Haritha Saranga

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 ...

Performance evaluation of purchasing and supply management using value chain DEA approach

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.

The Indian auto component industry – Estimation of operational efficiency and its determinants using DEA

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.

Determinants of operational efficiencies in the Indian pharmaceutical 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.

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.

Design for the environment: Impact of regulatory policies on product development

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.

Productivity and technical changes in the Indian pharmaceutical industry

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.

Analysis of hedge fund strategies using slack-based DEA models

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.

Commonality decisions when designing for multiple markets

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.

Sustainable supply chains for supply chain sustainability: impact of sustainability efforts on supply chain risk

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 have also been adopting various kinds of environmental and social sustainability practices in recent times to reduce carbon footprint and improve their image on the social front. However, very few studies in the extant literature have examined the impact of sustainability practices on supply chain risk. We address this important gap in literature by empirically testing this relationship, using primary data from six manufacturing sectors and 21 different countries including developed as well as emerging markets across the globe. Our findings indicate that risk mitigation strategies do not always reduce the actual supply chain risk experienced by firms, whereas sustainability efforts help reduce supply chain risk, especially in emerging market contexts. In addition, we find that, while reactive risk mitigation strategies on their own fail to reduce supply chain risk, they are effective when used in conjunction with sustainability efforts. We also find that preventive risk mitigation efforts are only effective in mature supply chains such as the OECD countries.