Optimal Warranty Period for Free-replacement Policy of Agm Batteries

The objective of this study is to analyze the suitability of the age-based warranty model and a millage based warranty model for absorbent glass mat batteries (AGM) for the automobile industry. The battery life expectancy can be assessed and described by a combination of different terms such as: state of health (SOH), deep of discharge (DOD), state of energy (SOE) and state of charge (SOC). However, using actual data from the field, the implementation of reliability engineering and statistical modeling we aim to calculate optimal limits for warranty policies that minimize warranty costs. The outcomes of this research will enable battery manufacturers, motor companies and warranty managers in decisions making strategies for cost savings in warranty projects without negatively affecting customer satisfaction

Some contributions to modeling usage sensitive warranty servicing strategies and their analyses

Providing a warranty as a part of a product\u27s sale is a common practice in industry. Parameters of such warranties (e.g., its duration limits, intensity of use) must be carefully specified to ensure their financial viability. A great deal of effort has been accordingly devoted in attempts to reduce the costs of warranties via appropriately designed strategies to service them. many such strategies, that aim to reduce the total expected costs of the warrantor or / and are appealing in other ways such as being more pragmatic to implement - have been suggested in the literature. Design, analysis and optimization of such servicing strategies is thus a topic of great research interest in many fields. In this dissertation, several warranty servicing strategies in two-dimensional warranty regimes, typically defined by a rectangle in the age-usage plane, have been proposed, analyzed and numerically illustrated. Two different approaches of modeling such usage sensitive warranty strategies are considered in the spirit of Jack, Iskandar and Murthy (2009) and Iskandar (2005). An `Accelerated Failure Time\u27 (AFT) formulation is employed to model product degradation resulting due to excessive usage rate of consumers. The focus of this research is on the analysis of warranty costs borne by the manufacturer (or seller or third party warranty providers) subject to various factors such as product\u27s sale price, consumer\u27s usage rate, types and costs of repair actions. By taking into account the impact of the rate of use of an item on its lifetime, a central focus of our research is on warranty cost models that are sensitive to the usage rate. Specifically, except the model in Chapter 4 where the rate at which an item is used is considered to be a random variable; all other warranty servicing policies that we consider, have usage rate as a fixed parameter, and hence are policies conditional on the rate of use. Such an approach allows us to examine the impact of a consumer\u27s usage rate on the expected warranty costs. For the purpose of designing warranties, exploring such sensitivity analysis may in fact suggest putting an upper limit on the rate of use within the warranty contract, as for example in case of new or leased vehicle warranties. A Bayesian approach of modeling 2-D Pro-rated warranty (PRW) with preventive maintenance is considered and explored in the spirit of Huang and Fang (2008). A decision regarding the optimal PRW proportion (paid by the manufacturer to repair failed item) and optimal warranty period that maximizes the expected profit of the rm under different usage rates of the consumers is explored in this research. A Bayesian updating process used in this context combines expert opinions with market data to improve the accuracy of the parameter estimates. The expected profit model investigated here captures the impact of juggling decision variables of 2-D pro-rated warranty and investigates the sensitivity of the total expected profit to the extent of mis-specification in prior information

Electronic Part Total Cost Of Ownership And Sourcing Decisions For Long Life Cycle Products

The manufacture and support of long life cycle products rely on the availability of suitable parts from competent suppliers which, over long periods of time, leaves parts susceptible to a number of possible long-term supply chain disruptions. Potential supply chain failures can be supplier-related (e.g., bankruptcy, changes in manufacturing process, non-compliance), parts-related (e.g., obsolescence, reliability, design changes), logistical (e.g., transportation mishaps, natural disasters, accidental occurrences) and political/legislative (e.g., trade regulations, embargo, national conflict). Solutions to mitigating the risk of supply chain failure include the strategic formulation of suitable part sourcing strategies. Sourcing strategies refer to the selection of a set of suppliers from which to purchase parts; sourcing strategies include sole, single, dual, second and multi-sourcing. Utilizing various sourcing strategies offer one way of offsetting or avoiding the risk of part unavailability (and its associated penalties) as well as possible benefits from competitive pricing. Although supply chain risks and sourcing strategies have been extensively studied for high-volume, short life cycle products, the applicability of existing work to long life cycle products is unknown. Existing methods used to study part sourcing decisions in high-volume consumer oriented applications are procurement-centric where cost tradeoffs on the part level focus on part pricing, negotiation practices and purchase volumes. These studies are commonplace for strategic part management for short life cycle products; however, conventional procurement approaches offer only a limited view for parts used in long life cycle products. Procurement-driven decision making provides little to no insight into the accumulation of life cycle cost (attributed to the adoption, use and support of the part), which can be significantly larger than procurement costs in long life cycle products. This dissertation defines the sourcing constraints imposed by the shortage of suppliers as a part becomes obsolete or is subject to other long-term supply chain disruptions. A life cycle approach is presented to compare the total cost of ownership of introducing and supporting a set of suppliers, for electronic parts in long life cycle products, against the benefit of reduced long-term supply chain disruption risk. The estimation of risk combines the likelihood or probability of long-term supply chain disruptions (throughout the part's procurement and support life within an OEM's product portfolio) with the consequence of the disruption (impact on the part's total cost of ownership) to determine the "expected cost" associated with a particular sourcing strategy. This dissertation focuses on comparing sourcing strategies used in long life cycle systems and provides application-specific insight into the cost benefits of sourcing strategies towards proactively mitigating DMSMS type part obsolescence

The photovoltaic market analysis program : background, model development, applications and extensions

The purpose of this report is to describe and motivate the market analysis program for photovoltaics that has developed over the last several years. The main objective of the program is to develop tools and procedures to help guide government spending decisions associated with stimulating photovoltaic market penetration.The program has three main components: (1) theoretical analysis aimed at understanding qualitatively what general types of policies are likely to be most cost effective in stimuating PV market penetration; (2) operational model development (PV1), providing a user oriented tool to study quantitatively the relative effectiveness of specific government spending options and (3) field measurements, aimed at providing objective estimates of the parameters used in the diffusion model used in (2) above.Much of this report is structured around the development and use of PV1, an interactive computer model designed to determine allocation strategies for (constrained) government spending that will best accelerate private sector adoption of PV. To motivate the model's development, existing models of solar technology diffusion are reviewed, and it is shown that they a) have not used sound diffusion principles and b) are not empirically based. The structure of the PV1 model is described and shown to address these problems.Theoretical results on optimal strategies for spending federal market development and subsidy funds are then reviewed. The validity of these results is checked by comparing them with PV1 projections of penetration and cost forecasts for fifteen government policy strategies which were simulated on the PV1 model. Analyses of these forecasts indicate that photovoltaics will not diffuse significantly during the time horizon studied if government market development funds (money allocated to the purchase and installation of PV systems) are withheld. Market development spending has the most positive effect on photovoltaic diffusion in strategies where it is deployed early and concentrated in the residential and commercial sectors. Early subsidy spending had little influence on ultimate diffusion. The analyses suggest that any subsidies for PV should be delayed until photovoltaic costs drop substantially. Extensions of the model and approach to other technologies are discussed

Data-driven Warehouse Management in Global Supply Chains

Warehouse management has emerged as a determinant for success of global supply chain management. This thesis focuses on how to solve warehouse challenges in global supply chain management (SCM) that is characterized by large volume uncertainty, great responsiveness needs and complex order-fulfilment collaboration with other functionalities. We employ data analytic methods to exploit the rich data information obtained from detailed registration of daily warehouse operations to address these challenges. By providing actual application examples in real-world situations we showcase the potency of such data-driven warehouse management. In this dissertation, data-driven warehouse management is presented by four-steps in the time horizon of warehouse operations: Long-term opportunities (for the coming years) are examined by predictive analytics for expanding cross-border e-commerce in the European Union. Mid-term demand for spare parts during the end-of-life phase (of several months) are forecasted by means of data-driven modelling for installed base. Short-term operational opportunity (weekly or daily) are presented by employing detailed productivity data to sustain effective operation of variable warehouse resources. Real-time (hourly or shorter) data applications are introduced for job priority allocation to improve daily responsiveness in warehouse order fulfilment. All these data analytic methods can be incorporated in warehouse management systems where practitioners can tune the specific strategies according to their warehouse constraints, including location cost, labour cost, time criticality, and freight company flexibility. In this way, data analytics at the warehouse level offers great opportunities for managing increasing uncertainties and performance requirements in global SCM

A General Approach to Electrical Vehicle Battery Remanufacturing System Design

One of the major difficulties electrical vehicle (EV) industry facing today is the production and lifetime cost of battery packs. Studies show that using remanufactured batteries can dramatically lower the cost. The major difference between remanufacturing and traditional manufacturing is the supply and demand variabilities and uncertainties differences. The returned core for remanufacturing operations (supply side) can vary considerably in terms of the time of returns and the quality of returned products. On the other hand, because different contracts can be used to regulate suppliers, it is almost always assumed zero uncertainty and variability for traditional manufacturing systems. Similarly, customers demand traditional manufacturers to sell newly produced products in constant high quality. But, remanufacturers usually sell in aftermarket, and the quality of the products demanded can vary depends on the price range, usage, customer segment and many other factors. The key is to match supply and demand side variabilities so the overlapping between them can be maximized. Because of these differences, a new framework is needed for remanufacturing system design. This research aims at developing a new approach to use remanufactured battery packs to fulfill EV warranties and customer aftermarket demands and to match supply and demand side variabilities. First, a market lifetime EV battery return (supply side) forecasting method is develop, and it is validated using Monte Carlo simulation. Second, a discrete event simulation method is developed to estimate EV battery lifetime cost for both customer and manufacturer/remanufacturer. Third, a new remanufacturing business model and a simulation framework are developed so both the quality and quantity aspects of supply and demand can be altered and the lifetime cost for both customer and manufacturer/remanufacturer can be minimized. The business models and methodologies developed in this dissertation provide managerial insights to benefit both the manufacturer/remanufacturer and customers in EV industry. Many findings and methodologies can also be readily used in other remanufacturing settings. The effectiveness of the proposed models is illustrated and validated by case studies.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143955/1/xrliang_1.pd

Post-Sale Cost Modeling and Optimization Linking Warranty and Preventive Maintenance

Ph.DDOCTOR OF PHILOSOPH