Consumer-deviant behavior costs global utility firms USD 96 billion yearly, attributable to Non-Technical Losses (NTLs). NTLs affect the operations of power systems by overloading lines and transformers, resulting in voltage imbalances and, thereby, impacting services. They also impact the electricity price paid by the honest customers. Traditional meters constitute 98 % of the total electricity meters in India. This paper argues that while traditional meters have their limitation in checking consumer-deviant behavior, this issue can be resolved with ML-based algorithms. These algorithms can predict suspected cases of theft with reasonable certainty, thereby enabling distribution companies to save money and provide consistent and dependable services to honest customers at reasonable costs. The key learning from this paper is that even if data is noisy, it is possible to create a Machine Learning Model to detect NTL with 80 percentage plus accuracy.

Purpose Project delivery organizations (PDO) have to develop competitive advantage against new entrants. This study aims to explore the knowledge conversion transactions proposed by Nonaka and Takeuchi (1995) in project phases through the interplay of dynamic and operational capabilities. This study is based on a case study for a PDO in the engineering industry. Design/methodology/approach This study proposes a model of dynamics between the constructs, and its illustration with a case study of a PDO. The research extends the socialization, externalization, combination and internalization (SECI) model of knowledge management (KM). Findings This study provides an overview of existing research related to the constructs like applicability of operational and dynamic capabilities, knowledge configuration and knowledge management processes to individual projects delivered by a PDO for its clients. Further, this study provides an overview of the knowledge configuration adopted by an organization and how it helps to build the competitive advantage of an organization. Research limitations/implications This study proposes a model for applying the constructs to each of the phases of a project. It then illustrates the knowledge value chain in a PDO in the field of engineering projects with detailed insights into the steps of sensing, seizing and sharing knowledge across the project life cycle. Practical implications Project-based firms can use the learnings and create their own SECI model linking the conceptual model of KM and PDO and KM value chain. Social implications In social projects implementation, this conceptual model and process will be helpful in building efficiency and effectiveness. Originality/value This case study presents the knowledge value chain in a PDO in the field of engineering projects with detailed insights into the steps of sensing, seizing and sharing knowledge across the project life cycle.

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In a knowledge-based economy, the issues of technology transfer and management of technology, especially in sensitive strategic industries, are of major concern. The transfer of technology is a complex multidisciplinary area of technology management involving technology transfers from overseas developing agencies and internal technology transfers. Technology is a combination of four basic com~onents-facilities, abilities, facts, and frameworks. Economics of scale and complexities in technologies, especially in major weapon systems, would increasingly render the concepts of self sufficiency and evenself-reliance impossible ideals to achieve, even by the developed countries. In such a scenario, transfer of technology will continue to be used as a powerful tool of global geopolitical power projection by the developed countries as an extension of their foreign policies. For nations like India, there is no option but to invest in the indigenous RCD and SCT base in sensitivelstrategic industries. Experience in transfer of technology with those of space, defence research, atomic energy, scientific and industrial research must be pooled into knowledge bank to achieve synergy.

OALP was introduced in 2016 by GoI with a less regulatory burden to the government and pricing freedom to investing firms. Earlier policy NELP was with exploration and production cost recovery and price control by GoI. The new policy OALP is with no-cost recovery, revenue sharing, and pricing freedom to the firm. The Oil and gas industry is involved with a wide variety of risks. In OALP, there is no regulatory monitoring and no cost recovery; this has increased the risk to the Indian oil and gas industry; a firm may go for cheap alternatives, which can cause accidents and create direct loss to the industry and the country's reputation. In addition, pricing freedom for the firm can increase the natural gas price, and the gas-dependent sectors face trouble in purchasing. This paper studied the role of regulatory monitoring in risk reduction in the Indian oil and gas sector