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When comparing and evaluating performance, decision-makers are concerned with providing a range of effective, efficient, and fair measures that can yield representative relative rankings for the units being evaluated. In this chapter, we apply three multicriteria benchmarking modeling techniques – weighted linear combination, data envelopment analysis (DEA), and ordered weighted average (OWA) – to an example dataset to provide a quantitative assessment of performance. Evaluation of the results demonstrates that each of these techniques has relative strengths and shortcomings. To take advantage of the relative strengths, and avoid some of the shortcomings that we observed, we develop and assess a promising new methodological approach, the order rated effectiveness (ORE) model. ORE uses the OWA unit ratings within a DEA optimization framework to provide an overall relative performance assessment.

The newsvendor problem is fundamental to many operations management models. The problem focuses on the trade-off between the gains from satisfying demand and losses from unsold products. The newsvendor model and its extensions have been applied to various areas, such as production plan and supply chain management. This chapter examines the study about newsvendor problem. In this research, there is a review of the contributions for the multiproduct newsvendor problem. It focuses on the current literature concerning the mathematical models and the solution methods for the multiitem newsvendor problems with single or multiple constraints, as well as with the risks. The objective of this research is to go over the newsvendor problem and bring into comparison different newsvendor models applied to the flower industry. A few case studies are described addressing topics related to the newsvendor problem such as discounting and replenishment policies, inventory inaccuracies, or demand estimation. Three newsvendor models are put into practice in the field of flower selling. A full database of the flowers sold by an anonymous retailer is available for the study. Computational experiments for practical example have been conducted with use of the CPLEX solver with AMPL programming language. Models are solved, and an analysis of different circumstances and cases is accomplished.

A study on convective aspects was carried out on a Couette flow in an irregular channel by applying a constant uniform magnetic field parallel to the channel flow.

The dynamic study of such a flow resulted in highly nonlinear coupled partial differential equations. To solve these partial differential equations analytically, regular perturbation method was invoked for velocity, temperature and concentration with a combined parameter of Soret and Forchheimer. The numerical computational results have been extracted for various nondimensional parameters with regard to fluid and particle flow as well as for temperature and solute concentration.

The current article presents a novel approach to assess the effects of drag force as well as the diffusion-based interactions between the velocity, temperature and concentrations with the aid of Soret and Dufour on two-dimensional MHD mixed with a dusty viscoelastic fluid.

The results found are in good agreement with the earlier studies in the absence of nonlinear effect of Forchheimer model.

The purpose of this paper is to identify factors related to rural healthcare services and establish a hierarchical model for the effective rural healthcare management in India.

A questionnaire survey identified and correlated numerous factors related to the Uttarakhand rural healthcare systems. Experts opinion were translated into a reachability matrix and an interpretive structural model. A fuzzy matriced impacts croises-multiplication applique and classment (FMICMAC) analysis arranged the factors as hierarchical stages using their driving power.

The interpretive structural and FMICMAC hierarchical models suggest four key driving factors: diseases, climatic conditions, population growth and political pressure.

Despite numerous issues, rural healthcare services can be improved by considering key driving factors that could be used as a prediction tool for policy makers.

Results demonstrate that population control, coordinating services with local bodies and rural health center annual maintenance can be game changers toward better healthcare services.

The purpose of this paper is to explore the way in which system dynamics (SD) can enhance some key success factors of the balanced scorecard (BSC) model and support decision-makers, specifically in analyzing and evaluating the results of hypothetical scenarios. Moreover, the paper aims to emphasize the role played by statistics not only in validating the SD-based BSC, but also in increasing managers’ confidence in the model reliability.

The paper presents a case study, developed according to an action research perspective, in which a three-step approach to the BSC implementation was followed. Specifically, the first step requires the development and implementation of a “traditional” BSC, which is refined and transformed into a simulation SD model in the second step. Last, the SD-based BSC is combined with statistics to develop policy making and scenario analysis.

The integration of BSC and SD modeling enables the development of a comprehensive approach to strategy formulation and implementation and, more importantly, provides a more reliable basis upon which to build and test sound cause-and-effect relationships, within a specific BSC. This paper exemplifies how an SD-based BSC can be used – and perceived reliable – to evaluate different scenarios and mutually exclusive policy effects in a multidimensional approach. In particular, this study illustrates how to forecast and depict trends for financial and non-financial indicators over the simulation period, with reference to three different scenarios.

This paper contributes to the ongoing debate on the BSC by exploring whether a combination of SD and statistics may enhance the BSC system’s advantages and facilitate its implementation process and use for decision-making and scenario analysis.

The learning outcomes of this paper is as follows: to review the basic differences between the two evolving bonds, i.e. green vs masala bonds in the Indian capital market; to comprehend the factors that need to be considered in deciding the type of bond to be issued; to assess complexities, such as process, timing, risk and location in relation to the issue of the green bonds; and to understanding the rudiments of bond economics, such as pricing, all-in-cost and yield-to-maturity of bonds and make a comparison of all-in-cost of the Reg-S bond and green bond to Indian Railway Finance Corporation (IRFC).

In September 2017, IRFC, a public sector undertaking registered as a Non-Banking Finance Company with Reserve Bank of India under the administrative control of the Ministry of Railways, was planning to raise US$500m 10-year green bonds from investors in Asia, Europe and the Middle East. The green bond proceeds were proposed to be used for low carbon transport and in this way, contribute significantly to the green initiatives of the Indian Railways. Many companies in India had issued regular bonds without labeling them as green but had used the proceeds of the bond for climate-aligned assets. Therefore, a bigger challenge before the IRFC management was the economics of green bond for getting a nod from the Board of Governors to go ahead. Some preliminary estimates on cost of green bonds were received from few bankers but to see that the terms of green bonds are met eventually, the Director (Finance) developed his own estimate of the cost of the new bonds. The Managing Director and Director (Finance) of IRFC were trying to figure out the economic advantage of green bonds besides its social benefits.

MBA Programme Executive Training.

Teaching notes are available for educators only.

CSS 1: Accounting and Finance.

This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.

The authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.

Fifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.

The authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.

Earlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.

A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.

Over the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.

This review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.

This paper provides a systematic insight into research trends in ML in both logistics and the supply chain.