• VENKATESWARANAIDU KOLLURI

Machine learning (ML), a subset of artificial intelligence, has revolutionized industries, dominating its presence in epidemiological dissemination. The notion of machine learning involves programming computers to optimize a performance criterion using example-generated data or past experience. Different algorithms underlie differing tasks based on their inputs and the type of desired output. Specifically, supervised algorithms involve learning a mapping from inputs to outputs, given examples of the desired mapping. Alternatively, unsupervised algorithms attempt to learn the structure or distribution of data, with the model required to make inferences from instances of incomplete input [1]. The plethora of methods may overshadow the mindset needed to make use of their capabilities. One must approach an analysis in a methodical and scientific manner. A clear definition of a problem should be established, followed by an approach in which performance is evaluated using data, then iterating the approach seeking improvement. Machine learning is a relatively new field in the context of healthcare supply chain management, with formidable relevance given its increasing complexity of operations and abundance of data [1]. The purpose of this paper is to provide a tutorial for healthcare practitioners on understanding the intricacies of machine learning and methods on how it can be used practically to optimize supply chain functions. This understanding is crucial, as the application of machine learning in an unsupervised fashion, without understanding on the part of the user, can lead to poor results and improper utilization of methods [1]. Through a basic understanding of these techniques, a user can make informed decisions on whether to seek collaboration with an analyst versed in machine learning, what type of analysis may be appropriate, and how to evaluate the analysis presented.

Healthcare, supply chain management, predictive analytics framework, challenges, machine learning, classification, regression, unsupervised learning, predictive analytics, data mining, supply chain management, data, Classification

"MACHINE LEARNING IN MANAGING HEALTHCARE SUPPLY CHAINS: HOW MACHINE LEARNING OPTIMIZES SUPPLY CHAINS, ENSURING THE TIMELY AVAILABILITY OF MEDICAL SUPPLIES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 6, page no.282-286, June-2016, Available :http://www.jetir.org/papers/JETIR1701B41.pdf

"MACHINE LEARNING IN MANAGING HEALTHCARE SUPPLY CHAINS: HOW MACHINE LEARNING OPTIMIZES SUPPLY CHAINS, ENSURING THE TIMELY AVAILABILITY OF MEDICAL SUPPLIES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 6, page no. pp282-286, June-2016, Available at : http://www.jetir.org/papers/JETIR1701B41.pdf