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Generis – 2015 American Manufacturing Summit
Bruce Gladwin, ProModel Corporation
bgladwin@promodel.com
2
 Introductions: Who, What & Why
 Evolution of (Lean) Process Analytics
 Simulation-based “Living VSMs”
◦ Understanding the “Physics of the Process”
 The Virtual Kaizen: your insurance policy
 Introducing Modeling & Sustaining the Gain
3
 Bruce Gladwin – fellow pilgrim on the Lean journey
 28 years in process analysis and improvement
◦ Hughes Aircraft – Industrial Engineer Role
◦ GE Global Research – Internal PI Consultant
◦ GE Power Systems – Six Sigma Black Belt
 ProModel Corporation – Orem, Utah
◦ Simulation Trainer, Consultant & Project Manager
◦ Product Manager, Process Simulation Tool
◦ Currently VP, Modeling Services Team
4
 What?
◦ How modeling tools, specifically discrete-event simulation,
can improve & accelerate your Lean journey
 Why?
◦ “The times they are a changin” – Bob Dylan
◦ Early modeling tools were not for “the faint of heart”
◦ Advances in simulation technology have put this valuable
technology within reach of more and more Lean practitioners
 Many tools today are “fill in the blank”, no programming required!
5
6
7
8
9
 Learning to See – Lean Enterprise Institute
10
Learning to See: © 1998 LEI, Inc., pp. 32-33
11
Learning to See: © 1998 LEI, Inc., pp. 78-79
12
 Value Stream Map
◦ Static model that describes
the states of current and/or
future processes
◦ Tells what is happening in the
process now and what the
process could be later
◦ Typically reports on lead time
and value added time
◦ Requires general information
regarding material arrivals,
operation times, labor and
equipment availability.
 e.g. average time = 15 sec
 Process Simulation
◦ Dynamic model that predicts the
behavior of current and/or future
processes
◦ Tells why a process behaves the
way it does or how it could behave
in the future
◦ Reports on throughput, inventory,
lead times, VA, NVA and resource
utilization
◦ Requires specific information about
material arrivals, batch constraints,
operation times, labor and
equipment uptime & availability,
transportation methods and times,
and any other capacity constraints.
◦ e.g. Time = T(5,10,25) sec
13
Non-functioning
Recirculation
Loop for empty
Tub carriers
14
Repairing the
Recirculation
Loop allowed
more WIP which
was key to fixing
problem
15
Throughput
Up 37% !!
WIP also
Up 37% !!
16
17
Interdependencies
Variation
SIMULATE
Use Simulation When You Cannot Ignore Variation and Interdependencies
Static
Tools
SPREADSHEET
ANALYSIS
MONTE-CARLO
SIMULATION
(Crystal Ball)
OPTIMIZATION ALGORITHMS (LP, MIP)
18
Using “Averages” can be dangerous.
How much
Risk can
you afford
to take?
19
Trying to optimize a
complex mfg system
is like trying to solve
the Rubic’s cube.
Simulation can
help understand
the trade-offs!!
Maximize Throughput
Reduce Cycle Time
Reduce Inventory
Minimize CapEx
Interdependencies
20
Exercise 1
…
from
Process
Simulator
Basics
Training
Course
Activity
Worker
Work Unit
(Exit)
5 minute
Process Time
100% Resource
Availability
Deterministic
Example
Activity 2
Worker 2
Work Unit 2
(Exit)
U(5,1) minute
Process Time
100% Resource
Availability
Stochastic
Example
Activity 3
Worker 3
Work Unit 3
(Exit)
U(4,1) minute
Process Time
80% Resource
Availability
Stochastic
Example with
Uncertainty
Activity 4
Worker 4
Work Unit 4
(Exit)
U(4,1) minute
Process Time
80% Resource
Availability
Assembly Opn
Stochastic
Example with
Uncertainty and
Interdependency
Activity 4A
Worker 4A
Work Unit 4A
BA
C D
Arrival Rate = 1 unit every 5 minutes
Identical for every arrival stream
No variation
21
Results
Are
Striking!
40 hr comparison of
System A to System D
Throughput: 4.6%
Avg Queue: 14x
Avg Cycle: 15x
22
23
 A Virtual Kaizen is simply using simulation first to
better understand the “Physics of the Process”…
 Then design the improved process, to produce the
desired outputs, within expect levels of variation…
 All in a risk free environment
◦ No interruptions to the existing line
◦ No need to make physical rearrangements (yet)
◦ Ability to ask “What if…?”
24
 Founded in 1975 by former Westinghouse engineers
 Ownership: privately held by founders
 Location: Pittsburgh area
 Employees: 150 total (50 salary, 100 hourly)
 Products:
◦ Mechanical Switches for Electric Utility Grids
◦ Automatic Distribution Motor Operators (ADMO)
 Allows remote control of switches by utility personnel
25
Vertical Switch
V2-C (copper)
Center Break Switch
CB-A (aluminum)
6’
6’
Switches = 50% Revenues
26
 Privately held, debt-free, profitable business with little
competition from low cost countries (for now).
 Capacity constrained. The market would buy every
additional unit that they could produce.
 Each additional unit sold would contribute $5k-$10k
to their bottom line.
 Want to maximize 1st shift capacity before adding
additional 2nd shift operations.
 Considering 30,000 sf expansion to their existing
plant, at a cost of about $1M.
27
Eliminate or Reduce the Risk through a “Virtual
Kaizen” using Simulation Technology.
28
29
 Value stream mapping
 Work standardization
 Point of use storage
 Single Piece Flow
 Visual management (Subassembly Feeder Lines)
 Kanban (pull) production (Subassembly Area)
 Setup reduction (Fabrication Area)
 Total productive maintenance (Fabrication Area)
30
31
 Simulation proved to be an accurate tool for modeling
the current state Vertical Switch Assembly process
 A Future-State model using Lean Production concepts
proved to be far more efficient
◦ 20% improvement in product throughput!
◦ 67% reduction in average unit cycle time!
◦ 50% reduction in floor space requirements!
◦ Elimination of need for 2nd shift!
32
 Identify the best opportunities
◦ Good ROI potential
 Every additional unit produced can be sold
◦ (Fairly) Low Risk
 Use a Sensei with years of modeling experience
 Involve all stakeholders, especially line operators
whenever possible
 Be patient and allow your team time to develop
modeling skillset. Make modeling a part of every
major process change.
33
 Update the models regularly so they are always
available to predict the future
◦ More & better data results in better decisions
 Link to real-time data sources
◦ More & better data results in more accurate results
◦ Strategic Analysis becomes Tactical Analysis
34
Generis – 2015 American Manufacturing Summit
Bruce Gladwin, ProModel Corporation
bgladwin@promodel.com

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ProModel Generis - Modeling Accelerates Lean

  • 1. 1 Generis – 2015 American Manufacturing Summit Bruce Gladwin, ProModel Corporation bgladwin@promodel.com
  • 2. 2  Introductions: Who, What & Why  Evolution of (Lean) Process Analytics  Simulation-based “Living VSMs” ◦ Understanding the “Physics of the Process”  The Virtual Kaizen: your insurance policy  Introducing Modeling & Sustaining the Gain
  • 3. 3  Bruce Gladwin – fellow pilgrim on the Lean journey  28 years in process analysis and improvement ◦ Hughes Aircraft – Industrial Engineer Role ◦ GE Global Research – Internal PI Consultant ◦ GE Power Systems – Six Sigma Black Belt  ProModel Corporation – Orem, Utah ◦ Simulation Trainer, Consultant & Project Manager ◦ Product Manager, Process Simulation Tool ◦ Currently VP, Modeling Services Team
  • 4. 4  What? ◦ How modeling tools, specifically discrete-event simulation, can improve & accelerate your Lean journey  Why? ◦ “The times they are a changin” – Bob Dylan ◦ Early modeling tools were not for “the faint of heart” ◦ Advances in simulation technology have put this valuable technology within reach of more and more Lean practitioners  Many tools today are “fill in the blank”, no programming required!
  • 5. 5
  • 6. 6
  • 7. 7
  • 8. 8
  • 9. 9  Learning to See – Lean Enterprise Institute
  • 10. 10 Learning to See: © 1998 LEI, Inc., pp. 32-33
  • 11. 11 Learning to See: © 1998 LEI, Inc., pp. 78-79
  • 12. 12  Value Stream Map ◦ Static model that describes the states of current and/or future processes ◦ Tells what is happening in the process now and what the process could be later ◦ Typically reports on lead time and value added time ◦ Requires general information regarding material arrivals, operation times, labor and equipment availability.  e.g. average time = 15 sec  Process Simulation ◦ Dynamic model that predicts the behavior of current and/or future processes ◦ Tells why a process behaves the way it does or how it could behave in the future ◦ Reports on throughput, inventory, lead times, VA, NVA and resource utilization ◦ Requires specific information about material arrivals, batch constraints, operation times, labor and equipment uptime & availability, transportation methods and times, and any other capacity constraints. ◦ e.g. Time = T(5,10,25) sec
  • 14. 14 Repairing the Recirculation Loop allowed more WIP which was key to fixing problem
  • 15. 15 Throughput Up 37% !! WIP also Up 37% !!
  • 16. 16
  • 17. 17 Interdependencies Variation SIMULATE Use Simulation When You Cannot Ignore Variation and Interdependencies Static Tools SPREADSHEET ANALYSIS MONTE-CARLO SIMULATION (Crystal Ball) OPTIMIZATION ALGORITHMS (LP, MIP)
  • 18. 18 Using “Averages” can be dangerous. How much Risk can you afford to take?
  • 19. 19 Trying to optimize a complex mfg system is like trying to solve the Rubic’s cube. Simulation can help understand the trade-offs!! Maximize Throughput Reduce Cycle Time Reduce Inventory Minimize CapEx Interdependencies
  • 20. 20 Exercise 1 … from Process Simulator Basics Training Course Activity Worker Work Unit (Exit) 5 minute Process Time 100% Resource Availability Deterministic Example Activity 2 Worker 2 Work Unit 2 (Exit) U(5,1) minute Process Time 100% Resource Availability Stochastic Example Activity 3 Worker 3 Work Unit 3 (Exit) U(4,1) minute Process Time 80% Resource Availability Stochastic Example with Uncertainty Activity 4 Worker 4 Work Unit 4 (Exit) U(4,1) minute Process Time 80% Resource Availability Assembly Opn Stochastic Example with Uncertainty and Interdependency Activity 4A Worker 4A Work Unit 4A BA C D Arrival Rate = 1 unit every 5 minutes Identical for every arrival stream No variation
  • 21. 21 Results Are Striking! 40 hr comparison of System A to System D Throughput: 4.6% Avg Queue: 14x Avg Cycle: 15x
  • 22. 22
  • 23. 23  A Virtual Kaizen is simply using simulation first to better understand the “Physics of the Process”…  Then design the improved process, to produce the desired outputs, within expect levels of variation…  All in a risk free environment ◦ No interruptions to the existing line ◦ No need to make physical rearrangements (yet) ◦ Ability to ask “What if…?”
  • 24. 24  Founded in 1975 by former Westinghouse engineers  Ownership: privately held by founders  Location: Pittsburgh area  Employees: 150 total (50 salary, 100 hourly)  Products: ◦ Mechanical Switches for Electric Utility Grids ◦ Automatic Distribution Motor Operators (ADMO)  Allows remote control of switches by utility personnel
  • 25. 25 Vertical Switch V2-C (copper) Center Break Switch CB-A (aluminum) 6’ 6’ Switches = 50% Revenues
  • 26. 26  Privately held, debt-free, profitable business with little competition from low cost countries (for now).  Capacity constrained. The market would buy every additional unit that they could produce.  Each additional unit sold would contribute $5k-$10k to their bottom line.  Want to maximize 1st shift capacity before adding additional 2nd shift operations.  Considering 30,000 sf expansion to their existing plant, at a cost of about $1M.
  • 27. 27 Eliminate or Reduce the Risk through a “Virtual Kaizen” using Simulation Technology.
  • 28. 28
  • 29. 29  Value stream mapping  Work standardization  Point of use storage  Single Piece Flow  Visual management (Subassembly Feeder Lines)  Kanban (pull) production (Subassembly Area)  Setup reduction (Fabrication Area)  Total productive maintenance (Fabrication Area)
  • 30. 30
  • 31. 31  Simulation proved to be an accurate tool for modeling the current state Vertical Switch Assembly process  A Future-State model using Lean Production concepts proved to be far more efficient ◦ 20% improvement in product throughput! ◦ 67% reduction in average unit cycle time! ◦ 50% reduction in floor space requirements! ◦ Elimination of need for 2nd shift!
  • 32. 32  Identify the best opportunities ◦ Good ROI potential  Every additional unit produced can be sold ◦ (Fairly) Low Risk  Use a Sensei with years of modeling experience  Involve all stakeholders, especially line operators whenever possible  Be patient and allow your team time to develop modeling skillset. Make modeling a part of every major process change.
  • 33. 33  Update the models regularly so they are always available to predict the future ◦ More & better data results in better decisions  Link to real-time data sources ◦ More & better data results in more accurate results ◦ Strategic Analysis becomes Tactical Analysis
  • 34. 34 Generis – 2015 American Manufacturing Summit Bruce Gladwin, ProModel Corporation bgladwin@promodel.com