Modern Management Through Ancient Indian Wisdom: Towards a More Sustainable Paradigm
The paper attempts to establish the relevance of the teachings of Ancient Indian scriptures like Vedas, Upanishads, Bhagavad Gita, Manu-smriti, Arthashastra, etc in the practice of Modern Management. In this paper, we will see that though Drucker's modern management paradigms like MBO are very ideal theoretically and can be derived from the ethical and spiritual frameworks of ancient Indian ethos however, in practice, companies are struggling to implement these frameworks effectively because they are still not aligned to the teachings of ancient Indian scriptures. Also, through the same body of knowledge derived from ancient Indian scriptures, we will try to understand the reasons behind individual decision-making failures ultimately leading to institutional failures and in the process would look forward towards integrating some elements into the existing frameworks to create a more sustainable management paradigm. In this regard, I have created a model which depicts a 6-fold path of “Jnan” or Knowledge through which we can not only better manage on an individual as well as institutional and global level, but also better able to contribute towards restoring World peace and uniform economic order through spiritual congruence.
Śrīmad Bhagavad Gītā and Knowledge Management with Special Focus on Jñāna Yoga
Jnāna yoga is the path of self-knowledge of achieving the farthest insights in personal and professional development. From the decades, the concepts of Gītā viz., wise Leadership, self-awareness, detachment from materials, Leader as enlightened sage, self-Awareness, self-knowledge, and self-realization are important forces to command the journey. This chapter portrays the special focus on Śrīmad Bhagavad Gītā and Jnāna yoga (Yoga of knowledge) to find the potentialities of knowledge workers amongst various organizations. The main objective of this chapter is to present the main abilities of knowledge workers in the organizational settings and to present characteristics they possess.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.