Prescriptive analytics

Summary

Prescriptive analytics is a form of business analytics which suggests decision options for how to take advantage of a future opportunity or mitigate a future risk, and shows the implication of each decision option. It enables an enterprise to consider "the best course of action to take" in the light of information derived from descriptive and predictive analytics. Overview Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and predictive analytics. Referred to as the "final frontier of analytic capabilities", prescriptive analytics entails the application of mathematical and computational sciences and suggests decision options for how to take advantage of the results of descriptive and predictive phases. The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today. Descriptive analytics looks at past performance and understands that performance by

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https://en.wikipedia.org/wiki/Prescriptive_analytics

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A prescriptive Dirichlet power allocation policy with deep reinforcement learning

Yuan Tian

Prescribing optimal operation based on the condition of the system, and thereby potentially prolonging its remaining useful lifetime, has tremendous potential in terms of actively managing the availability, maintenance, and costs of complex systems. Reinforcement learning (RL) algorithms are particularly suitable for this type of problem given their learning capabilities. A special case of a prescriptive operation is the power allocation task, which can be considered as a sequential allocation problem whereby the action space is bounded by a simplex constraint. A general continuous action-space solution of such sequential allocation problems has still remained an open research question for RL algorithms. In continuous action space, the standard Gaussian policy applied in reinforcement learning does not support simplex constraints, while the Gaussian-softmax policy introduces a bias during training. In this work, we propose the Dirichlet policy for continuous allocation tasks and analyze the bias and variance of its policy gradients. We demonstrate that the Dirichlet policy is bias-free and provides significantly faster convergence, better performance, and better robustness to hyperparameter changes as compared to the Gaussian-softmax policy. Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case in which we propose the Dirichlet power allocation policy and evaluate its performance on a case study of a set of multiple lithium-ion (Li-I) battery systems. The experimental results demonstrate the potential to prescribe optimal operation, improving the efficiency and sustainability of multi-power source systems.

2022

Challenges Laying Ahead for Future Digital Enterprises: A Research Perspective

Christopher Tucci, Gianluigi Viscusi

Nowadays, digital enterprises are confronted with disruptive technological advancements in their constant quest for innovation and creativity. In order to evolve towards new forms of enterprises, driven by constant business model transformation, a number of challenges need to be addressed from a research and practice perspective. In this paper, a glimpse of the technological trends and visionary scenarios for Enterprises of the Future is provided, leading to the elaboration of the research challenges along the following dimensions: (a) Collaborative, Real-time, Proactive Business Analytics-as-a-Service, (b) Innovative, Web-based Business Models for New Kinds of Economies, (c) Federated, Innovation-driven Enterprise Collaboration Platforms, (d) Dynamic Discovery and Negotiation of the Intellectual Property Rights’ Flow.

Springer International Publishing2015

Challenges Laying Ahead for Future Digital Enterprises: A Research Perspective

Christopher Tucci, Gianluigi Viscusi

Springer-Verlag Berlin2015