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The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

Although handloom is a significant source of livelihood for millions of people in India, it performs poorly compared to other sectors of the economy, which may be the root of technical inefficiency. Until now, to measure technical efficiency, no studies have been carried out; therefore, the purpose of this study is to estimate the technical efficiency in the handloom micro-enterprises in India.

This study includes 427 handloom micro-entrepreneurs from the Indian state of Assam. Using bootstrap truncated regression, the data envelopment analysis (DEA) was used to calculate the technical efficiency and identify the factors responsible for inefficiency.

The findings of this study reveal that handloom enterprises are 75% pure technically efficient, suggesting room for input reduction. The bootstrap truncated regression results show that education, prior experience, modern technology, ICT, bank loan, training, gender and location significantly influence the technical efficiency of handloom enterprises.

Despite recent advances in the DEA method, this study used a traditional form of DEA. This study used only one output and a limited set of inputs. Better results could have been obtained by expanding the number of inputs and output. Finally, the data for this study has been obtained from a very narrow geographic area. The production practices of the handloom enterprises in other parts of the region and other states might vary considerably.

Technical efficiency measurement has management implications for businesses because it allows entrepreneurs to determine how much less input is required to produce the same output. A meticulous analysis can pinpoint the causes of inefficiency.

This paper aims to make two significant contributions to the extant literature. First, to the best of the authors’ knowledge, no published document has analyzed the technical efficiency of handloom micro-enterprises anywhere in the world. The authors fill this void by systematically analyzing the technical efficiency of the handloom industry in Assam.

Farmers producer organisations (FPOs) play the most crucial role in the agriculture supply chain system, aiming to redress the balance between farming and marketing activities of agricultural produce. The purpose of this study is to assess the performance of FPOs using data envelopment analysis (usually referred to as DEA) on 34 FPO units selected from the state of Rajasthan.

One of the most commonly used techniques to examine business performance is the application of DEA. The application of DEA requires the selection of inputs and outputs. This study takes three inputs and three outputs based on the insights drawn from the field survey. While the input variables consist of total assets, paid-up capital and the number of economic activities, the three output variables are turnover, net profit and number of members benefitted. Broadly, these variables encapsulate the operational performance of the business units.

This study’s findings reveal that the estimated relative efficiency score of the input-oriented CCR (Charnes, Cooper, and Rhodes) model ranges from 0.06 to 1. Interestingly, only one FPO has reported a relative efficiency (RE) score of one, whereas the remaining FPOs fall below the efficiency frontier. However, 15 FPOs report an RE score of one in the output-oriented CCR approach. Considering the estimates obtained in the input- and output-oriented BCC (Banker, Charnes and Cooper) models, this study found that about 20% of the FPOs report an efficiency score greater than 0.80. Moreover, three FPOs are on the frontier line. An examination of the scale efficiency score in the input-oriented model, 45% of the FPOs have an efficiency score greater than 0.80, whereas almost all FPOs achieve a scale efficiency score greater than 0.80 in the output-oriented model. Overall, the results imply that the FPOs should place greater emphasis on the efficient utilisation of the inputs to enhance the overall business performance and productivity.

The findings of this study provide vital insights into the specific inputs and outputs that determine the performance efficiency of FPOs and identify the potential areas for improving the existing inefficient FPOs.

This study contributes to the repository of the existing empirical studies in three distinct ways. First, the authors hardly found any previous studies that quantitatively assess the business performance of FPOs using the DEA technique. Second, the effort to identify the slacks associated with each input and output variable in input- and output-oriented models gives insights on improvable areas for inefficient FPOs. Third, the authors attempt to demystify the empirical obfuscations by highlighting the major challenges FPOs face in the state of Rajasthan.

Indian textiles have a history of fine craftsmanship, and because of that, it has its own global appeal and international demand. India has its own extensive base of raw material and manufacturing. This makes India the second-largest textile exporter in the world after China. India's share in the global textiles and apparels trade was approximately 5% in 2017. In 2017, India's textile industry contributes 7% of total industry output and employs about 45 million people directly. Its contribution to the country's GDP and export earnings in the same year was 2% and 15%, respectively. With this backdrop, the present chapter aims to investigate twin objectives. Initially, the changing growth pattern of India's readymade garment export is investigated. Along with this, the impacts of trade openness on India's readymade garment export are also scrutinized. The entire study is conducted based on the secondary data, compiled from the various issues of “Handbook of Statistics on Indian Economy,” published by “Reverse Bank of India.” The data are compiled for the period 1987–1988 to 2018–2019. The investigation of the first objective is facilitated by the “Poirer's Spline function approach.” On the contrary, for the exploration of the second objective, we have calculated the “trade openness index.” It is measured as the sum of export and import as a percentage of GDP. The study concludes that the Indian readymade garment industry shows a declining growth rate and the industry is benefited from trade openness. The study ends with suitable policy prescriptions.

This paper aims to estimate the technical efficiency (TE) and its determinants in the handloom micro-enterprises of Assam (India) using the double-bootstrap data envelopment analysis (DEA) technique.

The study uses a random sample of 340 handloom micro-entrepreneurs from the three districts of Assam in India. The double-bootstrap DEA was used to calculate the TE and its determinants.

The findings reveal that handloom enterprises are only 60% technically efficient, suggesting room for improvement. The bootstrap truncated regression results demonstrate that the handloom firms’ TE is influenced by both entrepreneur-specific and firm-specific factors.

The implication lies in the fact that the management of a firm may figure out how much it can reduce its input utilization to produce the existing amount of output so that it can move along the TE ladder. Moreover, it can crosscheck the factors to weed out inefficiency.

This paper has made two significant contributions to the extant literature. Firstly, it fills the gap by way of accounting the TE of handloom micro-enterprises, which has so far been neglected. Secondly, it used the bootstrap approach, which otherwise is very rare in the discourse on the Indian manufacturing industry, let alone in the micro, small and medium scale enterprises sector.

The Indian garment industry has witnessed a significant change since the inception of the New Textile Policy 2000 that suggests removing the industry from the list of small‐scale industries with a view to improving its competitiveness in the global market. As productivity is the driving factor in enhancing the competitiveness of any decision‐making entity (firm), a study of total factor productivity (TFP) and its sources can provide vital inputs to a firm for improving its competitiveness. Keeping this as a backdrop, the paper attempts to measure the TFP in the Indian garment‐manufacturing firms; identify sources of the TFP; and suggest measures for the firms to enhance their productivity.

The study is based on the firm‐level panel data collected from the Centre for Monitoring Indian Economy for the years 2002‐2007. One output variable, namely, gross sale and four input variables, namely, net fixed assets, wages & salaries, raw material, and energy & fuel, have been selected. The DEA‐based Malmquist Productivity Index (MPI) approach has been applied to measure the TFP.

The Indian garment industry has achieved a moderate average TFP growth rate of 1.7 per cent per annum during the study period. The small‐scale firms are found to be more productive than the medium‐ and large‐scale firms. The decomposition of TFP growth into technical efficiency change (catch‐up effect) and technological change (frontier shift) reveals that the productivity growth is contributed largely by technical efficiency change rather than by technological change.

Earlier studies on the Indian garment industry have applied the partial factor productivity approach, which has several limitations. This paper measures the TFP and identifies its sources through applying a non‐parametric DEA‐based MPI approach. Through this approach, the productivity growth is decomposed into technical efficiency change and technological change. Further, an attempt has also been made to study the variation in the productivity growth rates across location, scale‐size and type of garments.

The purpose of this paper is to propose a novel integrated approach using analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods for evaluation and prioritization of appropriate manufacturing flexibility type required in the face of multiple environmental uncertainties.

Using a case study of an Indian fashion apparel firm, the study demonstrates the application of the proposed integrated framework for evaluation and prioritization of manufacturing flexibility. The study uses AHP method to determine importance weight of environmental uncertainty criteria and subcriteria and then employs TOPSIS method to determine the final ranking of manufacturing flexibility types required to cope up with these uncertainties.

The findings of the case suggest that the proposed integrated approach is feasible and practically implementable for manufacturing flexibility assessment.

AHP has been extensively studied and used, but the major limitation of this proposed approach is the involvement of large number of pairwise comparisons leading to difficulty in maintaining consistency in pairwise comparisons.

The proposed approach can work as a benchmarking tool to practitioners in evaluating and prioritizing manufacturing flexibility alternatives and to suggest strategic allocation of resource by prioritizing different manufacturing flexibilities types.

Unlike conventional approaches, the study provides meaningful knowledge to decision makers by demonstrating a simple, flexible, and efficient method to evaluate and rank the appropriate manufacturing flexibility types.

This paper aims to examine technical efficiency and its determinants in Indian textile garments industry in post-agreement on textiles and clothing regime and evaluate the technical efficiency among micro, small and medium enterprises (MSMEs) firms.

This study uses unbalanced panel data for the period 2005–2010 to 2015–2016. The stochastic frontier function is used to estimate technical efficiency and its determinants.

The results show that the overall ecosystem of textile garments’ value chains could be improved to enhance the technical efficiency thereof. The result also reveals that small-scale firms have the highest technical efficiency scores, and medium-scale firms have the least technical efficiency score among all the categories of MSMEs.

The textile garments industry needs to define its innovation strategies, as these strategies lead to different results that can be achieved only through the management of resources dedicated to the generation and implementation of innovations.

This study has shown that to offset India’s cost disadvantage in the international markets, there is a need to develop an ecosystem of textile manufacturing and value chains, eliminate the inverted duty structure (where inputs are taxed at a higher rate than the final product) and switch over from shuttle looms toward shuttle-less looms. This would unleash the potential of textile and garments industry and make it globally competitive and technically efficient. Further, there will be an alignment with the ease of doing business with an appropriate mix of policy, technology, institution, infrastructure, information and services.

Using frontier production function takes stochastic context into account for the dynamic character of technical efficiency and its components. Most of the past studies have assessed technical efficiency at the aggregate level using three-digit National Industrial Classification (NIC) or four-digit NIC code. An analysis at higher levels of aggregation masks the variation in technical efficiency. This study used five-digit NIC data to measure the firm-specific technical efficiency of the textile industry. According to the authors’ knowledge, this study is the first of its kind in the Indian textile industry using stochastic frontier approach and panel data. Further, it also looks at the contribution of different determinants in technical efficiency to the firms.

The purpose of this paper is to measure technical efficiencies, slacks and input/output targets for 50 large Indian pharmaceutical firms.

The data are collected from Prowess of Centre for Monitoring of Indian Economy for the financial year 2010-2011. This study uses data envelopment analysis approach, taking raw material, salaries and wages, advertisement and marketing and capital usage cost as input variables and net sales revenue as output variable.

The paper finds that out of 50 firms, nine firms were overall technical efficient while 19 firms pure technical efficient and thus defined the efficient frontier. The BCC model identified that the inefficiency is either due to inefficient managerial performance or scale utilization. Further, firms are classified as high, low and middle robust firms on the basis of peer count. The study also analysed the slacks which were found to be significant in regard of some inputs, especially advertisement and marketing. The targets setting results have shown that all the inputs have significant scope for reduction.

The empirical results are useful in assessing the relative efficiency of the large Indian drug and pharmaceutical industry (ID&P) firms. The managers and owners can take corrective actions to reduce the cost of operations by optimizing advertising and marketing cost, capital usage cost and salary and wages so as to improve their efficiency.

Unlike the previous studies on the efficiency of the ID&P industry, the paper have shown the significance of improvement in managerial performance and scale utilization. In addition to this, excess inputs used in the production process and also possible target values of inputs and outputs are shown in the study. The robustness and stability of efficiency scores is also checked.

The study estimates total factor productivity growth (TFPG) and its components of the 4-digit manufacturing industries of chemical and chemical products in India from 1998–1999 to 2017–2018, pre-economic crises period (from 1998–1999 to 2007–2008) and post-economic crises period (from 2008–2009 to 2017–2018) using frontier approaches, that is, data envelope analysis DEA and stochastic frontier approach (SFA). The components of TFPG are technological progress (TP), technical efficiency change (TEC) and economic scale change (SC). It is found that the growth rates of total factor productivity (TFP) in most of the 4-digit industries of chemical and chemical products in India increased during the post-economic crises period (from 2008–2009 to 2017–2018) and the increase in TFPG of them during that period is mainly accounted for by the increase in TP of the same during that period. The TEC of almost all the industries remains the same, however, declined during the post-economic crises (from 2008–2009 to 2017–2018) and SC of them remains very low or even negative during the aforementioned study periods.