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Purpose This paper aims to examine the relationship between environmental factors, risk perception and decision-making in risk management. Specifically, using attribution theory, the authors study the influence of macro-level logistical capabilities of a host country on a firm’s actual and perceived supply chain risk, and examine if this country-level factor and the firm level perception of risk affect a firm’s decision-making in risk management. Design/methodology/approach This study uses a combination of primary data from 932 manufacturing firms from 22 countries and secondary data from the logistics performance index (LPI), and empirically tests the conceptual framework using partial least squares structural equation modeling. Findings Key results reveal that a country’s logistical capabilities, measured using LPI, have a significant impact on managers’ risk perception. Firms located in countries with high LPI perceive lower risk in their supply chain both in the upstream and downstream, and therefore do not invest much in external integration, compared to firms in low LPI countries, and hence are exposed to high risk. Originality/value This is one of the first empirical studies linking a country’s logistical capabilities with supply chain risk perceptions, objective supply chain risk and supply chain risk management efforts of a firm using the International Manufacturing Strategy Survey database.

In this study, we empirically test the antecedents and consequences of sustainable manufacturing practices across emerging as well as developed countries such as India, China and OECD. We use data ...

Supply chain managers across the globe are finding it difficult to manage the increasingly complex supply chains despite adopting a variety of risk mitigation strategies. Firms on the other hand ha...

We draw on prior work in the strategy domain and provide empirical evidence of how interactions of resources (or resource configurations) underlying an important capability (i.e., product development capability) lead to differential levels of competitive advantage in a unique emerging economy setting. Our work provides a nuanced understanding of how the efficacy of a specific capability varies depending on changes in the product market environment, such that certain resource configurations facilitate competitive advantage during particular periods of time, while others do not. The study uses rich qualitative and quantitative data gathered through primary and secondary sources to test the conjectures. Our work also demonstrates that while interactions of resources matter significantly in providing competitive advantage, in isolation, these resources do not matter.

In order to remain competitive in the market, firms are forced to expand their product offerings and offer high levels of customization, bringing about high uncertainty in their supply chain. Firms that face high environmental uncertainty are increasingly facing higher risks in terms of supply disruptions, production and delivery delays that ultimately result in poor operational performance. This study aims at understanding the antecedents of supply chain operational risk faced by firms and the conditions under which such risks can be mitigated. Using Indian data from the sixth edition of International Manufacturing Strategy Survey (IMSS) and structural equation modeling, we investigate the relationships between environmental uncertainty and supply chain risk and the moderating effect of supply chain flexibility. We identify appropriate types of flexibility to mitigate the three major aspects of supply chain risk: supply risk, manufacturing process risk and delivery risk. Our empirical investigation reveals that uncertainty in the supply chain leads to high supply chain risk; and in uncertain environments, supply and manufacturing flexibility help in reducing the supply and manufacturing process risks respectively. However, our results also indicate that, in emerging markets such as India where logistic infrastructure is less developed, internal capabilities alone may not be sufficient in reducing supply chain delivery risk. Our findings not only contribute towards filling certain gaps in the supply chain risk management literature, but also provide practicing managers and researchers a better understanding of the types of flexibility that can mitigate supply chain risk in different business environments.

It is well-established that a manufacturer’s commonality decisions between related products are driven by trade-offs between cost savings from standardization and demand effects of customization. Prior research has examined these trade-offs in the context of product line design for a single geography where cannibalization is a primary concern. However, the insights from a single market may not readily apply when serving related products for different geographies where cannibalization ceases to be a concern. The downside of commonality for geographically separated markets arises from a mismatch in customer preferences, which results in disutilities. In this paper we model the trade-off between the consumer-side disutilities and the cost-side scale economies in the presence of demand uncertainty, and derive the optimal extent of commonality between products for both the markets in the context of two product development strategies: (i) primary market centric design (PMD) and (ii) design for multiple markets (DFM). Our insights from analytical and numerical analysis show that when consumers are very picky and market uncertainty is low, the DFM strategy outperforms the PMD strategy. Counter to the practice of introducing primary market centric products into emerging markets our study demonstrates the need for more customization, especially in small, but uncertain markets. Our research has important implications for automakers in integrating the diversity in tastes while making commonality decisions for multiple markets.

India is considered to be one of the toughest aviation markets in the world, due to high fuel prices, overcapacity and intense price competition. It is therefore important to identify critical drivers of performance, which enable the airlines to survive and succeed in this emerging market with huge growth potential. In the current empirical study, we investigate the linkages between various performance drivers, operational efficiencies and market performance. An extensive data collection using primary and secondary sources enabled us to gather data on all the airlines operating in India, both private and public, for the period 2005–2012, on a variety of important parameters. We carried out a two-stage empirical analysis, which involved estimation of operational efficiencies during the first stage using Data Envelopment Analysis, and determination of performance drivers during the second stage using a two-way random effects GLS regression and also a Tobit model. Our findings suggest that while some of the structural and regulatory factors have an undesirable impact on airline performance, the low cost carriers in India have managed to achieve significant operational efficiencies. In addition, we find that, while cost efficiency is driven by a variety of factors, it is the technical efficiency which brings in better market performance through pricing power in the Indian airline industry.

Globally automakers are facing pressure from their stakeholders to follow sustainable business practices and produce products that are less harmful to the environment. The introduction of gas guzzling automobiles in the US market despite the increasingly stringent emission norms highlights the widening gap between the goals of the regulators and the automakers, demanding a fresh outlook at the regulatory framework. In this paper, we therefore propose a composite regulatory standard that not only allows the regulators to control various environmental standards, but also provides automakers with an opportunity to exploit scale economies and synergies in product development. Our results show that under composite regulations, sufficiently high economies of scale will ensure higher traditional and environmental qualities as well as higher profits for the automaker while operating in two markets as opposed to a single market. We also find under the composite regulations that, when more demanding norms are in place, despite positive synergies between traditional and environmental quality attributes, higher environmental quality is not guaranteed unless the scale economies are sufficiently high. Our work has implications for regulatory authorities in evaluating alternative policy design under heterogeneous market characteristics and technological synergies.

The Indian civil aviation industry was relatively small until the 1990s when the broader economic liberalization deregulated the civil aviation sector. The entry of private airlines and low-cost airlines resulted in a tremendous growth of the industry as the volume of passenger traffic more than tripled and freight more than doubled in the last decade alone. However, Indian aviation is currently facing a variety of challenges including high taxes, airport charges, maintenance costs, fare regulation, slot allocation, air space restriction and adherence to route guidelines. While the Indian government has enabled private participation in airport infrastructure, the growth in capacity seems to be lagging behind the demand. Policy changes aimed at increasing airport capacity, facilitating MRO services and generating investment in infrastructure are necessary. This chapter assesses the role played by deregulation and examines future opportunities that can be leveraged to make India a global aviation power house.

The rapid development of emerging market firms and their foray into global value chains has attracted significant attention. In this perspectives paper, we draw on case studies from the automotive industries in India and China, to describe the coevolution of domestic firms and advanced economy multinational enterprises (AMNE) entrants. We first show that domestic firms that used catch-up strategies such as capability upgradation, investments into internal R&D and globalization through mergers & acquisitions have managed to succeed in local markets as well as climb into global value chains. We next illustrate that the strategy adopted by the most successful AMNEs involves combining the formation of vertical partnerships with local sub-assembly suppliers and horizontal collaborations with local network orchestrators. Simultaneously weaving together embeddedness in these two cortical sides of the local business eco-system on the one hand and within its global corporate value chain networks on the other – generates a “double helix” effect, whereby its local and global capabilities reinforce each other. The double helix improves cost competitiveness and pushes the product innovative envelope in both local and global markets.

This study considers four categories of business models in healthcare in India and Ireland, using Porter’s generic strategies concept as a framework. The study finds that in both countries, business models exist in all four categories – broad low-cost, broad premium, focused low-cost and focused premium. However, the mechanism of implementation of the business models is different in each country and the mode of implementation is matched to each country’s economic and social circumstances. The paper concludes that cross-country learning in healthcare is not one-way: that developed economies have much to learn from emerging economies, and vice versa.

We study the linkages between firm-level quality initiatives such as quality management systems (QMS) and total quality management (TQM) and output productivity in the Indian auto component industry. We use externally validated quality certification and quality awards as proxies for QMS and TQM, respectively, as it is difficult to directly measure the QMS and TQM efforts of firms. We use an unbalanced panel of 220 firms and a balanced panel of 73 firms from the Indian auto component industry over the period 1993–2006 to study these links. Both parametric as well as non-parametric approaches are used, as appropriate, to measure the rate of change in productivity and the impact of quality initiatives on productivity change during this period. We determine the proportion of productivity resulting from technical change and relative efficiency change, thus providing insights into the structure of productivity improvements. We find that TQM efforts resulted in a high rate of productivity change (11%) in the award-winning firms after the award. On the other hand, pre-certification productivity change due to QMS was 5% and post-certification change was 3.6%. In the periods prior to certification, productivity change was driven mainly by technical change; whereas the source of productivity change after certification is mixed. However, prior to awards, productivity change was driven mainly by relative efficiency change, whereas post-award productivity change was due to technical change. The results suggest that management focus on attaining certification did generate conceptual learning (linked to technical change) during the period leading to certification, but these effects were not significant after certification. The results also suggest that the TQM programs generated significant productivity gains in the long run, although setting the associated systems in place did not result in significant productivity change prior to winning awards. Thus, the study provides direct but nuanced evidence linking quality certification as well as the adoption of TQM programs to the associated conceptual and operational learning processes and their impact on the change in productivity.

Market liberalization in emerging-market economies and the entry of multinational firms spur significant changes to the industry/institutional environment faced by domestic firms. Prior studies have described how such changes tend to be disruptive to the relatively backward domestic firms, and negatively affect their performance and survival prospects. In this paper, we study how domestic supplier firms may adapt and continue to perform, as market liberalization progresses, through catch-up strategies aimed at integrating with the industry's global value chain. Drawing on internalization theory and the literatures on upgrading and catch-up processes, learning and relational networks, we hypothesize that, for continued performance, domestic supplier firms need to adapt their strategies from catching up initially through technology licensing/collaborations and joint ventures with multinational enterprises (MNEs) to also developing strong customer relationships with downstream firms (especially MNEs). Further, we propose that successful catch-up through these two strategies lays the foundation for a strategy of knowledge creation during the integration of domestic industry with the global value chain. Our analysis of data from the auto components industry in India during the period 1992–2002, that is, the decade since liberalization began in 1991, offers support for our hypotheses.

We study the productivity change and factors driving this change in the Indian pharmaceutical industry during 1994–2003, in the backdrop of economic liberalization and change in regulatory norms. We use a non parametric Data Envelopment based-methodology to estimate productivity change and decompose it into technical and relative efficiency changes. We find that, the long-term strategic measures by a section of innovative firms that foresaw the implications from competitive forces of globalization and a change in the regulatory environment have sphereheaded the technical change. Consequently, few innovative firms, characterized by greater R&D investments, transition into higher value-added products and businesses as a step towards more technically sophisticated new drug development have pushed the production frontier, increasing the technical and productivity gains. The higher technical and R&D capabilities and wider new product portfolios of multinational companies also have contributed to the positive technical and productivity changes in the Indian pharmaceutical industry.

Hedge funds have made a significant impact on the performance of world financial markets in recent times. Our objective in this paper is to develop a robust framework for the evaluation of hedge funds by incorporating a maximum number of performance measures through public data sources. We analyse the hedge fund strategies (styles) using a variety of classical risk-return measures with the help of slack-based Data Envelopment Analysis (DEA) models to determine a unique performance indicator. The main thrust is to investigate the risk return profile of 4730 hedge funds classified under 18 different strategies using multiple inputs and outputs. The originality of the work lies in applying Slack-Based DEA to decipher the risk-return profile of these strategies using advanced risk-return measures such as Value at Risk, drawdown, lower and higher partial moments and skewness. We find that the correlation between the ranking of hedge fund strategies based on Sharpe ratio and the DEA models is very low; at the same time, there is a significant correlation between rankings obtained by the application of DEA using different sets of input/output measures. We have also compared the DEA rankings with other traditional financial ratios such as modified Sharpe ratio, Sortino ratio and Calmar ratio. The paper also studies the impact of events such as the Asian financial crisis on the performance of hedge funds. The study around the event shows that only a relatively small number of strategies performed better during times of turmoil.

Purchasing and Supply Management (PSM) today is increasingly becoming more important to senior management due to its potential to strategically influence both operational performance as well as financial performance outcomes. However the cross-functional nature of many PSM activities has led to inadequate data collection and performance measurement resulting in weak performance evaluation methodologies and mixed results. We address this gap in the current study, firstly by using an external assessment survey methodology that complements the internal perceptional measures of PSM performance, to collect data for a sample of over 120 firms across the globe with more than 3 billion US dollar turnover, representing seven industry sectors. Next, we develop a comprehensive performance measurement framework using the classical and two-stage Value Chain Data Envelopment Analysis models, which make use of multiple PSM measures at various stages and provide a single efficiency measure that estimates the all-round performance of a PSM function and its contribution to the long term corporate performance in each of these seven industry sectors. The relevance of this measurement methodology is demonstrated through an in-depth analysis of the distribution of efficiencies within and across industry sectors and through the estimation of target PSM performance levels.

Obsolescence of embedded parts is a serious concern for managers of complex systems where the design life of the system typically exceeds 20 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.

In this paper, the performance analysis of the Indian auto component industry is carried out from the perspectives of an original equipment manufacturer and a component supplier. Various efficiency measures are estimated using Data Envelopment Analysis with publicly available financial data on a representative sample of 50 firms. The first stage analysis reveals various operational inefficiencies in the auto component industry which are subsequently decomposed into technical, input mix and scale efficiencies. The study finds evidence that a majority of the inefficient firms are operating in the diminishing returns to scale region and demonstrates potential savings through benchmark input targets. A second stage analysis aimed at exploring root causes of inefficiencies finds that substitution of labour for capital could be causing a variety of inefficiencies including the input mix inefficiency in the Indian component industry. The empirical results also suggest that, unlike the global auto supply chain, higher average inventories are required for higher operational efficiencies in the Indian context. Contrary to the popular expectations, the technology licensing does not show significant influence on efficiency, at least in the short term, whereas efficient working capital management does result in higher operational efficiencies. The study also unearths the need to reform labour laws which are significantly contributing to various inefficiencies in the Indian component industry.

Operational efficiencies of a firm play a crucial role in determining the survival and growth of a firm, especially when the industry is going through a dynamic structural transformation owing to external changes. In this paper, we explore the effect of managerial and strategic parameters on the degree of operational efficiency achieved by a firm in the Indian pharmaceutical industry using data envelopment analysis (DEA). During the period 1992–2002, the relaxation of import restrictions and foreign direct investment, along with a major change in the regulatory norms, resulted in increased competition from firms with superior resources in this industry. We use non-parametric DEA models and parametric methods such as regression analysis to determine the factors that have contributed to the internal operational efficiencies of these firms. The findings indicate that domestic firms, most of which are controlled by family-based governance structures, enjoy higher efficiencies than affiliates of multinational pharmaceutical majors. After controlling for firm size and initial efficiency levels, we find that firms with higher levels of innovation through higher R&D investments and older establishments are associated with higher efficiencies, when compared with their less R&D intensive and younger counterparts, respectively.

PurposeThe 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/approachThis research provides the identification of important inputs and outputs for six sigma projects that are then analyzed using data envelopment analysis (DEA) to identify projects, which result in maximum benefit. Maximum benefit here provides a Pareto optimal solution based on inputs and outputs directly related to the efficiency of the six sigma projects under study. A sensitivity analysis of efficiency measurement is also carried out to study the impact of variation in projects' inputs and outputs on project performance and to identify the critical inputs and outputs.FindingsDEA, often used for relative efficiency analysis and productivity analysis, is now successfully constructed for six sigma project selection.Practical implicationsProvides a practical approach to guide the selection of six sigma projects for implementation, especially for companies with limited resources. The sensitivity analysis discussed in the paper helps to understand the uncertainties in project inputs and outputs.Originality/valueThis paper introduces DEA as a tool for six sigma project selection.

Level of repair analysis (LORA) is an approach used during the design stage of complex equipment for analysis of the cost effectiveness of competing maintenance strategies. LORA is carried as a part of the life cycle cost and cost of ownership analysis and plays a significant role in minimizing the life cycle cost and cost of ownership of the capital equipment. Since many purchasing decisions of complex equipment are based on cost of ownership, it has become essential to carry out LORA to compete in the market. In this paper, we develop a mathematical model for LORA and propose a solution methodology based on genetic algorithms. The concept is illustrated using a hypothetical aircraft engine.

Opportunistic maintenance gives the maintenance crew an opportunity to replace or repair those items, which are found to be defective or needs replacement in the immediate future, during the maintenance of a sub‐system or a module. This paper tries to address the questions of how to decide whether a particular item needs opportunistic maintenance, and if so how cost effective the opportunistic maintenance is in comparison to a later grounding. These questions play an important role, especially in case of complex systems containing expensive items with hard lives and condition monitoring maintenance strategies. A systematic analysis of selection of components that require opportunistic maintenance is carried out, after which genetic algorithms are used to decide whether opportunistic maintenance is cost effective or not. A hypothetical example is used to describe the methodology for genetic algorithms.

The great need for an optimum preventive maintenance strategy coupled with the fast‐developing condition‐monitoring techniques has given rise to the invention of relevant condition predictor (RCP)‐based maintenance approach. The main purpose of this approach is to prevent the failures due to gradual deterioration of mechanical items in order to improve system reliability and availability. This is done by monitoring relevant condition predictors of constituent maintenance significant items of the system, taking into account the availability and cost‐effectiveness of the monitoring techniques. A comprehensive review of all constituent items is carried out and a systematic approach is used to decide an optimum maintenance policy for each corresponding group of items. An optimum time to the examination of relevant condition predictors is derived mathematically with required reliability as the optimisation criterion in order to implement the RCP‐based maintenance activities.

The paper presents a methodology based on relevant condition predictor (RCP) for reliability prediction for systems under condition-based maintenance. By considering the event of not being able to recognise the fault initiation or the critical state in RCP-based maintenance, the methodology uses Markov models for reliability prediction. The cases of single and multiple relevant condition predictors are presented along with a numerical procedure to obtain the reliability functions for RCP-based maintained systems. Numerical examples are used to illustrate the methodology and the values of reliability are obtained at discrete time points using the numerical algorithm.

Mathematical models for reliability prediction under multiple condition parameters are discussed. The models are developed under the assumption that the different condition parameters can be independent or dependent. Presents a model based on the continuous time Markov chain where the rate of degradation of the system depends on the state of the system and the fact that different parameters that cause system degradation can be dependent. The models are illustrated using an example.

Innovative resources and capabilities have been found to be essential to climb up the value chain in most industries. However, development of innovative resources and effective deployment of these resources requires significant investments on a sustained basis, which is a difficult task for domestic firms in many emerging economies, especially if they operated with obsolete technologies under protected regimes for long periods of time. To understand how and what type of resources and capabilities are developed in emerging economies, which are going through institutional transitions from protected to liberalized regimes, we examine various management theories and propose a conceptual framework that integrates relevant theories and helps us study the process of resource and capability development. Based on the existing literature on resource based view and dynamic capability approach, we also frame certain hypotheses on innovative resources and capabilities and their impact on firm performance. We collected primary and secondary data from the Indian auto component industry to test these hypotheses and carried out detailed interviews with industry experts to understand the industry dynamics and the results from our data analysis. We use the proposed conceptual framework and qualitative inputs from industry experts to explain our empirical results which provide very interesting insights into the selection and development of innovative resources and capabilities by the emerging economy firms and their impact on various measures of firm performance.

India is attracting significant attention as an attractive location for manufacturing industries in recent times. There have been many studies that demonstrate the rapid productivity and technological growth of Indian manufacturing industries post‐ industrial liberalization. We focus on the firm level strategic operational decisions with regard to Scale and Operating Focus, Product and Process Technologies and Planning, Control, and Execution Systems. We find little evidence that firms have taken advantage of scale economies in India during the post‐liberalized era. However, we find evidence that firms in industries such as chemicals and pharmaceuticals imported sophisticated process technologies and developed in‐house R&D capabilities to adapt them to the local environment even prior to liberalization. These capabilities seem to have enabled them to undertake more technology‐intensive activities and take further advantage of liberalization to improve product technologies.

We study the linkages between firm level quality initiatives such as Quality Management Systems (QMS) and Total Quality Management (TQM) and output productivity in the Indian auto component industry. We use externally validated quality certification and quality awards as proxies for QMS and TQM respectively, since it is difficult to directly measure the QMS and TQM efforts of firms.We use an unbalanced panel of 220 firms and a balanced panel of 73 firms from the Indian auto component industry over the period 1993 to 2006 to study these links. Both parametric as well as non parametric approaches are used, as appropriate, to measure the rate of change in productivity and the impact of quality initiatives on productivity change during this period. We determine the proportion of productivity resulting from technical change and relative efficiency change, thus providing insights into the structure of productivity improvements. We find that TQM efforts resulted in a high rate of productivity change (11%) in the award winning firms post-award. On the other hand, pre-certification productivity change due to QMS was 5% and post-certification change was 3.6%. In the periods prior to certification, productivity change was driven mainly by technical change; while the source of productivity change post-certification is mixed. However, prior to awards, productivity change was driven mainly by relative efficiency change while post-award productivity change was due to technical change. The results suggest that management focus on attaining certification did generate conceptual learning (linked to technical change) during the period leading to certification, but these effects were not significant post-certification. The results also suggest that the TQM programs generated significant productivity gains in the long run, although setting the associated systems in place did not result in significant productivity change prior to winning awards. Thus, the study provides direct but nuanced evidence linking quality certification as well as the adoption of TQM programs to the associated conceptual and operational learning processes and their impact on the change in productivity.

Inventory Management has emerged as one of the important tools to improve operational efficiency over the last 30-40 years across the globe. Japanese companies such as Toyota pioneered lean manufacturing, which emphasizes on the need to maintain low inventory levels across the supply chain through practices like JIT, Kanban and vendor managed inventory etc. Anecdotal evidence suggests that, inventory levels in general have been falling in the Indian manufacturing industries too in the recent past. The Japanese influence on the Indian manufacturing industry began with the entry of Suzuki into the Indian automobile industry in mid eighties. Since then, the principles of lean manufacturing have permeated across many industries, especially the automotive sector in India. However, there is scant empirical research in the literature that documents the inventory trends and the determining factors in India. The current study aims at filling this gap through a comprehensive inventory trend analysis in the Indian automotive industry during the 14 year period 1992-2005 with an objective to determine the inventory trends and identify the influencing factors. We use advanced econometric models to study the impact of various factors, such as the firm’s cluster, tier, export and import intensity on inventory levels. The study finds that average inventory has been steadily declining, with all three inventory components, viz., raw material, work-in-process and finished goods inventory contributing to this decline. The results suggest that the efficient working capital management and the quality improvement efforts of Tier 1 firms have been one of the major contributions to the decline in average inventory levels in the Indian auto industry.

Obsolescence of embedded parts is a serious concern for managers of complex systems where the design life of the system typically exceeds 20 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), arm-acquiring bandit 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.

Speed in product development can be defined as the thin line that determines success or failure in many fast growing industries today. Strategies like set based concurrent engineering and parallel team deployment have been used to accelerate new product introduction by successful companies like Toyota. In the current paper, these two strategies are combined to investigate the impact of generating sets of alternatives using parallel teams on the revenue generated from new product development. The trade-off between cost and speed of development due to deployment of parallel teams in product development using the Multi Armed Bandit framework are also explored. For sequential and overlapped development strategies, quantitative models to determine the optimum number of product development teams at different stages of development have been constructed using the Gittins Index strategy. These models have been tested empirically using hypothetical data and the results show that the higher flexibility induced quality due to larger set of design alternatives results in higher rewards, despite the higher cost of deploying multiple teams.

The change of Intellectual Property Protection (IPP) from a softer process patenting to a stronger product patenting in Indian Pharmaceutical Industry (IPI) is attracting many global drug majors to source their production from India, which is the fourth largest producer of pharmaceuticals in the world. In this paper, the interests of different stake holders like the buyers (multinational enterprises), who are searching for efficient partners and the vendors (Indian drug producers) that are competing for the contracts, are analysed for a suitable efficiency evaluation criterion. The primary objective of this paper is to study how various firms in the IPI with different business strategies, competing for the same opportunities can find suitable benchmarking peer groups to meet the challenges of a dynamic business environment using data envelopment analysis (DEA). A multiple objective DEA model that determines suitable peer groups for inefficient companies is discussed along with more traditional DEA models. The proposed model has the flexibility to include inputs like R&D expenditure and outputs like Exports that are not homogeneously distributed across the firms and address the interests of various stake holders like buyers and vendors simultaneously.

The Indian Pharmaceutical Industry (IPI) will be going through a major shift in its business model, from the year 2005 onwards, as the existing Process Patent regime gives way to the Product Patent regime, in order to comply with the Trade Related Intellectual Property Rights System (TRIPS) agreement. As a result, IPI, comprising of more than 20,000 players, is slowly consolidating with mergers, acquisitions and alliances; and getting ready to adapt to the new environment. In such a dynamic environment it would be imperative to examine whether there are any common firm level factors which aid in the survival and growth of a firm. This assumes crucial importance due to the fact that it is almost impossible for any firm to control the factors which affect the industry as a whole. This is particularly true when the changes are driven due to the process of globalization and not due to any policy changes of individual governments. With this objective, we have used Data Envelopment Analysis (DEA) on a sample of 44 companies that have survived at least the past one-decade, to determine the best practices in the Indian Pharmaceutical Industry. The results of DEA have been analysed along with their Compounded Annual Growth Rate (CAGR) to see if internal efficiencies and growth rate are related in the Indian Pharmaceutical Industry. We have also used regression analysis to see the correlations between various inputs/outputs and the growth rates. Various models of DEA like Constant Returns to Scale (CCR), Variable Returns to Scale (BCC) and Assurance Region (AR) are used to substantiate the results obtained.

Recently, it has been shown by Manoranjan et al. [l] that this class of equations possesses an interesting solitary wave interaction mechanism. Moreover using semi group theoretic approch they have established global existence of a generalized solution, provided the initial data are in a potential well. Further it is shown that finite time blowup may occur for weak solution in R, if the initial data are not in the potential well and the total energy at t = 0 exceeds the depth of the well. In the first part of this paper we shall discuss existence, uniqueness and regularity results for the problem (1 .I)-( 1.3) using the Faedo-Galerkin method. In the literature, very few results are available pertaining to the numerical study of (1 .l)-( 1.4). In [2], Defrutos et al. have analysed pseudo spectral Fourier method for the equation (I. I)- (I .2) with periodic boundary conditions. They have also discussed the nonlinear stability and convergence results in the analytic framework introduced earlier by Lopez, Marcos and Sanz- Serna [3]. For related results on finite difference schemes, one may refer to Ortega and Sanz- Serna [4]. Recently, Manoranjan et al. [5] have applied Petrov-Galerkin finite element method for Cauchy problem without rigorous error analysis and have presented some numerical results. In the later part of the present paper, we have derived optima1 error estimates for both semidiscrete

In the current paper, using a sample data of 58 firms consisting of automakers and auto component suppliers across a 14-year period, we study the factors contributing to efficient inventory management in the Indian automotive Industry. We use fixed effects regression models to document trends in inventory holdings over time and how this varies across inventory types and across tiers in the supply chain. Our results show that inventory holdings have declined differentially across tiers and across different types of inventories. We find tier-1 suppliers reduced all components of their inventories with the help of TQM and lean efforts.