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The purpose of the paper is to confirm the adherence of double bottom line objectives by the microfinance institutions (MFIs) of India and, further, to identify the causal factors that work out their double bottom line commitments.

The study uses an empirical data set for the period of 10 years, i.e. from 2005–2006 to 2014–2015, gathered from www.mixmarket.org. It follows an exploratory approach with overall and segmented performance analysis. Further, a panel data regression model is applied to identify the causal factors of double bottom line.

The study finds that MFIs are adhering to the notion of double bottom line. The segregated analysis does not give any solid indications of trade-off. The mature and small MFIs are found to be better in attaining social objective but the new and large are better in sustainability. The non-governmental organization (NGO) category is more committed to the double bottom line than the non-NGO. The causal analysis could not show any relationship between financial performance and outreach. Though age and outreach size show relationships with small loan sizes, they do not influence sustainability. The operating and financial expenses along with portfolio quality are found to be the main causal variables of sustainability.

There are indications for the policy makers to frame regulations and prepare a roadmap for the mature and the large MFIs. This would help them adhere to the double bottom line, which would further streamline the operations of the MFIs in the long run. Containing operating expenses and controlling the asset quality still remain to be the challenges which need to be addressed with proper policy guidelines.

The analysis of the study focuses on industry classifications, which make it more intriguing in nature given the fact of the varied features like age, legal status and outreach. India being the largest microfinance market in the world has limited studies. Most of the studies in double bottom line are based on a cross-country analysis, which generalizes the individual characteristics. The study fills this gap and adds to the understanding of the double bottom line commitments in the Indian context.

Logistics is the part of the supply chain (SC) that plans, executes and handles forward and reverse movement and storage of products, services and related information, in order to respond to customers' needs effectively and efficiently. The main concern for logistics is to ensure that the correct product is placed at the right time. This paper introduces a linear model of shipping focused on decision-making, which includes configuration of shipping network, choosing of transport means and transfer of individual customer shipments through a particular transport system.

In this study, authors try to address the problem of supply chain network (SCN) where the primary goal is to determine the appropriate order allocation of products from different sources to different destinations. They also seek to minimize total transportation cost and inventory cost by simultaneously determining optimal locations, flows and shipment composition. The formulated problem of getting optimal allocation turns out to be a problem of multi-objective programming, and it is solved by using the max-addition fuzzy goal programming approach, for obtaining optimal order allocation of products. Furthermore, the problem demand and supply parameters have been considered random in nature, and the maximum likelihood estimation approach has been used to assess the unknown probabilistic distribution parameters with a specified probability level (SPL).

A case study has also been applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. Results of this study are very relevant for the manufacturing sector in particular, for those facing logistics issues in SCN. It enables researchers and managers to cope with various types of uncertainty and logistics risks associated with SCN.

The principal contribution of the proposed model is the improved modelling of transportation and inventory, which are affected by different characteristics of SCN. To demonstrate computational information of the suggested methods and proposed model, a case illustration of SCN is provided. Also, environmentalism is increasingly becoming a significant global concern. Hence, the concept proposed could be extended to include environmental aspects as an objective function or constraint.

Efficient integration of logistical cost components, such as transportation costs, inventory costs, with mathematical programming models is an important open issue in logistics optimization. This study expands conventional facility location models to incorporate a range of logistic system elements such as transportation cost and different types of inventory cost, in a multi-product, multi-site network. The research is original and is focused on case studies of real life.