This dissertation investigates two broad management accounting questions in the context of electric utility industry: How do regulators for electricity industry use the information generated from accounting systems to make pricing decisions? What are the economic consequences of these decisions? In Chapter 2, I review regulatory reforms and discuss existing issues of using DEA models for efficiency benchmarking in four aspects. Suggestions are given for improving the use of DEA models based on the review and discussion. In Chapter 3, I empirically investigate the effect of incentive regulation with DEA benchmarking on operational efficiency using a panel of electricity distribution firms in Brazil. In Chapter 4, I examine the effect of restructuring and retail competition on cost reduction using a sample of US investor-owned electric utilities. The effects of privatization, industrial restructuring, incentive regulation and benchmarking are effectively disentangled from one another using the research setting in Brazil and US electricity industry. In Chapter 5, I combine the idea of activity based costing and data envelopment analysis to further develop a detailed benchmarking model for incentive regulation. / Business Administration/Accounting

For the last two decades, the road maintenance concept has been gaining tremendous attention. This has brought about new institutional changes, predominant of which is the challenge for maintenance managers to achieve maximum performance from the existing road system. Such challenge makes it imperative to implement comprehensive systems that measure road maintenance performance. However, the road maintenance performance measurement systems developed and implemented by researchers and state departments of transportation (DOTs) mainly focus on the effectiveness measures, e.g., the level-of-service. Such measurement systems do not sufficiently elaborate on the efficiency concept, e.g., the amount of resources utilized to achieve such level-of-service. Not knowing how "efficient" state DOTs are in being "effective" can lead to excessive and unrealistic maintenance budget expectations. This issue indicates the need for a performance measurement approach that can take the efficiency concept into account. Another important concept that is not investigated in the current road maintenance performance measurement systems is the effect of the environmental factors (e.g., climate, location, and etc.) and operational factors (e.g., traffic, load, design-construction adequacy, and etc.) on the performance of the road maintenance process. This issue, again, indicates the need for a performance measurement approach that can take such external and uncontrollable factors into account. The purpose of this research is to develop and implement a comprehensive framework that can measure the relative efficiency of different road maintenance strategies given the (i) multiple inputs and outputs that characterize the road maintenance process and (ii) uncontrollable factors (e.g., climate, traffic, etc.) that affect the performance of such process. It is challenging to measure the overall efficiency of a process when such process is a multiple input-multiple output process and when such process is affected by multiple factors. To address this challenge, an innovative approach to efficiency measurement, Data Envelopment Analysis, is used in this research. It is believed that this research, by taking the efficiency concept into account, will significantly improve the ways that are currently used to model and measure the performance of road maintenance. The findings of this research will contribute new knowledge to the asset management field in the road maintenance domain by providing a framework that is able to differentiate effective and efficient maintenance strategies from effective and inefficient ones. / Ph. D.

Determining the best way for evaluating organizational performance is a complex problem as it involves assessment of indicators in multiple dimensions. In the case of nonprofit social service provision this evaluation needs to consider also the outcomes of the service. This research develops a performance measurement system that collects performance indicators, evaluates them and provides concrete performance improvement recommendations to decision-makers in the nonprofit sector. Three dimensions of performance are identified for social services: effectiveness or outcome achievement, service quality and efficiency. A framework for measuring performance in four stages or nodes is advanced. The nodes represent the most important production functions for nonprofit organizations dedicated to social services. These are: (a) financial (fundraising or income generation activities); (b) capacity creation; (c) service delivery; and, (d) effectiveness. Survey instruments were developed to collect service quality and effectiveness indicators for the last two nodes. Effectiveness measures were identified following a well-structured 7-step approach to develop outcome-based objectives. To effectively deal with this problem, the Data Envelopment Analysis (DEA) formulation was adapted to evaluate performance at each node. DEA computes performance scores, optimal target performance levels, and the performance frontier for different branches, units, or other comparable decision-making units (DMUs). Two basic formulations were developed for this framework as follows: Model I as a four stage formulation that carries the actual values of output variables of one node to the successive node, and Model II as a formulation that carries the projections — i.e. the recommended targets' from one node to the other. This last formulation assumes that the DMUs have undergone a reengineering effort and that their indicators are set at their maximum potential. Several environmental factors affecting social service provision were included in the analysis. Additionally, variable selection recommendations were developed for DEA analysis and DEA graphical reports produced. It was concluded that decision makers could use Model I to identify performance improvement targets in each production node. The results from Model II can be used for resource planning after the targets are achieved. Finally, this performance measurement framework is being implemented at one of largest national social service agencies in the United States. / Ph. D.

The implementation of lean manufacturing concepts has shown significant impacts on various industries. Numerous tools and techniques have been developed to tackle specific problems in order to eliminate wastes and carry out lean concepts. With the focus on "how to make a system leaner," little effort has been made on determining "how lean the system is." Lean assessment surveys evaluate the current status of a system qualitatively against predefined lean indicators. Lean metrics are developed to quantify performance of improvement initiatives, but each metric only focuses on one specific area. Value Stream Maps demonstrate the current and future states graphically with the emphasis on time-based performance only. A truly quantitative and synthesized measure for overall leanness has not been established. In some circumstances, being lean may not be the only goal for manufacturers. In order to compete in the rapidly changing marketplace, manufacturing systems should also be agile to respond quickly to uncertain demands. Nevertheless, being extremely agile may increase the cost of regular operations and reduce the leanness of the system. Similarly, being extremely lean may reduce flexibility and lower the agility level. Therefore, a manufacturing system should be agile enough to handle the uncertainty of demands and meanwhile be lean enough to deliver goods with competitive prices and lead time. In order to achieve the appropriate leanness level, a leanness measure is needed to address not only "how lean the system is" but also "how lean it should be." In this research, a methodology is proposed to quantitatively measure leanness level of manufacturing systems using the Data Envelopment Analysis (DEA) technique. The production process of each work piece is defined as a Decision Making Unit (DMU) that transforms inputs of Cost and Time into output Value. Using a Slacks-Based Measure (SBM) model, the DEA-Leanness Measure is developed to quantify the leanness level of each DMU by comparing the DMU against the frontier of leanness. A Cost-Time-Value analysis is developed to create virtual DMUs to push the frontier towards ideal leanness so that an effective benchmark can be established. The DEA-Leanness Measure provides a unit-invariant leanness score valued between 0 and 1, which is an indication of "how lean the system is" and also "how much leaner the system can be." With the help of Cost-Time Profiling technique, directions of potential improvement can be identified by comparing the profiles of DMUs with different leanness scores. The leanness measure can also be weighted between Cost, Time and Value variables. The weighted DEA-Leanness Measure provides a way to evaluate the impacts of improvement initiatives with an emphasis on the company's strategic focus. Performing the DEA-Leanness measurement requires detailed cost and time data. A Web-Based Kanban is developed to facilitate automated data collection and real-time performance analysis. In some circumstances where detailed data is not readily available but a Value Stream Maps (VSM) has been constructed, the applications of DEA-Leanness Measure based on existing VSM are explored. Besides pursuing leanness, satisfying a customer's demand pattern requires certain level of agility. Based on the DEA-Leanness Measure, appropriate leanness targets can be identified for manufacturing systems considering sufficient agility level. The Online-Delay and Offline-Delay Targets are determined to represent the minimum acceptable delays considering inevitable waste within and beyond a manufacturing system. Combining the two targets, a Lean-Agile Performance Index can then be derived to evaluate if the system has achieved an appropriate level of leanness with sufficient agility for meeting the customers' demand. Hypothetical cases mimicking real manufacturing systems are developed to verify the proposed methodologies. An Excel-based DEA-Leanness Solver and a Web-Kanban System have been developed to solve the mathematical models and to substantiate potential applications of the leanness measure in real world. Finally, future research directions are suggested to further enhance the results of this research. / Ph. D.

This research explores the feasibility of providing innovative and effective solutions for traffic congestion. The design of reservation systems is being considered as an alternative and/or complementary travel demand management (TDM) strategy. A reservation indicates that a user will follow a booking procedure defined by the reservation system before traveling so as to obtain the right to access a facility or resource. In this research, the reservation system is introduced for a cordon-based downtown road network, hereafter called the Downtown Space Reservation System (DSRS). The research is executed in three steps. In the first step, the DSRS is developed using classic optimization techniques in conjunction with an artificial intelligence technology. The development of this system is the foundation of the entire research, and the second and third steps build upon it. In the second step, traffic simulation models are executed so as to assess the impact of the DSRS on a hypothetical transportation road network. A simulation model provides various transportation measures and helps the decision maker analyze the system from a transportation perspective. In this step, multiple simulation runs (demand scenarios) are conducted and performance insights are generated. However, additional performance measurement and system design issues need to be addressed beyond the simulation paradigm. First, it is not the absolute representation of performance that matters, but the concept of relative performance that is important. Moreover, a simulation does not directly demonstrate how key performance measures interact with each other, which is critical when trying to understand a system structure. To address these issues, in the third step, a comprehensive performance measurement framework has been applied. An analytical technique for measuring the relative efficiency of organizational units, or in this case, demand scenarios called network Data Envelopment Analysis (DEA), is used. The network model combines the perspectives of the transportation service provider, the user and the community, who are the major stakeholders in the transportation system. This framework enables the decision maker to gain an in-depth appreciation of the system design and performance measurement issues. / Ph. D.

Highly deteriorated U.S. road infrastructure, major budgetary restrictions and the significant growth in traffic have led to an emerging need for improving efficiency and effectiveness of highway maintenance practices that preserve the road infrastructure so as to better support society's needs. Effectiveness and efficiency are relative terms in which the performance of a production unit or decision making unit (DMU) is compared with a benchmark (best practice). Constructing the benchmark requires making a choice between an "estimation approach" based on observed best practices (i.e., using data from input and output variables corresponding to observed production units (DMUs) to estimate the benchmark with no elaboration on the details of the production process inside the black box) or an "engineering approach" to find the superior blueprint (i.e., focusing on the transformation process inside the black box for a better understanding of the sources of inefficiencies). This research discusses: (i) the application of the estimation approach (non-parametric approach) for evaluating and comparing the performance of different highway maintenance contracting strategies (performance-based contracting versus traditional contracting) and proposes a five-stage meta-frontier and bootstrapping analytical approach to account for the heterogeneity in the DMUs, the resulting bias in the estimated efficiency scores, and the effect of uncontrollable variables; (ii) the application of the engineering approach by developing a dynamic micro-level simulation model for the highway deterioration and renewal processes and its coupling with calibration and optimization to find optimum maintenance policies that can be used as a benchmark for evaluating performance of road authorities. This research also recognizes and discusses the fact that utilization of the maintenance budget and treatments that are performed in a road section in a specific year directly affect the road condition and required maintenance operations in consecutive years. Given this dynamic nature of highway maintenance operations, any "static" efficiency measurement framework that ignores the inter-temporal effects of inputs and managerial decisions in future streams of outputs (i.e., future road conditions) is likely to be inaccurate. This research discusses the importance of developing a dynamic performance measurement framework that takes into account the time interdependence between the input utilization and output realization of a road authority in consecutive periods. Finally, this research provides an overview of the most relevant studies in the literature with respect to evaluating dynamic performance and proposes a classification taxonomy for dynamic performance measurement frameworks according to five issues. These issues account for major sources of the inter-temporal dependence between input and output levels over different time periods and include the following: (i) material and information delays; (ii) inventories; (iii) capital or generally quasi-fixed factors and the related topic of embodied technological change; (iv) adjustment costs; and (v) incremental improvement and learning models (disembodied technological change). In the long-term, this line of research could contribute to a more efficient use of societal resources, greater level of maintenance services, and a highway and roadway system that is not only safe and reliable, but also efficient. / Ph. D.

Affordability of products and services is an economic benefit that should accrue to consumers, whether they are corporations, government agencies or individuals. This concept of affordability goes beyond conventional wisdom that considers affordability as the ability to pay the price of a product or service. This dissertation defines and explores a broader concept of affordability – one of fitness to perform at the level of quality required by the consumer, to perform at that level whenever the product or service is used, and to do so with minimum consumption of resources. This concept of affordability is applied to technological systems by using the complexity sciences concept of fitness as the metaphor for technological systems' fitness. During a system design evolution, the specific design outcome is determined by that set of design search paths followed – it is path dependent. Dynamic mechanisms create, dictate and maintain path dependence. Initial conditions define the start and direction of a path. During subsequent design steps, positive feedback influences the designer to continue on that path. This dissertation describes underlying mechanisms that create, dictate and maintain path dependence; discusses the effects of path dependence on system design and system affordability; models these effects using system dynamics modeling; and suggests actions to address its effects. This dissertation also addresses several types of fitness landscapes, and suggests that the Data Envelopment Analysis (DEA) solution space is a form of fitness landscape suitable for evaluating the efficiency, and thus the fitness, of research and development (R&D) projects. It describes the use of DEA to evaluate and select Department of Defense (D0D) R&D projects as a new application of DEA. / Ph. D.

Safety is a continually significant issue in the construction industry. The Occupation Safety and Health Administration as well as individual construction companies are constantly working on verifying that their selected safety plans have a positive effect on reduction of workplace injuries. Worker safety is a large concern for both the workers and employers in construction and the government also attempts to impose effective regulations concerning minimum safety requirements. There are many different methods for creating and implementing a safety plan, most notably the Construction Industry Institute's (CII) Zero Accidents Techniques (ZAT). This study will attempt to identify a relationship between the level of ZAT implementation and safety performance on industrial construction projects. This research also proposes that focusing efforts on certain ZAT elements over others will show different safety performance results. There are three findings in this study that can be used to assist safety professionals in designing efficient construction safety plans. The first is a significant log-log relationship that is identified between the DEA efficiency scores and Recordable Incident Rate (RIR). There is also a significant difference in safety performance found between the Light Industrial and Heavy Industrial sectors. Lastly, regression is used to show that the pre-construction and worker selection ZAT components can predict a better safety performance. / Master of Science

Linkages between Complex Adaptive Systems (CAS) thinking and efficiency analysis remain in their infancy. This research associates the basic building blocks of the CAS 'flocking' metaphor with the essential building block concepts of Data Envelopment Analysis (DEA). Within a proposed framework DEA "decision-making units" (DMUs) are represented as agents in the agent-based modeling (ABM) paradigm. Guided by simple rules, agent DMUs representing business units of a larger management system, 'align' with one another to achieve mutual protection/risk reduction and 'cohere' with the most efficient DMUs among them to achieve the greatest possible efficiency in the least possible time. Analysis of the resulting patterns of behavior can provide policy insights that are both evidence-based and intuitive. This research introduces a consistent methodology that will be called here the Complex Adaptive Productive Efficiency Method (CAPEM) and employs it to bridge these domains. This research formalizes CAPEM mathematically and graphically. It then conducts experimentation employing using the resulting CAPEM simulation using data of a sample of electric power plants obtained from Rungsuriyawiboon and Stefanou (2003). Guided by rules, individual agent DMUs (power plants) representing business units of a larger management system,'align' with one another to achieve mutual protection/risk reduction and 'cohere' with the most efficient DMUs among them to achieve the greatest possible efficiency in the least possible time. Using a CAS ABM simulation, it is found that the flocking rules (alignment, cohesion and separation), taken individually and in selected combinations, increased the mean technical efficiency of the power plant population and conversely decreased the time to reach the frontier. It is found however that these effects were limited to a smaller than expected sub-set of these combinations of the flocking factors. Having been successful in finding even a limited sub-set of flocking rules that increased efficiency was sufficient to support the hypotheses and conclude that employing the flocking metaphor offers useful options to decision-makers for increasing the efficiency of management systems. / Ph. D.

U.S. higher education is an example of a large multi-organizational system within the service sector. Its performance regarding workforce development can be analyzed through the lens of industrial and systems engineering. In this three-essay dissertation, we seek the answer to the following question: How can the U.S. higher education system achieve an equal representation of female and minority members in its student and faculty populations? In essay 1, we model the education pipeline with a focus on the system's gender composition from k-12 to graduate school. We use a system dynamics approach to present a systems view of the mechanisms that affect the dynamics of higher education, replicate historical enrollment data, and forecast future trends of higher education's gender composition. Our results indicate that, in the next two decades, women will be the majority of advanced degree holders. In essay 2, we look at the support mechanisms for new-parent, tenure-track faculty in universities with a specific focus on tenure-clock extension policies. We construct a unique data set to answer questions around the effectiveness of removing the stigma connected with automatic tenure-clock policies. Our results show that such policies are successful in removing the stigma and that, overall, faculty members that have newborns and are employed by universities that adopt auto-TCE policies stay one year longer in their positions than other faculty members. In addition, although faculty employed at universities that adopt such policies are generally more satisfied with their jobs, there is no statistically significant effect of auto TCE policies on the chances of obtaining tenure. In essay 3, we focus on the effectiveness of training underrepresented minorities (e.g., African Americans and Hispanics) in U.S. higher education institutions using a Data Envelopment Analysis approach. Our results indicate that graduation rates, average GPAs, and post-graduate salaries of minority students are higher in selective universities and those located in more diverse towns/cities. Furthermore, the graduation rate of minority students in private universities and those with affirmative action programs is higher than in other institutions. Overall, this dissertation provides new insights into improving diversity within the science workforce at different organizational levels by using industrial and systems engineering and management sciences methods. / Ph. D.