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Milk is a perishable food product, one of the primary sources of nutrition. Reports worldwide indicate numerous food frauds and foodborne diseases associated with adulterated milk products. These safety concerns highlight the importance of a visible milk supply chain, which can be achieved by cutting-edge technologies. However, these technologies come with high costs. So, this study aims to propose a framework that integrates blockchain, Internet of Things (IoT) and cloud to enhance visibility with reduced cost in an Australian milk supply chain (AMSC).

A design science research methodology is used, where a proof of concept is also developed at the retailer end to show how blockchain, IoT and cloud can improve visibility with reduced cost in an AMSC.

According to cost and visibility analysis, blockchain implementation in AMSC would generate a high return on investment (ROI). For the given case, ROI becomes positive for all stakeholders after 750 cycles. Integrating IoT, cloud and blockchain is more profitable than just using blockchain. Additionally, technology implementation may not benefit all stakeholders equally. For example, the retailer needs 10 cycles to benefit, but the transporter needs 50 in the given case.

The findings of this study assist milk industries in decision-making regarding technology implementation in their supply chain and motivate them to implement these technologies, resulting in improved trust and coordination among entities and consumers.

A cost and visibility analysis are performed to evaluate the impact of technology implementation on cost and visibility in an AMSC. A SOAR (Strength Opportunities Aspiration Results) analysis is also performed for the strategic planning framework.

This chapter has been seminal work of Dr K.S.S. Iyer, which has taken time to develop, for over the last 56 years to be presented here. The method in advance predictive analytics has developed, from his several other applications, in predictive modeling by using the stochastic point process technique. In the chapter on advance predictive analytics, Dr Iyer is collecting his approaches and generalizing it in this chapter. In this chapter, two of the techniques of stochastic point process known as Product Density and Random point process used in modelling problems in High energy particles and cancer, are redefined to suit problems currently in demand in IoT and customer equity in marketing (Iyer, Patil, & Chetlapalli, 2014b). This formulation arises from these techniques being used in different fields like energy requirement in Internet of Things (IoT) devices, growth of cancer cells, cosmic rays’ study, to customer equity and many more approaches.

This case highlights the challenges faced by a young and inexperienced Area Sales Manager (ASM), early in her career. This is an often-encountered situation by fresh graduates. Through the analysis of the case, the students will be able to: ■ understand challenges a young manager faces in taking over a new, unfamiliar and underperforming territory; ■ analyze and learn to manage data and identify performance gaps in the territory, by selecting the right metrics; ■ learn the factors for evaluating the performance of current intermediaries (distributors); and ■ appreciate the importance of managing all stakeholders – internal team and building a strong relationship with the intermediaries – both distributors and retailers.

Kavita Kaur, the new Area Sales Manager at Broadway India Pvt. Ltd. (BIPL), had just taken over the Chhattisgarh sales territory in January 2020. Fresh out of a B-school, it was her first job, and her allotted territory was severely lagging growth at only 1%, compared to an all-India country growth at 13% in 2019, over the previous year. The territory was operated by established intermediaries (distributors) with long associations with BIPL. Based on her data analyses of distributors’ performance, Kaur started her retail visit with the highest selling distributor’s area (Sharda Agencies) to confirm her understanding of what the data had shown her. Following her retail visit and a meeting with Sharda Agencies, the situation turned for the worse. An email bordering to a threat from him took her aback. Kaur now had to make a choice to ensure growth in her new territory. Her options were between placating the current distributor or appointing a new one – should she retain or replace? Both had their own risks.

This case is intended for use at the postgraduate level in courses such as sales management, channel management and strategic marketing courses, as well as in executive management programs. The case is relevant from the context of channel management in India, where channel intermediaries can be very demanding. The case will give students a practical hands-on decision-making situation, where there are complexities of quantitative and qualitative nature. It will also help young graduates prepare for real life situations where the assigned territory is struggling in performance and a lot is expected from the new recruits.

Teaching notes are available for educators only.

CSS 8: Marketing.

Reverse logistics has attracted many industries due to product recalls, enormous waste generation, competitive reasons, vast opportunity in the waste management market, and to get the maximum value out of waste recovery. Selection of the right implementation strategy is vital for reverse logistics to function efficiently. Therefore, this research aims to evaluate the criteria for selecting reverse logistics strategy and help to choose the preferred strategy for its implementation.

Three reverse logistics implementation strategies, namely, in-house, joint venture and outsourcing, are proposed. A novel hybrid fuzzy analytical hierarchy process (F-AHP) and fuzzy measurement of alternatives and ranking according to COmpromise Solution (F-MARCOS) based framework is developed to fulfil the research objective. A survey is performed on Indian manufacturing industry to demonstrate the applicability of the proposed framework.

The result shows that government policy and regulations, reverse logistics risks and reduced emission have prime importance for a manufacturing industry which needs to implement reverse logistics into its supply chain. Outsourcing is the preferred reverse logistics strategy followed by joint venture and in-house that a manufacturing firm in India can implement.

The research results are based on the responses of the survey received. This research considers various industry sectors to test the applicability of the framework. However, for actual implementation, this survey must first be limited to a particular industry as the results will apply to that industrial sector only.

This developed framework simplifies the procedure of selecting the strategy when the industry needs to implement reverse logistics. For industries working with a smaller set of criteria, this framework is a powerful and dynamic approach for reducing and choosing the most pertinent one that helps accomplish their objectives of reverse logistics implementation strategy selection.

Based on the literature and current applicability of reverse logistics, this research proposes three models to implement reverse logistics in Indian industries. A novel hybrid F-AHP and F-MARCOS based framework is developed to handle the selection of suitable reverse logistics strategy.

In recent years, there has been a growing emphasis on competency-based systems as a means of assessing employee performance. These systems assess the degree to which the competencies of employees align with the requirements of their employment positions. This study aims to identify, prioritize, and make contextual interrelationships of the competency dimensions that are relevant for evaluating employees in the context of Indian manufacturing MSMEs.

These dimensions were identified through an extensive literature review and interviews with industry experts. Further, a mixed-methods approach, including the “Bayesian Best-Worst Method” (BBWM), is applied for prioritizing important dimensions, whereas for making mutual relationships, the “Interpretive Structural Modeling” (ISM) method is utilized. “Matrice d'impacts croisés multiplication appliquée á un classment” (MICMAC) is also known as “cross-impact matrix multiplication applied to classification” is used for clustering competency dimensions based on their “driving power” and “dependence power”.

The findings reveal that among the primary dimensions, “creative performance,” and among the sub-dimensions, “innovative behaviors,” are the most critical competency dimensions for an employee assessment. The study also found that “smart working”, “factual and theoretical knowledge”, “empathy at work”, “understanding of specific knowledge”, and “engagement ideas and activities” are the main dimensions driving employees' competency.

This paper provides contribution to the competence literature by identifying and evaluating competency dimensions for assessing employees' performance within manufacturing MSMEs in an emerging economy such as India. The study also assesses the rank and contextual relationship between the identified dimensions as no past research focused on the same by using BBWM and ISM in the Indian manufacturing MSMEs context.

Green entrepreneurship (GE) is a novel concept in business and enhances environmentally friendly production and operation activities for “sustainable development” (SD). The aim of this study is to determine the drivers that contribute to the growth and success of “micro, small, and medium enterprises” (MSMEs) in the manufacturing sector in India. The study also examines the mutual and cause-and-effect relationships among these identified drivers.

The study used integrated research methodology and identified nine key drivers of GE (GEDs) through extensive literature reviews, theoretical perspectives (i.e. “resource-based view” (RBV), “natural resource-based view” (NRBV) and “critical success factor theory” (CSFT)), and expert opinions. Further, “total interpretive structural modeling” (TISM) and “matrice d'impacts croisés multiplication appliquée á un classment” (MICMAC) analysis are used here to develop a hierarchical model and cluster the drivers, and fuzzy “decision-making trial and evaluation laboratory” (fuzzy-DEMATEL) is used to develop causal relationships among the drivers. Further, a sensitivity analysis is conducted to ensure the robustness of the results.

Results indicated that green manufacturing and operation capability development, green business process management and attitudes toward developing sustainable business models significantly impacted GE and SD. The findings of this study help managers, policymakers, and practitioners gain an in-depth understanding of the drivers of GE.

The study considers a limited number of drivers and is specific to Indian manufacturing MSMEs only. Further, a limited number of experts from different enterprises are considered for data analysis. This study is also based on interrelationships and their relative importance based on multicriteria decision-making techniques. This study aids government decision-making, policy formulation and strategic decision-making for manufacturing businesses in achieving SD goals. In addition, this research also encourages green entrepreneurs to start eco-driven companies and facilitate the use of environmentally friendly goods to offset environmental challenges and accomplish sustainable development goals.

This study proposes an integrated methodology that will benefit managers, practitioners and others in developing strategies and innovations to improve and develop green practices. This study further helps with responsive, sustainable business development in various manufacturing MSMEs.

The purpose of this paper is to explore the key reasons of lack of promotion of Buddhist tourist sites in India and compare the awareness of foreign and Indian tourists towards India as a destination for Buddhist sites vis‐à‐vis other countries such as Japan, Thailand and China.

The paper uses an exploratory research design. Data were collected using structured questionnaires administered to Indian and foreign tourists, interviews of officials in the tourism department and through internet sources.

Findings show a low level of awareness among both Indian and foreign tourists about India as a destination for Buddhist sites as compared to countries such as Japan, Thailand and China. Further, the findings to show a low level of engagement in promoting India as a destination for Buddhist tourism on part of tourist officials.

The paper develops insights into better promotion of Buddhist tourist sites in India.

The paper serves as a stepping stone towards future research on overlooked religious sites and their promotion.

The purpose of this paper is to explore the factors that could contribute towards the enhancement of the tourist experience at the Golden Temple site in Amritsar. The paper also seeks to explore if there is any difference in the above mentioned factors based on the religion and the nationality of the respondents.

The paper uses an exploratory research design. Data were collected using structured questionnaires with open‐ended and close‐ended questions administered to the various tourist groups visiting the Golden Temple.

Significant differences between the factors contributing to the enhancement of the experience of the various tourist groups are expected.

The research findings will help the tourism industry to identify the factors that are important for various groups of tourists. This will help to improve the customer experience and thereby attract more tourists.

The study will add to better understanding of the factors contributing to enhancement of the tourist experience at religious sites.

The objective of this study is to systematically identify, categorize and assess the driving factors and interdependencies associated with various types of healthcare waste. The study specifically focuses on waste that has been managed or is recommended for treatment through the application of Lean Six Sigma (LSS) methodologies.

To accomplish the study’s objectives, interpretive structural modeling (ISM) was utilized. This analytical tool aided in quantifying the driving power and dependencies of each form of healthcare waste, referred to as “enablers,” as well as their related variables. As a result, these enablers were classified into four distinct categories: autonomous, dependent, linkage and drivers or independents.

In the healthcare sector, the “high cost” (HC) emerges as an autonomous variable, operating with substantial independence. Conversely, variables such as skill wastage, poor service quality and low patient satisfaction are identified as dependent variables. These are distinguished by their low driving power and high dependency. On the flip side, variables related to transportation, production, processing and defect waste manifest strong driving forces and minimal dependencies, categorizing them as independent factors. Notably, inventory waste (IW) is highlighted as a salient issue within the healthcare domain, given its propensity to engender additional forms of waste.

Employing the ISM model, along with comprehensive case study analyses, provides a detailed framework for examining the complex hierarchies of waste existing within the healthcare sector. This methodological approach equips healthcare leaders with the tools to accurately pinpoint and eliminate unnecessary expenditures, thereby optimizing operational efficiency and enhancing patient satisfaction. Of particular significance, the study calls attention to the key role of IW, which often acts as a trigger for other forms of waste in the sector, thus identifying a crucial area requiring focused intervention and improvement.

This research reveals new insights into how waste variables are structured in healthcare, offering a useful guide for managers looking to make their waste-reduction strategies more efficient. These insights are highly relevant not just for healthcare providers but also for the administrators and researchers who are helping to shape the industry. Using the classification and ranking model developed in this study, healthcare organizations can more easily spot and address common types of waste. In addition, the model serves as a useful tool for practitioners, helping them gain a deeper, more detailed understanding of how different factors are connected in efforts to reduce waste.

This study aims to scrutinize the role of environmental, social and governance (ESG) performance and its indicators in achieving working capital management (WCM) efficiency.

Using a sample comprising 520 firm-year observations from Indian listed firms for the period from 2017 to 2021, the current study estimates WCM efficiency using the Banker, Charnes and Cooper (BCC) input-oriented model of data envelopment analysis (DEA). In addition, this study performs baseline, robustness and heterogeneity tests to examine the effect of ESG performance and its components on WCM efficiency.

Our findings show that firms with high ESG performance better manage short-term liquidity. Also, environmental performance (EP), social performance (SP) and governance performance (GP) highlight a similar positive association with WCM efficiency. As per the heterogeneity test results, both high- and low-sustainable firms showcase the necessity of ESG performance to achieve efficiency in managing their working capital.

The findings emphasize the need for managers and policymakers to integrate sustainable practices with financial strategies, enhancing both short-term stability and long-term sustainability goals and thereby guiding effective policy and governance enhancements.

We attempt to adjudicate the role of sustainability on WCM efficiency from an emerging country perspective, which has not yet been explored. Our study also makes a methodological contribution by pioneering the DEA in the context of ESG and working capital.