Prescriptive analytics

About: Prescriptive analytics is a research topic. Over the lifetime, 362 publications have been published within this topic receiving 4086 citations.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Abstract: The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Abstract: The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue To date, substantial works have been conducted on smart meter data analytics To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management We also review the techniques and methodologies adopted or developed to address each application In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security

From Predictive to Prescriptive Analytics

Abstract: We combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisi...

Impact of business analytics and enterprise systems on managerial accounting

Abstract: The nature of management accountants' responsibility is evolving from merely reporting aggregated historical value to also including organizational performance measurement and providing management with decision related information. Corporate information systems such as enterprise resource planning (ERP) systems have provided management accountants with both expanded data storage power and enhanced computational power. With big data extracted from both internal and external data sources, management accountants now could utilize data analytics techniques to answer the questions including: what has happened (descriptive analytics), what will happen (predictive analytics), and what is the optimized solution (prescriptive analytics). However, research shows that the nature and scope of managerial accounting has barely changed and that management accountants employ mostly descriptive analytics, some predictive analytics, and a bare minimum of prescriptive analytics. This paper proposes a Managerial Accounting Data Analytics (MADA) framework based on the balanced scorecard theory in a business intelligence context. MADA provides management accountants the ability to utilize comprehensive business analytics to conduct performance measurement and provide decision related information. With MADA, three types of business analytics (descriptive, predictive, and prescriptive) are implemented into four corporate performance measurement perspectives (financial, customer, internal process, and learning and growth) in an enterprise system environment. Other related issues that affect the successful utilization of business analytics within a corporate-wide business intelligence (BI) system, such as data quality and data integrity, are also discussed. This paper contributes to the literature by discussing the impact of business analytics on managerial accounting from an enterprise systems and BI perspective and by providing the Managerial Accounting Data Analytics (MADA) framework that incorporates balanced scorecard methodology.

Prescriptive analytics: Literature review and research challenges

Abstract: Business analytics aims to enable organizations to make quicker, better, and more intelligent decisions with the aim to create business value. To date, the major focus in the academic and industrial realms is on descriptive and predictive analytics. Nevertheless, prescriptive analytics, which seeks to find the best course of action for the future, has been increasingly gathering the research interest. Prescriptive analytics is often considered as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time for business performance improvement. This paper investigates the existing literature pertaining to prescriptive analytics and prominent methods for its implementation, provides clarity on the research field of prescriptive analytics, synthesizes the literature review in order to identify the existing research challenges, and outlines directions for future research.