Manufacturing
Systems Research
Chris Hicks
© C.Hicks, University of Newcastle
Computer Aided Production
Management Systems in
Engineer to Order Companies
• ACME grant in collaboration with NEI
Objectives
• Identify the characteristics of
companies in ETO/MTO sector
• Evaluate the status of CAPM
• Identify common CAPM problems
• Develop methods for modelling CAPM
systems in ETO/MTO environments
• Apply modelling methods to identify
solutions to CAPM problems
© C.Hicks, University of Newcastle
Identification of Company
Characteristics and CAPM
problems
• Nine one day visits
• Three long visits (3 days - 1 month)
• Developed semi-structured “audit”
methodology
• Developed methods for company
classification
© C.Hicks, University of Newcastle
Audit
•
•
•
•
•
Markets
Products
Processes
Manufacturing Systems
CAPM Systems
© C.Hicks, University of Newcastle
Company Classification
Jobbing
Company Type “A”
Main product
Batch
Spares
Subcontract
Flow
Deep
Shallow
Product Structure
© C.Hicks, University of Newcastle
Company Type “B”
Jobbing
Batch
Valves & Pumps
Motors
Cabs
Flow
Deep
Shallow
Product Structure
© C.Hicks, University of Newcastle
Control Approaches
Project
Management
Jobbing
MRP
MRP
+ JIT
Batch
JIT
Flow
Deep
Shallow
Product Structure
© C.Hicks, University of Newcastle
Audit Conclusions
• Markets - demand highly variable and
lumpy
• Products - complex, highly customised,
mix of products
• Processes - wide range yet all areas
tend to be controlled in same way
• Manufacturing systems - mainly
functional layouts, high capital
employed
• CAPM systems - poor integration, wide
variety of subsystems, incorrect data
structures, poor operational
procedures, generally unsuccessful
© C.Hicks, University of Newcastle
Systems Modelling
• Functional models decompose
complex systems using a hierarchical
top-down approach. They provide a
means of understanding activities and
interrelationships
• Information models enable structure of
information to be described
• Dynamic models show changing
behaviour over time.
© C.Hicks, University of Newcastle
Company X - Context Diagram
a
Customer
a
Customer
ITT
Contract
Awarded
Progress
Report
Tender
Company X
ITT
Quote
b
Supplier
© C.Hicks, University of Newcastle
Order
b
Supplier
Data Flow Diagram - High Level
4
Gen. Manager
D
1
Approval Of
Tender
Supplier
Tender
Quote
Quote
ITT (copy)
1
Designs, TPS, PP
Recommend Suppliers
Design for
Tender
Tendering
Designs
M
5
Pref. Suppliers
M
6
Historic Designs
ITT (copy)
CQAR
5
Projects
Progress
Report
Project
Plan
Update
Plan & Coordinate
Project
Quality
Project
Report
9
Approve
P.O.
Contract
Progress
File (copy)
Report
Drawings,
Manuals
7
Engineering
6
Conceptual &
Detailed Design
New Designs
M
5
M
6
Supplier
Approval
Supplier
Pref. Suppliers
Historic Designs
Purchasing
Supplier Selection,
Ordering &
Expediting
M
3
Contract File
D/M
4
Client Corresp
BRR
Action
Drawings
Progress
Report
Customer
G.M
Prepare
CQAR
Contract
File (copy)
Contract
Awarded
Tender
ITT
Contract
File
Supplier Details
Suppliers
& Costing
ITT (copy)
Supplier
3
D
2
Prepare
Tender
Engineering
2
ITT & Tender
Progress
Report
Drawings
ITP
Contract
File (copy)
Purchase
Order
(copy)
Supplier
Approval
8
Quality
ITP & Supplier
Approval
Supplier
M/D
Suppliers
7
Supplier
Purchase
Expedite
Quote Order
D
Previous Suppliers
Designs
8
Supplier
© C.Hicks, University of Newcastle
Inspection
report
Data Flow Diagram - Low Level :
Supplier Selection, Ordering & Expediting
7 . Supplier Selection,
Ordering & Expediting
Projects
Drawings
7.1
Purchasing
Generate Bill
of Materials
Components
(grouped)
7.2
Contract
File (Copy)
Supplier
Details Possible
Supplier
Previous Suppliers
M/D
7
Supplier Literature
New
Supplier
Purchasing
Select
Supplier(s)
D
8
Approve
Supplier
Engineering
Quote
Supplier Selected
7.3
Purchasing
Order
Component
Agreed Delivery
Date, Location
7.4
Purchasing
Purchase
Order
Project Plan
(Delivery Date)
Purchase
Order
(Copy)
Approve
Order
Supplier
Projects
Project Progress
Check
Expedite
Progress
© C.Hicks, University of Newcastle
Supplier
Method Limitations
• Audit, systems analysis and data
modelling provide static “snapshot”
views. Longitudinal studies are a
series of snapshots. No model of
system dynamics.
• At best enable “best practice” or
potential solutions to be described and
documented.
• Not possible to perform experiments to
examine alternative configurations and
evaluate them in terms of specified
performance criteria
© C.Hicks, University of Newcastle
Simulation
• Allows modelling of system dynamics
• Very expensive in terms of model
building and computational resources
• Validation often a problem
• Predominantly used for either small
scale models or rough-cut high level
models
© C.Hicks, University of Newcastle
Simulation Model CAPM Modules
PP
MPS
FIN
MRP
CP
BOMP
P/A P
INV
POC
SFC
CAPM Modules
Existing
Developing
SIMULATION
© C.Hicks, University of Newcastle
STATIC DATA
OPERATIONAL DATA
RESOURCE DATA
RESOURCE DATA
Resource code
Name
Optional resource
Location (x,y)
Rectangular size (w,h)
Coordinates
Company reference
Shift pattern
Audit period
Data update period
Availability
Dispatching rule
Optional resource rule
Batch splitting rule
Batch sizes
Transfer device
Minimum set up time
Minimum processing time
Minimum transfer time
Stochastic distribution
Efficiency
Overhead cost per hour
Cost per hour
PRODUCT DATA
PART CODE
Part code
Name
Component codes
Component quantities
PRODUCT STRUCTURE
Product structure codes
Product instance codes
MINOR RESOURCE DATA
Minor resource code
Location
Quantity
Cost per hour
Minimum use time
SHIFT DATA
CONDITIONAL DATA
RESOURCE DATA
Resource status
Work in progress (non added value)
Work in progress (added value)
Queue length
Maximum queue length
Operations per family
Sum set up time
Sum processing time
Last part
Batch counter
PRODUCT DATA
PRODUCT STRUCTURE
Current operation
Current operation type
Current value
Actual operation start times
Queue times before each operation
Actual set up times
Actual processing times
Queue times after each operation
Actual transfer time
Shift number
Daily work pattern
PRODUCT DATA
PART CODE
Routing
Operation types
Minor resources used
Minor resource quantities
When minor resources used
Planned set up times
Planned operation times
Planned transfer times
Lead time
Lot size
Lot sizing rule
Safety stock
Safety lead time
Planned product structure
PRODUCT STRUCTURE
Product family
Original due time
Planned due time
Planned operation start times
MASTER PRODUCTION SCHEDULE
MPS Item Number
Part code
Quantity
Static data - time invarient
Operational data - specified configuration
Conditional data - dynamic stochastic data
DEFAULT DATA
Refers to
Minimum set up time
Minimum processing time
Minimum transfer time
Minimum queue before operation
Minimum queue after operation
© C.Hicks, University of Newcastle
Key Features
• Large scale model allows whole
manufacturing facilities to be
represented
• Models facilities, products, processes
and planning and control systems
• Many product families can be
represented with shallow, medium or
deep product structure
• Data structures match ETO/MTO
requirements
• Allows variety of planning and control
methods to meet local requirements
• May be used as a research tool or for
planning and simulation.
© C.Hicks, University of Newcastle
Simulation Case Study
• Heavy Machine Shop used for case
study
• Static configuration used layout and
other resource constraint information
• BOM information obtained for products
• Process data and planning data
obtained for 18 month period.
© C.Hicks, University of Newcastle
Experimental Design
• Series of full factorial experiments
Factors
• Process - assembly lead time,
minimum set-up, machining and
transfer
• CAPM, scheduling methods, BOMs,
dispatching rules, capacity planning
• Product mix / load
• Operational - data update period
© C.Hicks, University of Newcastle
Conclusions
• Assembly planning and capacity
planning important CAPM subsystems
• “Dispatching” rules not very significant
• Manufacturing performance sensitive
to transfer times.
• Significant advantages gained through
having close control of key resources
• Real time data recording led to
improved manufacturing performance
© C.Hicks, University of Newcastle
Consequent research
topics
• Manufacturing Layout (Hinrichs, Wall)
• Capacity planning (Tay, Holmes, Hines,
Pongcharoen)
• Assembly planning (Sullivan ..)
• Planning of product development
activities
• Planning under uncertainty (Wall,
Brand, Song)
• Integration of project planning methods
with MRP type approaches (EPSRC
proposal)
• Plan Optimisation through Genetic
Algorithms
© C.Hicks, University of Newcastle
Manufacturing Layout
• All data required for layout analysis,
clustering and generation in simulation
data structures
• Work started with Chris Lee who was
interested in improving the layout of
Vickers’ Scotswood Road factory
• Much work in layout has focused on
moving from functional layout to
cellular layouts
© C.Hicks, University of Newcastle
Methods
• Clustering
– Matrix based methods
– Similarity coefficient methods
• Layout generation
– Starting with some candidate
solution generate new layout that
minimises (maximises) some
objective function
– Simulated annealing
– Genetic algorithms
© C.Hicks, University of Newcastle
Capacity Planning
•
•
•
•
•
Finite / infinite loading
Re-planning rules (Tay)
Finite loading rules (Holmes)
Interactive tools (Hines, Poncharoen)
Schedule “optimisation” (Poncharoen)
© C.Hicks, University of Newcastle
Supply Chain
Management
• ETO companies moving towards buy
rather than make
• Business process analysis approach
(McGovern, Earl, Harrison, Hamilton)
• Agent based modelling of supply
chains (Harvey, McLeay, Hines)
© C.Hicks, University of Newcastle
IT Implementation
• Embodies audit, business process
analysis and requirements definition
• Transfer of computing expertise
• 3 Teaching Company Schemes
© C.Hicks, University of Newcastle
Layout design & effect
on benchmarks
• Tony Wells Siemens Semiconductors
• Data from North Tyneside, US,
Germany, Taiwan.
• Pareto analysis of costs
• Identification of cost drivers
• Relating cost drivers to plant design
configurations
• Results so far: potential cost reduction
of 50% on £30m/annum -pity the plant
has closed!
© C.Hicks, University of Newcastle
Management of Knowledge
• Data modelling / systems analysis
based upon thematic knowledge that is
formal, explicit and easily shared
• Knowledge management requires
knowledge of product and process
structure
• “Tacit” or embedded knowledge that is
disorganised, informal, context
dependent and relatively inaccessible
often important.
• Interested in developing
methodologies for systems integration
that include both thematic and tacit
knowledge
• LABS Proposal with Paul Braiden
© C.Hicks, University of Newcastle
Other Activities
• £180k EDF grant with ISRU to develop
distance learning material (Dave
Stewardson & Mark Gary)
• TCS with House of Hardy aimed at
improving manufacturing efficiency
(Rob Davidson & Paul Braiden)
© C.Hicks, University of Newcastle
Summary
• Wide portfolio of manufacturing
systems research
• Various types of research undertaken
from the theoretical through to applied.
• “Market led” rather than “product led”
© C.Hicks, University of Newcastle