Bharatendra K. Rai

Resilience and competitiveness of small and medium size enterprises: an empirical research

A small- or medium-sized enterprise (SME) is normally a company that employs about 50–200 people. Because of the globalisation of market and operations, and technological advances, the competition among SMEs has radically increased over the years, and their survival is increasingly dependent on a number of factors including resilience of SMEs to refocus some of their strategies and technologies. In this article, a review of selected literature has been undertaken on some of the SME characteristics and new strategies, techniques and technologies that can provide a competitive advantage and sustainability in the global market and operations. Based on the literature review, a framework has been developed with key factors/enablers that determine the resilience and competitiveness of SMEs. This framework has been empirically studied by collecting data from SMEs. It involves a sample of 40 SMEs in the Southcoast of Massachusetts and provides further insight into the key characteristics associated with resilience and competitiveness of SMEs that are influenced by advances in operations strategies, technology and globalisation. Finally, a detailed summary of findings and conclusions are presented.

Hazard rate estimation from incomplete and unclean warranty data

Abstract Warranty data are a rich source of information for feedback on product reliability. However, two-dimensional automobile warranties that include both time and mileage limits, pose two interesting and challenging problems in reliability studies. First, warranty data are restricted only to the reported failures within warranty coverage and such incompleteness can lead to inaccurate estimates of field failure rate or hazard rate. Second, factors such as inexact time/mileage data and vague reported failures in a warranty claim make warranty data unclean that can suppress inherent failure pattern. In this paper we discuss two parameter estimation methods that address the incompleteness issue. We use a simulation-based experiment to study these estimation methods when departure from normality and varying amount of truncation exists. Using a life cycle model of the vehicle, we also highlight and explore issues that lead to warranty data not being very clean. We then propose a five-step methodology to arrive at meaningful component level empirical hazard plots from incomplete and unclean warranty data.

A modeling framework for assessing the impact of new time/mileage warranty limits on the number and cost of automotive warranty claims

Abstract An automobile with over 7000 parts is a highly complex product. In spite of employing the best quality and reliability practices during product development, manufacturing, and assembly, unexpected failures during warranty period do occur and cost automobile companies billions of dollars annually in warranty alone. Warranty coverage for an automobile is generally stated in terms of mileage (in miles) and time (in months or years). The coverage expires when any of the two limits is crossed. Any change in warranty coverage too, influences warranty cost significantly. However, changes made to warranty coverage are often market driven. In either case, a company needs to plan for maintaining a large cash reserve to pay for the warranty services on their products. In this paper, we present a simple method to assess the impact of new time/mileage warranty limits on the number and cost of warranty claims for components/sub-systems of a new product. We highlight the use of mileage accumulation rates of a population of vehicles to arrive at claims per thousand vehicles, sold with new time/mileage warranty limits. We also discuss the bias in warranty cost estimates that may result in using cumulative cost per repair information. We recommend the use of incremental cost per repair especially when populations with different mileage accumulation rates are under consideration. Application examples are included to illustrate the use of the proposed methodology.

Customer-Rush Near Warranty Expiration Limit, and Nonparametric Hazard Rate Estimation From Known Mileage Accumulation Rates

Time or mileage data obtained from warranty claims are generally more accurate for hard failures than for soft failures. For soft failures, automobile users sometimes delay reporting the warranty claim until the warranty coverage is about to expire. This results in an unusually high number of warranty claims near the end of warranty coverage. Because such a phenomenon of customer-rush near the warranty expiration limit occurs due to user behavior rather than due to the vehicle design, it creates a bias in the warranty dataset. Design improvement activities that use field reliability studies based on such data can potentially obtain a distorted picture of the reality, and lead to unwarranted, costly design changes. Research in the area of field reliability studies using warranty data provides several methods for warranty claims resulting from hard failures, and assumes reported time or mileage as actual time or mileage at failure. In this article, the phenomenon of customer-rush near the warranty expiration limit is addressed for arriving at nonparametric hazard rate estimates. The proposed methodology involves situations where estimates of mileage accumulation rates in the vehicle population are available. The claims influenced by soft failures are treated as left-censored, and are identified using information in technician comments about the repair carried out plus, if required, a more involved engineering analysis of field returned parts. Maximum likelihood estimates for the hazard function and their confidence limits are then obtained using Turnbulls iterative procedure. An application example illustrates use of the proposed methodology

Modeling and analysis of automobile warranty data in presence of bias due to customer-rush near warranty expiration limit

Abstract Automobile users experiencing soft failures, often delay reporting of warranty claims till the coverage is about to expire. This results into a customer-rush near the warranty expiration limit leading to an occurrence of ‘spikes’ in warranty claims towards the end of warranty period and thereby introducing a bias into the dataset. At the same time, an occurrence of manufacturing/assembly defects in addition to the usage related failures, lead to ‘spikes’ in warranty claims near the beginning of the warranty period. When such data are used to capture the field failures for obtaining feedback on product quality/reliability, it may lead product or reliability engineers to potentially obtain a distorted picture of the reality. Although in reliability studies from automobile warranty data, several authors have addressed the well-recognized issues of incomplete and unclean nature of warranty data, the issue of ‘spikes’ has not received much attention. In this article, we address the issue of ‘spikes’ in the presence of the incomplete and unclean nature of warranty data and provide a methodology to arrive at component-level empirical hazard plots from such automobile warranty data.

Prediction of drill-bit breakage from degradation signals using Mahalanobis-Taguchi system analysis

Drilling is a widely used machining process. As the hole drilling operation progresses, a drill-bit gradually degrades until it breaks at the end of its life. Replacing a drill-bit after it is breakage can be costly in certain special applications. At the same time an early tool replacement decision may lead to lower tool life utilisation. This calls for methods that enable accurate prediction of tool failures. In such situations degradation signals using appropriate features are utilised to arrive at the tool replacement decision. In this paper we study two such degradation signals viz., thrust force and torque using 20 derived features (10 features per degradation signal). The methodology used involves a successful application of Mahalanobis-Taguchi System (MTS) analysis that enables an accurate prediction of drill-bit breakage ensuring high tool life utilisation.

Reliability Analysis and Prediction with Warranty Data : Issues, Strategies, and Methods

Thank you for reading reliability analysis and prediction with warranty data issues strategies and methods. Maybe you have knowledge that, people have look numerous times for their favorite novels like this reliability analysis and prediction with warranty data issues strategies and methods, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful virus inside their desktop computer.

Forecasting warranty performance in the presence of the ‘maturing data’ phenomenon

Forecasting of warranty performance helps car engineers to fine-tune their strategies for warranty cost reduction. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at a certain future time, but also future MIS values. However, the ‘maturing data’ phenomenon that causes a warranty performance measure at specific MIS values to change with time make such forecasting challenging. Although dynamic linear models have been used for forecasting warranty performance, the focus mainly has been to utilize previous-model-year vehicle data for the analysis. In this paper, we apply a neural network model to forecast year-end warranty performance in the presence of the ‘maturing data’ phenomenon. We use a special type of neural network, viz. radial basis function (RBF), and optimize its parameters by minimizing training and testing errors through planned experimentation. This application shows the effectiveness of RBF neural networks to forecast warranty performance in the presence of the ‘maturing data’ phenomenon.

Classification, feature selection and prediction with Neural-network Taguchi System

Mahalanobis-Taguchi System (MTS) is often compared with artificial neural networks as both methodologies share common application areas. However, the comparison has been strictly limited to latter as a standalone process. Neural networks in a MTS framework, due to availability of a large array of architectures, has potential to lend flexibility needed to deal with a wide variety of application areas. This paper proposes a Neural-network Taguchi System (NTS) approach that incorporates neural networks in a MTS framework and consists of four stages viz., plan, validate, identify, and monitor. The workability of the proposed approach is illustrated using a tool-breakage prediction problem.

Robust design of an interior hard trim to improve occupant safety in a vehicle crash

Head injury ranks among the top contributors in automobile accidents. Consequently, although styling is treated important, safety of occupants in a crash receives preemptive priority in the design of automotive interior components. Additionally, the Federal Motor Vehicle Safety Standard (FMVSS) 201 has laid down certain requirements to be fulfilled by automobile manufacturers for producing a safe vehicle. One of the requirements stipulate dummy equivalent of the Head Injury Criteria, i.e. HIC(d) value for the interior components of a vehicle to be below 1000 under certain stated conditions. In this paper, we provide a robust design approach to achieve the requirements for one such interior component, viz. an interior hard trim that covers the pillar closest to the drivers head on the left-hand side of the vehicle.