Enhancing discrete-event simulation with big data analytics: A review

The need to control rising costs in healthcare has led to an increase in the use of data analytics to develop more efficient healthcare business models. This article discusses a simulation that uses data analytics to minimize the number of physicians and nurses needed in healthcare facilities during a crisis situation. Using a hypothetical emergency scenario, the hospital uses a healthcare analytical system to predict the necessary resources to govern the situation. Based on historical data regarding the flow of patients through the facility, a discrete-event simulation estimates resource scheduling and the resulting impact on both wait times and personnel demand. Furthermore, the value of multiple replications for discrete-event simulation models is discussed and defined, along with factors that enable greater control of multiple design points with this simulated experiment. The results of this study demonstrate the value of simulation modeling in effective resource planning. The addition of only a single doctor significantly reduced predicted wait times for patients during the crisis. Further, the findings support the use of data analytics and predictive modeling to mitigate rising healthcare costs in the United States through efficient planning and resource allocation.

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Stochastic Predictions of Ore Production in an Underground Limestone Mine Using Different Probability Density Functions: A Comparative Study Using Big Data from ICT System

Applied Sciences ◽

10.3390/app11094301 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4301

Author(s):

Dahee Jung ◽

Jieun Baek ◽

Yosoon Choi

Keyword(s):

Big Data ◽

Probability Distribution ◽

Probability Density ◽

Discrete Event Simulation ◽

Discrete Event ◽

Real Data ◽

Probability Density Functions ◽

Density Functions ◽

Travel Times ◽

Event Simulation

This study stochastically predicted ore production through discrete event simulation using four different probability density functions for truck travel times. An underground limestone mine was selected as the study area. The truck travel time was measured by analyzing the big data acquired from information and communications technology (ICT) systems in October 2018, and probability density functions (uniform, triangular, normal, and observed probability distribution of real data) were determined using statistical values. A discrete event simulation model for a truck haulage system was designed, and truck travel times were randomly generated using a Monte Carlo simulation. The ore production that stochastically predicted fifty times for each probability density function was analyzed and represented as a value of lower 10% (P10), 50% (P50), and 90% (P90). Ore production was underestimated when a uniform and triangular distribution was used, as the actual ore production was similar to that of P90. Conversely, the predicted ore production of P50 was relatively consistent with the actual ore production when using the normal and observed probability distribution of real data. The root mean squared error (RMSE) for predicting ore production for ten days in October 2018 was the lowest (24.9 ton/day) when using the observed probability distribution.

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Human systems integration: A discrete event simulation of operational utility for control of multiple small unmanned aerial systems

PsycEXTRA Dataset ◽

10.1037/e578862012-006 ◽

2010 ◽

Author(s):

John M. Colombi ◽

Jill R. Ward

Keyword(s):

Discrete Event Simulation ◽

Discrete Event ◽

Systems Integration ◽

Unmanned Aerial Systems ◽

Event Simulation ◽

Human Systems Integration ◽

Aerial Systems ◽

Human Systems

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Healthcare Operations Improvement with an Integration of Discrete-Event Simulation and Lean Thinking

International conference on Innovative Engineering Technologies (ICIET’2014) Dec. 28-29, 2014 Bangkok (Thailand) ◽

10.15242/iie.e1214018 ◽

2014 ◽

Keyword(s):

Discrete Event Simulation ◽

Discrete Event ◽

Healthcare Operations ◽

Lean Thinking ◽

Event Simulation

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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