Reliability Engineering

System performance analysis often demands more than traditional reliability block diagramming (RBD) tools can juggle. It’s critical to take into account the relationship between components. How does the reset of one component affect the whole system? How can you identify potential failures? How can you accurately predict and manage the risks around assets that could fail and cause unnecessary and expensive downtime? How can you resolve uncertainty in multi-stage manufacturing systems?

The Reliability module in ExtendSim Pro is the missing link bridging reliability block diagrams with the pinpoint accuracy of simulation to mimic the behavior of systems using dynamic reliability modeling. Maintenance reliability professionals, asset managers, and predictive maintenance teams are turning to simulating RBDs in ExtendSim to help manage their asset reliability program, reduce rate failures, optimize alternate flow paths, deal with intermediate product storage, and improve the reliability of plant assets. Pro

Procter & GambleProcter & Gamble (P&G) has used ExtendSim to "model all product lines - from soap to nuts" for design of equipment and production lines, scheduling, commerce, quality, etc. The models they build are used to interface with engineers who are not necessarily simulation experts, but can use it for analysis and design. Production LinesUltimately becoming simulation experts while using it because of ExtendSim's design. Military aircraftA consultant for a branch of the US Military is using ExtendSim to build inventory and reliability models of the frequency of failure of parts on military aircraft. Once a part has failed, how does it get replaced and will the replacement part be in inventory or not? And if the aircraft is out of service, Parts failurewill there be another one to replace it?
Smart Grid SecurityDeployment of digital smart grid sensing, communication, and control technologies that improve electric grid security, reliability, and efficiency is growing exponentially. ExtendSim is being used to dynamically monitor grid operations - identifying appropriate security controls Smart Gridbased on parameters and constraints then simulating mission assurance indicators before and after defense actuation to gauge effectiveness. go Understanding failure impactDow Chemical Company performs reliability modeling in ExtendSim to identify and understand the impact of different failures on overall production capabilities in chemical plants. The model is used for understanding the key equipment components that contribute towards maximum production loss and for analyzing the impact of change policies, such as the installation of new equipment or an increased stock level for failure-prone components. Dow ChemicalA Failure Summary Report provides information for further phases of the analysis.


Simulating Reliability-Based Systems

Accurately determine the MTTR and MTBR of systems with shutdown.

Model the relationship between components and how the behavior of one component affects the other.

Identify and document areas of potential failures.

Quantifiably report changes in performance given changes in design, capacity, operations, maintenance, or logistics.

Rate failures in terms of likelihood and consequence.

Maintain assets in a safe, efficient manner.

Consider system up and down times.

Measure and maintain system repeatability.

Dynamic reliability modeling.

Predict future performance.

Who Is Using ExtendSim for Reliability Modeling

DNV ReliabiilityIn the first pilot project of its kind, DNV GL found all possible root causes for critical system failures directly from design documentation. They made a list of cut sets from an ExtendSim model of the signal flow where fault tree is the result, not the input. This project resulted in more reliable while being less expensive safety analysis of the system.

Who Should be Using ExtendSim

Complex manufacturing production lines.

Intricate communication networks.

Case Studies Case Studies

Bane Nor Reliability Modelling of ERTMs/ETCS
Raja Gopal Kalvakunta
MSc. Reliability Availability Maintainability and Safety (RAMS), Norwegian University of Science & Technology • June 2017

The European railway industry is continuously advancing and in recent years, they have adopted a new system called European Railway Traffic Management System/ European Train Control System (ERTMS/ETCS) for the interoperability of railways among different European nations. Currently, this has been used more extensively for transportation by commuters and for freight. The foremost quality of such transportation system is to operate in a reliable manner and maintain punctuality. In this context, Bane Nor (Norwegian National Rail Administration) is planning to convert the entire conventional signalling system to ERTMS signaling system, as a part of their ERTMS National Implementation project.

ERTMS/ETCS is a complex infrastructure of various systems on trackside, lineside and train onboard and these systems have different sub systems comprising of software, hardware, network and signalling components. Due to its complexity, determining the failures and resolving them is challenging. An existing line operated on ERTMS is taken as case study from Bane NOR for developing a reliability model.

Kalvakunta modelPrimarily a reliability block diagram method is used to model the Østfoldbanen Østre Linje (ØØL) ERTMS pilot line as a case study in ExtendSim's Reliability module incorporating a combination of single station and bidirectional (BiDi) sections, then conducting 1000 simulations to assess ØØL ERTMS infrastructure. It is estimated from the results that this model has the potential to determine the performance of the infrastructure, and it is deduced that predominant infrastructure failures that cause delays are due to partial interlocking fail, maintenance and track fracture, followed by failure of balise, axle counters and points.
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University of Oslo Safety Instrumented Systems Operated in the Intermediate Demand Mode
Kristine Tveit
University of Oslo - Master of Science in Modeling and Data Science • December 2015

The frequency of demands are crucial when analyzing a safety instrumented system (SIS). IEC 61508 distinguishes between low and high demand mode when calculating risk for such a system. In reality there are systems that can not clearly be placed in one of the two modes. These types of systems are called intermediate demand mode systems, which are analyzed in this thesis. Not many published SIS reliability studies focus on the problems related to this borderline. Oliveira predicts somewhat strange behavior for the hazard rate in the intermediate demand mode, as well as with a focus on the demand duration.

The results from the analyses of a redundant system show that the standard Probability of Failure on Demand (PFD) formulae are usable for very low demand rates, but become increasingly more conservative as one moves into the intermediate mode, while the Probability of Failure per Hour (PFH) is non-conservative. This can cause major consequences for the operator of a safety system in the sense of not obtaining the optimal testing strategy, or even worse encounter a hazard.

tveit modelFor more complex systems with several components the Markov approach has its limits, choice of distributions and maintenance details are also restricted. Discrete Event simulation can deal with such complex systems, and also the rare event problem that often is a challenge for safety system analysis can be handled satisfactorily.

By use of Harel Statechart and discrete event Monte Carlo simulations for different safety systems, it is shown that the intermediate demand mode is dependent on the relationship between the proof-tests, demands and repair duration. When a demand rate increases to a significant level, demands can be used as tests. With Harel Statecharts we can calculate realistic models that go beyond what a Markov model is capable of.
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University of Calgary Studying the Impact of Uncertainty in Operational Release Planning - An Integrated Method and its Initial Evaluation
Ahmed Al-Emran, Puneet Kapur, Dietmar Pfahl, & Guenther Ruhe
Information and Software Technology, April 2010
International Conference on Software Process (ICSP) 2010

Uncertainty is an unavoidable issue in software engineering and an important area of investigation. This paper studies the impact of uncertainty on total duration (i.e., make-span) for implementing all features in operational release planning, including:
• the number of new features arriving during release construction
•the estimated effort needed to implement features
• the availability of developers
• the productivity of developers.

Al-Emran modelAn integrated method is presented combining Monte-Carlo simulation (to model uncertainty in the operational release planning (ORP) process) with process simulation (to model the ORP process steps and their dependencies as well as an associated optimization heuristic representing an organization-specific staffing policy for make-span minimization). The method allows for evaluating the impact of uncertainty on make-span. The impact of uncertainty factors both in isolation and in combination are studied in three different pessimism levels through comparison with a baseline plan. Initial evaluation of the method is done by an explorative case study at Chartwell Technology Inc. to demonstrate its applicability and its usefulness.

Results. The impact of uncertainty on release make-span increases – both in terms of magnitude and variance – with an increase of pessimism level as well as with an increase of the number of uncertainty factors. Among the four uncertainty factors, we found that the strongest impact stems from the number of new features arriving during release construction. We have also demonstrated that for any combination of uncertainty factors their combined (i.e., simultaneous) impact is bigger than the addition of their individual impacts.

The added value of the presented method is that managers are able to study the impact of uncertainty on existing (i.e., baseline) operational release plans pro-actively.Download pdf

Procter & Gamble Improved Manufacturing Processes Save Company One Billion Dollars, October 12, 2011

Procter & Gamble partnered with the Energy Department's Los Alamos National Laboratory (LANL) in the 1990s. LANL scientists helped P&G engineers develop simulations to improve the reliability of P&G's complex production lines. P&G's 150 facilities worldwide saw a 44 percent increase in plant productivity and 30 percent increase in equipment reliability since they started using the software.

The pairing of the lab and corporations' data led to the creation of simulation software called Reliability Technology in 1993. With the software, engineers could configure both the machines and their maintenance schedules based on reliability. In addition, engineers could foresee and possibly avoids product jams, intervals of a component breakage or variations in a machine speeds. In other cases, engineers could triage the production line. Large-scale implementation of the technology helped save P&G $1 billion in manufacturing costs, according to Procter & Gamble. These cost-saving benefits are applicable towards production lines across the manufacturing sector.Go to page...


Play VideoModeling Reliability with ExtendSim

ExtendSim SimCast describing the different methods used to model reliability in ExtendSim. It features examples that use both blocks and items to represent failures in the system or process. This SimCast includes a first look at the Reliability module in ExtendSim Pro while it was still early in its development stage.

Rome wasn't built in a day, nor was ExtendSim. After 30 years of construction, it has remained an industry leading, trusted, and pretty amazing simulation least that's what our users are telling us! We'd like to introduce you to this powerful, leading edge simulation tool.

Using ExtendSim, you can develop dynamic models of existing or proposed processes in a wide variety of fields. Use ExtendSim to create models from building blocks, explore the processes involved, and see how they relate. Then change assumptions to arrive at an optimum solution. ExtendSim and your imagination are all you need to create professional models that meet your business, industrial, and academic needs.

Simulation with ExtendSim

ExtendSim is an easy-to-use, yet extremely powerful, tool for simulating processes. It helps you understand complex systems and produce better results faster. With ExtendSim you can:

Predict the course and results of certain actions

Gain insight and stimulate creative thinking

Visualize your processes logically or in a virtual environment

Identify problem areas before implementation

Explore the potential effects of modifications

Confirm that all variables are known

Optimize your operations

Evaluate ideas and identify inefficiencies

Understand why observed events occur

Communicate the integrity and feasibility of your plans


What ExtendSim can do

ExtendSim allows you to simulate any system or process by creating a logical representation in an easy-to-use format.


Modeling capabilities

With ExtendSim, you get powerful modeling constructs, including:

A full set of building blocks that allow you to build models rapidly

A customizable graphical interface that depicts the relationships in the modeled system

Unlimited hierarchical decomposition making enterprise-wide models easy to build and understand

Dialogs, Notebooks, and integrated relational databases for changing model values, so you can quickly try out assumptions and interface with your model dynamically

Animation of the model for enhanced presentation

A full-featured authoring environment for building user-friendly front ends that simplify model interaction and enhance communication

The ability to adjust settings dynamically, while the simulation is running

An equation editor for creating custom-compiled equations

The ability to create new blocks with custom dialogs and icons

Complete scalability since model size is limited only by the limits of your system

Evolutionary optimization, scenario management, confidence intervals, Monte Carlo, batch-mode, and sensitivity analysis

Customizable reports and charts for presentation and in-depth analysis

Activity-based costing capabilities for analyzing cost contributors

Advanced resource management to maximize resource utilization

Full connectivity and interactivity with other programs and platforms


Simulation architecture

A robust architecture adds advanced features to make it the most scalable simulation system available:

Multi-purpose simulation. ExtendSim is a multi-domain environment so you can dynamically model continuous, discrete event, discrete rate, reliability block diagramming, agent-based, linear, non-linear, and mixed-mode systems.

Library based. The blocks you build can be saved in libraries and easily reused in other models.

Integrated compiled programming language and dialog editor, optimized for simulation. Modify ExtendSim's blocks or build your own for specialized applications.

Scripting support. Build and run models remotely, either from an ExtendSim block or from another application.

Integrated support for other programming languages. Use ExtendSim's built-in APIs to access code created in Delphi, C++ Builder, Visual Basic, Visual C++, etc.

Over 1000 functions. Directly access functions for integration, statistics, queueing, animation, IEEE math, matrix, sounds, arrays, FFT, debugging, DLLs, string and bit manipulation, I/O, and so on; you can also define your own functions.

Message sending. Blocks can send messages to other blocks interactively for sub processing.

Sophisticated data-passing capabilities. Pass values, arrays, or structures composed of arrays.

Full support for a wide range of data types and structures. Arrays, linked-lists, and integers, real, and string data types are built in.

Integrated data linking. Connect block dialog data to internal databases.

More than 3 decades in the making... ExtendSim is an innovative, revolutionary simulation tool.


ExtendSim packages

All ExtendSim Model Developer Editions have the same core set of capabilities. ExtendSim CP is the base package and is used for continuous modeling. Subsequent packages add modules designed for specific markets -- discrete event in ExtendSim DE and discrete rate, reliability block diagramming, plus advanced simulation technology in ExtendSim Pro. For more details on available packages, see ExtendSim Product Line.


Levels of use

You can use ExtendSim on many levels:

Run pre-assembled models and explore alternatives by changing the data. If you work in a group environment, one or more authors can create models for others to run for experimentation. The author can also build a custom front end to facilitate user interaction with the model. Non-modelers can run pre-assembled models, change data, and obtain results.

Assemble your own models from the blocks that come with ExtendSim. ExtendSim is shipped with libraries of blocks to handle most modeling needs. To assemble a model, pull blocks from libraries and link connectors on the blocks. You can also assemble your own hierarchical blocks of subsystems and save them in libraries. This saves starting from scratch when you're building a model of a process that has elements in common with a previous model.

Use the integrated development environment to create new blocks that conform to the ExtendSim modeling architecture. The development environment is optimized for simulation and allows you to create blocks with custom code, dialogs, and icons and use them in your models just as you would other ExtendSim blocks. You can also modify the blocks that come with ExtendSim to work with your specific needs.

Develop your own modeling architecture, conventions, and features. With the ExtendSim development environment, you can create a custom set of blocks with unique interfaces, communication protocols, and behaviors. This new architecture can be continuous, discrete event, discrete rate, agent-based, or an entirely new type of simulation.

Automate your model building using the scripting functions to build wizards, or by using ActiveX/COM. You can use ActiveX/COM or block-based wizards to cause models to be automatically created or modified. Models can be also be programmatically created from a user input form or data file. This allows the modeling environment to be utilized indirectly by end-users who have little or no simulation experience.

SpotlightSimulation Spotlight

New and innovative solutions powered by ExtendSim.


Socio-Economic Planning SciencesPost-Disaster Collaboration Strategies
2019 October
Assessing Hospital System Resilience to Events Involving Physical Damage and Demand Surge
Bahar Shahverdi, Mersedeh Tariverdi, Elise Miller-Hooks
George Mason University • Department of Civil, Environmental, and Infrastructure Engineering • PhD in Transportation
Published 25 July 2019 in Socio-Economic Planning Sciences

 X X X How effective are formalized collaboration strategies when responding to a disaster incident involving mass casualties and area-wide damage? How best can patients be transferred and resources, including staff, equipment and supplies, be shared across hospitals? ExtendSim was used to help quantify hospital system resilience to pandemic, MCI, and disaster events with damage. Patient flows through 9 critical units of multiple hospitals spaced over a large metropolitan region were analyzed to determine impacts on critical resources, physical spaces, and demand. Hospital resilience to these hazard events was evaluated to show the potential of strategies involving not only transfers and resource sharing, but also joint capacity enhancement alternatives to improve post-disaster emergency health care service delivery through joint action.
Read more

IEOMSupply Chain Sustainability
2019 June
A Simulation Study of Sustainable Agri-Food Supply Chain
Hagar Amer, Noha Galal, Khaled El-Kilany • Dept of Industrial and Management Engineering • Arab Academy for Science, Technology, and Maritime Transport
Presented at the International Conference on Industrial Engineering and Operations Management • Paris, France, July 2018

Agri-Food Supply ChainDue to the globally increasing concern of public health regarding food safety, and quality, the management of agri-food supply chains (ASC) has become even more complex given its special characteristics of perishability, uncertainty of supply and demand, and managing the carbon dioxide equivalent emissions produced throughout the supply chain due to cooling, transportation, and disposal of fresh produce. Using ExtendSim, researchers modeled a two-echelon real life supply chain to study the effect of changing order quantity under uncertain demand and lead time on a set of economic, social, and environmental performance measures. Model results help to minimize food wastage, offer higher quality fresh produce, and lower emissions while maintaining the highest profit and an acceptable service level.
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IEOMReentrant Flow Lines
2019 May
Simulation Analysis of Segmented CONWIP: Application to Reentrant Flow Lines
Yassin Shaalan, Ingy El-Khouly, and Khaled El-Kilan • Dept of Industrial and Management Engineering • Arab Academy for Science, Technology, and Maritime Transport
Presented at the International Conference on Industrial Engineering and Operations Management • March 2018

Reentrant flow linesReentrant flow lines are a special type of production flow line in which a job may visit a machine or group of machines more than once. This reentrancy characteristic results in higher variability of cycle time and throughput rates when compared to traditional production flow lines. A simulation study using ExtendSim shows that by dividing a production line into segments using different CONWIP levels affects the line performance. In fact, in some instances, compared to a single segment CONWIP and push system, using segmented CONWIP can achieve the same throughput rate in a shorter cycle time.
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Frontiers in Energy ResearchBiomass Supply Chain Design
2019 April
Simulation Modeling for Reliable Biomass Supply Chain Design Under Operational Disruptions
Bhavna Sharma1, Robin Clark, Michael R. Hilliard, and Erin G. Webb
Frontiers in Energy Research • Bioenergy & Biofuels
25 September 2018

Lignocellulosic biomass derived fuels and chemicals are a promising and sustainable supplement for petroleum-based products. Currently, the lignocellulosic biofuel industry relies on a conventional system where feedstock is harvested, baled, stored locally, and then delivered in a low-density format to the biorefinery. However, the conventional supply chain system causes operational disruptions at the biorefinery mainly due to seasonal availability, handling problems, and quality variability in biomass feedstock. Operational disruptions decrease facility uptime, production efficiencies, and increase maintenance costs. For a low-value high-volume product where margins are very tight, system disruptions are especially problematic. In this work we evaluate an advanced system strategy in which a network of biomass processing centers (depots) are utilized for storing and preprocessing biomass into stable, dense, and uniform material to reduce feedstock supply disruptions, and facility downtime in order to boost economic returns to the bioenergy industry. A database centric discrete event supply chain simulation model was developed, and the impact of operational disruptions on supply chain cost, inventory and production levels, farm metrics and facility metrics were evaluated. Three scenarios were evaluated for a 7-year time-period: (1) bale-delivery scenario with biorefinery uptime varying from 20 to 85%; (2) pellet-delivery scenario with depot uptime varying from 20 to 85% and biorefinery uptime at 85%; and (3) pellet-delivery scenario with depot and biorefinery uptime at 85%. In scenarios 1 and 2, tonnage discarded at the field edge could be reduced by increasing uptime at facility, contracting fewer farms at the beginning and subsequently increasing contracts as facility uptime increases, or determining alternative corn stover markets. Harvest cost was the biggest contributor to the average delivered costs and inventory levels were dependent on facility uptimes. We found a cascading effect of failure propagating through the system from depot to biorefinery. Therefore, mitigating risk at a facility level is not enough and conducting a system-level reliability simulation incorporating failure dependencies among subsystems is critical. Read more

yehia ieemDynamic Pricing and Inventory Management of Perishable Products
2018 December
Sustainable Dynamic Pricing for Perishable Food with Stochastic Demand
Ghada Yehia Mostafa, N.M. Galal, K.S. El-Kilany
IEEM 2018 -- International Conference on Industrial Engineering and Engineering Management
Masters in Industrial Engineering • Arab Academy for Science, Technology, and Maritime Transport

yehia foodEstablishing a pricing strategy and managing inventory of perishable products can easily be a retailer’s nightmare. How do you maximize revenue while minimizing food waste? As a product nears the end of its lifetime, will customers still purchase it at the same price as when it was fresh? If you reduce the price as the product ages, what might that price point be so customers will purchase it and you will be left with little to no waste? Or do you cut inventory so product will sell out before it ages, but leaving some customers without needed product? This paper investigates dynamic pricing strategy with the objective of maximizing revenue and minimizing food waste to ensure sustainability and customer satisfaction. Read more

ISAT 2018Forecasting Demand for Healthcare Services Using Hybrid Simulation Techniques
2018 September
Overcoming Challenges in Hybrid Simulation Design and Experiment
Jacek Zabawa and Bozena Mielczarek, Faculty of Computer Science and Management, Wroclaw University of Science and Technology
ISAT 2018 -- 39th International Conference

Zabawa modelThis study builds on earlier research which focuses on the use of combined simulation methods to support healthcare demand predictions. The authors developed a hybrid simulation model composed of two submodels which were built using different simulation paradigms. Both models, i.e. a population model based on a continuous modeling approach and an arrivals model built with discrete-event methodology, were created in ExtendSim. This combined model simulates the consequences of demographic changes, the variability in incidence rates that result from an aging population, and the seasonal fluctuations in epidemic trends on future demand for healthcare services.

OCEAN CERTAINCreating Certainty About the Contributions of our Oceans
2018 April
Ocean Certain
Ocean Certain consists of 11 partners from 8 European countries plus Chile and Australia
The project was initiated in Norway by NTNU (the project coordinator) and the University of Bergen

OCEAN CERTAIN brochureOCEAN-CERTAIN is an ambitious collaborative project working to create more certainty about the contributions of our oceans during climate change. Climate change poses serious risks for both natural systems and human beings, and plausible and feasible policy decisions and strategies to mitigate these risks are urgently needed. However, there are important knowledge gaps surrounding the large-scale natural processes and interactions with social-economic processes that play an important role for our oceans. The multi-disciplinary project OCEAN-CERTAIN (Ocean Food-web Patrol – Climate Effects: Reducing Targeted Uncertainties with an Interactive Network) aims to shed light on these processes. OCEAN-CERTAIN is an EU FP7 project that comprises 11 partners from eight European countries, Chile and Australia. It includes both natural scientists, who will work with the ecosystem and the biological pump, and social scientists who will study possible consequences for society.

Using a combination of metamodel simulations, system dynamics with the custom-designed GUI functionalities of ExtendSim, Ocean Certain has created a a powerful tool for the evaluation and demonstration of policy alternatives under different climate and socio-economic conditions. This decision support system has been designed to serve as knowledgebase and knowledge generator to examine the feedback mechanisms linking the ocean food web system to the social-economic system of the coastal zones.

PolarimeterInstrument Simulation
2018 March
Richard Morrison
University of Melbourne • School of Chemistry

Polarimeter modelSimulations offer valuable insights into how an instrument can be expected to perform and can also point to areas for improvement. In the case of polarimeters described in this article, it is of interest to simulate the motion of the analysing polarizer relative to the fixed defining polarizer as this forms the basis for the optical rotation measurements. To make the simulation more realistic the author incorporated additional factors including (i) the broadband nature of the LED’s emission spectrum, (ii) the variation of the specific rotation of the sample as a function of wavelength, (iii) the spectral response of the detector and (iv) the temperature at which measurements are performed.
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Auckland University of TechnologyPerishable and Substitutable Product Supply Chains
2018 February
Replenishment Policy for Perishable and Substitutable Products at Suppliers and Retailers: A Multi-Criteria Approach
Linh Nguyen Khanh Duong
PhD in Supply Chain Management
Auckland University of Technology • Faculty of Business and Law

duong perishableDefining replenishment policies for perishable products is an important activity, particularly where suppliers have a range of products. As product ranges increase, consumers can substitute products if their preferred product is out of stock. Such substitution considered simultaneously as perishability makes it difficult to achieve balanced results over different departments/companies in the face of fluctuating demand. Given these circumstances, a financially calculated replenishment policy makes communicating the impact of operational changes difficult. In contrast, non-financial measures improve the communication between departments and staff (e.g., between warehousing, procurement, and sales), and allows them to set operational targets from broad corporate strategies. This study contributes to inventory management theory by being the first research to develop a non-financial framework and demonstrate its comparability to financial approaches for perishable and substitutable inventory.
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WSC 18Minimizing Uncertainties in Shipbuilding
2017 December
Application of a Multi-Level Simulation Model for Aggregate and Detailed Planning in Shipbuilding
(El Astillero 4.0: Modelado y Simulación del Astillero de Navantia - Ferrol)
Mar Cebral Fernández, Marcos Rouco-Couzo, Marta Quiroga Pazos - UMI Navantia, UDC; Rafael Morgade Abeal - Navantia Ferrol; Alejandro García del Valle & Diego Crespo-Pereira - Universidade da Coruña.
Presented at the Winter Simulation Conference 2018

mfg navantiaShipbuilding is one of the most complex existing manufacturing processes. The large number of operations necessary to produce the parts that make up a vessel coupled with the need to synchronize multiple workflows and numerous resources where serial production is practically non-existent, make the management of such a production system very difficult. mfg navantiashipSimulation is an extremely powerful tool for the decision-making process to test and evaluate different scenarios, to efficiently plan future investments, to determine the allocation of resources, and to drastically reduce the risks of making incorrect decisions.

This case study uses ExtendSim to model the manufacturing process of a frigate from the shipyard of Navantia Ferr to minimize the uncertainties of its construction.
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Carollo Engineers, Inc.2017 Software & Technology Innovation Award
2017 November
Carollo’s Blue Plan-it® Team Wins 2017 Software and Technology Innovation Award
Chao-an Chiu & Charlie He, Carollo Engineers, Inc.
Corporate Vision Magazine's 2017 Software & Technology Innovation Award
Blue Plan-it Award

Carollo Engineers, Inc. Blue Plan-it® was selected from a shortlist of 200 firms for the 2017 Software Technology Innovation Award, given by Corporate Vision Magazine. This is an exceptional achievement, since that Carollo is really not a software company.

Developed by Carollo to help municipal service providers manage complex, interconnected water and wastewater infrastructure, storage, treatment, conveyance, and distribution systems, Blue Plan-it is a fully customizable simulation and optimization model suite developed in ExtendSim. Blue Plan-it takes an agency’s vast quantity of data, including maintenance, geographic, regulatory, and financial information, and creates a customized graphical user interface that can be easily modified, understood, and presented.
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APICS 2017Fortune 500 Medical Device Company
2017 October
Using Simulation and Optimization in Complex Manufacturing Operations
Jim Curry, Founder, OpStat Group Inc. & Alvaro Brisolla, Production Planning and Logistics Senior Manager, Ethicon (a Johnson & Johnson subsidiary)
APICS Annual Conference 2017

APICS 2017 modelThis presentation outlines the story of a Medical Device Fortune 500 company’s experience in implementing a combined simulation and optimization solution not integrated to the primary ERP system of a critical manufacturing location. You'll learn about the pros and cons, and the challenges and lessons learned, during the implementation process using a discrete simulation model using OpStat methodology launched in two critical areas in 2015 and 2016. After a weekly simulation and optimization process was enacted, customer service rose to unprecedented levels, while plant financial performance became enterprise benchmarks. The process knowledge acquired by the team during the implementation also resulted in a major shift in the nature of the work of the planning organization, evolving from a group of schedulers to a team of holistic capacity planners. To accomplish this task in an evidence-based way, a simulation model was developed using ExtendSim. This approach helped the clinical, managerial, and facility staff to not just understand their existing department workflow but also to provide them the opportunity to test trans-disciplinary scenarios to guide the design of the new emergency department. Furthermore it provided the client with the statistics to evaluate the impact a new Chest Pain & General Observation Center and Results Waiting Lounge would have on their entire system. Read more

EnergiesBiomass Supply Chain
2017 August
Development of the IBSAL-SimMOpt Method for the Optimization of Quality in a Corn Stover Supply Chain
Hernan Chavez & Krystel K. Castillo-Villar from the Mechanical Engineering Department at The University of Texas at San Antonio and Erin Web from the Environmental Sciences Division at Oak Ridge National Laboratory
Energies, Volume 10, Issue 8 

IBSAL-SimMOpt model
Up until now, most of the models developed to optimize biomass supply chains have failed to quantify the effect of biomass quality and the preprocessing operations required to meet biomass specifications on overall cost and performance. However, the IBSAL-SimMOpt method provides a novel approach for finding near-optimal solutions to the problem of designing biomass supply chains that improve the physical characteristics of the feedstock at an acceptable cost. The bioenergy sector and the research community now have access to a simulation-based optimization model with activity-based costing that is capable of representing the stochastic behavior of some elements intervening in the corn stover supply chain for the production of bioethanol. This analytical model can be used for decision making as well as educational and training purposes since it allows the user to conduct “what if” scenarios.
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NTNUOil & Gas Subsea Production
2017 June
Production Availability Analysis: Implications on Modelling due to Subsea Conditions
Subsea viewTianqi Sun
Masters Thesis in Reliability, Availability, Maintainability and Safety (RAMS)
Norwegian University of Science and Technology
Department of Mechanical and Industrial Engineering

Subsea production and processing systems have become a hot topic among research institutes and industries. While highlighting the advantages on production and economy, the reliability issues show a different picture with limited access, difficulty of maintenance and possibly lower availability. The influence of these issues on the system performance is studied in this paper to evaluate the benefit of subsea systems.
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SDMPH Disaster Med Public Health Preparedness
2017 February
Evaluating the Impact of Pharmacies on Pandemic Influenza Vaccine Administration
Schwerzmann J, Graitcer SB, Jester B, Krahl D, Jernigan D, Bridges CB, Miller J

Is it really beneficial for pharmacists to administer pandemic influenza vaccines? Absolutely! Using ExtendSim, researchers showed that weekly national vaccine administration capacity increased to 25 million doses per week when pharmacist vaccinators are integrated into pandemic vaccine response planning. In addition, the time to achieve 80% vaccination coverage nationally was reduced by 7 weeks, assuming high public demand for vaccination.  Read more


Modeling Methodologies

The formalism used to specify a system is termed a modeling methodology. Continuous modeling (sometimes known as process modeling) is used to describe a flow of values. Discrete Event models track unique entities. Discrete Rate models share some aspects of both continuous and discrete event modeling.

In all three types of simulations, what is of concern is the granularity of what is being modeled and what causes the state of the model to change.


The time step is fixed at the beginning of the simulation, time advances in equal increments, and values change based directly on changes in time. In this type of model, values reflect the state of the modeled system at any particular time, and simulated time advances evenly from one time step to the next.

For example, an airplane flying on autopilot represents a continuous system since its state (such as position or velocity) changes continuously with respect to time. Continuous simulations are analogous to a constant stream of fluid passing through a pipe. The volume may increase or decrease at each time step, but the flow is continuous.

Discrete Event

The system changes state as events occur and only when those events occur; the mere passing of time has no direct effect on the model. Unlike a continuous model, simulated time advances from one event to the next and it is unlikely that the time between events will be equal.

A factory that assembles parts is a good example of a discrete event system. The individual entities (parts) are assembled based on events (receipt or anticipation of orders). Using the pipe analogy for discrete event simulations, the pipe could be empty or have any number of separate buckets of water traveling through it. Rather than a continuous flow, buckets of water would come out of the pipe at random intervals.

Discrete Rate

Discrete rate simulations are a hybrid type, combining aspects of continuous and discrete event modeling. Like continuous models they simulate the flow of stuff rather than items; like discrete event models they recalculate rates and values whenever events occur.

Using the pipe analogy for a discrete rate simulation, there is a constant stream of fluid passing through the pipe. But the rates of flow and the routing can change when an event occurs.

These 3 main modeling types are compared and contrasted in the tables below.

In some branches of engineering, the term discrete is used to describe a system with periodic or constant time steps. Discrete, when it refers to time steps, indicates a continuous model; it does not have the same meaning as discrete event or discrete rate. Continuous models in ExtendSim are stepped using constant time intervals; discrete event and discrete rate models are not.


Other Modeling Approaches

Although there are several other approaches to modeling, they usually fit within one of the three major categories. For example, System Dynamics and Bond graphs are subsets of continuous modeling, and queuing theory models are subsets of discrete event modeling.

Because of their specialized use, three specific modeling approaches - Monte Carlo, State/Action, and Agent Based - are described below.

Monte Carlo

Widely used to solve certain problems in statistics, Monte Carlo simulations provide a range of results rather than a single value. This approach can be applied to any ExtendSim model and used wherever uncertainty is a factor.

Monte Carlo modeling uses random numbers to vary input parameters for a series of calculations. These calculations are performed many times and the results from each individual calculation are recorded as an observation. The individual observations are statistically summarized, giving an indication of the likely result and the range of possible results. This not only tells what could happen in a given situation, but how likely it is that it will happen.

You build a Monte Carlo simulation in ExtendSim by incorporating random elements in a model and obtaining multiple observations. There are two ways to do this:

The classical Monte Carlo method is to take a single mathematical equation or set of equations, then cause the equation to be calculated many times. In this type of simulation, time is not a factor. The entire model is run to completion and evaluated at each step; each subsequent step performs a new calculation.

An alternative Monte Carlo approach, typically applied in a discrete event model, is to either divide a single simulation run into multiple sections (batch means) or run the simulation many times (multi-run analysis). Monte Carlo is incorporated by adding randomness to the model, running it many times, and analyzing the results. This method can be applied to any continuous, discrete event, or discrete rate model.


With agent-based modeling you usually do not know model dynamics in advance; instead, you obtain that information from the interaction of the agents in the model.

Agent-based models share the following characteristics:

The identification of individual entities within the model.

A set of rules that govern individual behavior.

The premise that local entities affect each other's behavior.

Agent-based modeling is concerned with individual entities (called "agents") that interact with other agents within their specified locality. All the agents have a set of rules to follow but they also have a degree of autonomy such that model dynamics cannot be predefined. This is because agents can have intelligence, memory, social interaction, contextual and spatial awareness, and the ability to learn.


With state/action modeling a system is modeled as a collection of discrete states. Sometimes known as a state chart, a state/action model represents a system that responds to an event by transitioning to another state. The model is composed of a series of states where each state depends on a previous state. A state has an associated action and an event that will cause that state to change to another. The transition from one state to the next is not sequential; each state can lead to any other state.

There are rules that govern the communication and transition between the states:

All states accept events.

One or more states may create an event as a result of a transition by another state or group of states.

A group of states can be set to transition conditionally, for instance to only change if another state or group of states achieve a specific stage. These are known as guard conditions.

State/action models are independent of any of the three modeling methodologies (continuous, discrete event, or discrete rate.) They are useful for specification and verification in many areas, from computer programs to business processes.


Comparison Table

Modeling Method Continuous Discrete event Discrete rate
ExtendSim library Value library
Electronics library
Item library Rate library
What is modeled Processes Individual items Flows of stuff
Examples Processes:
chemical, biological, economic, electronic, geological.
traffic, equipment, work product, people.
data, messages, network protocols at the packet level.
Rate-based flows of stuff:
homogeneous products (powders, fluids, oil, and gas), high-speed or high-volume production and packaging, data feeds and streams, mining.

Table of continuous, discrete event, and discrete rate differences

Use this table as a guide to help determine which style to use when modeling a system.

class="tablebody" style="text-align: center;"> 

Factor Continuous Discrete event Discrete rate
What is modeled Values that flow through the model. Distinct entities ("items" or "things"). Bulk flows of homogeneous stuff. Or flows of otherwise distinct entities where sorting or separating is not necessary.
What causes a change in state A time change An event An event
Time steps Interval between time steps is constant. Model recalculations are sequential and time dependent. Interval between events is dependent on when events occur. Model only recalculates when events occur. Interval between events is dependent on when events occur. Model only recalculates when events occur.
Characteristics of what is modeled Track characteristics in a database or assume the flow is homogeneous. Using attributes, items are assigned unique characteristics and can then be tracked throughout the model. Track characteristics in a database or assume the flow is homogeneous.
Ordering FIFO Items can move in FIFO, LIFO, Priority, time-delayed, or customized order. FIFO
Routing Values need to be explicitly routed by being turned off at one branch and turned on at the other (values can go to multiple places at the same time). By default, items are automatically routed to the first available branch (items can only be in one place at a time). Flow is routed based on constraint rates and rules that are defined in the model (flow can be divided into multiple branches).
Statistical detail General statistics about the system: amount, efficiency, etc. In addition to general statistics, each item can be individually tracked: count, utilization, cycle time. In addition to general statistics, effective rates, cumulative amount.
Typical uses Scientific (biology, chemistry, physics), engineering (electronics, control systems), finance and economics, System Dynamics. Manufacturing, service industries, business operations, networks, systems engineering. Manufacturing of powders, fluids, and high speed, high volume processes. Chemical processes, ATM transactions. Supply chains.
Recommended package ExtendSim CP
ExtendSim CP
ExtendSim DE
ExtendSim DE
ExtendSim Pro
ExtendSim Pro

Awards & SponsorshipsAwards & Sponsorships



4th International Conference on Decision Support Technology
"Sustainable Data-Driven & Evidence-based Decision Support with applications to the Environment and Energy sector"
22 to 25 May 2018 • Heraklion, Crete, Greece

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Carollo 2017 Award2017 Software & Technology Innovation Award
Chao-an Chiu & Charlie He, Carollo Engineers, Inc.
Corporate Vision Magazine's 2017 Software & Technology Innovation Award

Carollo’s Blue Plan-it® Team Wins 2017 Software and Technology Innovation Award
Carollo Engineers, Inc. Blue Plan-it® was selected from a shortlist of 200 firms for the 2017 Software Technology Innovation Award, given by Corporate Vision Magazine. This is an exceptional achievement, since that Carollo is really not a software company and they won a software award.

Blue Plan-ItDeveloped by Carollo to help municipal service providers manage complex, interconnected water and wastewater infrastructure, storage, treatment, conveyance, and distribution systems, Blue Plan-it is a fully customizable simulation and optimization model suite developed in ExtendSim. Blue Plan-it takes an agency’s vast quantity of data, including maintenance, geographic, regulatory, and financial information, and creates a customized graphical user interface that can be easily modified, understood, and presented.
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3rd International Conference on Decision Support Technology
"Data, Information and Knowledge Visualisation in Decision Making"
29 to 31 May 2017 • Namur, Belgium

Assessing Supply Chain Risk through a Project Risk Management Approach
Mame Gningue, Daouda Kamissoko and Sonia Froufe

ICDSST 17 WinnersFor exemplary work at the conference, Daouda Kamissoko of Ecole des Mines d'Albi-Carmaux was awarded a complimentary license of ExtendSim AT.
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2nd International Conference on Decision Support Technology
"Decision Support Systems Addressing Sustainability & Societal Challenges"
23 to 25 May 2016 • Plymouth, UK

Workload Reduction Through Usability Improvement of Hospital Information Systems - The Case of Order Set Optimization
Daniel Gartner, Yiye Zhang, and Rema Padman

Professor Rema Padman of Carnegie Mellon University, USA was awarded a complimentary license of ExtendSim AT for her part in the development of a heuristic solution procedure to minimize physician workload associated with prescribing order sets.

Knowledge identification, categorisation and prioritisation for ERP implementation success
Uchitha Jayawickrama, Shaofeng Liu, and Melanie Hudson Smith

Dr Uchitha Jayawickrama, Staffordshire University, UK was awarded a complimentary license of ExtendSim AT for participation in a study is to examine the effectiveness of knowledge identification, categorisation and prioritisation that would contribute to achieve ERP implementation success.
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VINCI 2015Prix de l’Innovation VINCI 2015 pour la région Grand Ouest
Innovation prize for the West Region of France
Processes and Techiques Prize
4 September 2015 • France

Application OSILI
Daniel Belard and Didier Lesage, COSEA/VINCI Contruction

TGVVINCI gave its “Prix de l’Innovation VINCI 2015 pour la région Grand Ouest” to the ExtendSim model called OSILI. This model studies the design and operational issues of the TGV between Tours and Bordeaux, to determine whether the high-speed trains meet reliability, availability, and regularity criteria.

OSILI represents the whole of the LGV SEA line. It allows the simulation of  incidents on the line to better understand an incident's impact and calculate the ensuing delays. Simulation provides precise results, making it possible to replay incidents and better evaluate arbitration rules.
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2015 Franz Edelman Prize Awarded to Syngenta for a Project Utilizing ExtendSim
2015 INFORMS Conference on Business Analytics & Operations Research
13 April 2015 • Huntington Beach, CA USA

Edelman awardsExtendSim was one of the key tools used by Syngenta and KROMITE LLC in their award-winning project "Good Growth through Advanced Analytics" earning the 2015 INFORMS Franz Edelman Award for Achievement in Operations Research and the Management Sciences.

The "Good Growth through Advanced Analytics" program is a Syngenta soybean breeding team initiative using advanced mathematics and new technologies to develop higher yielding soybean varieties. Through increased genetic gain, the program is improving soybean variety accuracy, selection intensity, genetic variation and generation time without using more land, water or inputs.
 Syngenta recognizes the positive impact these tools have on soybean R&D and is initiating a multi-year effort to customize and launch similar tools across all major crops.

Soybeans"Winning the Franz Edelman Award demonstrates the scientific excellence and leadership science is bringing to the farmer in the field," said Joe Byrum, Syngenta Global Head Soybean R&D. "It's a level of leadership that has hasn't been demonstrated in agriculture until now. We're honored to win the Franz Edelman Award... It further demonstrates the impact our Good Growth Plan is having on the world, and we're proud to share that message with the masses."

Additional information about the INFORMS Franz Edelman Award and Competition and all the finalists can be found online at

ARLOG AwardArgentine Assocation of Business Logistics (ARLOG)
5th edition of the ARLOG Award
16 July 2008 • Argentina

UADE and GL&A Win the Prize ARLOG 2008

ARLOG 2008 AwardIn 2008, the Argentine Association of Business Logistics (ARLOG) selected  the Universidad Argentina de la Empresa (UADE) for the prestigious ARLOG Award. In conjunction with GL&A Consultants, UADE developed a discrete stochastic simulator for freight trains carrying cereals, oils, and other products to the ports of Bahía Blanca and Rosario from producing areas in Argentina to improve the logistical capacity of the company. The operation of the entire system is very complex as it models a multitude of locomotives in constant motion to move several thousand wagons for the rail network, subject to mechanical, climatic, and human contingencies.

The prestigious ARLOG award is granted biennially to projects that deepen and promote the development, innovation, and professionalization of logistics management. 
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