1. 1 © Sathi Mahesh 2014 Discrete Event Simulation for Systems Engineering Sathiadev Mahesh Department of Management, UNO

2. 2 © Sathi Mahesh 2014 Systems Engineering Optimal design of a complex system – systems with interacting human, mechanical, and digital components – focus on total system resiliency Engineering expertise is typically focused in one field – mechanical design, electrical systems, digital controls – few people understand the interaction especially with digital systems and worse when human factors are involved

3. 3 © Sathi Mahesh 2014 Case Service business with staffing decision change in staff as part of operational efficiency improvement, cost reduction service time increases customers not satisfied fewer new customers need for second round of cost reductions worsening loop - need to understand complete loop Based on Brailsford, Desai, Viana, 2010 paper

4. 4 © Sathi Mahesh 2014 Systems Modeling Challenges (i) the wide range of interlinked technologies with no single individual possessing adequate expertise in all the disciplines (ii) the difficulty of modeling non-technology disciplines such as group dynamics and human behavior (iii) the exponential growth of the simulation model making it unintelligible and therefore unreliable

5. 5 © Sathi Mahesh 2014 Systems Model Requirements All parties must understand model need to estimate the scenarios and likelihood of catastrophic collapse through cascading failures security breaches as a result of planned attacks at weak points in the system

6. 6 © Sathi Mahesh 2014 Systems Modeling with Incomplete Information Bayesian Analysis - DM with incomplete datasets Analysis of a complex system – too many gaps in the data can result in models with little or no value – case: derivative mortgage securities and market crash of 2008 – case: increased velocity of customer feedback and impact of C2C e- commerce – do we withdraw from complexity with Plan B?

7. 7 © Sathi Mahesh 2014 Systems Dynamics based Simulation Uses causal loops for feedback between sub-systems (website to customer) accumulation (build up/down resources) time delays (response to changes) mathematical model of loops... helps understand interplay between sub systems does not effectively capture complex in-house processes and differences in individual behavior

8. 8 © Sathi Mahesh 2014 SD Example - Anylogic.com Image from Anylogic.com

9. 9 © Sathi Mahesh 2014 Agent Based Simulation Interaction between objects Ability to capture emergent behavior – groups of agents performing complex tasks while individually possessing limited capability – ants and anthills Requires a high level of computational skill Need in net based commerce with autonomous robots and intelligent agents

10. 10 © Sathi Mahesh 2014 Agent Based Simulation http://www.youtube.com/watch?v=PN4GflvJr7c

11. 11 © Sathi Mahesh 2014 Discrete Event Simulation Entities following an observed pattern – arrival data - validated (customers, patients, service calls) Interactions with the model steps – model set up as flowchart, easy to ensure validity of model – resources consumed at each activity step many tools such as ARENA, VisSim, AnyLogic, Flexsim, ProModel, SimCAD Pro, Simul8

12. 12 © Sathi Mahesh 2014 Tutorials https://www.youtube.com/watch?v=CY69tm7qMXI

13. 13 © Sathi Mahesh 2014 Systems Engineering typically impacts multiple disciplines DES is a tool to model and test a business process Systems Dynamics Simulation describes feedback looks, accumulation, and time delays in response transfer between systems Combined DES-SD Simulation models capture both effects. Agent based models are not yet prime time. Summary