U. Dinesh Kumar

Six sigma project selection using data envelopment analysis

Purpose – The evolution of six sigma has morphed from a method or set of techniques to a movement focused on business‐process improvement. Business processes are transformed through the successful selection and implementation of competing six sigma projects. However, the efforts to implement a six sigma process improvement initiative alone do not guarantee success. To meet aggressive schedules and tight budget constraints, a successful six sigma project needs to follow the proven define, measure, analyze, improve, and control methodology. Any slip in schedule or cost overrun is likely to offset the potential benefits achieved by implementing six sigma projects. The purpose of this paper is to focus on six sigma projects targeted at improving the overall customer satisfaction called Big Q projects. The aim is to develop a mathematical model to select one or more six sigma projects that result in the maximum benefit to the organization.Design/methodology/approach – This research provides the identification ...

Maintenance free operating period – an alternative measure to MTBF and failure rate for specifying reliability?

Abstract The paper analyses the concept of maintenance free operating period (MFOP), the reliability requirement driven by the Ministry of Defence (UK) for the next generation of future aircraft to be included in the fleet. Since the traditional reliability requirement MTBF (mean operating time between failure) has several drawbacks, the immediate reaction would be to analyse the credibility of the new measure MFOP against MTBF. The paper discusses various issues associated with MFOP. Two mathematical models are developed to predict the maintenance free operating period survivability (MFOPS), one using mission reliability approach and the other using alternating renewal theory. The paper also analyses cost implications of MFOP to the customer and to the producer.

Availability based spare optimization using renewal process

Abstract In this article, we develop mathematical models for spare components with exponential, gamma, normal and Weibull time to failure distribution using a renewal process. The models can be used to predict the number of spares required for a component or system to achieve specified inherent availability. The paper also presents optimization models for systems with components in series where the time to failure distribution of the components follow any general distribution. The objective of the optimization problem is to maximize the availability of the system satisfying constraints on cost and weight. The optimization model developed in the paper can be solved using many general purpose software like LINDO, SOLVER of EXCEL etc. An efficient branch and bound procedure which can be used to solve the optimization problem is also presented in the article.

New trends in aircraft reliability and maintenance measures

Discusses two measures, namely, maintenance free operating period (MFOP) and failure free operating period (FFOP) that are seen as the future reliability and maintenance measures. The concept of MFOP is a new reliability metric driven by the Ministry of Defence, UK, which is becoming popular among projects such as future offensive aircraft systems (FOAS), joint strike fighter (JSF) and ultra reliable aircraft (URA) etc. Analyses how these two measures can change the whole operation of military and commercial aircraft operations.

Optimization models for recovery block schemes

This paper presents optimization models for a fault tolerant software by selecting a set of versions for a given program. The objective is to maximize the reliability of the software satisfying a budget limitation. Optimization models are developed for two block recovery schemes: (1) independent recovery block and (2) consensus recovery block. The paper also presents simple formulas to calculate the reliability of the two schemes.

Evolutionary maintenance for aircraft engines

This paper looks at the concept of evolutionary maintenance. This approach is especially useful for expensive, complex systems, such as aircraft engines. In evolutionary maintenance, the maintenance and inspection schedule is adjusted after every service activity. The requires knowledge of the time-to-failure of each module of the engine and the corresponding hard lives of any of its components if applicable. After every maintenance and inspection activity, the hard life and preventive maintenance schedule are revised by adjusting the maintenance interval without increasing the risk of failure (risk initially committed by selecting a maintenance policy). The paper compares the effectiveness of the evolutionary maintenance against the traditional approach based on the required reliability or acceptable number of failures between maintenance.

Efficiency and productivity analysis of Indian pharmaceutical industry using data envelopment analysis

In this paper, we have used data envelopment analysis (DEA) models to analyse the relative efficiency and productivity change in Indian pharmaceutical industry (IPI) between 1998 and 2007 which covers the post-TRIPS (1995) and post Indian Patent Act Amendment (2005) period. BCC DEA model and Malmquist productivity index are used to estimate the relative efficiency and productivity change of Indian pharmaceutical companies over the 10 year period. We have proposed and tested several hypotheses on the average efficiency and the productivity change of IPI to check if the indigenous and multinational companies differ in their efficiency and productivity change over the aforementioned period. Also, we have analysed the effect of firm size on several performance measures. Exploring the relationship between DEA efficiency and innovation, we find that innovative firms with R&D and patents have higher efficiency than non-innovative firms.

Optimal selection of obsolescence mitigation strategies using a restless bandit model

Obsolescence of embedded parts is a serious concern for managers of complex systems where the design life of the system typically exceeds 20Â years. Capital asset management teams have been exploring several strategies to mitigate risks associated with Diminishing Manufacturing Sources (DMS) and repeated life extensions of complex systems. Asset management cost and the performance of a system depend heavily on the obsolescence mitigation strategy chosen by the decision maker. We have developed mathematical models that can be used to calculate the impact of various obsolescence mitigation strategies on the Total Cost of Ownership (TCO) of a system. We have used classical multi-arm bandit (MAB) and restless bandit models to identify the best strategy for managing obsolescence in such instances wherein organizations have to deal with continuous technological evolution under uncertainty. The results of dynamic programming and greedy heuristic are compared with Gittins index solution.

A GOAL PROGRAMMING MODEL FOR OPTIMIZING RELIABILITY, MAINTAINABILITY AND SUPPORTABILITY UNDER PERFORMANCE BASED LOGISTICS

Reliability, Maintainability and Supportability (R, M and S) are the main drivers of the system operational effectiveness (SOE). New procurement strategies have been developed by both public and private sectors to focus on the R, M and S characteristics inherent to the design of a system. One such strategy known as Performance Based Logistics (PBL) has gained popularity due to its success in improving the operational effectiveness of the system. In a PBL contract the customer buys performance, typically measured using R, M and S metrics, instead of contracting for a specified collection of resources defining the underlying support infrastructure. In this paper we have developed a mathematical model, using Goal Programming to optimize multiple performance measures of a design. We show how the best design is chosen from competing design alternatives when systems engineering principles are considered in defining the evaluation measures. The proposed mathematical model simultaneously considers multiple system engineering metrics during the design stage of the product development. The engineering metrics considered are a representation of the systems operational availability, reliability, maintainability, supportability and total cost of ownership. The Goal Programming model developed in the paper can be easily solved using software such as LINDO, LINGO and Excel Solver.

OPTIMAL COMPONENT SELECTION OF COTS BASED SOFTWARE SYSTEM UNDER CONSENSUS RECOVERY BLOCK SCHEME INCORPORATING EXECUTION TIME

Computer based systems have increased dramatically in scope, complexity, pervasiveness. Most industries are highly dependent on computers for their basic day to day functioning. Safe & reliable software operations are an essential requirement for many systems across different industries. The number of functions to be included in a software system is decided during the software development. Any software system must be constructed in such a way that execution can resume even after the occurrence of failure with minimal loss of data and time. Such software systems which can continue execution even in presence of faults are called fault tolerant software. When failure occurs one of the redundant software modules get executed and prevent system failure. The fault tolerant software systems are usually developed by integrating COTS (commercial off-the-shelf) software components. The motivation for using COTS components is that they will reduce overall system development costs and reduce development time. In this paper, reliability models for fault tolerant consensus recovery blocks are analyzed. In first optimization model, we formulate joint optimization problem in which reliability maximization of software system and execution time minimization for each function of software system are considered under budgetary constraint. In the second model the issue of compatibility among alternatives available for different modules, is discussed. Numerical illustrations are provided to demonstrate the developed models.