Transcript Nomination for Rockwell Collins Engineer of the Year Dr MC

Producing Prosperity - An Industrial
Engineer’s Role in Economic Expansion
Dr M C Jothishankar
Advanced Manufacturing Technology
Defining the “New Economy”

Past 100 years, vitality of US economy was determined
by success of “traditional” manufacturing industries automobiles, steel, oil, and chemicals

Today, information technology, communications and
intellectual capital determine success

The driving forces of the new economy are ideas,
knowledge, services and higher-order skills

Manufacturing remains important - innovation,
adaptation, and reengineering are the watchwords of
success for today’s workers and businesses.
What Does the New Economy Mean?

To those in information technologies - describes the
power of new communication tools

To venture capitalists - hundreds of investment
opportunities each day

To corporate leaders - new alliances, partnerships or
mergers

To trade advocates - accelarates an integrated, global
economy

To educators - lifelong learning opportunities

To the average citizen - numerous opportunities at home
and at work, and more connectivity worldwide
What are the Characteristics

Products are increasingly non-tangible: knowledge
is the major input

Productivity is increasing: deployment of
technology driving force

Markets are global and competitive: labor and
expertise vs. location and physical structure

Entrepreneurs are spurring economic growth

New partnerships are the wave: co-competition
creates a flexible economy
How Can an IE Become More
Competitive in the New Economy?

IEs must build upon their core strengths and focus
on the economic foundations of the New Economy:
– Cross functional skills
– Access to new technologies on which new
products and processes are based
• Consortium participation
– Collaborative work among:
• Industries, academia, government and labor
Case Study I
Traditional Approach
(Part 1)
Material Movement &
Fleet Management
This project aims to study the best possible
routing/distribution of mails/material
between Rockwell facilities in Cedar
Rapids.
Facts

Over 30 buildings are served

Seven drivers moved material between buildings

Some stops were delivered 12 times a day

Type of material moved:
– Internal mail
– Dispatches
– “Hot” Dispatches
– Security Dispatches
– Skids
Present Routes
Traditional IE Approach

Time study on the routes

Foot prints of the routes

Cost calculations on resources
Recommendations

Reduce current seven drivers to four drivers

Establish a “hub” at 120 Mailroom for relay of
dispatches between routes

Reduce frequency of visits to a maximum of 8
per day
Final
Results
Total Savings out of this
project:
$121,000
Case Study I
Same Problem in New Economy
- A Collaborative Approach
(Part 2)
U. S. Region
• Dr. M. C. Jothishankar
Japan Region
• Mr. Tomomitsu
Murano,
• Dr. Dennis Bricker
THE UNIVERSITY
OF IOWA
• Mr. Hisaya
Watanabe
• Dr. Seiichi Kawata
European Region
• Prof. Dr.-Ing. Heinz Wörn
• Mr. Daniel Frey
University of Karlsruhe,
Germany
Quote to Note
“The improvements achieved in one
company can be easily be wasted in the
subsequent phases of logistics chain.”
- Heard on the Street!
Objective of GALAXI
To develop an optimization and simulation
model that will minimize the overall fleet
operations cost and most effectively
distribute material between different
manufacturing plants.
Description of the Model

In this manufacturing system, there are a number
of factories

Each factory manufactures parts, which are used
for assembly of products at the same or another
factory

Parts are transferred between factories by using
several kinds of vehicles (trucks) on demand
Example of Plants
P4
P3
P5
P2
P1
P6
P7
P8
Proposed Solution Method

The problem will be modeled as a
minimum-cost, multi-period, multicommodity network flow problem.

One set of variables will specify the
routes and schedules for the trucks,
while another set of variables will
specify the movement of the parts.
Total Cost to be Minimized
h kti Z ikt
f vmYvm +    c kp lX lkt + k



K t=1 iI
mM
t=1 l k
T
T
cost of
vehicles
shipping
costs
storage costs
& penalties
for late delivery
Constraints

capacity restrictions

conservation of flow for each material

limit on # vehicles of each type

integrality of vehicles
Solution Approach

Benders’ Decomposition

Lagrangian Relaxation

Cross-Decomposition

Genetic Algorithm

Simulation
Cross Decomposition
Genetic Algorithms
Truck schedules
Lagrangian
Subproblem
Manual Input
Benders'
Subproblem
(Solves Lagrangian Relaxation) (Determines Material movement)
Lagrangian multipliers
Inter-Relationship Among Models
Deterministic Model
USA
Cross-Decomposition Method
F(Y)
Japan
Genetic Algorithms
Stochastic Model
Germany
Simulation Model
Sample Simulation
Expectations of This Project

We hope to reduce our total material
movement cost by 30% a savings of almost $
250,000 annually

This software will help the truck schedulers to
make better decisions and to reduce the time
spent in scheduling

Increased truck utilization
Case Study II
Setup Reduction
(Part 1)
Concepts

Look beyond the problem under study Instead of “Point” solution approach the
problem to provide a “System Solution”

Use Re-engineering principles

Involve the users
Problem Overview

PCB assembly machines have high pick-andplace rates, but their set-up times are
typically very long

PCBs scheduled in Process Center on firstcome-first-serve basis

Set-up is changed for every PCB batch

Large set-up times and underutilized
resources
Setup Details

Kits run per day : 30

Feeder changes between kits : 40

Feeder changes per day : 1200

Time to change a feeder : 30 Seconds

Time to change feeders / month :
1200 x 22 x 0.5 = 220 hours
(10 hours a day!)
Process Center Operation
Improvement Objectives

Set-up time reduction

Scheduling time reduction

Increase machine utilization

Decrease manufacturing lead time

Increase throughput
Process Center Optimization
Project Approach

PCB manufacturing process reengineering

Development of optimization algorithms

Software development

Simulation studies
Process Center Optimization

Cluster PCBs into groups

Sequence the PCB groups to minimize the total
set-up time

Optimize assignment of feeder locations to
minimize the number of feeder changeovers

Use simulation to evaluate system performance
for generated schedules
Clustering PCBs

Clustering PCBs into a minimum set of groups
such that:
– Groups are formed based on similar
components
– Total number of unique component types
should be less than the number of feeders
– Within each PCB group, no set-up is necessary
when changing from one PCB type to another
Clustering PCBs - Example (Before)
Printed Circuit Board
Component
1
a
b
c
d
e
f
g
h
2
3
4
5
6
1
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
1
1
1
1
8
1
1
1
1
1
Clustering PCBs - Example (After)
Printed Circuit Board
Component
g
f
a
b
d
e
c
h
4
1
7
9
8
1
1
1
1
1
1
1
1
1
1
1
2
5
6
3
1
1
1
1
1
1
1
1
1
1
1
1
1
PCB Groups:
(4,1,7)
(9,8)
(2,5)
(6,3)
Select work orders to be
scheduled for production
Balance workload
on assembly lines
Split assembly components
between machines
Group similar assemblies
into families
Generate family and
assembly sequences
Generate machine
placement programs
Optel Procedure
Interacting Elements
Part
Inventory
Productio
n
Plan
Process
Center
Data
OPTEL
Schedule
PCB Design
Data
Case Study II
Manufacturing Optimization
and Execution System (MOES)
for a PCB Assembly Plant
(Part 2)
Present Optel Framework
Production
Scheduling
PDM
ERP/
MRP
Assembly
Data
Modeling
Material
Management
O
P
T
E
L
Plant Data
Manager
Machine
Optimization
Setup
Verification
Plant Process
Monitor
Shop
Floor
Benefits of Using Optel

Setup time reduction: 70%

Increase in machine utilization: 20%

Increase in component placement: 60%

Reduction in machine programming time: 95%

Eliminated night shift and week end operations
Results
Total Savings out of this
OPTEL project:
$1.5 M/Year/site
Case Study III
E- Manufacturing
Partners of this E-Manufacturing Project
Virtual Factory
ANY PRODUCT
ANY TIME
ANY WHERE
Vision
Our vision is E-manufacturing where we have

Seamless, scalable and robust evolution of
products from design to manufacturing

Computer tools (such as simulators, rule-bases,
visualizing environments) to rapidly plan, validate
and deploy manufacturing instructions

Flexible manufacturing systems for simultaneous
production of multiple products and minimum
system change over
Present flow
Design Release
Physical
Constraints
and Functions
Detailed
Design
MFG. Review Trial&Error
(DFM Checks) Production
Proposed Flow
Physical
Constraints
and Functions
Engineering
Design
Design For
Manufacturing
Virtual
Environment
Enterprise
Resource
Planning
Computer
Integrated
Manufacturing
Optel Manufacturing
Execution System
Design Release
Production
Virtual Environment
Rules
Designs
Resources
Rule
Inference
Engines
Manufacturing
Execution
Systems
Simulator
Process
Planners
Process
Plans
Virtual
Products
Visualizers
Machine
Programs
Manufacturing
Analysis
Flow Diagram
Assembly
configuration
file
AP 210 file
(2D)
Placement
Sequence
(OPTEL)
Visualizer
(STEP
OIV)
Design Facts
DFM
Rule files
Machine
library
DFM Rule
Checker
Simulator
AP 210
file (3D)
2D to 3D converter

ECAD translators generate AP 210 files
containing 2D Geometry

Simulate 3D view of the assembly board

Converter
– Input : AP 210 file (2D geometry)
– Extrudes them into solids (Advanced BREP)
– Output:
• AP 210 file (2D + 3D geometry)
• AP 203 files
2D to 3D converter

AP 203 files
– Individual packages
– Board

These files then converted
into Open Inventor format
– Inventor: Graphics
package used for rendering
DFM Rule Facility

Subset of Rockwell Collins DFM rules were
chosen for implementation.

Interface to the simulator
– DFM rules which were violated
– Components which violated these rules.
Assembly Data

Establish an assembly usage view
– Components
• Organization by package / part family
• Part numbers, version
• Configuration management data
• Location, orientation
• Reference Designators

Integrated with OPTEL
– Magazine Setup
– Optimized placement sequence
Simulator
DFM Rule
Check
Feeder
information
Virtual
Board
Placement
Simulator
Component
information
Placement
Sequence
Geometric
Models
Components
violating
DFM rules
Work Done

Accepting an AP210 2D design file

Manufacturability analysis

Extracting component information

Generating 3D models of components and
assemblies

Generate
– “as designed” view
– “as simulated” view
Rules of New Economy

Change happens
– Anticipate change

Be ready to change quickly and enjoy the
change
– Adapt to change quickly
“It is not the strongest of the
species that survive, not the most
intelligent, but the one most
responsive to change”
Charles Darwin