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.

Evaluating intensive care unit admission and discharge policies using a discrete event simulation model

SIMULATION ◽  
2020 ◽  
Vol 96 (6) ◽  
pp. 501-518 ◽  
Author(s):  
Imran Hasan ◽  
Esmaeil Bahalkeh ◽  
Yuehwern Yih
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Discrete Event ◽  
Wait Time ◽  
Average Waiting Time ◽  
Event Simulation ◽  
The Impact ◽  
Patient Mix

The efficient utilization and management of a scarce resource such as the intensive care unit (ICU) is critical to the smooth functioning of a hospital. This study investigates the impact of a set of operational policies on ICU behavior and performance. Specifically, the implemented policies are (a) wait time thresholds on how long patients can wait for an ICU bed, (b) the time windows during which patient discharges and transfers take place, and (c) different patient mix combinations. The average waiting time of patients for ICU beds and the admission ratio, the ratio of admitted patients to total ICU bed requests, are the performance measures under consideration. Using discrete event simulation, followed by analysis of variance and post hoc tests (Tukey multiple comparison), it is shown that increasing discharge windows has a statistically significant impact on the total number of admissions and average patient wait times. Moreover, average waiting time increased when wait time thresholds increased, especially when the number of emergency surgeries in the mix increased. In addition, larger proportions of elective surgery patients in the patient mix population can lead to significantly reduced ICU performance.

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.

scholarly journals Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation

2021 ◽  
Author(s):  
G.J. Melman ◽  
A.K. Parlikad ◽  
E.A.B. Cameron
Keyword(s):  
Resource Allocation ◽  
Discrete Event Simulation ◽  
Elective Surgery ◽  
Discrete Event ◽  
Specific Patient ◽  
Event Simulation ◽  
The Impact ◽  
Allocation Decisions ◽  
Allocation Strategies ◽  
Resource Allocation Decisions

AbstractCOVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

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.

scholarly journals Measuring lean implementation for maintenance service companies

2016 ◽  
Vol 7 (1) ◽  
pp. 35-61 ◽  
Author(s):  
Stephan J. de Jong ◽  
Wouter W.A. Beelaerts van Blokland
Keyword(s):  
Transaction Cost ◽  
Discrete Event Simulation ◽  
Continuous Improvement ◽  
Lean Manufacturing ◽  
Discrete Event ◽  
Asset Specificity ◽  
Lean Implementation ◽  
Content Type ◽  
Event Simulation ◽  
Maintenance Service

Purpose – Implementation of lean manufacturing is currently performed in the production industry; however, for the airline maintenance service industry, it is still in its infancy. Indicators such as work in process, cycle time, on-time performance and inventory are useful indicators to measure lean implementation; however, a financial economic perspective taking fixed assets into consideration is still missing. Hence, the purpose of this paper is to propose a method to measure lean implementation from a fixed asset perspective for this type of industry. With the indicators, continuous improvement scenarios can be explored by value stream discrete event simulation. Design/methodology/approach – From literature, indicators regarding asset specificity to measure lean implementation are found. These indicators are analysed by a linear least square method to know if variables are interrelated to form a preliminary model. The indicators are tested by value stream-based discrete event simulation regarding continuous improvement scenarios. Findings – With the new found lean transaction cost efficiency indicators, namely, turnover, gross margin and inventory pre-fixed asset (T/FA, GM/FA and I/FA, respectively), it is possible to measure operation performance from an asset specificity perspective under the influence of lean implementation. Secondly, the results of implementing continuous improvement scenarios are measured with the new indicators by a discrete event simulation. Research limitations/implications – This research is limited to the airline maintenance, repair and overhaul (MRO) service industry regarding component repair. Further research is necessary to test the indicators regarding other airline MRO service companies and other sectors of complex service industries like health care. Practical implications – The lean transaction cost efficiency model provides the capability for a maintenance service company to simulate the effects of process improvements on operation performance for service-based companies prior to implementation. Social/implications – Simulation of a Greenfield process can involve employees with possible changes in processes. This approach supports the adoption of anticipated changes. Originality/value – The found indicators form a preliminary model, which contributes to the usage and linkage of theories on lean manufacturing and transaction cost theory – asset specificity.

Improving outpatient phlebotomy service efficiency and patient experience using discrete-event simulation

2016 ◽  
Vol 29 (7) ◽  
pp. 733-743 ◽  
Author(s):  
Kenneth Yip ◽  
Suk-King Pang ◽  
Kui-Tim Chan ◽  
Chi-Kuen Chan ◽  
Tsz-Leung Lee
Keyword(s):  
Discrete Event Simulation ◽  
Scenario Analysis ◽  
Performance Metrics ◽  
Discrete Event ◽  
Evidence Based ◽  
Content Type ◽  
Event Simulation ◽  
Service Efficiency ◽  
Related Service ◽  
The Relationship

Purpose – The purpose of this paper is to present a simulation modeling application to reconfigure the outpatient phlebotomy service of an acute regional and teaching hospital in Hong Kong, with an aim to improve service efficiency, shorten patient queuing time and enhance workforce utilization. Design/methodology/approach – The system was modeled as an inhomogeneous Poisson process and a discrete-event simulation model was developed to simulate the current setting, and to evaluate how various performance metrics would change if switched from a decentralized to a centralized model. Variations were then made to the model to test different workforce arrangements for the centralized service, so that managers could decide on the service’s final configuration via an evidence-based and data-driven approach. Findings – This paper provides empirical insights about the relationship between staffing arrangement and system performance via a detailed scenario analysis. One particular staffing scenario was chosen by manages as it was considered to strike the best balance between performance and workforce scheduled. The resulting centralized phlebotomy service was successfully commissioned. Practical implications – This paper demonstrates how analytics could be used for operational planning at the hospital level. The authors show that a transparent and evidence-based scenario analysis, made available through analytics and simulation, greatly facilitates management and clinical stakeholders to arrive at the ideal service configuration. Originality/value – The authors provide a robust method in evaluating the relationship between workforce investment, queuing reduction and workforce utilization, which is crucial for managers when deciding the delivery model for any outpatient-related service.

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