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@SIMUL8 Virtual User Group, September: Brian Harrington, Less is More


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@SIMUL8 Virtual User Group

We know not everyone can take time out to attend conferences and user meetings, so we're making it easy for you to get involved with our series of virtual user groups.

Learning Zone:

Brian Harrington will call on his experience as a Six Sigma Black Belt Black Belt to share his thoughts on "A Six Sigma approach to building successful simulations.

Published in: Technology
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@SIMUL8 Virtual User Group, September: Brian Harrington, Less is More

  1. 1. Successful Simulations using Less is More By: Brian Harrington 9/17/2013 MTN-SIM, LLC 1
  2. 2. Chaos of Change We need to batch build the Bodyside Outers. The new laser welder looks like it will be over 58 seconds. How many carriers are on the FS delivery system? We will be running this 2-10 hr shifts. How many units between Seg1 and Seg2? The Main Framing conveyor runs at 45fpm. There are 3 repair resources available. We have 3 different Pallet types. What is the build schedule? Last week… we had a 3 hr breakdown. Looks like we are 2 weeks behind! 3 seconds for the lift table to go up, then another 3 seconds to drop. 2
  3. 3. Complex Systems and Shorten Development Time This is a good equation for stress! We’ll simulate it! This just pushed the stress on the simulation team! 3
  4. 4. Less is More using 6-Sigma DMAIC or DMADV steps: Define, Measure, Analyze, Improve, Control Define, Measure, Analyze, Design, Verify DES Steps: Objective, Assumptions, Data Collection, Build Model, Verify, Validate, Experimentation, Results Very Similar steps! 4
  5. 5. Y=f(x’s) Transfer Function 6-Sigma focuses on Key Input Factors (x’s) to deliver your Response. All of the x’s can be measured & controlled to increase accuracy & precision of hitting your Target (Y). System/Process Trivial Many (N’s) Vital Few (X’s) Inputs (N’s & X’s) Output (Y) 5
  6. 6. The Art & Science of DES Experience modelers tend to create a model with the least amount of objects (data) to meet the objective. A common modeling error is to add too much detail to the model; attempting to simulate every move or event within the system. The two key questions become: 1. What is the significant input? 2. How do we control it? 6
  7. 7. The P-Diagram The P-Diagram not only helps engineers to define the Key Parameters for a robust design, but also acts as an excellent communication tool for team reviews. 7
  8. 8. Basic Building Blocks The 6 Basic Building Blocks: Start Point, Queue, Activity, Conveyor, Resource, and End Point. 8
  9. 9. 6 is all you Need 1. Work Item Types: Can represent parts, carriers, signals, phone calls, just about anything that requires a “Label Profile”. 2. Activities: Work Centers, machines, tasks, process steps, anything that requires a “Cycle Time”. 3. Storage Areas: Buffers, de-couplers, banks, magazines, anything that requires a finite space to occupy over time. 4. Conveyors: Moving parts from pt A to Pt B; Number of parts & Speed of conveyor. 5. Resources: Manpower, crews, forklifts, tugs; anything that require a certain resource to be present. 6. End Pt: Keep track of statistics and free memory! 9
  10. 10. Manufacturing Example Problem Statement: 30% proposed increase in throughput of an overhead sequencing bank of finite capacity Questions from management: Do we need to add capacity (additional lane), or can we maintain the current size and improve our sequencing routing logic? The two key questions from Simulation Team: 1. What is the significant input? 2. How do we control it? 10
  11. 11. Sequence Bank (60 units) Net JPH & Gross JPH into System Vehicle Mix Percent # of Designated Lanes Capacity # Size of Deck Overflow lanes Sequencing Logic Repair Times Conveyor Cycle Times # of vehicles on input transfers The last goes on… Throughput Net JPH INPUT SYSTEM OUTPUT Priority of Work # Resources Build Schedule Routing Logic Consecutive jobs The list goes on… CONTROLS Human Decisions Internal Failures Crew Factors Market Demand NOISE Objective: Do we need to add an additional Lane? P-Diagram (Seq Bank) 11
  12. 12. Reduce Inputs & Get Answers 1. Examine the system assuming ideal (perfect) sequence logic. 2. Some of the N’s & X’s can be ignored. 3. Determine the breakpoint where the system fails; maybe it’s 23%.  Use the least amount of data to get initial answers to management ASAP! 12
  13. 13. Establish Credibility 1. The program team might have critical answers sooner; hence managers have additional time to secure funds. 2. The initial answers provided and backed by data will keep the team engaged, and eager to provide further details! 3. The simulation engineer learns more about the system, and can add additional details as necessary. 13
  14. 14. Graph your Data! One of the most basic steps in 6-Sigma; Exploit your data! Stat-Fit for SIMUL8 14
  15. 15. Leverage Statistical Distributions! • Curve fit your data! Instead of using lengthy spreadsheets. • Black-box; entire segments of the model can be collapsed using distributions. • If using empirical datasets, drop them into a “S8 Probability Profile Distribution” 15
  16. 16. Use Known Distributions The data collection phase of modeling can be the lengthiest and most time consuming. i.e.) Downtime (MTBF & MTTR); such as Exponential & Erlang respectively. Cycle times often use a Fixed distribution; that is the “Design Cycle Time”. 16
  17. 17. Steady State A common data collection error is to capture all data points, and attempt to force them into one distribution. – Filter out the outliers; usually catastrophic points are outside the scope of the steady state system. 17
  18. 18. Tornado of Change Utilizing Six-Sigma tools in conjunction with model building keeps the team informed on what is of most importance to get the program launched. This is how several large manufacturing companies are able to shorten the overall launch time, and bring their new product to the market before the competition If you find yourself caught up in the tornado of change; just remember “Less is More”! 18
  19. 19. Questions 19