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This case describes the innovations of a medical practitioner over six decades of practice. It provides an opportunity to the participants to analyse the motivation, processes, constraints and outcomes or innovating efforts of individuals as opposed to corporates. During his career Dr. Shah has experimented with novel methods of treatment, applications of known solutions and new product formulations. At the fag end of his career Dr. Shah is wondering whether the innovating efforts were worth their white.

The case presents an insight into the Guru Shishya Parampara. It is presents the outcome of a dialogue the case writers had with Rajvaidya Dhanshankarji G Pandit (Vaidyaji), a practitioner of Ayurveda system of medicine using NadiNidan–pulse diagnosis. The case presents Vaidyaji's family background, initial learning, and the admission process and describes the various stages of his learning and improvisation. It also explores the attributes of Guru and Shishya, acceptance of the Shishya by the Guru, processes of learning and evaluation, graduation, and revisit and renewal of learning.

The case presents the efforts of Dr. Dhanesh Jain, founder of Ratna Sagar Publications to innovate in children's publishing space. His innovations received mixed response. Commercial success was delayed. Adoption of innovations by the educational institutions did not happen as expected, the innovations, however, created opportunities for other business ventures. Dr. Jain is wondering whether the innovation efforts were worth their while.

The AirAsia India 2017 (AAI) case presents the situation faced by Tony Fernandes, the CEO of the AirAsia group of companies, in 2017, when he had to respond to the changes in aviation policy made by the Ministry of Civil Aviation (MCA). As per the changes, an airline operating in India could start its international operations without having five years of domestic flying experience provided it deployed 20 of its aircraft or 20% of the total capacity, whichever was higher, for domestic operations. The objective of this case is to help discuss issues relating to sustaining late entry and exploring new growth opportunities in the context of regulatory changes.

This case provides an opportunity to develop criteria for the selection of a leader who would lead a well established conglomerate. It provides details about the initiatives of the predecessors, the changing environment of businesses, internal concerns and the challenges before the new leader. A list of the contenders for the top position in Tata Sons Ltd is given to facilitate evaluation. The participants are required to review the strategy and functioning of Tata Sons Ltd and develop their criteria for selection.

This case provides an account of an informal round table organized by the dean of a Regional Management Institute to identify issues relating to the management of Regional Management Institutes. It raises questions relating to pedagogy, placement, roles of leaders, local innovations, programme portfolio and sustenance of these institutes. The participants are required to review the outcome of the first round table and take the discussion forward.

This case describes the financial and non-financial performance of Starbucks, a large organisation provided as on 2007. Howard Schultz, the promoter and chairman of the corporation is disturbed by the decline in the performance of Starbucks, especially the dilution of customer experience. He is required to analyse what happened and adopt a course of action to strengthen Starbucks' performance vis a vis competitive attacks. The participants are required to analyse the situation, generate options for Starbucks and make recommendations for the future, including whether Jim Donald, the current incubent, needs to retained as the CEO of Starbucks.

The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters.

The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases.

Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic.

Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.

Purpose: This chapter describes the design and implementation, at the computer hardware and software level, of the Greggg robot. Greggg is a scalable high performance, low cost hospitality robot constructed from off-the-shelf parts. Greggg has a robust architecture and acts as a tour guide on-campus, both indoors or outdoors. This research allows one to build a customized robot at a low cost, under U.S. $2,000, for accomplishing the desired hospitality tasks, and scale, and expand the capability of the robot as required.

Practical Implications: The practical implication of the research is the capability to build and program a robot for hospitality tasks. Greggg is a customizable robot capable of giving on-campus tours both indoors and outdoors. In its current architecture, Greggg can be trained to be a museum docent and give directions to visitors on-campus or at an airport and scaled up for other hospitality tasks using off-the-shelf components. Enhancing the robot by scaling it up and expanding it, in addition to testing it with a range of increasingly more difficult tasks using machine learning algorithms, is highly beneficial to advancing research on the use of robots in the hospitality sector. Greggg can also be used for Robot-as-a-service (Rass) applications.

Societal Implications: The economic implication of Greggg is the ease and low cost with which one, with minimal technology know-how, can construct an autonomous hospitality industry robot. This chapter details the hardware and software needed to build a low cost scalable and customizable autonomous robot for the hospitality industry without having to pay an exorbitant price.

Research/Limitations/Implications: This research allows one to build their own customized hospitality robot under U.S. $2,000. Given the cost of building the robot, it has limitations on the hospitality tasks it can perform. It can navigate on flat surfaces, has limited vision and speech processing capabilities and has a battery life not exceeding an hour. Furthermore, it does not have any robotic manipulators or tactile processing capabilities.