ExtendSim popularity in higher institutions has experienced phenomenal growth as researchers have learned of ExtendSim's ease of use combined with its high level of accuracy. ExtendSim has become an indispensable component supporting essential phases of innovative research projects in universities worldwide.
In response to the growing popularity of using ExtendSim in research projects, we established the ExtendSim Academic Research Grant program. Under the Grant, the ExtendSim Team supports students who are obtaining an advanced degree (Masters Thesis, PhD, or PostDoc) by subsidizing the cost of a full Model Developer Edition of ExtendSim for use by the student during the term of their research project. In exchange, the student provides a description of the research and quarterly updates throughout the term of the project. At the end of the research, all findings (ie. paper, project, etc.) and the ExtendSim model formulated are passed on to ANDRITZ Inc. for publication on the ExtendSim web site.
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Current Academic Research Grants
These research projects have been awarded an ExtendSim Academic Research Grant and are currently in progress. To learn about past projects completed under the ExtendSim Academic Research Grant program, please see Academic Research Grants Fulfilled.
Digital Twin • Critical Services Provider
Model-Based Systems Engineering
Xueping Li plus a team of Grad Students in the Department of Industrial & Systems Engineering
University of Tennessee, Knoxville
Grant awarded: May 10, 2022
To be completed: December 31, 2022
Hypothesis.
We propose the concept of using simulation as a soft digital twin to address complex decision-making processes in the age of digital transformation. The central hypothesis is that a well-developed simulation model is able to provide insights into critical business processes, conduct scenario analysis, and ultimately optimize the overall system performance.
Project description.
We adopt a multi-phased research plan for an undisclosed organization that provides critical systems.
Phase 1: Identify one area of interest (e.g., the maintenance organization) for detailed systems analysis. Identify model elements such as: variables, stocks, flows, and inter-relationships. Develop conceptual model structures. Establish functional requirements (e.g., real-time data acquisition, intelligent adaptation (machine learning), what-if scenarios, optimization capability, etc.) Define and establish the data sources, systems process mapping (including initial causal loop diagrams). Identify optimal modeling approaches (in consideration of multi-method modeling). Develop final causal flow diagrams, interfaces, and modeling elements for other appropriate methods.
Phase 2: Develop prototype model(s) for designated systems. Efforts include defining desired functionality, design and operational requirements, operating parameters, data resource needs, visualization, user interface, portability and access, system boundaries, and programing dictionaries (data, variables, parameters, links, interfaces, machine intelligence functionality etc.). Initial models will be constructed for proof-of-concept demonstration and preliminary V&V.
Phase 3: Complete design of the systems model, finalize verification and validation, complete UI and data visualization, complete documentation, and validate systems interface operability.
Questions this research will answer.
- RQ1: Will the soft digital twin, i.e., the simulation model, be able to produce results (e.g., throughput, overall job cycle time, resource utilization, etc.) that is comparable to historical system performance through a data-driven approach?
- RQ2: Will the soft digital twin help conduct what-if analysis with high confidence?
- RQ3: Will the soft digital twin help with system optimization under given certain operational constraints?
How research strategy will be designed to help answer these questions.
Please refer to the aforementioned multi-phase research plan.
How ExtendSim will be used in this project.
We plan to use ExtendSim to build the simulation model for the identified system (i.e., the maintenance system).
What is unique about this project?
The undisclosed organization that we work with provides critical services that demand high availability and any downtime will lead to exorbitant high costs.
Why the interest in the indicated subject?
Modeling and simulation have been one of my major research areas. The potential use of simulation as a digital twin is of particular interest to me.
What impact will this research make to the existing current state of knowledge?
We will have graduate research students working on the project. We intend to publish our research findings that will be disseminated widely.
Publications.
I have published 140+ peer-review journal and conference papers. Below are a few recent publications.
Zeyu Liu, Anahita Khojandi, Xueping Li, Akram Mohammed, Robert Davis, and Rishikesan Kamaleswarn (2022). A Machine Learning-Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction. INFORMS Journal on Computing.
Rodney Kizito, Zeyu Liu, Xueping Li, Kai Sun (2022). Multi-stage Stochastic Optimization Of Islanded Utility-microgrids After Natural Disasters. Operations Research Perspectives, No. 100235.
Chien-fei Chen, et al. (2022). Extreme Events, Energy Security and Equality through Micro and Macro Levels: Concepts, Challenges and Methods. Energy Research & Social Science, Vol. 85, No. 102401.
Yulin Sun, Cong Guo and Xueping Li (2022). An Order-Splitting Model for Supplier Selection and Order Allocation in a Multi-echelon Supply Chain. Computers and Operations Research, Vol. 137, No. 105515.
Rodney Kizito, Zeyu Liu, Xueping Li and Kai Sun (2021). Stochastic Optimization of Distributed Generator Location and Sizing in an Islanded Utility Microgrid During a Large-Scale Grid Disturbance. Sustainable Energy, Grids and Networks, Vol. 27, No. 100516.
Kaike Zhang, Xueping Li, and Mingzhou Jin (2021). Efficient solution methods for a general r-interdiction median problem with fortification. INFORMS Journal on Computing.
Rodney Kizito, Phillip Scruggs, Xueping Li, Michael DeVinney, Joseph Jansen, and Reid Kress (2021). Long Short-Term Memory Networks for Facility Infrastructure Failure and Remaining Useful Life Prediction. IEEE Access, Vol. 9, pp. 67585 - 67594. (doi.org/10.1109/ACCESS.2021.3077192).
Zeyu Liu, Anahita Khojandi, Akram Mohammed, Xueping Li, Lokesh K. Chinthala, Robert L. Davis, Rishikesan Kamaleswaran (2021). HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study. Computers in Biology and Medicine, Vol. 2021, No. 104255.
Chuang Liu, Huaping Chen, Xueping Li, Zeyu Liu (2021). A Scheduling Decision Support Model For Minimizing The Number Of Drones With Dynamic Package Arrivals And Personalized Deadlines. Expert Systems With Applications, Vol. 167, No. 114157.
Thomas Berg, Xueping Li, Tami Wyatt, Rupy Sawhney (2021). Agent-Based Modeling Simulation of Nurse Medication Administration Errors. Computers, and Nursing Informatics, Vol. 39, No. 4, pp. 187-197.
Yongzhen Li, Xueping Li, Jia Shu, Miao Song, Kaike Zhang (2021). A General Model and Efficient Algorithms for Reliable Facility Location Problem under Uncertain Disruptions. INFORMS Journal on Computing.
Updates.
Coming soon!
Digital Twin • Sow Farms
Anticipate future performance problems and issue alerts
Mihai Catalin Doja
Masters in Computer Science
University of Lleida, Spain
Grant awarded: May 17, 2023
To be completed: March 1, 2025
Hypothesis.
Digital twins can be useful to monitor farming operations at different degrees of digitalisation. They can anticipate future performance problems and issue alerts.
Project description.
The research plan will embrace three different tasks:
- Implement a simulation model of a sow farm.
- Develop metrics to measure technical and economic performance of the system.
- Integrate the model with an Excel spreadsheet to ease the use for practitioners.
Questions this research will answer.
The main answer is to test if a digital twin may help to delineate a path way for digitization in sow farms since many data sources could feed the model by using sensors. Not less important is to progress an interface development for an effective adoption of such decision support systems.
How research strategy will be designed to help answer these questions.
The support of her supervisor, Professor Lluis Miguel Pla Aragon, will be important in this strategy since he has already developed several approaches to the system Mihai that will have to review and update his existing models. For validation, they have an agreement with the Official Spanish databank of sow farms (i.e. BDPorc) who can provide them with real data to test and validate the model.
How ExtendSim will be used in this project.
The main use will be the development of the simulation model, using animation to refine the code, and connections to input/output with Excel spreadsheets to speed up the running of simulations allowing further and deeper data analysis.
What is unique about this project?
There are no simulation models for sow farms developed with the idea of digital twin (and digitization) in mind.
Why the interest in the indicated subject?
Because it is a strategic research line of the Department of Mathematics in the University of Lleida.
What impact will this research make to the existing current state of knowledge?
If the research progresses adequately, it is intended to go further in depth as Mihai is considering the development of a PhD.
Publications.
Mateo, J., Pagès-Bernaus, A., Pla-Aragones, L.M., Castells-Gasia, J.P., Babot-Gaspa, D. (2021) An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock Sensors.
Pla-Aragones, L.M. 2021. The Evolution of DSS in the Pig Industry and Future Perspectives. In: Papathanasiou J., Zaraté P., Freire de Sousa J. (eds) EURO Working Group on DSS. Integrated Series in Information Systems. Springer, Cham.
Updates.
Coming soon!
Healthcare • Optimization of System Operations
Healthcare System
Maram Battisha
Bachelor's Degree
Arab Academy For Science, Technology And Maritime Transport, Industrial & Management Engineering - Alexandria, Egypt
Grant awarded: January 6, 2025
To be completed: October 1, 2025
Hypothesis.
The hypothesis for this project is that integrating a dynamic, data-driven simulation model can optimize healthcare system operations by improving resource allocation, reducing patient wait times, and enhancing overall patient satisfaction. Specifically, using tools like ExtendSim to simulate healthcare processes will highlight inefficiencies and offer strategies for improvement in real-world healthcare settings.
Project description.
The research plan involves creating a comprehensive model of a healthcare system, incorporating elements like patient flow, staffing, resource allocation (such as beds, medical equipment, and medications), and wait times. This model will be constructed using ExtendSim, a simulation software that allows for visualizing and analyzing complex systems. The goal is to identify bottlenecks in healthcare operations and propose strategies to enhance efficiency without compromising patient care. The project will involve data collection from existing healthcare systems, setting up the simulation model, and running various scenarios to assess potential improvements.
Questions this research will answer.
- What are the primary factors contributing to inefficiencies in healthcare systems, particularly in terms of patient wait times and resource allocation?
- How can a simulation model optimize resource allocation to reduce wait times and improve patient care?
How research strategy will be designed to help answer these questions.
- Literature Review: To understand current inefficiencies and identify relevant factors affecting healthcare systems.
- Data Collection: Obtain data on patient flow, wait times, resource usage, and staffing from healthcare facilities
- Model Building: Develop a dynamic simulation model using ExtendSim to replicate the healthcare system.
- Scenario Testing: Run multiple scenarios with varying parameters (e.g., staffing levels, patient arrivals, and resource availability) to identify improvements.
- Analysis: Analyze the simulation results to uncover inefficiencies and suggest actionable solutions for improving healthcare operations.
- Optimization: Refine the model based on findings and explore optimization strategies to enhance patient care and system performance.
How ExtendSim will be used in this project.
ExtendSim will be used to create a simulation model of the healthcare system that integrates various components, such as patient flow, resource allocation, and staffing. The tool will simulate different operational scenarios and allow for testing various interventions (e.g., changes in staffing, resource levels) to assess their impact on system performance. By adjusting the parameters within ExtendSim, the research will uncover solutions to improve efficiency and patient satisfaction while minimizing wait times and maximizing resource utilization.
What is unique about this project?
This project is unique because it applies simulation modeling to a comprehensive healthcare system, taking into account multiple dynamic factors such as patient arrival rates, resource availability, and staffing. Unlike simpler models that focus on one aspect of healthcare, this project aims to simulate the entire system's operations. Furthermore, by using ExtendSim, a robust simulation tool, the project provides a more accurate and actionable framework for real-world healthcare system improvements.
Why the interest in the indicated subject?
The healthcare sector faces numerous challenges, including long patient wait times, overburdened resources, and inefficiencies in care delivery. These challenges directly affect patient satisfaction and the overall quality of care. I am interested in this project because improving healthcare delivery using data-driven methods and simulations can lead to meaningful changes that improve public health and reduce inefficiencies. Additionally, healthcare systems are complex and multifaceted, which presents a rewarding challenge for creating impactful solutions.
What impact will this research make to the existing current state of knowledge?
This research will contribute to the healthcare field by providing actionable insights for improving system efficiency. By applying simulation modeling, healthcare administrators will gain a clearer understanding of how various operational changes affect patient outcomes and resource utilization. Ultimately, this project could inform decision-making processes in healthcare systems, leading to improved care delivery, reduced costs, and better patient satisfaction.
Publications.
As this is an initial research proposal, no publications related to this specific topic have been authored yet. However, I am confident that the results of this project will lead to valuable findings and potential publications in healthcare management or simulation modeling journals.
Updates.
Coming soon!
Healthcare • Policy Setting for Operating Room Management
Surgery cancellation: causes, hospital-related strategies
Mona Koushan
Otago University
PhD in Management
Grant awarded: June 5, 2019
To be completed: May 31, 2021 & January 2022
Updated completion date: December 2022
Project description.
ExtendSim will be used to set a policy for OR management that balances the rate of surgery cancellation with hospital utilization taking into account the relationship and inter-relationship of different circumstances (e.g., scheduling policies, case mix, and hospital size) that affect policies.
Questions this research will answer.
- Which policy will be suitable for the hospital according to the circumstances?
- How does a manager set a policy that balances the rate of surgery cancellation and hospital utilization accounting for the circumstances they face?
How research strategy will be designed to help answer these questions.
To answer these questions, a discrete-event simulation will be used to investigate these relationships so Ms. Kaushan can develop a model/framework that will help the hospital manager to determine the most applicable policy considering the hospital’s circumstances.
How ExtendSim will be used in this project.
ExtendSim will be used to set a policy for OR management that balances the rate of surgery cancellation with hospital utilization taking into account the relationship and inter-relationship of different circumstances (e.g., scheduling policies, case mix, and hospital size) that affect policies.
What is unique about this project?
In general, studies consider a single policy to manage operation room capacity (and treat the policy as ‘fixed’ or ‘given’), but there are few articles that compare these policies and find which one can be more efficient (Y. B. Ferrand et al., 2014; Van Riet & Demeulemeester, 2015). While Duma & Aringhieri (2019) used each policy, they didn’t consider how hospital circumstances and various factors (outlined by Van Riet and Demeulemeester (2015), such as:
- the available number of ORs
- the available beds in downstream (ICU & PICU)
- operation duration characteristics
- scheduling policy
- patient volume
- case mix
...can affect the managers’ decision for setting an appropriate policy.
Why the interest in the indicated subject?
Increasing demand for healthcare service with limited resources has led to hospitals paying more attention to the use of resources. Given that the hospitals are naturally faced with different kinds of variability and uncertainty (such as surgery duration, length of stay (LOS), recovery duration and emergency arrivals), the fully planned of resources without any time buffers will require some level of surgery cancellations that will affect patient satisfaction. There are various factors that cause surgical cancellations. So, it become a main issue for the hospital manager to set a policy that balances the rate of surgery cancellation and hospital resource utilization, accounting for the hospital’s circumstances.
What impact will this research make to the existing current state of knowledge?
Today’s increasing demand for surgery and limited hospital resources has led to more surgery cancellations and resource under-utilization. Thus, solving this problem helps to even the patient flow while reducing the surgery cancellation (hospital-related causes) and managing the OR’s buffers with resource utilization according to the inherent uncertainty.
Publications.
Jahani, N., Jabalameli, M.S., Koushan, M., Rezaei Vandchali, H., Wood, L.C., "Bio-objective model for sustainable biofuel supply chain network design under uncertainties", Bioresource Technology Report (Derived from an external research project). Contribution: Validated the methodology and statistical analysis, interpreted, and wrote the final version of the manuscript. (Under review 2022).
Koushan, M., Wood, L.C., Greatbanks R. "A Scenario-based robust optimisation approach for multi-objective, multi-stage operating room scheduling with time and demand uncertainties", European Journal of Operational Research. (Under review 2022).
Koushan, M., Wood, L.C., Greatbanks R. Evaluating Factors Associated with the Cancellation and Delay of Elective Surgical Procedures: A Systematic Review, International Journal for Quality in Health Care. 2021, 33(2).
Duong, L. N. K.; Wang, J. X.; Wood, L. C.; Reiners, T.; Koushan, M. The Value of Incremental Environmental Sustainability Innovation in the Construction Industry: An Event Study. Constr. Manag. Econ. 2021, 0 (0), 1–21.
Koushan, M. Wood, L.C., Greatbanks, R. "Cause of surgery cancellation: A systematic literature review", Health Policy (submitted February 2020).
Koushan, M., Jolai, F., Wood, L.C., Hybrid Differential Evolution-Data mining (HDEDM) Algorithm for uncertain dynamic CMS problem, 48th International Conference of Computer and Industrial engineering, Auckland, New Zealand, December 2018.
Azadeh, A., Sheikhalishahi, M., Koushan, M. An integrated fuzzy DEA–Fuzzy simulation approach for optimization of operator allocation with learning effects in multi products CMS. Applied Mathematical Modelling (2013) 9922-9933.
Rafiei, H., Rabbani, M., Koushan, M., Effect of Motivation & Learning Curve In Dynamic Cell Formation And The Worker Assignment Problem, International Journal of Engineering Sciences & Research Technology (2012) 481-497.
Koushan, M., Jolai, F., Wood, L.C., Hybrid Differential Evolution-Data mining
(HDEDM) Algorithm for uncertain dynamic CMS problem, 48th International Conference of Computer and Industrial engineering, Auckland, New Zealand, December 2018.
Yadegari, M., Koushan, M., Inventory control, pricing of perishable items, taking into account time-dependent effects of inflation, 3th International Conference on Industrial Engineering and Sustainable Management, Isfahan, Iran, December 2016. (It was named one of the seven top articles).
Updates.
September 6, 2022 - I was granted my Ph.D. degree and the thesis is embargoed in the University library for further publication. After a discussion with my supervisor, I decided to add more analysis to make the paper ready for publishing in a leading journal.
The model is ready, only further analysis is required which might take up to the end of the year.
Small-sized hospital hybrid policyJanuary 24, 2021 - I am working on extracting articles from my thesis that require further analysis. I have submitted my PhD thesis at the end of December 2021, which the estimated time for oral examination would be in April 2022. After the oral examination, I need to apply examiners’ comments on my thesis. ExtendSim models are designed, but further analysis is required to present the output of the model. View screenshots of in-progress models here.
Extended Grant to September 2022 for Mona to improve the research.
September 21, 2021 - The models are designed for three policies separately and the experiments are designed. Now it is the time to run the model with different policies and scenarios. There are 90 different statuses that must be examined with these models. View screenshots of in-progress models here.
Extended Grant to January 2022 due to COVID related delays in data collection.
May 3, 2021 - Since this project is directly related to health bodies, my projects has delayed in data collection stage due to COVID-19, which has affected simulation study. Please extend the Grant through September 2021.
In this stage I am preparing input data for simulation model which are extracted from conducted surgery scheduling model with MATLAB. The ExtendSim model is almost designed and it is examined in the small-size of hospital. The model is going to be run with different scenarios and different size of hospitals. In this case some part of model might be changed.
July 27, 2020 - I am modeling my simulation model to check with a usual surgery scheduling how different policies can help hospitals to improve performance (less surgery cancellation and more resource utilization). I intend to check the model and different policies with comprehensive surgery scheduling modeling too. Comparing generated data from these two models, hospital managers can easily decide which policy works more efficiently given their hospital circumstances.
My model is almost complete. I just need to add some formulation to calculate rate of surgery cancellation rate and add inputs.
Industrial Symbiosis Network • Agri-Food Sector
Modelling and Evaluating an Industrial Symbiosis Network in Agri-Food Sector through advanced simulation - Moving towards circular economy in the UK
Vikram Ramakrishnan
PhD in Management
Queen's University Belfast, United Kingdom
Grant awarded: May 25, 2024
To be completed: September 30, 2025
Hypothesis.
The hypothesis of this research proposes that developing an Industrial Symbiosis Network centered around a cheese factory, with symbiotic partnerships involving a beer brewery, soap producer, anaerobic digester, dairy farm, and sports nutrition factory, can significantly contribute to sustainable production practices and circular economy principles. Leveraging Material Flow Analysis, the study aims to develop and quantify resource flows within the network and evaluate its technical feasibility, economic viability, and environmental implications. Additionally, by constructing a process-based simulation model of the cheese factory, the research seeks to assess the network's performance in terms of technical feasibility, economic viability and environmental implications against operational disruptions. It is hypothesised that the establishment of such an integrated network will enhance resource efficiency, minimize waste generation, and foster mutually beneficial relationships among the symbiotic partners, ultimately promoting sustainable industrial practices and circular economy initiatives.
Project description.
The research follows a 3-paper format for each year (currently in Year 3):
- Year 1: Paper 1 - Literature Review on Industrial Symbiosis modelling approaches and IS indicators.
- Year 2: Paper 2 - Analysing Industrial Symbiosis Opportunity through Material Flow Cost Accounting: A Perspective of Dairy Production & Processing Unit in Northern Ireland.
- Year 3: Paper 3 - An Investigation into the Operational Vulnerabilities in Industrial Symbiosis Network Involving Dairy Processors and Producers.
Questions this research will answer.
- Research Question 1 (Paper 1): What are the suitable modelling approaches for designing an Industrial Symbiosis Network anchored around dairy production and processing?
- Research Question 2 (Paper 2): What are the technical feasibility, economic viability and environmental implications of an Industrial Symbiosis Network developed around Dairy Processors and Producers?
- Research Question 3 (Paper 3): What are the operational vulnerabilities in Dairy Processing & Production and what will be their implications on the technical, economic and environmental performance of the Industrial Symbiosis Network with dairy processors as anchors?
How research strategy will be designed to help answer these questions.
To model and evaluate Industrial Symbiosis in Agri-Food Networks, particularly taking the case of dairy production and processing, this research has been broadly divided into three phases.
- Phase I (Year 1)
- To find a suitable method which can model such an IS network to capture key resource flows and the dynamic nature of those flows, further indicators that can evaluate the synergic performance.
- Methodology: Literature Review on IS modelling approaches and IS indicators.
- Result: Material Flow Analysis to develop the Industrial Symbiosis Network and Discrete Event Simulation to incorporate dynamic properties to analyse its performance under vulnerabilities.
- Phase II (Year 2)
- To capture the key resource flows (quantification), which will be key synergy flows in the proposed IS network model and key stages in the production which can have an impact on the quantification of by-products.
- Methodology: Material Flow Modelling using STAN2.7 software; Literature and Empirical data collection to validate.
- Result: Performance of Industrial Symbiosis Network in terms of technical feasibility, economic viability and environmental implications.
- Phase III (Year 3)
- A Process-based Simulation Model to evaluate the performance of a developed IS network.
- Methodology: Simulation Model of a Cheese Factory in ExtendSim Pro software using Discrete Events and Discrete Rate Library; Empirical Data used to Validate.
- Expected Result: Performance of Industrial Symbiosis in terms of technical feasibility, economic viability and environmental implications under operational disruption in Cheese Factory affecting cheese whey flow.
How ExtendSim will be used in this project.
ExtendSim will be used in phase 3 i.e. year 3 of this research project to develop an advanced process-based simulation model of a cheddar cheese factory using the discrete rate and discrete event module in the ExtendSim Pro. The model will be simulated with scenarios developed where the impact on quality & quantity of cheese whey (by-product) due to disruptions and the factors which affect them will be analysed. The performance of the developed Industrial Symbiosis Network will be analysed under each scenario in terms of technical feasibility, economic viability and environmental performance.
What is unique about this project?
This research stands out due to its focus on the operational vulnerabilities within symbiotic industrial networks, particularly in sectors characterised by dynamic resources like milk and its derivatives. Unlike existing literature, this study aims to fill the gap by employing discrete event simulation techniques to comprehensively analyse the network's functioning in a process-based manner. By combining principles of circular economy, waste management and advanced simulation technique, this research offers a novel approach to understanding and mitigating risks within Industrial Symbiosis Networks. This unique perspective facilitates a deeper exploration of resource optimisation, waste minimisation, and environmental enhancement, contributing significantly to the advancement of circular economy practices.
Why the interest in the indicated subject?
This topic holds immense importance due to the escalating challenges posed by population growth, resource scarcity, and environmental degradation. The agri-food sector, a major contributor to these issues, impacts the environment through deforestation, water depletion, greenhouse gas emissions etc. Implementing sustainability and circular economy practices is crucial to mitigate these impacts. Investigating and embracing these principles can potentially unlock efficient resource utilisation, reduce environmental footprint, and foster resilience in the face of adversity.
What impact will this research make to the existing current state of knowledge?
The findings can help the dairy industry and the symbiont partners to understand and integrate sustainable supply chain practices and waste management techniques, and further unlock potential benefits of symbiotic relationships among themselves. Also, the insights gained through this research can be further used for developing Industrial Symbiosis models for more complex scenarios.
Publications.
Not yet. Working on the call for papers in a couple of ABS 3 Journals.
Updates.
January 2, 2025 - The model has been updated and improved in two main aspects:
- Enhance and update the dairy farm and cheese factory operations and processes to make them more realistic.
- To improve randomness using more suitable distribution in multiple blocks involved in the model.
Progress on ExtendSim model: The model has been revised and updated through more interviews and factory visits conducted in the last 4 months. Dairy Farms have been further categorised into High-Tech Dairy Farms (Sophisticated equipment involved in production) and Normal Dairy Farms (Less sophisticated equipment). In the Cheese Factory part of the model, more processes involving whey processing have been developed and updated in the model with parameters collected from a local large-scale cheese factory in Northern Ireland.
July 10, 2024 - Modeling Dairy Production from a Dairy Farm and Dairy Processing center to a Cheese Factory was worked on in this quarter. Discrete Rate and Discrete Event libraries were used to build the model using Valve, Tank, Diverge, Create, Queue, Exit, and Interchange blocks. The model has been developed but the parameters considered in the model needs to work on to develop realistic scenarios to study the model under different types of disruptions. The base of the simulation model has been built, now the coming weeks the model will be worked on to enhance it with realistic parameters in terms of flows/travel times or other operations and what distributions to use under each scenarios developed. The grantee has planned to speak to individuals from the dairy industry (particularly cheese producers) in the coming weeks/months to enhance this simulation model of dairy farm and cheese factory particularly in the parameters to include randomness into the model and enhance the study to more realistic scenarios.
Inventory Management • Implementing RFID Technology
Implementing Smart Solutions in Inventory
Jana Mohamed Shaban
Bachelor's Degree in Industrial & Management Engineering
Arab Academy For Science, Technology And Maritime Transport, Industrial & Management Engineering - Alexandria, Egypt
Grant awarded: December 23, 2024
To be completed: September 1, 2025
Hypothesis.
By implementing RFID (Radio Frequency Identification) technology in inventory management, the system will improve accuracy, efficiency, and real-time visibility in identifying the location and quantities of pallets within a warehouse environment, leading to optimized inventory control and reduced operational costs.
Project description.
The research will focus on developing and testing an RFID-based solution for warehouse inventory management. The system will utilize RFID tags attached to pallets, and RFID readers placed strategically within the warehouse to track their movements and quantities. The system will provide real-time data on the location and status of the pallets, helping to improve inventory accuracy, reduce manual errors, and speed up stock-taking and order fulfillment processes.
Steps for the research plan:
- Literature review: Review existing studies on RFID in inventory management and smart warehouses.
- System design: Create a plan for the placement of RFID tags and readers, and integrate it into warehouse management software.
- Pilot testing: Implement a small-scale pilot of the RFID system in a controlled warehouse environment.
- Data collection: Measure efficiency improvements, accuracy of inventory tracking, and potential cost savings.
- Analysis: Analyze the results from the pilot to assess the impact of RFID on operational efficiency and inventory management.
- Optimization: Identify areas for system enhancement based on pilot results.
- Full-scale implementation: Roll out the RFID system across the entire inventory management system if the pilot is successful.
Questions this research will answer.
- How accurate is the RFID-based system in tracking the location of pallets compared to traditional manual methods?
- What are the improvements in inventory management efficiency (e.g., time savings, error reduction) after implementing RFID technology?
- How does RFID impact the overall cost of inventory management?
- What are the technical and operational challenges involved in deploying RFID in a warehouse environment?
- What is the return on investment (ROI) of implementing RFID-based inventory management?
- How well does the RFID system integrate with existing warehouse management systems (WMS)?
How research strategy will be designed to help answer these questions.
- Comparative analysis: Conduct a direct comparison between RFID-based inventory tracking and traditional manual methods to assess accuracy and efficiency
- Time and cost analysis: Track the time spent on inventory management tasks before and after RFID implementation, and compare costs related to labor, equipment, and errors.
- Pilot study: Implement the RFID system in a small-scale environment, measuring key performance indicators (KPIs) such as accuracy, efficiency, and cost.
- Interviews and surveys: Gather feedback from warehouse staff on their experience with the new system and any operational challenges faced.
- Data-driven assessment: Use data analytics tools to assess the impact of RFID on operational metrics, providing quantifiable evidence for ROI.
How ExtendSim will be used in this project.
ExtendSim will be used to simulate the flow of pallets within the warehouse environment, integrating the RFID tracking system into the simulation model. This will allow for the testing of various scenarios, such as different tag placement strategies, warehouse layouts, and operational workflows. By using ExtendSim, we can model and predict the potential impact of the RFID system on inventory efficiency and identify areas for optimization before full-scale deployment.
What is unique about this project?
The uniqueness of this project lies in its practical approach to applying RFID technology to real-world inventory management challenges, coupled with a detailed simulation of warehouse operations using ExtendSim. Unlike theoretical studies or isolated case studies, this project aims to provide a comprehensive solution that integrates RFID tracking with real-world operational metrics, offering measurable improvements in efficiency and cost reduction.
Why the interest in the indicated subject?
I am interested in this subject because inventory management is a critical aspect of modern supply chains, and RFID technology offers a way to revolutionize how inventory is tracked and managed. The potential to improve operational efficiency, reduce errors, and lower costs is a strong motivator, especially in industries with complex logistics and large-scale warehouses.
What impact will this research make to the existing current state of knowledge?
This research will contribute to the field of supply chain management by demonstrating the real-world benefits and challenges of implementing RFID for inventory management. It will provide insights into how RFID technology can be effectively integrated into existing warehouse operations, improving inventory accuracy and operational efficiency. The findings could influence how industries adopt and implement smart technologies, offering a blueprint for best practices in inventory management.
Publications.
Currently, I do not have any published papers, but this research project could potentially lead to future publications in supply chain management and technology adoption journals.
Updates.
Coming soon!
Lean Manufacturing • Impact of Preventative Maintenance
Examining the Impact of Preventative Maintenance in a Lean Manufacturing Environment using Simulation Models
Mohammed Al Ghaithi
Bachelor's Degree in Mechanical & Manufacturing Engineering
Dublin City University - Dublin, Ireland
Grant awarded: January 31, 2025
To be completed: July 1, 2025
Hypothesis.
Setting up different variations in maintenance plans influences the production cycle time especially with highly utilized machines.
Project description.
Making an example simulation model and applying preventative maintenance to it to experiment with variations in production cycle time.
Questions this research will answer.
Studying the impact of scheduled and unscheduled maintenance plans and their different variations on an example production system by measuring different KPIs.
How research strategy will be designed to help answer these questions.
Simulating an example manufacturing system and applying lean manufacturing techniques and shutdowns to apply maintenance and failure of machines.
How ExtendSim will be used in this project.
Used as a simulation tool of the system and machine failures and maintenance plans.
What is unique about this project?
This project proves the basics of applying maintenance plans as close as it can be to a real world application.
Why the interest in the indicated subject?
As part of my course, it is to my interest to learn more about organisation of manufacturing systems and their care maintenance plans which keep them efficient.
What impact will this research make to the existing current state of knowledge?
The results obtained from the study will go towards general education purposes for upcoming generations of manufacturing engineering students.
Publications.
Coming soon!
Updates.
Coming soon!
Oil Processing Plant • Integrating Smart Heat Exchanger Systems
Smart Heat Exchanger for Oil Plants
Jana Amr, Ahmed Abdellatif, Moataz Khaled, Omar El Sayed, and Yassin Awny
Bachelor's Degree in Industrial & Management Engineering
Arab Academy For Science, Technology And Maritime Transport, Industrial & Management Engineering - Alexandria, Egypt
Grant awarded: May 16, 2025
To be completed: February 1, 2026
Hypothesis.
The integration of smart heat exchanger systems into oil processing plants can significantly improve cooling efficiency, reduce idle time, and enhance overall production performance compared to traditional natural cooling methods.
Project description.
- Analyze the current cooling process in the oil plant and identify inefficiencies.
- Design a customized smart heat exchanger system incorporating sensors, pumps, and nanofluids.
- Evaluate the impact of reduced cooling time on productivity through simulation and prototyping.
- Implement and test a physical prototype to validate performance improvements
- Assess the economic feasibility through benefit-cost analysis and calculate the payback period.
- Assess the economic feasibility through benefit-cost analysis and calculate the payback period.
Questions this research will answer.
- What inefficiencies exist in the current oil cooling system?
- How can smart technologies enhance the performance of heat exchangers?
- What are the economic and environmental impacts of implementing a smart heat exchanger system?
- How does the use of nanofluids and sensor integration affect cooling efficiency?
How research strategy will be designed to help answer these questions.
- Conduct a literature review to identify current challenges and technologies.
- Use Fishbone and gap analysis to understand root causes and bridge performance gaps.
- Perform system design including geometry and fluid modifications.
- Use AHP analysis to select optimal system components.
- Conduct economic impact analysis to measure ROI, savings, and payback period.
- Develop a prototype and test it to measure improvements in cooling time and production output.
How ExtendSim will be used in this project.
ExtendSim will be used to:
- Simulate the existing and proposed cooling processes.
- Model system dynamics including flow, temperature changes, and idle time reduction.
- Evaluate various heat exchanger configurations and operational parameters.
- Visualize and quantify the performance improvements before physical implementation.
What is unique about this project?
- Combines smart sensor integration and nanofluid-based thermal management.
- Applies industrial engineering principles to modernize traditional oil plant operations.
- Designs a customized double-pipe heat exchanger with advanced geometry for improved heat transfer.
- Includes a prototype and cost-benefit analysis to bridge theoretical design and real-world application.
Why the interest in the indicated subject?
The subject combines multiple engineering domains—mechanical, industrial, and energy—with a clear real-world application. It aligns with the global push for smarter, energy-efficient systems in traditional industries and offers an opportunity to make a measurable impact on industrial sustainability and performance.
What impact will this research make to the existing current state of knowledge?
- Sets a precedent for modernizing thermal management systems in oil and other process industries.
- Demonstrates how smart technologies can replace outdated cooling methods.
- Encourages adoption of IoT-based monitoring and nanotechnology in industrial applications.
- Provides a scalable model for other facilities facing similar challenges.
Publications.
None yet.
Updates.
Coming soon!
Operational Flow • Nuclear Waste Management
Nuclear Waste Dispositioning
Alexis Andaverde
PhD in Industrial Engineering
Florida State University, College of Engineering - Tallahassee, FL USA
Grant awarded: October 31, 2024
To be completed: July 1, 2028
Hypothesis.
We hypothesize that making structural changes to a typical nuclear waste disposal process can enhance waste handling throughput and process time. We can leverage ExtendSim to develop a discrete event model, or digital twin, of a nuclear waste disposal process. This discrete event simulation will provide valuable insights into the processes bottlenecks and required resource allocation to achieve steady and favorable system dynamics.
Project description.
The research will focus on modeling the operational flow of a general nuclear waste management through discrete event simulation. This approach will offer a more comprehensive understanding of waste disposal processing improvements that ensure long-term safety, regulatory compliance, and nuclear accountability in nuclear waste management.
The study will begin with the development of a baseline model that represents typical waste disposal operations. This baseline model includes processes from the moment waste arrives at the site, waste inspection, transportation of waste to underground facilities, waste emplacement, and truck departure. The baseline model will be a composition of three sequential processes.
Once the baseline model is created, a detailed analysis will be conducted to identify inefficiencies and bottlenecks at various stages. Stress tests and sensitivity analyses will follow to assess system performance under varying conditions, such as increased waste loads, equipment failures, and varying safety risks.
The final phase of the research will involve making structural changes to baseline model. The structural changes will be informed by the inefficiencies identified and consultations with practitioners in the field. Overall, this work will help us gain insights into safer, more cost-effective, and efficient nuclear waste dispositioning processes.
Questions this research will answer.
- What are the major bottlenecks in nuclear waste disposal operations from waste arrival to final truck departure?
- How does resource allocation (e.g., trucks, staff, equipment) impact the efficiency and cost of operations?
- What are the key performance indicators that affect the system's performance, and how sensitive is the system to changes in these variables?
- How can different operational scenarios, such as increased waste volumes or changes in schedules, be optimized for better overall system performance?
- How can a typical nuclear waste disposal process be made more resilient to disruptions, ensuring regulatory compliance and safety?
- What structural enhancements can be made to the nuclear waste disposal process?
How research strategy will be designed to help answer these questions.
- Modeling Current System: Using ExtendSim, we will create a detailed discrete event simulation of a nuclear waste disposal system, representing the flow from waste arrival to underground placement to final truck departure.
- Data Collection: Use existing public operational data on current processes, including waste transportation, storage, and handling times. Use expert opinion to provide estimates of any remaining required inputs.
- Managerial Insight Analysis: Identify bottlenecks, inefficiencies, and strengths in the current system, using data from the model.
- Stress Testing & Sensitivity Analysis: Perform stress tests and sensitivity analysis to evaluate how the system responds to changes in key parameters, such as increased waste loads, equipment breakdowns, or scheduling changes.
- Propose and Evaluate System Improvements: Develop additional discrete event simulation models that mimic different operational scenarios evaluate scenario performance of KPIs compared to the baseline simulation.
- Final Recommendations: Present a comprehensive set of recommendations for efficient nuclear waste disposal systems based on the research findings.
How ExtendSim will be used in this project.
ExtendSim will be the primary simulation tool used to build a detailed model of a nuclear waste disposal system. ExtendSim will allow for the representation of discrete events and the capturing of complex interactions between different stages of nuclear waste management, including transportation, storage, and disposal. ExtendSim’s capabilities in discrete event simulation, efficiency/utilization analysis, and scenario exploration will be critical in developing stress tests, conducting sensitivity analyses, and optimizing the system for enhanced performance. The tool's flexibility will enable the exploration of multiple scenarios, which will inform decision-making for improving operational efficiency.
What is unique about this project?
This research will offer one of the first comprehensive discrete event simulations focused specifically on the nuclear waste disposal from end-to-end operations. The combination of sensitivity analysis, stress testing, and scenario optimization within the nuclear waste management field is rare. Furthermore, by using advanced simulation techniques, this project will provide actionable insights not only for improving day-to-day operations but also for addressing long-term challenges such as waste volume increases and regulatory changes. The ability to model and optimize different scenarios will offer valuable strategies for improving safety and efficiency in nuclear waste disposition.
Why the interest in the indicated subject?
Nuclear waste management is a critical global challenge, with far-reaching consequences for safety, sustainability, and public health. My interest stems from the opportunity to contribute to a field where technological and operational improvements can have long-term environmental and societal impacts. Studying nuclear waste disposal operations can provide a model for future nuclear waste management systems globally, which aligns with my broader interest in sustainable environmental management and risk mitigation.
What impact will this research make to the existing current state of knowledge?
This research will provide novel insights into the nuclear waste management processes through the use of discrete event modeling. It will help set new standards for operational efficiency, safety, and cost-effectiveness in the handling and disposal of nuclear waste. The research findings can potentially be applied to other nuclear waste management facilities worldwide, promoting best practices in handling hazardous waste. Moreover, stress tests and sensitivity analyses will contribute to better preparedness for future challenges in nuclear waste management, including regulatory changes and the introduction of new waste streams.
Publications.
Andaverde, Alexis. "The Development and Analysis of Practical Decision Support Tool Implementations: Case Studies From Small Business to Large Government Enterprise." MS thesis. The University of Texas Rio Grande Valley, 2024.
Andaverde, Alexis, and Hiram Moya. "Development and Analysis of a Small-Business Inventory Control System; A Case Study." IISE Annual Conference and Expo. IISE, 2023.
Updates.
February 6, 2025 -This quarter, we focused on creating the current state of a nuclear waste processing system by structuring and implementing a Discrete Event Simulation (DES) model. The work involved developing a systematic framework for modeling key processes, incorporating various simulation components, and improving resource allocation strategies to enhance operational efficiency. Additional refinements were made to better represent system constraints and workflow dynamics.
The model has been structured to represent key aspects of the nuclear waste disposition process. It includes essential process flows, resource management strategies, and simulation components that allow for an analysis of system operations. Further refinements are planned to enhance accuracy and such as tracking of all necessary data points over multi simulation runs, implementing a schedule for workers in the system, and determining a steady state of the system.
Optimization • Dairy Production Prediction
Use of AI tools and synergies in conjunction with operations research models
Osvaldo Palma Rubio
Doctorate of Mathematics
University of Lleida, Spain
Grant awarded: May 25, 2023
To be completed: September 1, 2026
Hypothesis.
The ability to predict cow's milk production has improved over the years with the use of data analytics tools. Many researchers mention the benefits of using simulation methods or artificial intelligence in the field of milk production. However, a combination of such techniques is not observed in the scientific literature, which presents an important research opportunity. Such a combination is expected to generate greater degrees of adjustment than by using the two techniques separately.
Project description.
- Year 1: Scoping review on dairy production prediction topics using artificial intelligence tools. Paper number 1.
- Year 2: Development of a discrete event simulation model that allows economic evaluation of productive policies and predictions of cow's milk production. Paper number 2.
- Year 3: Development of one or several neural networks for the prediction of cow's milk production and its comparison with traditional models. Paper number 3.
- Year 4: Development of a hybrid model that combines operational research methodologies and artificial intelligence for the productive improvement of dairy farms. Paper number 4.
Questions this research will answer.
How can the use of data analytics tools improve the prediction of cow's milk production?
How ExtendSim will be used in this project.
ExtendSim will be used to simulate the behavior of a dairy cattle and explore the net present value impact of different policies.
What is unique about this project?
According to a review of the current literature, there is no combination of simulation and artificial intelligence tools that try to make predictions about cow's milk production. The problem to be solved is important because the rapid growth of the world population will increase the demand for milk, therefore the use of technology will play an important role in being able to supply this demand.
Why the interest in the indicated subject?
The interest of this research is that it will be possible to study the production of cow's milk in order to meet the growing global demand for this important food through the use of data analysis tools.
What impact will this research make to the existing current state of knowledge?
Cow's milk production is important (Godfray et al., 2010). and depends on factors such as the productive potential of the animal and the conditions of exploitation. A common estimator of the current production of an animal is past production (time series) (Dongre et al., 2016), but there are other health or environmental factors that can condition the expression of this potential such as the presence of lameness and other diseases that can benefit from digital measurements of productive variables (Contla et al., 2021) such as body weight, diet control (composition and intake), early detection of pre- and postpartum diseases, assessment of body condition, etc. The main objective of the research is to explore the application of artificial intelligence (AI) tools for the analysis and prediction of productive variables such as cow's milk production. This study aims to be a contribution to data analytics (Data Analytics) combining traditional tools of operational research with AI.
Publications.
Currently in the last stage of preparation of my first publication consisting of the bibliographic review of this topic.
Updates.
Coming soon!
Optimization • Warehouse Operations
Optimizing Warehouse Operations for Enhanced Efficiency and Demand Fulfillment
Zeina Amr Abdel Moniem
Bachelor's Degree
Arab Academy For Science, Technology And Maritime Transport, Industrial & Management Engineering - Alexandria, Egypt
Grant awarded: January 2, 2025
To be completed: September 1, 2025
Hypothesis.
Efficient utilization of warehouse resources through simulation-based optimization can significantly improve operational efficiency, minimize resource waste, and ensure timely customer order fulfillment.
Project description.
This research focuses on leveraging simulation modeling to analyze and optimize warehouse operations. The study will identify inefficiencies in resource allocation, order picking, and storage systems. Using ExtendSim, various scenarios and layouts will be simulated to determine the most effective strategies for:
- Reducing picking times.
- Improving space utilization.
- Enhancing order processing rates.
- Minimizing operational costs.
Questions this research will answer.
- What are the key bottlenecks in the current warehouse operations?
- How can resource allocation be optimized to balance workloads and minimize idle times?
- What picking strategies (e.g., zone, batch, wave) yield the highest efficiency for varying demand levels?
- How does warehouse layout impact operational throughput and order accuracy?
How research strategy will be designed to help answer these questions.
- Data Collection: Gather data on current warehouse operations, including order volumes, resource allocation, and workflow patterns
- Model Development: Use ExtendSim to create a detailed simulation model of the warehouse.
- Scenario Analysis: Test various strategies for layout optimization, picking methods, and resource utilization.
- Performance Metrics: Measure key performance indicators such as picking time, order accuracy, throughput, and cost efficiency.
- Validation: Compare simulation results with real-world operations to validate the model.
How ExtendSim will be used in this project.
- Modeling Warehouse Processes: Develop a detailed simulation of warehouse workflows, including storage, picking, and packing.
- Scenario Testing: Simulate alternative layouts, resource allocation strategies, and order fulfillment techniques to evaluate their impact.
- Optimization: Use ExtendSim's built-in optimization tools to identify the best configurations for maximizing efficiency.
- Visualization: Create dynamic visual representations of the warehouse to understand resource flows and process bottlenecks.
What is unique about this project?
This project uniquely combines simulation modeling with a focus on real-time adaptability to changing customer demands and resource availability. It emphasizes optimizing end-to-end warehouse operations rather than isolated processes, offering a holistic approach to efficiency improvements.
Why the interest in the indicated subject?
The growing demand for efficient supply chain operations in e-commerce and retail highlights the need for robust warehouse systems. Optimizing these operations is crucial for ensuring timely delivery and reducing costs, aligning with my academic and professional interests in supply chain management and operations research.
What impact will this research make to the existing current state of knowledge?
The research will provide actionable insights into improving warehouse efficiency and customer satisfaction. It will also contribute to the development of adaptable, simulation-based methodologies for operational optimization in logistics and supply chain management.
Publications.
Coming Soon!
Updates.
Coming soon!
Packaging Line • Enhancing Efficiency & KPI Performance
Lean Transformation and Digitalization of the 2S Packaging Line at Mondelez
Shady Ayman Shahen & Amin Menessi
Bachelor's Degree in Industrial & Management Engineering
Arab Academy For Science, Technology And Maritime Transport, Industrial & Management Engineering - Alexandria, Egypt
Grant awarded: November 22, 2024
To be completed: September 1, 2025
Hypothesis.
Implementing lean manufacturing principles and a digitalized system on the 2S packaging line will increase operational efficiency, reduce scrap and rework rates, and enhance KPI accuracy as compared to manual methods.
Project description.
- Project Overview and Objectives
- Goal: Enhance the efficiency and KPI performance of the 2S packaging line through lean manufacturing, digital validation, and Industry 4.0 integration.
- Objectives:
- Identify and analyze bottlenecks in the current process.
- Reduce the percentage of scrap and rework to improve yield.
- Validate the new digital system against manual methods for accuracy and reliability.
- Literature Review
- Lean Manufacturing in Packaging: Study existing lean manufacturing frameworks, with a focus on tools like 5S, Value Stream Mapping (VSM), and Total Productive Maintenance (TPM) to streamline packaging line processes.
- Industry 4.0 and Digitalization: Research the role of Industry 4.0 technologies, such as IoT, real-time data analytics, and automation in improving packaging line efficiency and accuracy. KPI Improvement and Waste Reduction: Review case studies or research articles on similar projects, focusing on strategies to decrease scrap, rework, and other inefficiencies.
- Current State Assessment (Problem Identification)
- Data Collection:
- Conduct a comprehensive analysis of the 2S packaging line using historical data, real-time observations, and interviews with line operators.
- Gather data on scrap and rework rates, downtime, production speed, and other key performance indicators (KPIs).
- Bottleneck Identification:
- Use tools like Value Stream Mapping (VSM) to map out the process and pinpoint areas with delays, excessive handling, or bottlenecks.
- Manual vs. Digital Comparison:
- Collect data from the existing manual tracking system and compare it with the new digital system.
- Identify any discrepancies in data accuracy, timeliness, and ease of access.
- Root Cause Analysis:
- Apply techniques such as the Fishbone Diagram or 5 Whys to identify the underlying causes of inefficiencies, high scrap rates, and bottlenecks.
- Data Collection:
- Solution Design and Proposal (Implementation Plan)
- Lean Implementation:
- Introduce lean methodologies like 5S to improve organization, Kanban for better flow control, and SMED (Single-Minute Exchange of Dies) if changeovers are frequent and time-consuming.
- Digital System Validation:
- Design a test plan to validate the digital system’s accuracy compared to the manual method over a defined period.
- Implement data capture and comparison metrics, such as accuracy rate, time savings, and ease of data retrieval.
- Industry 4.0 Integration:
- Explore incorporating IoT devices, sensors, and data analytics software to improve real-time monitoring and predictive maintenance capabilities.
- Training and Change Management:
- Develop a training program for operators and supervisors to ensure a smooth transition to new lean processes and digital tools.
- Lean Implementation:
- Data Collection and Analysis Post-Implementation
- After implementing lean changes and validating the digital system, collect data over a trial period on:
- Scrap and rework rates Line efficiency and speed improvements KPI performance, accuracy, and accessibility from the digital system versus manual tracking
- Analysis:
- Use statistical methods to analyze data trends and measure improvements compared to the baseline established in the current state assessment.
- After implementing lean changes and validating the digital system, collect data over a trial period on:
- Evaluation and Conclusions
- Hypothesis Testing:
- Assess if the proposed lean methodologies and digitalization efforts led to measurable improvements in line efficiency, reduced waste, and improved data accuracy.
- ROI Analysis:
- Calculate potential cost savings from scrap reduction, efficiency gains, and other improvements to demonstrate the economic impact of the project.
- Recommendations:
- Provide actionable insights for scaling these methods to other lines or areas of the factory.
- Hypothesis Testing:
- Documentation and Final Report
- Compile a final report that includes the project background, methodology, data analysis, findings, and recommendations for long-term improvement.
Questions this research will answer.
- What are the main bottlenecks in the current 2S packaging line?
- How do these bottlenecks impact overall efficiency and KPIs?
- What is the current percentage of scrap and rework in the 2S packaging line?
- What factors contribute to the high scrap and rework rates?
- How accurate is the new digitalized system in capturing production data compared to the manual tracking system?
- How do lean improvements and digitalization impact key performance indicators (KPIs) such as production rate, downtime, and overall equipment effectiveness (OEE)?
How research strategy will be designed to help answer these questions.
- Current State Analysis (Data Collection and Observation) by Direct Observations, Historical Data Review, Operator and Supervisor Interviews
- Lean Manufacturing Tool Application such as VSM, Root Cause Analysis (5 Whys and Fishbone Diagram)
- Digital System Validation and Comparison
- Implementation of Solutions and KPI Measurement
- Evaluation of Results and Cost-Benefit Analysis by ROI
How ExtendSim will be used in this project.
- Modeling the Current Packaging Line
- Bottleneck and Root Cause Analysis
- Testing Lean Manufacturing Interventions
- Scrap and Rework Reduction Analysis
- Digital System Validation
- Testing Industry 4.0 Technology Integration
- KPI Improvement and ROI Estimation
What is unique about this project?
My project stands out because it integrates both lean manufacturing and Industry 4.0 principles to tackle real-world efficiency challenges on a specific packaging line. This combination of traditional lean methodologies with advanced digital validation makes your approach comprehensive and forward-looking. By validating the digital system against manual data, you're addressing a unique aspect: assessing the accuracy and reliability of digital tools in a way that bridges traditional practices with emerging technologies.
Additionally, the use of simulation software like ExtendSim to model and test solutions adds a layer of precision and predictive capability that’s rare in many lean projects. This simulation will allow you to foresee the impact of interventions and tailor solutions based on specific KPI improvements, ultimately making your project a valuable case study in modern manufacturing optimization.
Is there anything specific you want to highlight as a unique feature, like the digital accuracy validation or the focus on scrap reduction?
Why the interest in the indicated subject?
- Passion for Process Optimization: Lean manufacturing principles, with their focus on improving efficiency, reducing waste, and maximizing value, align with a deep interest in solving operational challenges.
- Real-World Application: Working at Mondelez on a real-world packaging line gives you the opportunity to apply theoretical knowledge in a practical setting, with a direct impact on operational performance.
- Professional Growth: Working on this project provides the opportunity to develop a diverse set of skills, including lean management, digital system validation, data analysis, and simulation modeling.
What impact will this research make to the existing current state of knowledge?
- Refining KPI Metrics: The project will focus on improving key performance indicators (KPIs) like efficiency, downtime, and OEE (Overall Equipment Effectiveness). By proving the effectiveness of digital tools in tracking and improving these KPIs, your research will help redefine how manufacturing performance should be measured, encouraging a shift toward data-driven, real-time metrics.
- Cost-Benefit Analysis for Digital Investments: The ROI calculations from your research will provide other manufacturers with valuable benchmarks on the cost-effectiveness of implementing digital systems and lean methodologies, helping them make informed decisions about future investments in technology.
Publications.
Coming soon!
Updates.
Coming soon!
Pharmaceutical Production Line Optimization • Increasing Efficiency, Reducing Bottlenecks, & Enhancing Quality
Improving Production Line for Effervescent Sachets
Salma Ahmed Abdelghani
Bachelor's Degree in Engineering
Arab Academy For Science, Technology And Maritime Transport, Industrial & Management Engineering - Alexandria, Egypt
Grant awarded: January 2, 2025
To be completed: October 1, 2025
Hypothesis.
Optimizing the production line will increase efficiency, reduce bottlenecks, and enhance product quality.
Project description.
Study current inefficiencies, simulate potential improvements using ExtendSim, and evaluate results based on cycle time, throughput, and rejection rates.
Questions this research will answer.
- What are the main bottlenecks in the current process?
- How can variability and downtime be reduced?
- What improvements will maximize efficiency and cost-effectiveness?
How research strategy will be designed to help answer these questions.
- Collect real production data (e.g., process times, equipment performance).
- Simulate changes in ExtendSim to test solutions.
- Analyze and compare key performance metrics.
How ExtendSim will be used in this project.
Simulate the production process, test improvements, and visualize bottlenecks to optimize workflows.
What is unique about this project?
Focuses on improving production for effervescent sachets, a challenging product, using modern simulation tools.
Why the interest in the indicated subject?
Strong interest in optimizing pharmaceutical production to improve efficiency and product availability.
What impact will this research make to the existing current state of knowledge?
Will provide a practical framework for optimizing pharmaceutical manufacturing lines, reducing costs, and improving quality.
Publications.
No prior publications, but this project will contribute to my first.
Updates.
February 25, 2025 - We are currently working on a simulation project at Arvind Advanced Materials, focusing on optimizing production line efficiency through line balancing and defect analysis. We are using ExtendSim to model and test various scenarios for better workload distribution and defect reduction strategies.
So far, we have:
- Collected key production data, including SAM, defect rates, operator output, and rework data.
- Analyzed bottlenecks and inefficiencies within the line.
- Explored ExtendSim’s functionalities through available resources, including online tutorials and documentation, to better understand model creation.
Our mid-term review with our college faculty is scheduled for next week, on the 4th and 5th. Following this, we plan to start building and testing the simulation models, aiming for implementation next month.
Production Line Optimization • Line Balancing to Improve Production Efficiency
Optimising Production Line: A Simulation model for style flexibility and machine reliability
Thiruvedhini R. & Arya Sukhadev
Bachelor's Degree in Fashion Technology
National Institute of Fashion Technology - Chennai, India
Grant awarded: November 22, 2024
To be completed: June 1, 2025
Hypothesis.
- Implementing a simulation-based line balancing model improves production efficiency by minimizing workflow disruptions, reducing defect rates, and accommodating operator absenteeism.
- Enhancing machine reliability and style flexibility through simulation leads to a significant reduction in production downtime and operational inefficiencies.
- Simulation-based adjustments in line balancing during operator absenteeism can mitigate its negative impact on production output and quality.
Project description.
- Develop a simulation model to balance production lines effectively.
- Minimize workflow disruptions caused by machine failures or operator absenteeism.
- Improve defect rates by analyzing the interaction between operator efficiency and machine reliability.
- Enable style flexibility to meet varying production demands efficiently.
Questions this research will answer.
- How does operator absenteeism disrupt workflow and affect productivity?
- Can simulation techniques predict and mitigate the impact of absenteeism on line performance?
- What are the optimal strategies for reallocating resources during operator absences?
- How does machine downtime affect workflow disruptions and defect rates?
- Can a simulation model account for machine reliability to create more robust production plans?
- What preventive measures can reduce the impact of machine failures on productivity?
How research strategy will be designed to help answer these questions.
- Collect data on absenteeism and its impact on production output.
- Analyze how tasks get delayed or bottlenecked.
- Simulate production workflows with absenteeism scenarios to observe disruptions.
- Build a simulation model to predict line performance under absenteeism.
- Test strategies like reallocating operators or adjusting workflows in the simulation.
- Identify operations most affected by absences.
- Simulate resource reallocation strategies, like cross-training or assigning floaters.
- Analyze data on machine failures and their effects on production and defects.
- Simulate downtime scenarios to see how it disrupts workflows.
- Identify operations where downtime causes the most defects or delays.
- Include machine reliability metrics (e.g., failure rates) in the simulation.
- Test how different maintenance plans affect production performance.
- Validate the simulation by comparing its predictions to real-world performance.
- Analyze failure causes and test preventive actions like maintenance schedules or backups.
- Simulate their impact on reducing downtime and maintaining productivity.
- Recommend cost-effective measures based on simulation results.
How ExtendSim will be used in this project.
- Simulate workflows, tasks, and dependencies.
- Test the impact of operator absences on productivity.
- Optimize strategies for reallocating operators and machines.
- Run "what-if" scenarios for absenteeism, machine failures, and style changes.
What is unique about this project?
- Combines absenteeism, machine reliability, and style flexibility in one model.
- Focuses on real-time disruptions like operator absence and machine breakdowns.
- Uses predictive models for optimizing workflows and minimizing defects.
- Adaptable to various styles and production setups.
Why the interest in the indicated subject?
We are interested in this subject because it combines real-world challenges in apparel manufacturing with innovative solutions through simulation. By addressing issues like operator absenteeism, machine reliability, and line balancing, the project offers practical ways to improve productivity and quality. The use of simulation tools like ExtendSim allows for testing and optimizing strategies in a controlled environment, making it possible to find effective solutions without disrupting actual production. This makes the project both impactful and adaptable to various manufacturing settings, enhancing efficiency and problem-solving in the industry.
What impact will this research make to the existing current state of knowledge?
The research on Optimizing Production Lines will help apparel manufacturers improve flexibility, machine reliability, and workforce efficiency. By using simulation models, companies can quickly adapt to changing styles, reduce machine downtime, and better allocate workers for higher productivity. This will lead to faster production, lower costs, and reduced waste. The research will also provide data-driven insights for better decision-making, helping businesses become more scalable, adaptable, and sustainable while staying competitive in the market.
Publications.
Coming soon!
Updates.
Coming soon!
Production Scheduling • Multi-Product Powdered Mix Factory
Optimization of Production Scheduling in a Multi-Product Powdered Mix Factory
Farida Haytham
Bachelors of Science Degree in Industrial & Management Engineering
Arab Academy For Science, Technology And Maritime Transport, Industrial & Management Engineering - Alexandria, Egypt
Grant awarded: March 24, 2025
To be completed: September 1, 2025
Hypothesis.
By developing an optimized production scheduling plan that considers the constraints and limitations of Factory X's production system, it is possible to maximize throughput, minimize bottlenecks, reduce work-in-progress (WIP) levels, and improve machine utilization, ultimately enhancing the overall efficiency and productivity of the powdered mix factory.
Project description.
- Project Overview
- Factory X struggles with inefficiencies like long changeovers, bottlenecks, and low machine utilization, which impact production and demand fulfillment.
- Goal: Create an optimized production schedule to improve efficiency and throughput.
- Research Steps
- Literature Review: Study methods for improving scheduling, reducing bottlenecks, and managing WIP using tools like Lean Manufacturing, SMED, and simulation.
- Data Collection: Visit Factory X, interview staff, and observe operations to gather data on machine performance, product flow, and scheduling challenges.
- Model Development
- Build a simulation model using ExtendSim software to replicate Factory X’s production system.
- Inputs: Product demand, machine performance, cycle times, and changeover times.
- Outputs: Key performance indicators (KPIs) like throughput, WIP levels, and machine utilization.
- Testing and Optimization
- Run simulations to test different scheduling scenarios, including:
- Product allocation to packaging machines.
- Lean methods like Kanban and Just-In-Time (JIT).
- Choose the best schedule based on improved KPIs (e.g., higher utilization, lower WIP, and faster production).
- Validation and Results
- Validate the model by comparing simulation results with real factory performance.
- Refine the model and recommend the optimal schedule to Factory X.
- Conclusion
- Summarize findings and benefits of the new schedule (e.g., improved efficiency and reduced delays).
- Future work: Explore real-time scheduling with digital tools and expand improvements to other factory areas.
Questions this research will answer.
- How can bottlenecks in the production process be identified and minimized?
- What tools or strategies are most effective in reducing bottlenecks in multi-product production lines?
- What methods can be applied to optimize the allocation of product mixes to packaging machines?
- How can the new Machine X be integrated effectively into the scheduling plan to maximize throughput?
- How can changeover times be minimized to improve production efficiency?
- What scheduling or lean tools (e.g., SMED, 5S) can reduce downtime caused by changeovers?
- What strategies can be implemented to reduce Work-in-Progress (WIP) levels?
- Can techniques like Just-In-Time (JIT) or Kanban help maintain smooth production flow?
- What is the most efficient production scheduling plan for Factory X?
- Which scenario or schedule configuration offers the best balance of throughput, cycle time, and machine utilization?
- How can the proposed solutions be validated and measured for effectiveness?
- What KPIs (e.g., throughput, machine utilization, WIP) should be used to evaluate the success of the optimized schedule?
- What is the overall impact of optimized scheduling on Factory X’s performance?
- Can the proposed solutions meet demand while reducing delays and idle times without additional investment?
How research strategy will be designed to help answer these questions.
- Identify and Reduce Bottlenecks
- Observe production flow and use tools like Value Stream Mapping and simulation to locate bottlenecks.
- Test solutions such as load-leveling to reduce delays.
- Optimize Product Allocation
- Analyze machine performance data and test product-mix scenarios in ExtendSim, including the integration of Machine X.
- Minimize Changeover Times
- Evaluate the current changeover process and apply Lean tools like SMED to streamline operations.
- Reduce WIP Levels
- Implement Lean methods such as Kanban and Just-In-Time to control WIP and improve production flow..
- Develop the Best Schedule
- Use simulation to test various scheduling plans, focusing on throughput, machine utilization, and reduced idle time.
- Validate and Measure Results
- Compare simulation outcomes with real data using KPIs like throughput, cycle time, and machine utilization.
- Evaluate Overall Impact
- Assess how the optimized schedule improves Factory X’s efficiency and scalability.
How ExtendSim will be used in this project.
- Model Factory X’s Production System
- Build a simulation model replicating the production process, including product flow, machine performance, and production constraints.
- Incorporate key factors like cycle times, changeover times, demand percentages, and machine OEE.
- Test Scheduling Scenarios
- Use ExtendSim’s scenario testing features to evaluate various product allocation and scheduling strategies.
- Simulate different configurations, including the integration of the new Machine X, to find the most efficient solution.
- Analyze Bottlenecks and Inefficiencies
- Identify bottlenecks, idle times, and areas of inefficiency in the production line.
- Experiment with bottleneck reduction strategies using Lean tools and heuristic methods within the simulation.
- Optimize Machine Utilization
- Evaluate the impact of different scheduling scenarios on machine utilization rates and throughput.
- Aim to improve utilization above 80% while meeting production demand.
- Measure Key Performance Indicators (KPIs)
- Track KPIs such as average cycle time, throughput, WIP levels, and changeover time during simulations.
- Use these results to compare and select the best scheduling plan.
- Run “What-If” Scenarios
- Test various "what-if" scenarios to assess how changes in demand, machine breakdowns, or new scheduling policies impact production.
- Validate the Model
- Cross-check simulation outputs with real factory data to ensure the model’s accuracy and reliability.
What is unique about this project?
- Focus on a Real-World Factory (Factory X)
- The project directly addresses production scheduling challenges in a specific, real-world multi-product powdered mix factory, making the solutions practical and highly relevant.
- Combination of Lean and Simulation Techniques
- Integrates Lean Manufacturing tools (e.g., SMED, Kanban) with advanced simulation modeling using ExtendSim to optimize scheduling and improve production efficiency.
- Multi-Objective Problem-Solving
- Tackles complex, multi-objective scheduling challenges, such as minimizing bottlenecks, reducing changeover times, and improving machine utilization, all while meeting demand.
- Customization for Sequence-Dependent Scheduling
- Focuses on sequence-dependent scheduling, a critical but challenging aspect due to shared resources and frequent product changeovers.
- Integration of a New Machine
- Explores how to effectively integrate a newly introduced packaging machine (Machine X) into the production line, providing a fresh and actionable perspective.
- Use of ExtendSim for Tailored Solutions
- Employs simulation to replicate Factory X’s unique operations, allowing for precise testing and optimization of various scheduling strategies..
- Clear Business Impact
- Aims to enhance Factory X’s throughput, reduce delays, and lower idle time without requiring significant capital investment, offering immediate benefits to the business.
- Ethical and Practical Approach
- Maintains a strong focus on real-world feasibility, confidentiality, and sustainability, ensuring that the proposed solutions align with Factory X’s goals and ethical standards.
Why the interest in the indicated subject?
- Real-World Impact: Optimizing production scheduling directly improves efficiency, reduces costs, and boosts productivity.
- Complex Problem-Solving: Scheduling in a multi-product, sequence-dependent environment is a challenging and exciting problem to tackle.
- Passion for Operations Research: I enjoy applying tools like simulation and Lean principles to solve practical manufacturing issues.
- Career Relevance: This aligns with my goal to work in manufacturing, logistics, or operations management.
- Learning Opportunity: It allows me to deepen my knowledge of simulation, scheduling, and factory optimization.
- Collaboration with Industry: Working with Factory X provides hands-on experience and a chance to make meaningful improvements.
What impact will this research make to the existing current state of knowledge?
- Improved Scheduling Practices
- Provides a practical framework for optimizing production scheduling in multi-product, sequence-dependent environments, which can be adopted by other factories facing similar challenges.
- Integration of Lean and Simulation
- Demonstrates how combining Lean tools (e.g., SMED, Kanban) with simulation modeling can deliver efficient, cost-effective solutions.
- Enhancing Factory Efficiency
- Offers actionable strategies to reduce bottlenecks, minimize changeover times, and improve machine utilization, which can set new benchmarks for efficiency in the powdered mix industry.
- Scalable Solutions
- Develops scalable methods that can be adapted to other industries with shared resources and complex production lines.
- Promoting Data-Driven Decision-Making
- Encourages the use of simulation tools like ExtendSim for decision-making, improving accuracy and reducing risks in production planning.
- Boosting Academic and Industry Collaboration
- Acts as a case study for successful collaboration between academia and industry, highlighting the real-world application of research.
Publications.
Conference Paper (In Progress)
Title: Optimization of Production Scheduling in a Multi-Product Powdered Mix Factory
Description: Highlights the use of simulation and Lean techniques to solve scheduling challenges in sequence-dependent production environments.
Contribution: Co-authored methodology and simulation results sections.
Poster Presentation Event: Industrial Engineering Student Conference
Topic: Application of Lean Tools for Bottleneck Reduction in Manufacturing
Contribution: Presented research findings and practical applications of SMED and Kanban systems.
Collaborative Study (Planned)
Title: Improving Efficiency Through Simulation-Based Scheduling in Multi-Product Lines
Contribution: Assisted with case study analysis and ExtendSim model development.
Updates.
Coming soon!
Resource Allocation • Multitasking in Livechat Support Centers
Santiago Gallino
University of Pennsylvania • The Wharton School
Academic Journal Publication
Grant awarded: January 12, 2023
To be completed: January 11, 2024
Project description.
When workers multitask, their performance depends on the multitasking intensity and duration.
Questions this research will answer.
- What are the effects of multitasking on chat duration?
- What are the effects of multi-branding on chat duration?
- What are the implications for optimal task assignment?
How research strategy will be designed to help answer these questions.
We have collected data from a large contact center and we plan to use ExtendSim to generalize the insights coming from this setting.
What is unique about this project?
Combining field data with a mathematical model and simulation outcomes uncovered this understudy effect.
What impact will this research make to the existing current state of knowledge?
Help better allocate resources in contact centers.
Publications.
Please see: https://oid.wharton.upenn.edu/profile/sgallino/
Updates.
Coming soon!