Search results

Pricing is widely acknowledged as a market entry challenge for servitising companies. The purpose of this research is to ascertain the attributes that contribute to willingness to pay (WTP) for industrial services and suggest incorporating those attributes to a pricing model.

Three attributes (Quality of Service, Nearness of Service Provider and Brand Equity of Service Provider) were analyzed at three respective levels to ascertain their importance on WTP. Conventional conjoint analysis (CCA), using an orthogonal design, was the method used. The 346 respondents were decision-makers and top management professionals from various industries.

Brand Equity emerged as the most significant attribute contributing to WTP, having more than 45% importance – followed by the Quality and Nearness.

The scope of the study is limited to the industries and its Allies. However, the relative importance of the attributes may vary depending on the type of service.

The importance of attributes and their WTP preference helps future researchers create a pricing model involving these attributes. This helps service providers price their services rationally, thus succeeding in servitization.

Product life is extended because the manufacturers themselves are servicing it and also help recycle the product with their expertise. Servitization is also helpful for the Indian economy, as it is turning into a manufacturing economy.

This research investigates three attributes that contribute to WTP, in accordance with their level of contribution. It also provides a direction to establish an adequate pricing model for industrial services.

In Indian thermal power plants, the main cause of boiler tube failure is the presence of molten sulphates and vanadates, which deteriorate the tube material at high temperatures. To combat the hot corrosion failure of metals, thermal spray technology is adopted. This study aims to investigate and study the effect of hot corrosion behaviour of carbon nanotube (CNT)-reinforced ZrO₂-Y₂O₃ composite coatings on T-91 boiler tube steel in a molten salt environment at 900 °C for 50 cycles.

A plasma spray technique was used for development of the coatings. The samples were exposed to hot corrosion in a silicon tube furnace at 900 °C for 50 cycles. After testing, the test coupons were analysed by X-ray diffraction, scanning electron microscopy/energy dispersive spectroscopy and cross-sectional analysis techniques to aid understanding the kinetics of the corrosion reaction.

CNT-based reinforced coatings showed lower weight gain along with the formation of protective oxide scales during the experimentation. Improvement in protection against hot corrosion was observed with increase in CNT content in the coating matrix.

It is pertinent to mention here that the high temperature behaviour of CNT-reinforced ZrO₂-Y₂O₃ composite on T-91 steel at 900°C temperature in molten salt environment has never been studied. Thus, the present research was conducted to provide useful results for the application of CNT-reinforced composite coatings at elevated temperature.

Textile and Apparel (T&A) sector significantly influences socio-economic and environmental dimensions of the sustainability. The purpose of this paper is proposed to establish the interrelationship among the critical barriers to the sustainable development of T&A supply chains by using a multi-criteria decision-making approach and to obtain a ranking of the barriers.

In the present investigation through literature review and from expert opinions, 14 significant challenges to the sustainable growth of T&A sector have identified. For establishing the interrelationship and for developing a structural model of the identified challenges, interpretive structural modelling (ISM) methodology is employed.

The results of the investigation revealed that lack of effective governmental policies (B8), poor infrastructure (B4), lack of effective level of integration (B6), low foreign investment (B13) and demonetization (B12) are the top most significant challenges.

The model development based on the expert inputs from the industry and academia, these inputs could be biased influencing the accuracy of the model. Also, inclusion more factors for the analysis will improve the reliability of the model.

This research is intended to guide the policy and decision makers for improving overall the growth of the T&A supply chain.

The purpose of this study is to examine the impacts of board gender composition, board independence and the existence of a board sustainability committee on the corporate social and environmental performance of Australian firms.

The dataset comprises 2,188 Australian Securities Exchange listed firm-year observations (407 individual firms) from 2004 to 2015. The ASSET4 environmental, social and governance database is used to measure corporate social and environmental performance and their sub-dimensions.

Our results show that firms with higher board gender composition, greater board independence and sustainability committees tend to have better social and environmental performance. This paper also provides empirical evidence of the positive association of these variables on the sub-dimensions of social and environmental performance. The results are robust after controlling for self-selection and various forms of endogeneity.

This is the first study that examines the relationship between sustainability committees and corporate social and environmental performance in the context of Australia. This study also overcomes the relatively small sample size and shorter study period issues of similar studies in Australia that provide inconclusive evidence on the relationship between each of board gender composition, board independence and corporate social and environmental performance.

World over organizations are focusing on sustainable goals, where along with economic success their role in protecting the planet and people are becoming important. Whilst transforming the supply chain into a sustainable one, there would be some barriers which might hinder this process. This paper aims to study these barriers in the context of the electronics industry so that organizations can better implement sustainable supply chain programs.

In this research, barriers affecting sustainability implementation in the electronics supply chain are shortlisted from literature review and experts’ opinion. Using the combined methodology of Grey DEMATEL, the causal factors, the effect factors and degree of prominence of barriers is found out. The overall relationship among barriers is established by a diagraph. Sensitivity analysis is performed to check the robustness of the results.

It is found that lack of regulation and guidance from authorities is the primary causal barrier affecting operations of sustainable supply chain management. There are five barriers which fall in the influenced group and among them, complexity in measuring and monitoring sustainability practices has the largest net effect value on the implementation of a sustainable supply chain. The barrier having the highest correlation with other barriers is the high cost for disposal of hazardous wastes. The implications of these findings on managers and academicians is explored in the study.

In this research, the number of barriers shortlisted is limited to 11 in the context of the electronics supply chain. More factors could be added in future research based on the industry being studied.

The research analyses 11 barriers under categories of policy, technology, financial and human resources in the Indian electronics industry by evaluating the cause and effect group of barriers. These results can guide policymakers of the electronic sector and industry for mitigating barriers during the implementation of sustainable programs.

Presents the analysis of a two‐unit cold standby system in which the standby unit takes a random amount of time for operation whenever the operative unit fails. Each unit is first repaired by the assistant repairman and is then taken up for post‐repair if necessary. The failure and repair times of each unit are assumed to be correlated and their joint density is taken as bivariate exponential. Uses regenerative point technique to obtain various reliability characteristics of interest. Studies the behaviour of steady‐state availability through graphs. Verifies earlier results.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

This paper aims to develop an approach to design a warehouse that uses class-based storage policy in a way that minimizes both space cost and material handling cost.

The authors argue for and develop an optimization model for joint determination of lane depth, lateral width and product partitions for minimizing the sum of handling and space costs. In doing so, the assumption of perfect sharing is also relaxed. Using computational experiments, the authors characterize the operating conditions based on pick density and cost ratio. The authors further outline an approach to decide the conditions under which it is advantageous to implement multiple classes.

More classes are preferred when both the pick density and cost ratio are higher and vice versa. Factors such as demand skewness, lane depth and stacking height affect the space-sharing dynamics.

The paper gives the practical insights on when the conditions under which it is advisable to partition a warehouse into a certain number of classes instead of maintaining and when to maintain as a single-class block. It also gives a method to estimate the space-sharing factor, given a combination of operating parameters.

Very few studies have seen class-based storage policy in the context of block stacked warehouse layout. Further, block stacking designs have mostly been approached with the objective of minimizing just the space cost. This study contributes to the literature by developing an integrated model, which has the practical utility.

The purpose of this paper is to establish a freestream computational fluid dynamics (CFD) model of a three-dimensional non-spinning semi-cylindrical missile model with a single wrap around fin in Mach 2.70-3.00M range and 0° angle of attack, and ultimately establishing itself for future research study.

In this study, the behaviour of flow around the fin was investigated using a κ-ϵ turbulence model of second-order of discretization. This was done using a highly structured mesh. Additionally, an inviscid CFD simulation involving the same boundary conditions have also been carried out for comparison.

The obtained values of aerodynamic coefficients and pressure contours visualizations are compared against their experimental and computational counterparts. A typical missile aerodynamic characteristic trend can be seen in the current CFD.

The predicted values of the aerodynamic coefficients of this single fin model have also been compared to those of the full missile body comprising of four fins from the previous research studies, and a similar aerodynamic trend can be seen.

This study explores the possibility of the use of turbulence modelling in a single fin model of a missile and provides a basic computational model for further understanding the flow behaviour near the fin.

The purpose of this paper is to test the dynamic linkages among the stock markets of four South Asian countries (India, Pakistan, Bangladesh and Sri Lanka) in the backdrop of trade interdependency.

Listed indices are used to serve the proxy of stock markets of four countries for the period: January 2000–December 2018. The study uses the autoregressive distributed lag model and Granger causality techniques in multivariate frameworks while focusing on intraregional trade as an exogenous factor for testing the long- and short-run causality in the given data set, hence raising the quality of statistical inference.

The results highlight that India and Pakistan are net exporters to the South Asian region, while Bangladesh and Sri Lanka are net importers from the region. While testing the stock markets linkages, the expanded intraregional trade volumes (exports plus imports) have occurred with the significant cointegration of stock markets of India and Pakistan with the other stock markets in the long run. In the short run, the stock markets of India, Pakistan and Sri Lanka report bidirectional causality without having significant spillovers of intraregional trade on the stock prices.

The study relies on the multivariate techniques with stock prices and regional trade share as the exogenous variables. Further the regulatory, political and economic conditions of sample countries are fundamentally different which in turn affect their degree of trade interdependency and integration between the stock markets.

Nonsignificant cointegration of the stock markets of Sri Lanka and Bangladesh highlights the possibility of portfolio diversification in the long run, while the significant bidirectional causalities between the stock markets highlight the lesser degree of portfolio diversifications in the short run.

Pioneer efforts are made to examine the dynamic linkages between the South Asian stock markets while focusing on regional trade interdependency. The results provide new insight in the dynamics of stock returns of South Asian stock markets in the backdrop of intraregional trade.