Prescriptive analytics: Literature review and research challenges

Lepenioti, Katerina; Bousdekis, Alexandros; Apostolou, Dimitris; Mentzas, Gregoris

doi:10.1016/j.ijinfomgt.2019.04.003

Cited by 340 publications

(163 citation statements)

References 71 publications

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“…Menezes et al [55] explained that prescriptive analytics finds the best route to operate (outputs) in the view of given information and models (inputs). Similarly, Lepenioti et al [61] said that prescriptive analytics tries to locate the best action for the future in the manufacturing industry and it is frequently considered as the subsequent stage towards improving data analytics maturity for business execution improvement. Moreover, prescriptive analytic strategies, such as decision optimization, can handle profoundly complex issues running from hundreds to a large number of limitations that would never be analyzed manually, and Matyas et al [62] proposed a prescriptive maintenance methodology for manufacturing systems analysis, as well as simulation tools, that have been utilized to analyze past data, i.e., machine failure data and product quality data, to guarantee a high level of process flexibility and the quality of the product.…”

Section: Prescriptive Model Managementmentioning

confidence: 99%

Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review

et al. 2021

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Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.

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Section: Prescriptive Model Managementmentioning

confidence: 99%

Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review

et al. 2021

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“…prescriptive analytics is less mature [4], [5]. Recently, however, prescriptive analytics has been increasingly gathering research interest since it is considered as the next step towards increasing data analytics maturity and leading to optimized decision making, ahead of time, for business performance improvement [5]. Prescriptive analytics aims at suggesting (prescribing) the best decision options to take advantage of the predicted future utilizing large amounts of data [6].…”

Section: Introductionmentioning

confidence: 99%

“…Prescriptive analytics aims at suggesting (prescribing) the best decision options to take advantage of the predicted future utilizing large amounts of data [6]. To do this, it incorporates prediction events about what might occur and utilizes artificial intelligence, optimization algorithms and expert systems in a probabilistic context to provide adaptive, automated, constrained, time-dependent and optimal decisions [3], [5].…”

Section: Introductionmentioning

confidence: 99%

Sensor-driven Learning of Time-Dependent Parameters for Prescriptive Analytics

et al. 2020

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Big data analytics is rapidly emerging as a key Internet of Things (IoT) initiative aiming at providing meaningful insights and supporting optimal decision making under time constraints. In this direction, prescriptive analytics has just started to emerge. Prescriptive analytics moves beyond descriptive and predictive analytics aiming at providing adaptive, automated, constrained, time-dependent and optimal decisions. The use of time-dependent parameters in prescriptive analytics models provide a more reliable and realistic representation of the complex and dynamic environment and the associated decision making process; however, their estimation poses significant challenges due to the uncertainty derived from inaccurate user input, noisy data, and non-stationarity of real-world data streams. Since feedback and learning mechanisms for tracking the prescriptive analytics are crucial enablers for self-configuration and self-optimization, this paper proposes an approach for sensor-driven learning of time-dependent parameters for prescriptive analytics models deployed in streaming computational environments. The proposed approach was validated in an Industry 4.0 use case, while it was further evaluated through extensive simulation experiments. The proposed approach overcomes challenges related to uncertainty derived from user's input, non-stationary data and sensor noise and provides estimates of time-dependent parameters that lead to more reliable prescriptions. INDEX TERMS Big data, machine learning, data analytics, Internet of Things, non-stationary time-series.

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“…This justifies why the various algorithms developed for fitting data to mathematical equations are not used by many. Among the techniques for fitting data to mathematical equations, nonlinear regression represents one of the most used approaches [2]. It is a very helpful process in engineering, agricultural, and natural science, and it is used to capture and understand the underling relationships among variables (dependent and independent) of interest described by mathematical expressions.…”

Section: Introductionmentioning

confidence: 99%

Decision Support System for Fitting and Mapping Nonlinear Functions with Application to Insect Pest Management in the Biological Control Context

et al. 2020

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The process of moving from experimental data to modeling and characterizing the dynamics and interactions in natural processes is a challenging task. This paper proposes an interactive platform for fitting data derived from experiments to mathematical expressions and carrying out spatial visualization. The platform is designed using a component-based software architectural approach, implemented in R and the Java programming languages. It uses experimental data as input for model fitting, then applies the obtained model at the landscape level via a spatial temperature grid data to yield regional and continental maps. Different modules and functionalities of the tool are presented with a case study, in which the tool is used to establish a temperature-dependent virulence model and map the potential zone of efficacy of a fungal-based biopesticide. The decision support system (DSS) was developed in generic form, and it can be used by anyone interested in fitting mathematical equations to experimental data collected following the described protocol and, depending on the type of investigation, it offers the possibility of projecting the model at the landscape level.

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Prescriptive analytics: Literature review and research challenges

Cited by 340 publications

References 71 publications

Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review

Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review

Sensor-driven Learning of Time-Dependent Parameters for Prescriptive Analytics

Decision Support System for Fitting and Mapping Nonlinear Functions with Application to Insect Pest Management in the Biological Control Context

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