scholarly journals Assessing asset monitoring levels for maintenance operations

2015 ◽  
Vol 26 (5) ◽  
pp. 632-659 ◽  
Author(s):  
Abdullah A Alabdulkarim ◽  
Peter Ball ◽  
Ashutosh Tiwari
Keyword(s):  
Asset Management ◽  
Business Models ◽  
Discrete Event ◽  
Dynamic Modelling ◽  
Simulation Models ◽  
Content Type ◽  
Event Simulation ◽  
Product Service ◽  
Asset Monitoring ◽  
The Impact

Purpose – Asset management has recently gained significance due to emerging business models such as Product Service Systems where the sale of asset use, rather than the sale of the asset itself, is applied. This leaves the responsibility of the maintenance tasks to fall on the shoulders of the manufacturer/supplier to provide high asset availability. The use of asset monitoring assists in providing high availability but the level of monitoring and maintenance needs to be assessed for cost effectiveness. There is a lack of available tools and understanding of their value in assessing monitoring levels. The paper aims to discuss these issues. Design/methodology/approach – This research aims to develop a dynamic modelling approach using Discrete Event Simulation (DES) to assess such maintenance systems in order to provide a better understanding of the behaviour of complex maintenance operations. Interviews were conducted and literature was analysed to gather modelling requirements. Generic models were created, followed by simulation models, to examine how maintenance operation systems behave regarding different levels of asset monitoring. Findings – This research indicates that DES discerns varying levels of complexity of maintenance operations but that more sophisticated asset monitoring levels will not necessarily result in a higher asset performance. The paper shows that it is possible to assess the impact of monitoring levels as well as make other changes to system operation that may be more or less effective. Practical implications – The proposed tool supports the maintenance operations decision makers to select the appropriate asset monitoring level that suits their operational needs. Originality/value – A novel DES approach was developed to assess asset monitoring levels for maintenance operations. In applying this quantitative approach, it was demonstrated that higher asset monitoring levels do not necessarily result in higher asset availability. The work provides a means of evaluating the constraints in the system that an asset is part of rather than focusing on the asset in isolation.

Use of Predictive and Simulation Models to Develop Strategies for Better Access Specialists Care

2020 ◽  
pp. 109-136
Author(s):  
Siang Li Chua ◽  
Wai Leng Chow
Keyword(s):  
Lead Time ◽  
Discrete Event ◽  
Simulation Models ◽  
Multiple Logistic Regression ◽  
Specialist Care ◽  
Event Simulation ◽  
Patients At Risk ◽  
The Impact ◽  
Specialist Outpatient Clinic ◽  
Des Model

No-shows are patients who miss scheduled Specialist Outpatient Clinic (SOC) appointments. No-shows can impact patients' access to care and appointment lead time. This chapter describes a data-driven strategy of improving access to specialist care through first developing a stratified predictive scoring model to identify patients at risk of no-shows; second, studying the impact of a dynamic overbooking strategy that incorporates the use of the no-show prediction model using discrete event simulation (DES) on lead time. Seventeen variables related to new SOC appointments for subsidized patients in 2016 were analyzed. Multiple logistic regression (MLR) found eight variables independently associated with no-shows with area under receiver operation curve (AUC) 70%. The model was tested and validated. DES model simulated the appointment overbooking strategy as applied to the top highest volume specialties and concluded that lead time of Specialty 1 and 2 can be shortened by 27.5 days (49% improvement) and 21.3 (33%) respectively.

A Methodology to Evaluate Complex Manufacturing Systems through Discrete-Event Simulation Models

2012 ◽  
Vol 502 ◽  
pp. 7-12 ◽  
Author(s):  
L.P. Ferreira ◽  
E. Ares ◽  
G. Peláez ◽  
M. Marcos ◽  
M. Araújo
Keyword(s):  
Discrete Event Simulation ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Simulation Models ◽  
Network Configuration ◽  
Event Simulation ◽  
Automobile Assembly ◽  
Loop Network ◽  
Complex Manufacturing Systems ◽  
The Impact

This paper proposes a methodology to analyze complex manufacturing systems, based on discrete-event simulation models. The methodology was validated by performing different simulation experiments and will be applied to a multistage multiproduct production line, based on a real case, with a closed-loop network configuration of machines and intermediate buffers consisting of conveyors, which is very common in the automobile sector. A simulation model in an Arena environment was developed, which allowed for an analysis of the important aspects not yet studied in specialized literature, namely the assessment of the impact of the production sequence on the automobile assembly line. Various sequence rules were analyzed and the performance of each of the corresponding simulation models was registered.

Simulation of home-hospital impacts on crowding – FM implications

Facilities ◽  
2016 ◽  
Vol 34 (13/14) ◽  
pp. 748-765 ◽  
Author(s):  
John Fard ◽  
Kathy O. Roper ◽  
Jeremy Hess
Keyword(s):  
Discrete Event ◽  
Facility Management ◽  
Content Type ◽  
Hospital Capacity ◽  
Event Simulation ◽  
Hospital Facility ◽  
Ed Crowding ◽  
The Impact

Purpose This study aims to evaluate home-hospital implications for facility management (FM) and, in particular, ED crowding. Home-hospital programs, in which select patients receive hospital-level care at home, can extend hospital facility capacity. Emergency department (ED) crowding, a sensitive hospital capacity indicator, is associated with unsafe operations and reduced quality of care. Design/methodology/approach The impact of a home-hospital program on crowding was analyzed with a discrete-event simulation model using one month of historical data from a case hospital. Time ED patients waited for inpatient beds was the primary endpoint. Five scenarios with different levels of patient suitability for home-hospital were each run 30 times. Differences were evaluated using paired t-tests. Findings Implementing home-hospital reduced ED crowding by up to 3 per cent. Additionally, the simulation yielded insights regarding advantages and limitations of various home-hospital arrangements, suggested which hospital types may be the best candidates for home-hospital and highlighted the role of bed-cleaning turnaround times and environmental services staffing schedules in operations. Research limitations/implications This research examined home-hospital and crowding at one hospital. Developing a model that accounts for all hospital types requires significant data and many hospital partnerships but could allow for more informed decisions regarding implementation of such programs. Social implications This research has implications for ensuring access to ED care, an important source of acute care generally and particularly for the underserved. Originality/value This research systematically evaluates home-hospital’s impact on ED crowding. Simulation modeling resulted in analytical results and allowed for evaluation of what-if scenarios providing recommendations for hospital FMs on their role in decreasing ED boarding.

scholarly journals Data Analytics and Its Advantages for Addressing the Complexity of Healthcare: A Simulated Zika Case Study Example

2019 ◽  
Vol 9 (11) ◽  
pp. 2208 ◽  
Author(s):  
Lily Popova Zhuhadar ◽  
Evelyn Thrasher
Keyword(s):  
Discrete Event Simulation ◽  
Data Analytics ◽  
Business Models ◽  
Discrete Event ◽  
Simulation Models ◽  
Resource Planning ◽  
The United States ◽  
Wait Times ◽  
Event Simulation ◽  
Use Of Data

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.

scholarly journals Analysis of the effects of EHR implementation on timeliness of care in a dermatology clinic: a simulation study

2018 ◽  
Vol 25 (7) ◽  
pp. 827-832 ◽  
Author(s):  
Vahab Vahdat ◽  
Jacqueline A Griffin ◽  
James E Stahl ◽  
F Clarissa Yang
Keyword(s):  
Length Of Stay ◽  
Patient Care ◽  
Discrete Event ◽  
Wait Time ◽  
Simulation Models ◽  
Event Simulation ◽  
Dermatology Clinic ◽  
Average Patient ◽  
Encounter Time ◽  
The Impact

Abstract Objective Quantify the downstream impact on patient wait times and overall length of stay due to small increases in encounter times caused by the implementation of a new electronic health record (EHR) system. Methods A discrete-event simulation model was created to examine the effects of increasing the provider-patient encounter time by 1, 2, 5, or 10 min, due to an increase in in-room documentation as part of an EHR implementation. Simulation parameters were constructed from an analysis of 52 000 visits from a scheduling database and direct observation of 93 randomly selected patients to collect all the steps involved in an outpatient dermatology patient care visit. Results Analysis of the simulation results demonstrates that for a clinic session with an average booking appointment length of 15 min, the addition of 1, 2, 5, and 10 min for in-room physician documentation with an EHR system would result in a 5.2 (22%), 9.8 (41%), 31.8 (136%), and 87.2 (373%) minute increase in average patient wait time, and a 6.2 (12%), 11.7 (23%), 36.7 (73%), and 96.9 (193%) minute increase in length of stay, respectively. To offset the additional 1, 2, 5, or 10 min, patient volume would need to decrease by 10%, 20%, 40%, and >50%, respectively. Conclusions Small changes to processes, such as the addition of a few minutes of extra documentation time in the exam room, can cause significant delays in the timeliness of patient care. Simulation models can assist in quantifying the downstream effects and help analyze the impact of these operational changes.

Implementing lean six sigma and discrete-event simulation for tutoring operations in higher education institutions

2019 ◽  
Vol 10 (4) ◽  
pp. 909-927 ◽  
Author(s):  
Sandra L. Furterer ◽  
Kellie Schneider ◽  
Michael B. Key ◽  
Daniel Zalewski ◽  
Michael Laudenberger
Keyword(s):  
Discrete Event Simulation ◽  
Six Sigma ◽  
Discrete Event ◽  
Wait Time ◽  
Lean Six Sigma ◽  
Content Type ◽  
Event Simulation ◽  
The University ◽  
The Impact ◽  
University Of Dayton

Purpose The purpose of this study is to improve the utilization of walk-in tutors at the University of Dayton. Design/methodology/approach This paper discusses the application of mixed methods participatory action research with the use of the lean six sigma (LSS) define-measure-analyze-improve-control (DMAIC) methodology combined with discrete event simulation to improve the tutoring processes at the University of Dayton. Findings The impact of these improvements provided a great balance between tutor utilization and student wait time, and as a result, an increase in pay was negotiated for tutors. Additionally, two schools within the University, Engineering and Business, have seen the value of the tutoring for their students and have provided additional financial support for tutoring services. Specifically, these schools now have dedicated by-appointment tutors for their students. Originality/value Incorporating simulation within the LSS methodology provides a “prototype” simulation of the potential solutions prior to changing the process in the real world. This approach can minimize the risk of implementing solutions that are costly or ineffective. The improvement of increased tutor pay helped to demonstrate the university’s commitment to tutors and tutoring. Through continuous improvement efforts, the tutoring department has tripled the number of courses that they support through these enhanced relationships, improved utilization and the overall culture and work environment. The LSS methodology and tools combined with discrete event simulation can be used as a guide for improving other repetitive processes within the university.

scholarly journals Supporting scale-up of COVID-19 RT-PCR testing processes with discrete event simulation

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255214
Author(s):  
Jad El Hage ◽  
Patti Gravitt ◽  
Jacques Ravel ◽  
Nadia Lahrichi ◽  
Erica Gralla
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Scale Up ◽  
Simulation Models ◽  
The United States ◽  
Molecular Testing ◽  
Rt Pcr ◽  
Event Simulation ◽  
Trade Offs ◽  
The Impact

Testing is critical to mitigating the COVID-19 pandemic, but testing capacity has fallen short of the need in the United States and elsewhere, and long wait times have impeded rapid isolation of cases. Operational challenges such as supply problems and personnel shortages have led to these bottlenecks and inhibited the scale-up of testing to needed levels. This paper uses operational simulations to facilitate rapid scale-up of testing capacity during this public health emergency. Specifically, discrete event simulation models were developed to represent the RT-PCR testing process in a large University of Maryland testing center, which retrofitted high-throughput molecular testing capacity to meet pandemic demands in a partnership with the State of Maryland. The simulation models support analyses that identify process steps which create bottlenecks, and evaluate “what-if” scenarios for process changes that could expand testing capacity. This enables virtual experimentation to understand the trade-offs associated with different interventions that increase testing capacity, allowing the identification of solutions that have high leverage at a feasible and acceptable cost. For example, using a virucidal collection medium which enables safe discarding of swabs at the point of collection removed a time-consuming “deswabbing” step (a primary bottleneck in this laboratory) and nearly doubled the testing capacity. The models are also used to estimate the impact of demand variability on laboratory performance and the minimum equipment and personnel required to meet various target capacities, assisting in scale-up for any laboratories following the same process steps. In sum, the results demonstrate that by using simulation modeling of the operations of SARS-CoV-2 RT-PCR testing, preparedness planners are able to identify high-leverage process changes to increase testing capacity.

A decision support simulation model for bed management in healthcare

2019 ◽  
Vol 32 (2) ◽  
pp. 499-515 ◽  
Author(s):  
Elizabeth A. Cudney ◽  
Raja Anvesh Baru ◽  
Ivan Guardiola ◽  
Tejaswi Materla ◽  
William Cahill ◽  
...  
Keyword(s):  
Simulation Model ◽  
Discrete Event ◽  
Wait Time ◽  
Hospital System ◽  
Content Type ◽  
Bed Management ◽  
Event Simulation ◽  
Number Of Patients ◽  
Average Patient ◽  
The Impact

Purpose In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources. such as beds. Bed management is a key to the effective delivery of high quality and low-cost healthcare. The purpose of this paper is to develop a discrete event simulation to assist in planning and staff scheduling decisions. Design/methodology/approach A discrete event simulation model was developed for a hospital system to analyze admissions, patient transfer, length of stay (LOS), waiting time and queue time. The hospital system contained 50 beds and four departments. The data used to construct the model were from five years of patient records and contained information on 23,019 patients. Each department’s performance measures were taken into consideration separately to understand and quantify the behavior of departments individually, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time and LOS of patients. Findings Using the simulation model, it was determined that reducing the bed turnover time by 1 h resulted in a statistically significant reduction in patient wait time in queue. Further, reducing the average LOS by 10 h results in statistically significant reductions in the average patient wait time and average patient queue. A comparative analysis of department also showed considerable improvements in average wait time, average number of patients in queue and average LOS with the addition of two beds. Originality/value This research highlights the applicability of simulation in healthcare. Through data that are often readily available in bed management tracking systems, the operational behavior of a hospital can be modeled, which enables hospital management to test the impact of changes without cost and risk.

Performance Optimization of Semiconductor Manufacturing Equipment by the Application of Discrete Event Simulation

2008 ◽  
Author(s):  
Markus Pfeffer ◽  
Richard Oechsner ◽  
Lothar Pfitzner ◽  
Heiner Ryssel ◽  
Berthold Ocker ◽  
...  
Keyword(s):  
Discrete Event Simulation ◽  
Cycle Time ◽  
Performance Optimization ◽  
Semiconductor Manufacturing ◽  
Discrete Event ◽  
Simulation Models ◽  
Manufacturing Equipment ◽  
Event Simulation ◽  
Different Types ◽  
The Impact

Semiconductor wafer fabrication facilities (wafer fabs) are amongst the most complex production facilities. State-of-the-art wafer fabs comprise a large product variety, hundreds of processing steps per product, almost hundreds of machines of different types, and automated transportation systems combined with reentrant flows throughout the fab. In addition to the high complexity, wafer fabs require very high capital investment and an undisturbed operation. Semiconductor manufacturers are facing fierce competition as more global capacity is being added. Through this intense competition, semiconductor manufacturers have to improve their processes from a technological as well as from a logistical point of view in order to be successful within the global market. The logistics not only involves fab wide optimization strategies but also the individual equipment performance, for example cycle time and throughput, has to be considered. In this paper, the need for performance optimization of semiconductor manufacturing equipment is identified and the capability of discrete event simulation for such optimizations is being elaborated. Characteristics of different types of simulation models are described and the simulation model selection is explained. For case studies, several simulation models of different semiconductor manufacturing equipment have been developed. Using five examples, different optimization strategies, dependent on the application of the semiconductor manufacturing equipment, have been investigated by discrete event simulation. The paper shows the influence of the integration of metrology into deposition equipment, the impact of different batch sizes for oxidation processes, and the optimized dimensioning of photolithography equipment. Furthermore, the throughput and cycle time of different deposition equipment are optimized by the evaluation of various improvement strategies. All investigations have been performed with real data extracted from already utilized equipment or at least with data from the equipment suppliers of prototype equipment.

Cardiac Arrhythmia Simulations for Comparing Remote Telemetry Monitoring Systems

2018 ◽  
Vol 7 (1) ◽  
pp. 129-131
Author(s):  
Noa Segall ◽  
Ron’Nisha Franklin ◽  
Melanie C. Wright
Keyword(s):  
Response Times ◽  
Direct Method ◽  
Discrete Event ◽  
Heart Rhythm ◽  
Simulation Models ◽  
Monitoring Systems ◽  
Cardiac Events ◽  
Efficient System ◽  
Event Simulation ◽  
The Impact

To increase the potential for timely detection of cardiac events, hospitalized patients who are at risk for critical arrhythmias are put on telemetry to continuously monitor their heart rhythm. However, telemetry monitoring systems vary widely between hospitals, there are few guidelines for decisions regarding optimal practices, and few studies have compared the efficiency of different monitoring systems. Our goal was to determine the impact of different monitoring systems on the time to detect and respond to critical cardiac events. To this end, we compared the process of communicating a critical alarm to the patient’s nurse in 2 hospitals with different monitoring systems, to determine the most efficient system. We conducted in situ unannounced simulations of cardiac arrest in the 2 hospitals to measure the response times of monitor watchers and patient care unit staff. As expected, we found response times to be shorter in the hospital that had a more direct method for monitor watchers to contact patients’ nurses. In addition to the method for communicating arrhythmias, there are many other differences between the monitoring systems in the 2 hospitals that could also have affected response times. We are using discrete event simulation to develop computer simulation models of the hospitals that will allow us to take multiple factors into account when comparing them.

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