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The purpose of this paper is to evaluate the agility performance level of manufacturing supply chains using Taguchi loss functions (TLFs) and design of experiment (DoE).

The proposed methodology is used for capturing the various agility losses using appropriate TLFs and the aggregated agility loss is calculated at different situations using DoE. The aggregated agility loss is analysed for comparing manufacturing supply chain agility performance.

The proposed methodology was applied to three Indian auto component supply chains, i.e. X, Y and Z. In total, 27 experiments were carried out using DoE and obtained results show that agility performance level is the highest for X followed by Z, whereas agility performance level is the least for Y.

The proposed methodology is generic in nature and can be applied to a specific environment for comparing performance of different supply chains. The user has to identify the relevant agility enablers and capture the appropriate TLFs for the specific environment in which agility performance level has to be calculated and compared.

The proposed methodology provides an effective approach for evaluating agility performance. It can be used by the supply chain manger to assess the supply chain agility performance level of own company with its competitors. These comparisons will help the manufacturing company to find the areas where it should focus.

Many studies and researches related to implementation and evaluation of agile manufacturing are reported in the literature but very few studies are available for evaluating the supply chain agility performance. This study will definitely provide a guideline for measuring and comparing manufacturing supply chain agility performance in general and Indian automotive supply chain in specific.

The purpose of this paper is to empirically investigate the relationship between working capital management (WCM) efficiency and exogenous variables of the Indian manufacturing sector along with its sub-industries that are involved in export activities.

Panel regression (fixed effects) was used on a sample of 563 Indian manufacturing firms involved in export activities, covering a time period from 2008 to 2018.

Industry-wise results showed a significant relation of leverage, net fixed asset ratio, profitability, asset turnover ratio, total asset growth rate and productivity with cash conversion cycle (CCC).

Firstly, having taken a sample from a developing economy, the results of our study may be generalizable only among developing contexts. Secondly, the time period taken in this study (2008–2018) has witnessed several economic fluctuations such as recession and demonetization which might differ for the firms or countries in normal conditions.

An improved working capital model could advance the firms' performance by reducing the CCC of the firm, thereby creating efficiency in WCM. In addition, the results of this study could be helpful for many stakeholders such as working capital managers, debt holders, investors, financial consultants and others for monitoring the firms.

This study contributes to the existing literature in the relation between WCM efficiency and exogenous variables of the Indian manufacturing firms engaged in the export activities. Moreover, this study is one of the few research studies to investigate this relationship among Indian export firms in different industries, thus filling the gap in similar work done in other countries.

Strategic sourcing and supply risk management have become interesting topics of research in the recent years. Automotive industry experts are increasingly focussing on improving the supply efficiency and performance towards gaining sustainable competitive advantage. This study aims to classify, through an exhaustive review of past literature, the various enablers and barriers of strategic sourcing risk management (SSRM) and use them to identify the problems in the automobile sector.

For the purpose of this research, responses were collected through structured questionnaire from respondents belonging to senior management cadre in the industry. Factor analysis and force field analysis tools have been used for analysis.

Through independent exploratory factor analysis (EFA), four SSRM enablers, namely, supplier risk assessment, data sharing in supply network, partnership with supplier and supply flexibility, were identified. Similarly EFA revealed four SSRM barriers, namely, cost focus, ad hoc or poor planning, data security/privy breaches and hard visualization of SSRM benefits. Through a force field analysis, it was found out that the barriers had a higher impact on the SSRM initiatives than enablers.

The research suggests the ways how managers can reduce the impact of barriers and increase the enabling forces.

This paper enumerates the barriers and enablers together on the same platform to prioritize and evolve strategies to overpower the barriers and strengthen the enablers.

The paper develops a decision-making model for supplier selection combining quality function deployment (QFD), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). The efficacy of the model was demonstrated by applying it for supplier selection of lithium ion batteries.

The proposed methodology involved identifying customer requirements for lithium ion batteries and translating them to requisite technical characteristics using QFD. Further, separate sourcing, safety and sustainability-related supplier parameters were proposed taking into account the manufacturer's point of view. The relative weight of each parameter was then calculated using AHP, and finally, TOPSIS was used to select the best supplier.

The proposed methodology was applied to six suppliers of lithium ion batteries, and the obtained results were used to select the most and least preferred suppliers.

The obtained results cannot be generalized and are valid to the case environment. However, the proposed approach can be used for any environment related to supplier selection after capturing the corresponding parameters. The proposed approach does not restrict the number of parameters to be considered.

Many researches related to supplier evaluation are reported in literature, but few studies are available related to supplier performance evaluation for lithium ion batteries using QFD, AHP and TOPSIS. The study will provide a guideline for comparing and selecting supplier on the basis of performance in general and its application to lithium ion batteries in specific.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

The purpose of this paper is to study the supply chain resilience of Indian truckload transportation industry, in the event of potential disasters that affect the normalcy of their services. This study helped to identify factors affecting the two important dimensions of resilience, namely, resistive capacity and restorative capacity.

With the help of a comprehensive literature review, the different variables that capture both resistive and restorative capacities were identified. A framework for measurement of resilience was developed and an analytical model using Bayesian belief networks methodology was used to understand the linkages between the variables.

The results also suggest that at present resilience of the supply chains in Indian trucking firms is very low and there is a need for companies to invest in resources to build both in resistive and restorative capacities to enhance resilience.

The results of the model and the sensitivity analysis performed further helped to understand the major drivers that can enhance resilience of truckload firms.

The major contribution of this paper is to develop a quantitative model for resilience modeling in truckload transportation. This model can be updated when a new data arrives.

This study develops an integrated approach combining data envelopment analysis (DEA) and structural equation modeling (SEM) for estimating the working capital management (WCM) efficiency and evaluating the effects of diverse exogenous variables on the WCM efficiency and firms' performance.

DEA is applied for deriving WCM efficiency for 212 Indian manufacturing firms over a period from 2008 to 2019. Also, the effect of human capital (HC), structural capital (SC), cost of external financing (CEF), interest coverage (IC), leverage (LEV), net fixed asset ratio (NFA), asset turnover ratio (ATR) and productivity (PRD) on the WCM efficiency and firms' performance is examined using SEM.

The average mean efficiency scores ranging from 0.623 to 0.654 highlight the firms operating at around 60% of WCM efficiency only, which is a major concern for Indian manufacturing firms. Further, IC, LEV, NFA, ATR revealed direct effect on the WCM efficiency as well as indirect effect on firms' performance, whereas CEF had only a direct effect on WCM efficiency. HC, SC and PRD had no effects on WCM efficiency and firms' performance.

The findings offer vital insights in guiding policy decisions for Indian manufacturing firms.

This study is the first to identify the endogenous nature of the relationship of HC, SC, CEF, IC altogether with firms' performance, compounded by the WCM efficiency, by applying a comprehensive methodology of DEA and SEM and provides an efficiency performance model for better decision-making.

Selection of logistics service provider (LSP) (also known as Third-party logistics (3PL) is a critical decision, because logistics affects top and bottom line as well. Companies consider logistics as a cost driver and at the time of LSP selection decision, many important decision criteria’s are left out. 3PL selection is multi-criteria decision-making process. The purpose of this paper is to develop an integrated approach, combining quality function deployment (QFD), and Taguchi loss function (TLF) to select optimal 3PL.

Multiple criteria are derived from the company requirements using house of quality. The 3PL service attributes are developed using QFD and the relative importance of the attributes are assessed. TLFs are used to measure performance of each 3PL on each decision variable. Composite weighted loss scores are used to rank 3PLs.

QFD is a better tool which connects attributes used in a decision problem to decision maker’s requirements. In total, 15 criteria were used and TLF provides performance on these criteria.

The proposed model provides a methodology to make informed decision related to 3PL selection. The proposed model may be converted into decision support system.

Proposed approach in this paper is a novel approach that connects the 3PL selection problem to practice in terms of identifying criteria’s and provides a single numerical value in terms of Taghui loss.

The purpose of this paper is to present an approach to effective supply chain management by understanding the dynamics between various enablers of agile supply chain. Today’s business environment is characterized as a highly competitive, dynamic and volatile market. Agile supply chain is seen as the winning strategy to be adopted by manufacturers bracing themselves for dramatic performance enhancements to become national and international leaders.

Using interpretive structural modeling the research presents a hierarchy-based model and the mutual relationships among the enablers of agile supply chain.

The research shows that there exists a group of enablers having a high driving power and low dependence requiring maximum attention and of strategic importance, whereas another group consists of those variables which have high dependence and are the resultant actions.

This classification provides a useful tool to supply chain managers to differentiate between independent and dependent variables and their mutual relationships which would help them to focus on those key variables that are most important for building cost-effective and agile supply chains.

Presentation of enablers in a hierarchy and the classification into driver and dependent categories is unique effort in the area of agile supply chain management.

Globalization and outsourcing have rendered Indian automotive companies more vulnerable to supply chain (SC) risks. Consequently, companies adopt different supply chain risk management (SCRM) strategies to mitigate SC risks. The purpose of this paper is to explore SCRM strategies in Indian automobile industry and to classify automobile firms based on SCRM dimensions.

A survey instrument on SCRM dimensions was designed and data were collected from 79 automobile firms. Principle component analysis (PCA) was performed on the collected data to derive the factors underlying SCRM dimensions. Further, cluster analysis using extracted factors as a clustering variate was performed to identify strategic groups from the given set of firms.

PCA derived seven factors, namely: avoidance, supplier development, flexibility, risk pooling, redundancy, integration and control strategies. The surveyed firms were classified into two clusters as low and high SCRM level.

A limitation of this study is that data were collected from a single industry and in a single country.

Understanding of SCRM dimensions shall increase the use of these dimensions and firms can mitigate negative effects of SC risks. The detailed operationalization of SCRM strategies highlights the importance of three strategies: avoidance, integration and supplier development. Managers’ understanding of SCRM strategies will improve the firm's performance and business excellence.

This research empirically validates SCRM strategies and investigates how these create differences among firms.