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

Ph.DDOCTOR OF PHILOSOPH

A review on maintenance optimization

To this day, continuous developments of technical systems and increasing reliance on equipment have resulted in a growing importance of effective maintenance activities. During the last couple of decades, a substantial amount of research has been carried out on this topic. In this study we review more than two hundred papers on maintenance modeling and optimization that have appeared in the period 2001 to 2018. We begin by describing terms commonly used in the modeling process. Then, in our classification, we first distinguish single-unit and multi-unit systems. Further sub-classification follows, based on the state space of the deterioration process modeled. Other features that we discuss in this review are discrete and continuous condition monitoring, inspection, replacement, repair, and the various types of dependencies that may exist between units within systems. We end with the main developments during the review period and with potential future research directions

End-of-Life Inventory Decisions of Service Parts

With the spurt of technology and innovation the life cycles of parts and products have become shorter and service parts enter their final phases earlier. Final phase of a typical service part starts once the part production is ceased and ends when the last service or warranty contract expires. One popular tactic, in practice, to sustain service operations is placing a final order. The prime challenge of a firm while deciding a final order quantity is to minimize inventory-carrying costs together with the risk of obsolescence at the end of the planning period. In this study, end-of-life inventory decisions for an array of products including both consumer electronics and capital-intensive products are investigated. For consumer electronics we show that considering an alternative service policy, such as swapping the defective product with a new one, besides a regular repair policy improves cost efficiency. Moreover, for capital-intensive products we study systems with phase-out returns and systems with customer differentiation in the end-of-life phase. Our analysis reveals that taming the uncertainty associated with phase-out arrivals engenders a remarkable efficiency improvement. Moreover, including rationing decisions in the end-of-life phase enhances the performance of the system by a significant reduction in cost and risk of obsolescence

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

Simplified framework to evaluate software development warranty

This article addresses a simplified framework to evaluate the warranty costs of a software development process. The approach uses parameters required by the models from metrics commonly found associated with a software development project. Methods are proposed to extract and apply organizational baselines. The proposed framework is validated using simulation techniques based on the Monte Carlo method, allowing for the assessment of the likely distribution of the results and the sensitivity with the parameters used. Preliminary conclusions are extracted and future lines of work identified.Sociedad Argentina de Informática e Investigación Operativ

A study in joint maintenance scheduling and production planning

Master'sMASTER OF ENGINEERIN

A comparative analysis of the fuzzy and intuitionistic fuzzy environment for group and individual equipment replacement Models in order to achieve the optimized results

The main goal of this research is to compare group and individual replacement models based on fuzzy replacement theory and intuitionistic fuzzy replacement theory. The capital costs are assumed to be triangular fuzzy numbers, triangular intuitionistic fuzzy numbers, and trapezoidal intuitionistic fuzzy numbers, respectively. As a result, interpreting the direct relationship between volatility and ambiguity is critical. It is difficult to predict when specific equipment will unexpectedly fail. This problem can be solved by calculating the probability of failure distribution. Furthermore, the failure is assumed to occur only at the end of period t. In this situation, two types of replacement policies are used. The first is the Individual Replacement Policy, which states that if an item fails, it will be replaced immediately. The Group Replacement Policy states that all items must be replaced after a certain time period, with the option of replacing any item before the optimal time. The dimensions of the prosecution are fuzzy, and they are then assessed using mathematical and logical procedures. The fuzzy assessment criteria of the replacement model are provided as a set of outcomes, whereas the intuitionistic fuzzy replacement model has many advantages. A methodological technique is used to determine quality measurements in which fuzzy costs or values are kept without being merged into crisp values, allowing us to draw mathematical inferences in an uncertain setting. A comparison conceptualise is created for each fuzzy number, and in an uncertain environment, a comparison study on group and individual replacement was also conducted

An integrated decision support framework for remanufacturing in the automotive industry

In today\u27s global economy, firms are seeking any and every opportunity to differentiate from competitors by reducing supply chain costs and adding value to end customers. One increasingly popular option, under growing consumer awareness and increasing legislation, is to reintegrate returned products into the supply chain to achieve economic benefits as well as improve sustainability. An important class of such reverse goods flows has to do with remanufacturing (reman), which refers to activities that restore returned products ( cores ) or their major modules to operational condition for using in place of new product or distributing through other channels (e.g., spare parts). While opportunities abound, some key complications reported in the literature include: 1) difficulty in timing the launch of reman product (while accounting for uncertainties associated with product life-cycle demand and core supply), 2) difficulty with capacity planning for remanufacturing (while accounting for the fact that volumes can be low and that facilities/lines should target multiple product families for economies of scale), and 3) operational difficulties in maintaining efficiencies in production planning and control of remanufacturing activities. These difficulties are mostly attributable to limited visibility and higher levels of uncertainty in reverse logistics (in comparison with forward logistics). Despite advances in the remanufacturing literature in the last two decades (both in the academic literature and practitioner community), there is no integrated decision support framework that can guide companies to successful launch and execution of remanufacturing operations. This is particularly true for companies that engage in both original equipment (OE) service as well as the independent after-market (IAM) in the automotive industry. This research aims to address these limitations by developing a decision support framework and necessary models for effective remanufacturing in the automotive industry. At the strategic level, we propose a unified approach to explicitly model and address issues of capacities as well timing the launch of remanufacturing programs for new product. We derive the optimal remanufacturing policy and extensively studied the drivers of cost-effective remanufacturing program for aftermarket services. Our policies exploit the ability to leverage OE production to support both the OE service operations as well as demand from the IAM. To the best of our knowledge, this research is the first attempt of its kind in the remanufacturing literature, as prior research treated these interrelated decisions separately. Valuable managerial insights are obtained by minimizing the discounted cash outflows caused by appropriate investment and core return inventory building decisions. We show that, under certain conditions, it may be optimal to delay the launch of the remanufacturing program to build up an adequate initial core return inventory. This may help in perfectly substituting virgin parts with remanufactured parts after end of the OE production run. At operational level, efficient production planning and control of reman parts for the supplier heavily impinges on the ability to accurately forecast core returns from customers (e.g., dealers, distributors). There are several challenges to this, including, the volume and diversity of customers served by the supplier, differences among individual customer warehouses in returning cores, large reman product catalogs, changing customer behaviors (often improving core return delays), and data sparsity. In this research we report the evidence for the effectiveness of hazard rate regression models to estimate core return delays in the context of remanufacturing. We investigate a number of hazard rate modelling techniques (e.g., parametric, semi-parametric etc.) using real-world datasets from a leading Tier-1 automotive supplier. Results indicate the effectiveness of the proposed framework in terms of stability and face validity of the estimates and in predictive accuracy

Smart Maintenance Decision Support Systems (SMDSS)

Computerized information systems are used in all contemporary industries and have been applied to track maintenance information and history. To a lesser extent, such information systems have also been used to predict or simulate maintenance decisions and actions. This work details two models, a population data analysis, and a system infrastructure, to aid operations and maintenance managers with the difficult resource allocation decisions they face in the field. The first model addresses the consideration of component dependency for series network connections using a Markov Decision Process model and solution algorithm. The second model addresses the prioritization of maintenance activities for a fleet of equipment using an Analytical Hierarchy Process and solution algorithm. A recurrent event data analysis is performed for a population data set. The final element is the information system architecture linking these two models to a marketing information system in order to provide quotations for maintenance services. The specific industry of interest is the electrical power equipment industry with a focus on circuit breaker maintenance decision actions and priorities and the development of quotations for repair and replacement services. This dissertation is arranged in a three paper format in which each topic is self contained to one chapter of this document

Optimal number of remanufacturing in a circular economy platform

In reducing waste and protecting natural resources benefits in a circular economy platform, performing remanufacturing tasks are complex, as it may be associated with costs such as investment, setup and disposal cost. Thus, many studies those aims to find the optimal number of remanufacturing has been investigated whether it is an infinite or a constant number of remanufacturing via trial-and-error method. During the investigation, the disposal rate is assumed as a fixed value for each unique case, which needs further focus. The current study aims to propose a novel decision model to figure out an optimal number of remanufacturing regarding to the various ratio of used units returned for recovery. The proposed model was extended in context of remanufacturing opportunities of PVC products. The obtained findings are useful for companies in managing remanufacturing processes by knowing optimal remanufacturing times, and results in enhanced economic–ecological–social gains in the circular economy