International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A program’s success is measured by the three parameters of cost, schedule and performance. This is true of any acquisition program, including instrumentation development and procurement. The primary purpose of Logistics is to influence and reduce the Total Ownership Costs (TOCs) to the Department of Defense while procuring a supportable system that meets the customer’s needs. The time to influence the TOCs is as early in the life-cycle of the program as can be done - where it is cheapest to affect a “fix.” This paper will briefly describe where Logistics influences ownership costs in the acquisition process. Examples of cost drivers identified in the Hardened Subminiature Telemetry Sensor System (HSTSS) program will be provided and the role of Integrated Product Team (IPT) members in influencing and reducing the TOCs will be discussed. This process is not just in the purview of the traditional logistician, but is also performed by each member of the program’s IPT. Each IPT member brings his unique knowledge and experiences to the teams pool of corporate knowledge. Examples will be provided of decisions made by the IPT that reduced TOCs.

Investment in new technologies is considered by firms as a solution to improve their productivity, product and service quality and their competitive advantages in the global market. Unfortunately, not all technology adoption projects have met their intended objectives. The complexity of technology adoption along with little consideration of the long term cost of the technology, are among the factors that challenge companies while adopting a new technology. Companies often make new technology adoption decision without enough attention to the total cost of the technology over its lifecycle. Sometimes poor decision making while adopting a new technology can result in substantial recurring loss impacts. Therefore, estimating the total cost of the technology is an important step in justifying the technology adoption. Total Ownership Cost (TOC) is a wildly-accepted financial metric which can be applied to study the costs associated with the new technology throughout its lifecycle. TOC helps companies analyze not only the acquisition and procurement cost of the technology, but also other cost components occurring over the technology usage and service stage. The point is that, technology adoption cost estimation is a complex process involving consideration of various aspects such as the maintenance cost, technology upgrade cost and the cost related to the human-resource. Assessing the association between the technology characteristics (technology upgrades over its life cycle, compatibility with other systems, technology life span, etc) and the TOC encompasses a high degree of complexity. The complexity exists because there are many factors affecting the cost over time. Sometimes decisions made today can have long lasting impact on the system costs and there is a lag between the time the decision is taken and when outcomes occur. iv An original contribution of this dissertation is development of a System Dynamics (SD) model to estimate the TOC associated with the new technology adoption. The SD model creates casual linkage and relationships among various aspects of the technology adoption process and allows decision makers to explore the impact of their decisions on the total cost that the technology brings into the company. The SD model presented in this dissertation composes of seven sub-models including (1) technology implementation efforts, (2) workforce training, (3) technology-related workforce hiring process, (4) preventive and corrective maintenance process, (5) technology upgrade, (6) impact of technology on system performance and (7) total ownership cost sub model. A case study of Enterprise Resource Planning (ERP) system adoption has been used to show the application of the SD model. The results of the model show that maintenance, upgrade and workforce hiring costs are among the major cost components in the ERP adoption case study presented in Chapter 4. The simulation SD model developed in this dissertation supports trade-off analysis and provides a tool for technology scenarios evaluation. The SD model presented here can be extended to provide a basis for developing a decision support system for technology evaluation.

Plug-in Hybrid Electric vehicle (PHEV) technology has the potential to reduce operational costs, greenhouse gas (GHG) emissions, and gasoline consumption in the transportation market. However, the net benefits of using a PHEV depend critically on several aspects, such as individual travel patterns, vehicle powertrain design and battery technology. To examine these effects, a multi-objective optimization model was developed integrating vehicle physics simulations through a Matlab/Simulink model, battery durability, and Canadian driving survey data. Moreover, all the drivetrains are controlled implicitly by the ADVISOR powertrain simulation and analysis tool. The simulated model identifies Pareto optimal vehicle powertrain configurations using a multi-objective Pareto front pursuing genetic algorithm by varying combinations of powertrain components and allocation of vehicles to consumers for the least operational cost, and powertrain cost under various driving assumptions. A sensitivity analysis over the foremost cost parameters is included in determining the robustness of the optimized solution of the simulated model in the presence of uncertainty. Here, a comparative study is also established between conventional and hybrid electric vehicles (HEVs) to PHEVs with equivalent optimized solutions, size and performance (similar to Toyota Prius) under both the urban and highway driving environments. In addition, breakeven point analysis is carried out that indicates PHEV lifecycle cost must fall within a few percent of CVs or HEVs to become both the environmentally friendly and cost-effective transportation solutions. Finally, PHEV classes (a platform with multiple powertrain architectures) are optimized taking into account consumer diversity over various classes of light-duty vehicle to investigate consumer-appropriate architectures and manufacturer opportunities for vehicle fleet development utilizing simplified techno-financial analysis. / Graduate / 0540 / 0548 / ehtesham@uvic.ca

The U.S. Navy has been confronted with budget cuts and constraints during recent years. This reduction in budget compels the U.S. Navy to limit the number of manpower and personnel to control costs. Reducing the total ownership cost (TOC) has become a major topic of interest for the Navy as plans are made for current and future fleets. According to the U.S. Government Accountability Office (GAO, 2003), manpower is the most influential component of determining the life cycle cost of a ship. The vast majority of the TOC is comprised of operating and support (O&S) costs which account for approximately 65 percent of the TOC. Manpower and personnel costs account for approximately 50 percent of O&S costs. This research focused on tradeoff analysis and cost estimation between manpower and new technology implementation. Utilizing concepts from System Dynamics Modeling (SDM), System Dynamics Causal Loop diagrams (CLD) were built to identify major factors when implementing new technology, and then stocks and flows diagrams were developed to estimate manpower cost associated with new technology implementation. The SDM base model reflected an 18 months period for technology implementation, and then compared different technology implementation for different scenarios. This model had been tested by the public data from Department of the Navy (DoN) Budget estimates. The objective of this research was to develop a SDM to estimate manpower cost and technology tradeoff analysis associated with different technology implementations. This research will assist Navy decision makers and program managers when objectively considering the impacts of iii technology selection on manpower and associated TOC, and will provide managers with a better understanding of hidden costs associated with new technology adoption. Recommendations were made for future study in manpower cost estimation of ship systems. In future studies, one particular type of data should be located to test the model for a specific manpower configuration.