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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66411
Volume:
10 |
7 |
Jul 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Master of Computer Application
P. R. Pote Patil College of Engineering & Management, Amravati (M.H.)
Department of Master of Computer Application (AIML)
P. R. Pote Patil College of Engineering & Management, Amravati (M.H.)
Prescriptive analytics is a key area of business intelligence that helps organizations move beyond just forecasting future outcomes to suggesting the best possible actions. Unlike descriptive analytics, which looks at past data, and predictive analytics, which forecasts future trends, prescriptive analytics uses advanced methods like mathematical programming, machine learning, and artificial intelligence to help make complex decisions more efficiently. This paper explains the main parts of prescriptive analytics systems, looks at the key methods such as optimization, reinforcement learning, and causal analysis, and also shows how these are used in different industries like healthcare, finance, and supply chain. The paper also points out some of the ongoing challenges in this field, such as making systems work in real time, involving people in the process, and ensuring ethical use. Prescriptive analytics is a branch of business analytics that is used to find the best solutions for daily problems. It is closely connected to descriptive and predictive analytics. Prescriptive analytics uses data and algorithms to help make better decisions, both in the short and long term. It provides advice on different possible situations based on data and past experiences.
KEYWORDS
Despite the clear value generation, several technical and structural barriers prevent the widespread operationalization of prescriptive analytics: · Real-Time Big Data Processing: Most prescriptive optimization architectures operate in batch-processing mode due to the high mathematical complexity of the underlying solvers. Transitioning optimization algorithms into sub-second, real-time contexts remains an open computational hurdle. · The "Black Box" Trust Deficit: Advanced deep learning and DRL-driven prescriptions often fail to provide interpretable explanations for their outputs. Business leaders and healthcare professionals are resistant to delegating critical, high-risk choices to systems lacking transparent audit trails. · Algorithmic Bias Propagation: Prescriptive systems learn from historical distributions. If data collection methodologies reflect systemic inequities, the system's objective function will optimize for economic efficiency while compounding historical biases. Future research is converging on Responsible and Cognitive Analytics Systems. By nesting automated optimization engines inside interpretable rule-based systems (such as knowledge distillation or fuzzy logic), organizations can preserve mathematical maximization while enforcing strict ethical boundaries, transparency, and collaborative human oversight.
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Publication Date
7/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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