Operations Research J K Sharma
Quintin Downing
Neha Sharma is an Assistant Professor of Operations, Information and Decisions at The Wharton School, University of Pennsylvania. Her research focuses on design of online marketplaces using data, stochastic models, and game theory.
This course introduces basic concepts of operations management and application of the same in business practice today. We will examine the theoretical foundations of operations management and how these principles or models can be employed in both tactical and strategic decision making. Topics covered in detail are forecasting techniques, planning under deterministic and uncertain demand, operations planning and scheduling, queuing theory, service operations management, newsvendor models, risk pooling strategies in firms, capacity and revenue management, and supply chain coordination. We will conclude by discussing how supply chains evolve under technological change.
Dr. Jitendra Sharma is Professor of Operations Management and teaches an elective course in Quality Management Systems along with Six Sigma. His research involves using QFD to address quality and customer centric issues in product development. He also teaches Operations Management and Lean Systems. He has more than 40 international peer reviewed journal publications to his credit.
Professor Sharma has also authored numerous cases on the topics of operations and process management, including topics such as inventory management, MRP, AHP, forecasting, process planning and control, process and capacity management, six sigma, SQC, target costing, value analysis, sales and operations planning, etc. These cases are hosted with Harvard Business Publishing, Ivey Cases, Emerald and The Case Centre. Some of his best selling cases have been translated in Spanish and Chinese.
Professor Sharma does lean, six sigma, process and operations management training for executives of companies like Asian Paints. He holds a Ph.D. in Mechanical Engineering from the NIT, Raipur He also holds an M.Tech. from Nagpur University, MBA in Operations from IGNOU and a B. Engineering in Production from Nagpur University. Before diving into the world of academics he worked with different manufacturing industries as an engineer. He has a work experience of 30 years, of which 5 years were in industry and remaining in academics.
Nestled in the lap of nature, IMT Nagpur has accomplished much in over two decade. IMT Nagpur is home to some of the finest faculty with troves of knowledge and industry experience. The pedagogy adopted here is action-oriented, developed to meet the requirements of modern business world.
Sustainable finance is a rich field of research. Yet, existing reviews remain limited due to the piecemeal insights offered through a sub-set rather than the entire corpus of sustainable finance. To address this gap, this study aims to conduct a large-scale review that would provide a state-of-the-art overview of the performance and intellectual structure of sustainable finance. To do so, this study engages in a review of sustainable finance research using big data analytics through machine learning of scholarly research. In doing so, this study unpacks the most influential articles and top contributing journals, authors, institutions, and countries, as well as the methodological choices and research contexts for sustainable finance research. In addition, this study reveals insights into seven major themes of sustainable finance research, namely socially responsible investing, climate financing, green financing, impact investing, carbon financing, energy financing, and governance of sustainable financing and investing. To drive the field forward, this study proposes several suggestions for future sustainable finance research, which include developing and diffusing innovative sustainable financing instruments, magnifying and managing the profitability and returns of sustainable financing, making sustainable finance more sustainable, devising and unifying policies and frameworks for sustainable finance, tackling greenwashing of corporate sustainability reporting in sustainable finance, shining behavioral finance on sustainable finance, and leveraging the power of new-age technologies such as artificial intelligence, blockchain, internet of things, and machine learning for sustainable finance.
As a universal call to action to end poverty, protect the planet, and improve the lives and prospects of everyone around the world, the 17 Sustainable Development Goals (SDGs) are a part of the 2030 Agenda for Sustainable Development that have been adopted by all United Nations Member States in 2015 and expected to be achieved by 2030 (United Nations, 2020). The United Nations estimates an investment in the range of $5 trillion to $7 trillion to achieve the SDGs (Craig, 2021). With the unprecedent outbreak of a global pandemic in 2020, the United Nations Development Programme (UNDP) launched the SDG Finance Taxonomy to provide a roadmap for manage the financing and transaction costs of projects that are aligned to the SDGs (Wang et al., 2020). The taxonomy also calls for private capital, finance instruments, and support from financial institutions to contribute toward achieving the SDGs. SDG 17, which is about partnership for goals, is earmarked as a lynchpin for meeting the finance needs required for activities dedicated to achieving the SDGs (MacDonald et al., 2019; Rizzello & Kabli, 2020).
In this study, we aim to provide a state-of-the-art overview of sustainable finance research, taking into account all aspects and related articles in the field. That is to say, this study covers the entire spectrum of sustainable finance, and thus, it is not limited to any single aspect of the concept, as in the case of past reviews such as climate finance (Giglio et al., 2020) and green finance (Malhotra & Thakur, 2020). Moreover, this study uses an objective and a powerful review method, namely bibliometric analysis, which is highly suitable for reviewing fields with a large corpus of articles using quantitative techniques (Donthu et al., 2021a; Pattnaik et al., 2020; Paul et al., 2021). Specifically, bibliometric analysis exemplifies the use of big data analytics through machine learning of scholarly research in two major ways, namely
the search for big data (bibliometrics) is carried out on an artificial intelligence-powered scientific database (Scopus), wherein the scientific database uses specified keywords for supervised machine learning, as a subset of artificial intelligence, to extract large amounts of bibliometric data relating to articles relevant to sustainable finance, and
The insights from this review can be used in several useful ways. First, both new and seasoned researchers in sustainable finance can gain an overview and up-to-date understanding of its publication trend to gauge its interest in the scientific community over time (RQ1). Second, prospective authors can identify key literature (articles, journals) (RQ2), potential collaborators (authors, institutions, countries) (RQ3), as well as methodologies and contexts (RQ4) for sustainable finance research through this review. The same applies for policy makers and industry practitioners who wish to identify experts for consultancy, key literature to inform decisions, as well as methodological and contextual guides for applied research. Third, prospective authors can use the major themes and topics revealed through this review as a means to differentiate and position their contributions or novelty against existing streams of sustainable finance research (RQ5). Fourth and finally, prospective authors can gain inspiration from the curation of research directions herein to embark on new and potentially fruitful sustainable finance research (RQ6). These directions can also serve as a teaser into new knowledge that policy makers and industry practitioners can expect to see from the literature in the near future. These contributions, which are typically expected of well-done reviews, are in line with the authoritative guidelines for literature reviews of the field (e.g., Donthu et al., 2021a) and Paul et al., 2021).
The rest of this paper is organized as follows. The paper begins with an overview of sustainable finance. Next, the paper discloses the methodology and reports the findings of the review. Finally, the paper concludes with a future research agenda and a series of research questions for each major theme that can be used as a guide by prospective researchers to advance and fertilize the field of sustainable finance.
This study collects bibliometric data on sustainable finance research for its review. To do so, this study adopts and implements the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol, which consists of three major stages, namely assembling, arranging, and assessing of articles (Paul et al., 2021). The summary of the review procedure is illustrated in Fig. 2.
To assemble the corpus of articles on sustainable finance, this study identified its search keywords relating to sustainable finance from the preliminary review of relevant literature in the previous section and consulted 10 experts to ascertain the suitability of those keywords to represent sustainable finance. This led to a combination of 17 keywords that can be organized into the following search string:
Following that, we downloaded and read each article, and eliminated another 594 articles that mentioned the search keywords sparingly. That is to say, the aspects of sustainable finance did not take center stage in the investigation of those articles, resulting in their removal. This led to a final corpus of 936 articles for review, which was confirmed following a random cross-check using other databases such as Google Scholar and publishers website such as Elsevier, Emerald, Sage, Springer, and Taylor and Francis to avoid unintended exclusion of relevant studies in the field (Goyal et al., 2021; Harari et al., 2020; Lim et al., 2021).
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