Bootstrap Robust Prescriptive Analytics

We address the problem of prescribing an optimal decision in a framework where its cost depends on uncertain problem parameters $Y$ that need to be learned from data. Earlier work by Bertsimas and Kallus (2014) transforms classical machine learning methods that merely predict $Y$ from supervised training data $[(x_1, y_1), \dots, (x_n, y_n)]$ into prescriptive methods taking optimal decisions specific to a particular covariate context $X=\bar x$. Their prescriptive methods factor in additional observed contextual information on a potentially large number of covariates $X=\bar x$ to take context specific actions $z(\bar x)$ which are superior to any static decision $z$. Any naive use of limited training data may, however, lead to gullible decisions over-calibrated to one particular data set. In this paper, we borrow ideas from distributionally robust optimization and the statistical bootstrap of Efron (1982) to propose two novel prescriptive methods based on (nw) Nadaraya-Watson and (nn) nearest-neighbors learning which safeguard against overfitting and lead to improved out-of-sample performance. Both resulting robust prescriptive methods reduce to tractable convex optimization problems and enjoy a limited disappointment on bootstrap data. We illustrate the data-driven decision-making framework and our novel robustness notion on a small news vendor problem as well as a small portfolio allocation problem

What Types of Predictive Analytics are Being Used in Talent Management Organizations?

[Excerpt] Talent management organizations are increasingly deriving insights from data to make better decisions. Their use of data analytics is advancing from descriptive to predictive and prescriptive analytics. Descriptive analytics is the most basic form, providing the hindsight view of what happened and laying the foundation for turning data into information. More advanced uses are predictive (advanced forecasts and the ability to model future results) and prescriptive (“the top-tier of analytics that leverage machine learning techniques … to both interpret data and recommend actions”) analytics (1). Appendix A illustrates these differences. This report summarizes our most relevant findings about how both academic researchers and HR practitioners are successfully using data analytics to inform decision-making in workforce issues, with a focus on executive assessment and selection

Analysis of The Best Strategy for Great Education Policy Using Prescriptive Analytics (Indonesian School Experience)

This paper discusses an analytical prescriptive approach to analyzing education policy. This study uses an appropriate literature review method so that the results of the study are in the form of an overview based on a critical analysis of the subject matter of the benefits of prescriptive analytics in the field of Islamic education management. The main issues discussed in this study include a prescriptive analytics approach as an educational policy analysis approach, a prescriptive approach model in education policy analysis, and the advantages and disadvantages of a prescriptive approach in education policy analysis. This study finds that a project manager must undertake prescriptive analytics today and in the Industry 4.0 era. However, managers have a weakness in skills in this area because the future of data analysis is prescriptive, despite the impact of the obligation to change management. Research advises education managers to realize that prescriptive analytics is not always correct. Prescriptive analytics products can only be applied to one educational institution. However, the domino effect of prescriptive analytics has many benefits for educational institutions, both tangible and intangible

THE EFFECT OF ADVANCED ANALYTICS REAL-TIME DASHBOARDS ON COGNITIVE ABSORPTION AND TASK LOAD OF HUMAN END USERS

Advanced analytics can be used to gain comprehensive insights into real-time production processes. They are commonly subdivided into predictive and prescriptive analytics. While previously descriptive analytics only reflected current states of machinery, predictive analytics enables statements about the future. Going one step further, prescriptive analytics cover automated recommendations for action based on these predictions. On the one hand, humans as central stakeholders in production shall benefit from the information gained through advanced analytics to make better decisions. On the other hand, predictive and prescriptive analytics dashboards contain additional information and may potentially overwhelm human decision-makers in contrast to descriptive dashboards. In this study, we investigate the perception of human end users on information presented in descriptive, predictive, and prescriptive dashboards and investigate their cognitive absorption and task load. Our results show that more advanced predictive and prescriptive dashboards increase mental demand rather than lowering intellectual requirements. However, we found that prescriptive dashboards reduce user frustration by making decision alternatives and consequences more tangible

Proactive Buildings: A Prescriptive Maintenance Approach

Prescriptive maintenance has recently attracted a lot of scientific attention. It integrates the advantages of descriptive and predictive analytics to automate the process of detecting non nominal device functionality. Implementing such proactive measures in home or industrial settings may improve equipment dependability and minimize operational expenses. There are several techniques for prescriptive maintenance in diverse use cases, but none elaborates on a general methodology that permits successful prescriptive analysis for small size industrial or residential settings. This study reports on prescriptive analytics, while assessing recent research efforts on multi-domain prescriptive maintenance. Given the existing state of the art, the main contribution of this work is to propose a broad framework for prescriptive maintenance that may be interpreted as a high-level approach for enabling proactive buildings

A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics and ChatGPT

A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of prediction only. The component of predictive analytics concerned with interpreting the internals of the models and explaining their predictions for individual cases to stakeholders has largely been neglected. Additionally, works that attempt to employ data-driven prescriptive analytics to automatically generate evidence-based remedial advice for at-risk learners are in their infancy. eXplainable AI is a field that has recently emerged providing cutting-edge tools which support transparent predictive analytics and techniques for generating tailored advice for at-risk students. This study proposes a novel framework that unifies both transparent machine learning as well as techniques for enabling prescriptive analytics, while integrating the latest advances in large language models. This work practically demonstrates the proposed framework using predictive models for identifying at-risk learners of programme non-completion. The study then further demonstrates how predictive modelling can be augmented with prescriptive analytics on two case studies in order to generate human-readable prescriptive feedback for those who are at risk using ChatGPT.Comment: revision of the original paper to include ChatGPT integratio

Inventory Analytics

"Inventory Analytics provides a comprehensive and accessible introduction to the theory and practice of inventory control – a significant research area central to supply chain planning. The book outlines the foundations of inventory systems and surveys prescriptive analytics models for deterministic inventory control. It further discusses predictive analytics techniques for demand forecasting in inventory control and also examines prescriptive analytics models for stochastic inventory control. Inventory Analytics is the first book of its kind to adopt a practicable, Python-driven approach to illustrating theories and concepts via computational examples, with each model covered in the book accompanied by its Python code. Originating as a collection of self-contained lectures, Inventory Analytics will be an indispensable resource for practitioners, researchers, teachers, and students alike.

Don’t Get the Cart before the Horse: There Are No Shortcuts to Prescriptive Analytics

Davenport [5] argues that the most important component for putting big data into action within an organization is talent management, and this opinion is widely shared among academics. We interviewed the chief purchasing officers (CPOs) of 15 major corporations and found that they did not feel it was problematic to find the right people for data analytics teams, and did not feel it was difficult to get resources to support data analytics efforts. Instead, they were frustrated by data issues such as granularity, accuracy, and integration. They also were intimidated by what they perceived to be the requirements for prescriptive analytics, and generally had not progressed beyond descriptive analytics. This article summarizes the roadblocks that the CPOs encountered as they attempted to move from descriptive to predictive to prescriptive analytics, and presents a set of steps which must be followed if organizations are to move up the analytics hierarchy

How prescriptive analytics influences decision making in precision medicine

Failure of the old model of medical decision making, “one-size-fits-all”, has encouraged the healthcare/medicine landscape to take advantage of big data and analytics for tailoring the treatments[1], based on individual patient’s differences in gen, environment, and lifestyle [2]. Whereas literature has demonstrated a strong contribution to the adoption of healthcare analytics over patient’s data, for better decision making [3], understanding the level and the degree that each type of analytics influences decision making, is crucial for addressing the type of problems [4]. While descriptive, diagnostic, and predictive analytics generate knowledge for decision support systems, prescriptive analytics recommends a proactive decision[5]. This study aims to highlight the influential and effective role of prescriptive analytics for fulfilling precision medicine which is defined as an emerging approach in medical decision making .FCT – Fundação para a Ciência e Tecnologia within the Projects Scope: DSAIPA/DS/0084/201