In the current highly competitive and fast-changing business environment, in which the optimisation of all resources matters, creating an efficient supply chain is crucial. Earlier studies on supply chains have focussed on aligning product/services and information flows while neglecting the financial aspects. Due to this, in recent times, importance has been given to align financial flows with the other components of the supply chain. The interest in supply chain finance rose after the financial crisis when the bank loans declined considerably, as the need for better management and the optimisation of working capital became obvious. This paper reviews the articles on supply chain finance based on three themes—factors, outcomes, and solutions—while at the same time providing directions for future research on supply chain finance. This article is unique, as it investigates the factors affecting supply chains according to the existing literature. It also sheds light on the outcome of the supply chain without limiting the discussion only to the benefits. Further, it addresses the question: what are the solutions constituting supply chain finance?

While every new technology faces multiple challenges during market penetration, some technologies could be viewed by the adopters very differently than most others. The 3D Printing also called as Additive Manufacturing (AM), has been in the market for over a decade now, and is touted to be the next revolution in the industry. Technology has found wide applications in various industries, such as consumer electronics, automotive, medical devices, manufacturing and among many others. However, less is known with regards to the adoption and diffusion of 3D Printing technology, especially from the emerging economies. Using a survey method, this study aims to examine the adoption of 3D Printing technology in select industries in India. We found Relative Advantage, Ease of Use and Trialability to be significant. Whereas, Compatibility and Observability emerged as non-significant. We also explored the challenges with respect to 3D Printing Adoption. The knowledge of the major challenges along with the significant factors affecting adoption can help the manufacturers and suppliers of 3D Printing technology to focus on for increasing the rate of adoption.

Purpose: The present study aims to identify the critical factors of supply chain finance and the interrelationship between the factors using interpretive structural modeling. Methodology: Factors of supply chain finance were identified from the literature and experts from both industry and academia were consulted to assess the contextual relationships between the factors. Then, we applied interpretive structural modeling to examine the interrelationships between these factors and find out the critical factors. Findings: The model outcome indicates information sharing and workforce to be the most influential factors, followed by the automation of trade and financial attractiveness. Originality/value: Previous literature identified various factors that influence supply chain finance. However, studies showing interrelationships between these factors are lacking. This study is unique in the field as it applies total interpretive structural modeling for assessing the factors that affect supply chain finance. Our model will aid practitioners’ decision-making and the adoption of supply chain finance by providing a necessary framework.

PurposeThis paper attempts to measure the state-wise impact of Prime Minister's Jan Dhan Yojana (PMJDY) in 30 states and 6 union territories of India for the years 2016, 2017 and 2018; and tries to develop a state-wise plan for geographical expansion of outlets optimizing the overall impact of the scheme.Design/methodology/approachThe state-wise impact factor is calculated using demographic penetration of the scheme in rural areas, demographic penetration of the scheme in urban areas, percentage of accounts with Rupay cards and average balance in these accounts. The impact factor is postulated to be a linear function of literacy, per capita GDP, demographic and geographic penetration of banks and the number of poor people. The weights for the sub-parameters are derived through principal component analysis. A generalized linear model with heteroscedasticity and autocorrelation consistency method for estimation of the equation with robust standard errors is used.FindingsIt is found that the scheme has been more effective in the states with higher levels of illiteracy which is contrary to the findings of existing studies where illiteracy is identified as a barrier to financial inclusion. A state-wise plan for geographical expansion of outlets is proposed with a view to optimizing the overall impact of the scheme, along with suggestions for improvement.Research limitations/implicationsThe data for ATMs and bank mitras are available for some of the years, for some states and hence missing data were estimated using extrapolation or on an average basis. Furthermore, the panel data are available for three years making the period of panel small. These aspects might have affected the efficacy of our estimates.Originality/valueThe paper evaluates the newly launched ambitious program PMJDY by the Government of India (GoI), it will have far reaching impact on financial inclusion.

The aim of this study is to predict the profitability of Indian banks. Several factors both internal and external, affecting bank profitability were derived from extensive review of literature. We used Artificial Neural Network (ANN) with cross-validation technique to perform predictive analysis. ANN was chosen due to its flexibility and non-linear modelling capability. Several structures of ANN with a single and two hidden layers along with varying hidden neurons were implemented. Further, a comparison was made with the multiple linear regression (MLR) model. We found the models based on ANN to offer very accurate results in prediction and are marginally better as compared to the regression model. Higher accuracy of the model makes a significant difference due to the astronomically large size of the balance sheet of banks. This article is unique in the approach of handling the panel data for predictive analysis wherein the training of the model was done on a single bank’s data, thus, reducing the panel data to a time series data. This approach shows the ability to work with large panel data and make accurate predictions.