. Teams were assigned to use one of five different decision analysis approaches, including Weighted Sum, SMART, TOPSIS, AHP, and ELECTRE I. After applying the approach to either a simple or complex data set, team members were surveyed to determine perceptions including comfort with the approach, its results, and ease/difficulty of explaining the approach to coworkers. Discussions were also held with team members to gain additional insights. The study indicated that team members preferred less technically challenging decision analysis approaches, such as Weighted Sum and to a lesser degree SMART, when they were using a simple data set because they found the approach easier to use and to explain to others. The more technically challenging decision analysis approaches, such as AHP and ELECTRE I, were preferred with a complex data set because they provided better insight into the problem. Teams in this study did selected different alternatives when they used different decision analysis approaches. Demographic data was also collected but was not found to influence results in this study.     

Utilizing cluster analysis to structure concurrent engineeringteams

The problem of structuring a concurrent engineering team was studied. This research considered various mathematical clustering approaches to group product development design tasks together, and then constructed cross-functional teams based on the task clusters formed from each approach, Resultant team structures were evaluated against each other, and against a traditional discipline-centered hierarchical structure. The goal of this effort was to develop a structuring methodology for concurrent engineering teams that would allow projects to be completed faster, and with a lower risk of project failure. Team structures were developed using alternative clustering techniques and different combinations of data as inputs into the clustering techniques. Clustering approaches included single linkage, complete linkage, average linkage, the centroid method, and Ward's method. Data sources were from the initial stages of product development, and included task risk levels, task precedence relationships, disciplines required, personnel available, task technical importance, task difficulty, task priority, component requirement interactions, and projected communication levels between design tasks. Additional analysis was done on the effects of multiteam assignments for critical personnel. Team structures developed using the average linkage clustering approach and a data set composed of projected communication levels between tasks and discipline requirements for each design task were found to support the development of shorter duration projects with lower risk levels