Environmental marketing or green marketing or ecological marketing placed emphasis on sustainability of marketing activities of a firm and sustainable consumption of a consumer so that these create either a positive impact or lessen the negative impact on the environment. However, adopting green marketing is not easy as it involves a whole gamut of activities ranging from developing innovative or renovating existing products as per environment's ecological standards, changes to the production process, a modified form of advertising as well as packaging changes. Consequently , manufacturers as well as marketers faces various barriers or impediments while adopting green marketing and selection of green media for advertising . This paper aims to identify various impediments or barriers towards the adoption of green marketing in Indian context and to measure the interrelationships amongst the identified factors using Interpretive Structural Modelling (ISM) technique.

With every economy becoming globalized , operations of global manufacturing and logistics teams are becoming complex and challenging. Delayed shipments, inefficient plants, inconsistent suppliers can stall and delay the shipments thereby increasing the company's supply chain costs. Managing demand volatility and cost fluctuations in supply chain and making it visible globally are some of the challenges which supply chain managers are facing. As per Accenture report , only up to 17 % of the supply chain managers are comfortable implementing analytics to supply chain functions which means despite being a need for these supply chain managers and despite being the fact that analytics can serve as their problem solver , it cannot , and still has a long way to go to prove itself in this domain. The required foundation is still in its nascent stage. This research work thus focuses on studying and exploring the barriers to implementation of analytics or big data analytics to manufacturing supply chains. After exploring , it further study the interrelationship amongst them with the help of Interpretive Structural Modelling (ISM) methodology .

Technology advancements and the broad incorporation of digital practises into corporate operations have contributed significantly to the rapid development of customer relationship management during the last ten years. Because the firm's marketing activities have been using artificial intelligence (AI) to better understand its customers' shifting desires and requirements react more effectively to consumer enquiries, and deepen its connections with its clients, the company has been successful in reaching its goals. Automated responses to customer inquiries used to be monotonous and non-interactive; but, now that AI and other technologies are available, businesses are able to develop customised automation processes to enhance customer interactions and experiences. The abbreviation "Customer Relationship Management" (CRM) refers to a collection of computer software that is used to improve efficiency in dealing with customers and to boost sales. Artificial Intelligence (AI) is fast revolutionising how consumer enquiries are answered to within customer relationship management by examining wants and requirements, concentrating on developing unique customer interaction, and enhancing customer loyalty. This is being done within the context of Customer Relationship Management (CRM). The use of Artificial Intelligence (AI) in the workplace is fast becoming ubiquitous in the 21st century as an increasing number of businesses see its usefulness. The purpose of this empirical study is to investigate the key facets of AI that have an influence on Customer Relationship Management (CRM). The researchers intend to conduct their quantitative analysis using IBM SPSS, and a comprehensive report of their findings will be produced.

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