Business process quantitative analysis and optimization for service industries

The total time a customer spends in the business process system, called the customer cycle-time, is a major contributor to overall customer satisfaction. Business process analysts and designers are frequently asked to design process solutions with optimal performance. Simulation models have been very popular to quantitatively evaluate the business processes; however, simulation is time-consuming and it also requires extensive modeling experiences to develop simulation models. Moreover, simulation models neither provide recommendations nor yield optimal solutions for business process design. A queueing network model is a good analytical approach toward business process analysis and design, and can provide a useful abstraction of a business process. However, the existing queueing network models were developed based on telephone systems or applied to manufacturing processes in which machine servers dominate the system. In a business process, the servers are usually people. The characteristics of human servers should be taken into account by the queueing model, i.e. specialization and coordination. ^ The research described in this dissertation develops an open queueing network model to do a quick analysis of business processes. Additionally, optimization models are developed to provide optimal business process designs. The queueing network model extends and improves upon existing multi-class open-queueing network models (MOQN) so that the customer flow in the human-server oriented processes can be modeled. The optimization models help business process designers to find the optimal design of a business process with consideration of specialization and coordination. ^ The main findings of the research are, first, parallelization can reduce the cycle-time for those customer classes that require more than one parallel activity; however, the coordination time due to the parallelization overwhelms the savings from parallelization under the high utilization servers since the waiting time significantly increases, thus the cycle-time increases. Third, the level of industrial technology employed by a company and coordination time to mange the tasks have strongest impact on the business process design; as the level of industrial technology employed by the company is high; more division is required to improve the cycle-time; as the coordination time required is high; consolidation is required to improve the cycle-time. ^

The Importance of Dissimilatory Nitrate Reduction to Ammonium (DNRA) in the Nitrogen Cycle of Coastal Ecosystems

Until recently, it was believed that biological assimilation and gaseous nitrogen (N) loss through denitrification were the two major fates of nitrate entering or produced within most coastal ecosystems. Denitrification is often viewed as an important ecosystem service that removes reactive N from the ecosystem. However, there is a competing nitrate reduction process, dissimilatory nitrate reduction to ammonium (DNRA), that conserves N within the ecosystem. The recent application of nitrogen stable isotopes as tracers has generated growing evidence that DNRA is a major nitrogen pathway that cannot be ignored. Measurements comparing the importance of denitrification vs. DNRA in 55 coastal sites found that DNRA accounted for more than 30% of the nitrate reduction at 26 sites. DNRA was the dominant pathway at more than one-third of the sites. Understanding what controls the relative importance of denitrification and DNRA, and how the balance changes with increased nitrogen loading, is of critical importance for predicting eutrophication trajectories. Recent improvements in methods for assessing rates of DNRA have helped refine our understanding of the rates and controls of this process, but accurate measurements in vegetated sediment still remain a challenge.