Manufacturing
Systems III
Chris Hicks MMM Engineering
Email: [email protected]
MMM341/1
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Assessment
• End of year examination
• 2.5 hours duration
• Answer 4 questions from 6
MMM341/2
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Manufacturing Systems III
• Manufacturing Strategy
• JIT Manufacturing
• Manufacturing Planning and
control
• Company classification
• Modelling & Simulation
• Queuing theory (CFE)
MMM341/3
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Manufacturing
Strategy
MMM341/4
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Reference
• Hill, T (1986),”Manufacturing Strategy”,
MacMillan Education Ltd., London.
ISBN 0-333-39477-1
MMM341/5
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Manufacturing Strategy
• Long term planning
• Alignment of manufacturing to satisfy
market requirements
MMM341/6
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Significance of
Manufacturing
• Manufacturing often responsible for
majority of capital and recurrent
expenditure
• Long term nature of many
manufacturing decisions makes them
of strategic importance
• Manufacturing can have a large impact
on competitiveness
MMM341/7
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Manufacturing
Strategy
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Make / buy
Process choice
Technology
Infrastructure, systems, structures &
organisation
• Focus
• Integration with other functions
MMM341/8
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Strategy Development
• Define corporate objectives
• Determine marketing strategies to
meet these objectives
• Assess order qualifying and order
winning criteria for products
• Establish appropriate processes
• Provide infrastructure
MMM341/9
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Identifying Market
Requirements
• Order Qualifying criteria
• Order winning criteria
• Order losing criteria
MMM341/10
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Manufacturing
Influences
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Costs
Delivery
Quality
Demand flexibility
Product range
Standardisation / customisation
MMM341/11
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Profile Analysis
• Assess match between market
requirements and current performance
• Identify changes required to
manufacturing system
MMM341/12
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Market Requirements
Unimportant
V Imp.
Price
Quality
Delivery
CofOwn
Customisation
Other factors
MMM341/13
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Current Performance
Unimportant
V Imp.
Price
Quality
Delivery
CofOwn
Customisation
Other factors
MMM341/14
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Market requirement
Achieved performance
Unimportant
V Imp.
Price
Quality
Delivery
CofOwn
Customisation
Other factors
MMM341/15
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Process Choice
• Type of process: project, jobbing,
batch,line
• Flexibility
• Efficiency
• Robustness wrt product mix / volume
• Unique / generic technology?
• Capital employed
• How do processes help
competitiveness?
MMM341/16
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Manufacturing
Structure
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Layout: functional or cellular?
MTS / MTO
Flexibility of workforce
Organisation, team working etc.
Breakdown of costs
HRM issues
MMM341/17
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Products
• Relative importance, present and
future
• Mix
• Complexity
– Product structure
– Concurrency
– Standardisation / customisation
• Contribution
MMM341/18
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Measures of
performance
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What are they?
Frequency of measurement
Comparison with plan.
Orientation: product / process /
inventory
• Integration with other functions
MMM341/19
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Infrastructure
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Manufacturing planning & control
Sharing information / knowledge
CAD / CAM
Accounting systems
Quality systems
Performance measurement
MMM341/20
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Case studies
• Heavy engineering
– PIP teams, simplification, value
engineering, cellular manufacturing
• Automotive supplier
– “world class” but still relatively low
productivity compared with
Japanese sister company. Why?
MMM341/21
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
“Manufacturing is a business function
rather than a technical function. The
emphasis should be on supporting the
market” Terry Hill (1996)
MMM341/22
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Just-in-Time Manufacturing
MMM341/23
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
References
• APICS (1987),”APICS Dictionary”,
American Production and Inventory
Control Society, ISBN 0-935406-90-S
• Vollmann T.E., Berry W.L. & Whybark
D.C. (1992),”Manufacturing Planning
and Control Systems (3rd Edition)”,
Irwin, USA. ISBN 0-256-08808-X
• Browne J., Harhen J, & Shivnan J.
(1988),“Production Management
Systems: A CIM Perspective”,AddisonWesley, UK, ISBN 0-201-17820-6
MMM341/24
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Just-in-Time Manufacturing
“In the broad sense, an approach to
achieving excellence in a
manufacturing company based upon
the continuing elimination of waste
(waste being considered as those
things which do not add value to the
product). In the narrow sense, JIT
refers to the movement of material at
the necessary time. The implication is
that each operation is closely
synchronised with subsequent ones to
make that possible”
APICS Dictionary 1987
MMM341/25
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Just-in-Time
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Arose in Toyota, Japan in 1960s
Replacing complexity with simplicity
A philosophy, a way of thinking
A process of continuous improvement
Emphasis on minimising inventory
Focuses on eliminating waste, that is
anything that adds cost without adding
value
• Often a pragmatic choice of techniques
is used
MMM341/26
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Just-in-Time Goals
• “Zero” inventories
• “Zero” defects
– Traditional Western manufacturers
considered Lot Tolerance Per Cent
Defective (LTPD) or Acceptable
Quality Levels (AQLs)
• “Zero” disturbances
• “Zero” set-up time
• “Zero” lead time
MMM341/27
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Just-in-Time Goals
• “Zero” transactions
– Logistical transactions: ordering,
execution and confirmation of
material movement
– Balancing transactions: associated
with planning that generates
logistical transactions - production
control, purchasing, scheduling ..
– Quality transactions: specification,
certification etc.
– Change transactions: engineering
changes etc.
• Routine execution of schedule day in day out
MMM341/28
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Benefits of JIT
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Reduced costs
Waste elimination
Inventory reduction
Increased flexibility
Raw materials / parts reduction
Increased quality
Increased productivity
Reduced space requirements
Lower overheads
MMM341/29
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Just-in-Time
JIT links four fundamental areas
• Product design
• Process design
• Human / organisational issues
• Manufacturing planning and control
MMM341/30
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Process
design
Product design
JIT
Human /
organisation
Planning &
control
Elements of Just-in-time
MMM341/31
© Dr. C.Hicks, MMM
Engineering
Vollmann et al 1992
University of Newcastle upon Tyne
Product Design
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Design for manufacture
Design for assembly
Design for automation
Design to have flat product structure
Design to suit cellular manufacturing
Achievable and appropriate quality
Standard parts
Modular design
MMM341/32
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Process Design
• Set-up / lot size reduction
• Include “surge” capacity to deal with
variations in product mix and demand
• Cellular manufacturing
• Concentrate on low throughput times
• Quality is part of the process,
autonomation, machines with built in
capacity to check parts
• Continuous quality improvement
• No stock rooms - delivery to line/cell
• Flexible equipment
• Standard operations
MMM341/33
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Human / Organisational
Elements
• Whole person concept, hiring people,
not just their current skills / abilities
• Continual training / study
• Continual learning and improvement
• Workers capabilities and knowledge
are as important as equipment and
facilities
• Workers cross trained to take on many
tasks: process operation,
maintenance, scheduling, problem
solving etc.
• Job rotation / flexibility
• Life time employment / commitment?
MMM341/34
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Organisational Elements
• Little distinction between direct /
indirect labour
• Activity Based Cost (ABC) accounting
• Visible team performance
measurement
• Communication / information sharing
• Joint commitment
MMM341/35
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
JIT Techniques
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Manufacturing techniques
Production and material control
Inter-company JIT
Organisation for change
MMM341/36
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Manufacturing
Techniques
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Cellular manufacturing
Set-up time reduction
Pull scheduling
Smallest machine concept
Fool proofing (Pokayoke)
Line stopping (Jikoda)
I,U,W shaped material flow
Housekeeping
MMM341/37
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Group Technology / Cellular
Manufacturing
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Improved material flow
Reduced queuing time
Reduced inventory
Improved use of space
Improved team work
Reduced waste
Increased flexibility
MMM341/38
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Set-up Time Reduction
• Single minute exchange of dies
(SMED) - all changeovers < 10 mins.
1. Separate internal set-up from external
set-up. Internal set-up must have
machine turned off.
2. Convert as many tasks as possible
from being internal to external
3. Eliminate adjustment processes within
set-up
4. Abolish set-up where feasible
Shingo, S. (1985),”A Revolution in
Manufacturing: the SMED System”,
The Productivity Press, USA.
MMM341/39
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Basic Steps in a Traditional
Set-up Operation
1. Preparation, after process
adjustments, checking of materials and
tools (30%).
2. Mounting and removing blades, tools
and parts (5%) Generally internal.
3. Measurements, settings and calibration
(15%) includes activities such as
centring, dimensioning, measuring
temperature or pressure etc.
4. Trial runs and adjustments (50%) SMED
Typical proportion of set-up time given in
parenthesis.
MMM341/40
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Set-up Analysis
• Video whole set-up operation. Use
camera’s time and date functions
• Ask operators to describe tasks. As
group to share opinions about the
operation.
MMM341/41
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Three Stages of SMED
1. Separating internal and external set-up
doing obvious things like preparation
and transport while the machine is
running can save 30-50%.
2.Converting internal set-up to external
set-up
3. Streamlining all aspects of the set-up
operation
MMM341/42
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Separating Internal and
External Set-up
MMM341/43
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MMM341/44
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
ANDON
A board which shows if any operator on
the line has difficulties
• Red - machine trouble
• White - end of a production run
• Blue - defective unit
• Yellow - set-up required
• Line-stop - all operators can stop the
line to ensure compliance with
standards
• Flexible workers help each other when
problems arise
MMM341/45
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
JIT Material Control
• Pull scheduling
• Line balancing
• Schedule balance and smoothing
(Heijunka)
• Under capacity scheduling
• Visible control
• Material Requirements Planning
• Small lot & batch sizes
MMM341/46
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
“Pull” Systems
• Work centres only authorised to
produce when it has been signalled
that there is a need from a user /
downstream department
• No resources kept busy just to
increase utlilisation
Requires:
• Small lot-sizes
• Low inventory
• Fast throughput
• Guaranteed quality
MMM341/47
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Pull Systems
Implementations vary
• Visual / audio signal
• “Chalk” square
• One / two card Kanban
MMM341/48
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Material Requirements
Planning / JIT
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Stable Master Production Schedule
Flat bills of materials
Backflushing
Weekly MRP quantities with “call off” ,
a common approach
MMM341/49
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
JIT Purchasing
• JIT purchasing requires predictable
(usually synchronised) demand
• Single sourcing
• Supplier quality certification
• Point of use delivery
• Family of parts sourcing
• Frequent deliveries of small quantities
• Propagate JIT down supply chain,
suppliers need flexibility
• Suppliers part of the process vs.
adversarial relationships
MMM341/50
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
JIT Purchasing
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Controls and reduces inventory
Reduces space
Reduces material handling
Reduces waste
Reduces obsolescence
MMM341/51
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Organisation for Change
• Multi-skilled team working
• Quality Circles, Total Quality
Management
• Philosophy of joint commitment
• Visible performance measurement
– Statistical process control (SPC)
– Team targets / performance
measurement
• Enforced problem solving
• Continuous improvement
MMM341/52
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Total Quality
Management (TQM)
• Focus on the customer and their
requirements
• Right first time
• Competitive benchmarking
• Minimisation of cost of quality
– Prevention costs
– Appraisal costs
– Internal / external failure costs
– Cost of exceeding customer
requirements
• Founded on the principle that people
want to own problems
MMM341/53
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
JIT Flexibility
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Set-up time reduction
Small transfer batch sizes
Small lot sizes
Under capacity scheduling
Often labour is the variable resource
Smallest machine concept
MMM341/54
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Reducing Uncertainty
• Total Preventative Maintenance (TPM)
/ Total Productive Maintenance
• 100% quality
• Quality is part of the process - it can’t
be inspected in
• Stable and uniform schedules
• Supplier quality certification
MMM341/55
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Total Preventative
Maintenance (TPM)
• Strategy to prevent equipment and
facility downtime
• Planned schedule of maintenance
checks
• Routine maintenance performed by the
operator
• Maintenance departments train
workers, perform maintenance audits
and undertake more complicated work
MMM341/56
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Implementation of JIT
MMM341/57
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Implementation of JIT
Method:
1.
Lower inventory levels
2.
Identify problems
3.
Eliminate problems
4.
Improve use of resources
• Inventory
• People
• Capital
• Space
5.
Go back to step 1
MMM341/58
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
JIT Circle
Standardisation
Design - focus
TPM
JIT Purchasing
TQM
Visibility
JIT
Pull scheduling
Set-up
reduction
Multi-skill
Workforce
Plant
Layout
Small machines
MMM341/59
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
JIT Limitations
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Stable regular demand
Medium to high volume
Requires cultural change
Implementation costs
MMM341/60
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Computer Aided
Production Management
Systems (CAPM)
MMM341/61
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
References
• Vollmann T.E., Berry W.L. & Whybark
D.C. (1992),”Manufacturing Planning
and Control Systems (3rd Edition)”,
Irwin, USA. ISBN 0-256-08808-X
(Earlier editions just as good!)
• Browne J., Harhen J, & Shivnan J.
(1988),“Production Management
Systems: A CIM Perspective”,AddisonWesley, UK, ISBN 0-201-17820-6
MMM341/62
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Computer Aided Production
Management (CAPM) Systems
“All computer aids supplied to the
manager”
• Specification - ensuring that the
manufacturing task has been defined
and instructions provided
• Planning and control - scheduling,
adjusting resource usage and
priorities, controlling the production
activity
• Recording and reporting the status of
production and performance
MMM341/63
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Computer Aided Production
Management (CAPM) Systems
Information systems responsible for:
• Transaction processing - maintaining,
updating and making available
specifications, instructions and
production records
• Management information - for
exercising judgements about the use
of resources and customer priorities
• Automated decision making producing production decisions using
algorithms
MMM341/64
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MMM341/65
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
CAPM Systems
• Planning
• Control
• Performance measurement
MMM341/66
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Planning Modules
• Master Production Scheduling (MPS) high level production plan in terms of
quantity, timing and priority of planned
production
• Materials Requirements Planning
(mrp) / Manufacturing Resources
Planning (MRP)
• Capacity Planning
MMM341/67
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Control Modules
• Inventory control - keeping raw
material, work in process (WIP) and
finished goods stocks at desired levels
• Shop floor control (Production Activity
Control) - transforming planning
decisions into control commands for
the production process
• Vendor measurement - measuring
vendors’ performance to contract,
covering delivery, quality and price
MMM341/68
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Material Requirements
Planning (mrp)
“Material requirements plannning
originated in the 1960s as a
computerised approach for planning of
materials acquisition for production.
These early applications were based
upon a bill of materials processor
which converted demand for parent
items into demand for component
parts. This demand was compared with
available inventory and scheduled
receipts to plan order releases”
Browne et al (1986)
MMM341/69
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Manufacturing Resources
Planning (MRP)
• The combination of planning and
control modules was termed “closed
loop MRP”. With the addition of
financial modules an integrated
approach to the management of
resources was created. This was
termed Manufacturing Resources
Planning.
• Material Requirements Planning (mrp /
MRPI)
• Manufacturing Resources Planning
(MRP/MRPII)
MMM341/70
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Material Requirements
Planning
• Dependant demand
• Time phased planning
Inputs
• Master Production Schedule
• Bill of Materials
• Inventory status
Assumptions
• Infinite capacity
• Fixed lead times
• Fixed and predetermined product
structure
MMM341/71
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Production
Planning
Res ource
Planning
Dem and
Managem ent
Mas ter
Production
Scheduling
Routing
File
Detailed
capacity
planning
Bill of
Materials
Detailed
Material
Planning
FRONT END
Inventory
Status
Data
Timed-phased
requirement
(MRP) records
ENGINE
Material
and capacity
plans
Shop floor
systems
Vendor
System s
BACK END
MMM341/72
Figure 3 Manufacturing Planning and Control Systems (Vollman et. al. 1989)
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MRP Record Card
Period
1 2 3 4 5
Gross Requirements
40 10
10
Scheduled receipts
Projected available
4 50 44 44 4 44
balance
Planned order releases
50
Lead time = 1 period
Lot size = 50
MMM341/73
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MRP Conventions
• MRP time buckets
• Scheduled receipts at start of period
• Projected available balance at end of
period
• Planned order releases at the start of
period
• Planned orders vs. scheduled receipts
• Number of buckets = planning horizon
MMM341/74
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Representation of
Product
A
B
C
Simple Product Structure
MMM341/75
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Linked MRP Cards
Period
1 2 3 4 5
Gross Requirements
40 10
10
Scheduled receipts
Projected available
4 50 44 44 4 44
balance
Planned order releases
50
Lead time = 1 period
Lot size = 50
Period
Gross Requirements
Scheduled receipts
Projected available
9
balance
Planned order releases
Lead time = 2 periods
Lot size = 100
1
2 3
9
9
4 5
50
9 59 59
100
MMM341/76
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Backwards Scheduling
A
(2 days)
B
C
(1 day)
(3 days)
1
2
3
Work back from Due Date
Due Date
Backwards Scheduling
MMM341/77
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Forwards Scheduling
A
(2 days)
B
C
(1 day)
(3 days)
Slack
2
3
Work forwards from start time
Due Time
Start time
1
MMM341/78
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MRP Domain
• Steady state systems
• Low levels of uncertainty
• Shallow / medium or deep product
structure
• Stable demand
• Predominantly make to stock
• Manufacturing orientation
MMM341/79
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MRP Parameters
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Planning horizon
Size of time bucket
Lot sizing rules
Regeneration vs.. net change
MMM341/80
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Validity of MRP
Assumptions
• Infinite capacity vs. capacity planning
• Fixed lead times / varying load
• “Lead times are a result of the
schedule”
• Integration of planning levels requires
feasibility at high and low levels
MMM341/81
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Typical Control
Parameters
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Safety stock
Safety lead time
Yield
Order quantity category
Min/max order levels
Max. days supply
Min. days between orders
MMM341/82
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Lot sizing
• Lot-for-lot
• Economic Order Quantity (EOQ)
• Complex optimisation algorithms
MMM341/83
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Uncertainties in MRP
• Environmental uncertainty
– Customer orders
– Suppliers
• System uncertainty
– Product quality
– Scrap / rework
– Process times
– Design changes
• MRP nervousness / instability
MMM341/84
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Dealing with
uncertainty in MRP
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Safety stocks
Safety lead times
Safety due date
Hedging
Over-planning
Yield factors
MMM341/85
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Appropriate approaches
• Timing uncertainty: safety lead time
• Quantity uncertainty: safety stock
MMM341/86
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MRP Nervousness
• Significant changes in plans due to
minor changes in high level plans
• Frequent changes in plans make the
MRP system lose crdibility
MMM341/87
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Causes of Nervousness
• Demand uncertainty
• Product structure characteristics
• Incorrect lot-sizing rules
MMM341/88
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Nervousness: Solutions
• Stable MPS
• Carefully change any parameter
changes
• Use different lot sizing rules at the high
and low levels of the product structure
MMM341/89
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MRP Problems
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Quality of the model
Bill of materials structure
Non-material activities
Validity of the assumptions
Lack of 2 way time analysis
Quality of data
Regeneration / computational effort
Poor visibility
Operational aspects
MMM341/90
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
How to implement MRP
• Get accurate data
• Make sure you have accurate data
• Have good procedures to make sure
that the data is always accurate
• Remember approximately 75% of MRP
implementations fail
• Unsuccessful MRP costs nearly the
same as successful MRP
MMM341/91
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Capacity Planning
MMM341/92
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
References
• Vollmann T.E., Berry W.L. & Whybark
D.C. (1992),”Manufacturing Planning
and Control Systems (3rd Edition)”,
Irwin, USA. ISBN 0-256-08808-X
• Plossl G.W. & Wight O.W. (1973),
“Capacity Planning and Control”,
Production and Inventory
Management, 3rd quarter 1973 pp3167
MMM341/93
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Capacity Planning
“The function of establishing,
measuring and adjusting limits or
levels of capacity.
Capacity planning in this context is the
process of determining how much
labour and machine resources are
required to accomplish the tasks of
production.
Open shop orders and planned orders
in the MRP system are input to CRP
which “translates” these into hours of
work, by work centre, by time period”
APICS Dictionary 1987
MMM341/94
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Capacity Planning
• Plossl bath tub
• Lead-time = queuing time + set-up
time + processing time + transfer time
• Queuing time is dependant upon the
level of backlog in the system
• Three reasons why queues go out of
control
– Inadequate capacity
– Erratic input
– Inflated lead time estimates
MMM341/95
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Plossl Bath Tub
Planned
input
Backlog / load
Rated
capacity
Output
(demonstrated capacity)
MMM341/96
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Lead-time Syndrome
• Vicious circle which can occur when
queuing conditions change
• Increased demand may increase
backlog
• Increased backlog increases demand
• If the planned lead times are changed,
more orders are likely to arrive to meet
requirements during the increased lead
time.
• This further inflates lead times etc. etc.
MMM341/97
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Capacity Control
• Input-output control: ensure that the
demand never exceeds capacity
• In MTO, backlogs act as buffers
against workload variations. In this
case it’s a trade off between
maintaining resource utilisation and
minimising lead-times and inventory
MMM341/98
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Capacity Planning
Approaches
• Infinite loading: assume infinite
capacity, disregarding capacity
constraints
• Finite loading: work to capacity
constraints
MMM341/99
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Load
Infinite Loading
Capacity
0
1
2
3
4
5
Period
MMM341/100
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Load
Finite Loading
Capacity
1
2
3
4
5
6
Period
MMM341/101
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Infinite Loading
• Easier - less computation required
• Identifies and measures scheduled
over and under loads
• Shows how much capacity is required
to meet the plan (finite loading does
not)
MMM341/102
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Finite Loading
• Capacity of each resource specified in
terms of “standard” and “maximum”
capacity
• Jobs loaded onto each work centre in
priority order
• When resources are “full”, jobs are
rescheduled
• Horizontal vs. vertical loading
• The only way to revise a finite loading
schedule is to start from scratch,
rearranging jobs in a new priority
sequence
MMM341/103
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Capacity Planning
“A prerequisite to having an effective
capacity planning system is to have an
effective priority planning system.
If the due dates, or lead times are
incorrect, the schedule, the priorities
and the projection of when the load will
hit the resources will be fiction. The
system will not work”
Plossl & Wight 1973
MMM341/104
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
5 Levels of Capacity
Planning
• Resource planning: highly aggregated,
longest term level of capacity planning
• Rough-cut capacity planning: uses
MPS data
• Capacity Requirements Planning
(CRP)
• Finite loading
• Input / output control
MMM341/105
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Resource
Planning
Production
Planning
Rough-cut
Capacity
Planning
Master
Production
Scheduling
(MPS)
Capacity
Requirements
Planning
Material
Requirements
Planning
(MRP)
Demand
Management
Finite
Loading
Input/Output
Analysis
Shop Floor
Control
(SFC)
Vendor
Follow-up
Systems
Figure 4 Capacity Planning (Vollmann et al 1989)
MMM341/106
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Rough-cut Capacity
Planning
• Capacity Planning Using Overall
Factors (CPOF) calculates the overall
direct labour requirements for the MPS
and identifies load based upon historic
data
• Capacity Bills, uses BOM and planning
data
• Resource profiles, same as capacity
bills, but time phased
• See Vollmann et al for details
MMM341/107
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Capacity Requirements
Planning
• CRP utilises MRP information such as
lot sizing and inventory data
• Shop floor control provides information
of the current status of items: only the
capacity required to complete items is
considered
• CRP is based upon the infinite loading
approach
MMM341/108
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Company
Classification
MMM341/109
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
References
• Woodward J. (1965), “Industrial
Organisation: Theory and Practice”,
Oxford University Press, England
• New C.C. (1976), “Managing
Manufacturing Operations”, British
Institute of Management, Report No.
35.
• Barber K.D. & Hollier R.H. (1986),”The
Effects of Computer Aided Production
Management Systems on Defined
Company Types”, Int. J. Prod. Res.
24(2) pp311-327
MMM341/110
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
References
• Barber K.D. & Hollier R.H. (1986),”The
Use of Numerical Taxonomy to
Classify Companies According to
Production Control Complexity”, Int. J.
Prod. Res. 24(1) pp203-22
MMM341/111
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Company Classification
• Classification groups “like” items
together
• Dependent upon classification
variables
• Enables similarities and differences
between companies to be identified
• Identify appropriate planning & control
method
• Identify appropriate technology
MMM341/112
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Classification Approaches
General company classification
• Joan Woodward (1965) used Ministry of
Labour categories for investigating
organisational structure issues
• Sector based classification commonly used by
financial institutions (e.g. FT classification)
• DTI - SMEs
Classification of manufacturing
• Mode of production e.g. Burbidge (1971),
volume of production jobbing, batch, flow
• Goldratt (1980) VAT analysis based upon
pattern of material flow
• Production control complexity New (1976),
Barber & Hollier (1986)
MMM341/113
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Colin New Classification
• Survey of 186 companies to investigate
manufacturing management practice
Five classification areas:
• Market - customer environment
Relationship between cumulative lead time
and delivery lead time e.g. make to stock or
make to order
• Product range and rate of product innovation
• Product complexity - number of components
per product, depth of product structure
• Organisation of manufacturing system,
functional vs. group layout
• Cost structure of products
MMM341/114
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Market / Customer Environment
• Make to stock v/s make to order
• Marucheck & McClelland (1986)
Continuum from pure ETO - pure MTS
• Positioning of company usually a
strategic issue
• Effects competitive factors customisation vs. lead time and cost
• Position effects inventory
• Hicks (1994) Business process based
description
MMM341/115
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Product Complexity
• Depth of product structure
effects co-ordination of assembly
processes (phasing), uncertainties,
lead times etc.
• Number of components in product
• Source of components (make / buy)
• Standardisation / modular design vs.
pure ETO
• Concurrent engineering also increases
control complexity
MMM341/116
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Organisational Structure
•
•
•
•
Type of layout (process / cellular)
Management style
Company culture
Flexibility
MMM341/117
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Barber & Hollier (1986)
• Worked aimed establish suitability of
computer aided production
management techniques for different
types of company
• Based upon production control
complexity
• Developed work of Colin New (1976)
• Used numerical taxonomy to identify
clusters of common companies
• Work identified 6 groups of company
MMM341/118
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MANUFACTURING PROCESS
Chris Voss (1987)
JOBBING
PROJECT
PLANNING
MRP
MRP+JIT
BATCH
JIT
FLOW
SHALLOW
DEEP
DEPTH OF PRODUCT STRUCTURE
MMM341/119
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MANUFACTURING PROCESS
COMPANY TYPE "A"
JOBBING
MAIN PRODUCT
SPARES
SUBCONTRACT
BATCH
FLOW
SHALLOW
DEEP
DEPTH OF PRODUCT STRUCTURE
MMM341/120
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MANUFACTURING PROCESS
COMPANY TYPE "B"
VP
JOBBING
E
BATCH
FLOW
C
MAIN PRODUCT
SPARES
SUBCONTRACT
MINI BUSINESS
DIGGER CABS
ELECTRIC MOTORS
VALVES & PUMPS
SHALLOW
DEEP
DEPTH OF PRODUCT STRUCTURE
MMM341/121
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
MANUFACTURING PROCESS
COMPANY TYPE "A"
JOBBING
PROJECT
PLANNING
MRP
MRP+JIT
BATCH
JIT
FLOW
SHALLOW
DEEP
DEPTH OF PRODUCT STRUCTURE
MMM341/122
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Modelling &
Simulation
MMM341/123
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
References
• Kreutzer W. (1986), “System
Simulation: Programming Languages
and Styles”, Addison-Wesley
ISBN 0-201-12914-0
• Mitrani I (1982),”Simulation Techniques
for Discrete Event Systems”,
Cambridge University Press
ISBN 0-521-23885-4
MMM341/124
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Modelling
• Systems identification
• System representation
• Model design
• Model coding
• Validation
(last two points relate to simulation
modelling)
MMM341/125
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Types of Model
• Iconic models: e.g. a globe is an iconic
model of the earth
• Analytical models: general solutions to
families of problems based upon some
strong theory (close form solutions)
• Analytical models: represent systems
through some abstract notion of
similarity
• Symbolic models: use of symbols to
describe objects, relationships, actions
and processes
Churchman 1959
MMM341/126
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
• Induction: “deducing a general
principle from particular instances”
• Deduction: “deducing a particular
instance from a general law”
MMM341/127
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Descriptive Model
“Descriptive models offer some symbolic
representation of some problem space
without any guidance on how to search
it. The use of descriptive models is an
inductive, experimental technique for
exploring possible worlds”
Kreutzer 1986
MMM341/128
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Simulation
“The term simulation is used to describe
the exploration of a descriptive model
under a chosen experimental frame”
Kreutzer 1986
“Simulation is partly art, partly science.
The art is that of programming: a
simulation should do what is intended.
One should also know how to answer
questions about the system being
simulated”
Mitrani 1982
MMM341/129
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Limitations of Simulation
• Expensive in terms of manpower and
computing
• Often difficult to validate
• Often yields sub-optimum results
• Iterative problem solving technique
• Collection, analysis and interpretation
of results requires a good knowledge
of probability and statistics
• Difficult to convince others
• Often a method of last resort
MMM341/130
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
When to use Simulation
• The real system does not exist, or it is
expensive, time consuming, hazardous
or impossible to experiment with
prototypes
• Need to investigate past, present and
future performance in compressed, or
expanded time.
• When mathematical modelling is
impossible or they have no solutions
• Satisfactory validation is possible
• Expected accuracy meets
requirements
MMM341/131
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Simulation Methodology
•
•
•
•
•
•
•
•
•
•
System identification
System Representation
Model design
Data collection and parameter
estimation
Program design
Program implementation
Program verification
Model validation
Experimentation
Output analysis
MMM341/132
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
System Identification
“A system is defined as a collection of
objects, their relationships and
behaviour relevant to a set of
purposes, characterising some
relevant part of reality”
Kreutzer (1986)
MMM341/133
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
System Representation
“Symbolic images of objects,
relationships and behaviour patterns
are bound into structures as part of
some larger framework of beliefs,
background assumptions and theories
of the problem solver”
Kreutzer 1986
MMM341/134
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Model Design
“A model is an appropriate representation
of some mini-world. Models can very
quickly grow to form very complicated
structures. Control and the constraint
of complexity lie at the heart of any
modelling activity. Care must be
exercised to preserve only those
chracteristics that are essential. This
depends upon the purpose of the
model”
Kreutzer 1986
MMM341/135
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
“It is necessary to abstract from
the real system all those
components (and their
interactions that are considered to
be important”
Mitrani 1982
MMM341/136
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Model Coding
“This stage exists when computers are
being used as the modelling medium.
This stage seeks a formal
representation of symbolic structures
and their transformations into data
structures and computational
procedures in some programming
language”
Kreutzer 1986
MMM341/137
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Types of Simulation
Model
•
•
•
•
Monte Carlo
Quasi-continuous
Discrete event
Combined simulation
MMM341/138
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Monte Carlo Simulation
•
•
•
•
•
Derives name from roulette
Static simulation
Distribution sampling
No assumptions about model
Only statistical correlation between
input and output explored
• Results often summarised in frequency
tables
• Used for complex phenomena that are
not well understood, or too
complicated and expensive to produce
other models
MMM341/139
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Quasi- Continuous
Simulation
“Dynamic simulation. The clock is
sequenced by a clock in uniform fixed
length intervals. The size of the
increment determines the resolution of
the model”
Kreutzer 1986
MMM341/140
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Discrete Event
Simulation
• Asynchronous clock
• Assumes nothing interesting happens
between events
• Queuing networks in which the effects
of capacity limitations and routing
strategies often studied using DES
• This type of simulation most frequently
used for simulating manufacturing
systems
MMM341/141
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Types of Discrete Event
Simulation
•
•
•
•
Event scheduling
Process interaction
Object orientated
Activity scanning
MMM341/142
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Event Scheduling
Approach
• Event scheduling binds actions
associated with individual events into
event routines.
• The monitor selects event for
execution, processing a time ordered
agenda event notices.
• Event notices contain a time and a
reference to an event routine.
• Each event can schedule another
event, which is placed in the correct
position of the agenda.
• The clock is always set to the time of
the next immanent event”
MMM341/143
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Process Interaction
Approach
• Focuses on the flow of entities through
the model
• Views system as concurrent,
interacting processes
• Life cycle for each class of entities
• Monitor uses agenda to keep track of
pending tasks
• Monitor records activation times,
process identities and state that the
process was last suspended
MMM341/144
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Object Orientated
Programming
• Process records the values of all local
variables
• Object contains, attributes (data),
activities (processes) and lifecycle
• Communication between objects only
through well defined interfaces
provided by messages which an object
is programmed to respond to
• Classes / sub classes
• Instances
• Inheritance
MMM341/145
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Activity Scanning
Approach
• Each event is specified in terms of the
conditions that need to apply for the
event to start and finish
• Each event has a set of actions that
take place when it finishes
• Model execution is cyclic, scanning all
activities in the model testing which
can start / finish.
• Clock only moves when whole cycle
leaves status unchanged
• 3 phase structure computationally
expensive
• “Conditional Sequencing” since
programmer only states start and end
conditions
MMM341/146
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Types of Simulation
• Deterministic - no random component
• Stochastic - represents uncertainties
MMM341/147
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Stochastic Simulation
• Sampling experiments
• Standard statistical approaches such
as design of experiments used
• Random processes based upon
pseudo random number generators
MMM341/148
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Pseudo-Random
Number Generators
• Seed based: algorithm produces
“random” number from seed. Repeated
execution gives same streams of
random numbers
• Non-seed based, random number
generated using time, or status of
computer
MMM341/149
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
CDF(x)
1
0
1
X
Pseudo-random number picked in
range 0 to 1
2
Value of X determined from
Cumulative Distribution function as
shown
MMM341/150
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne
Validation
Model qualification
REALITY
Analysis
CONCEPTUAL
MODEL
Model
verification
Model
validation
Computer
Model
MMM341/151
© Dr. C.Hicks, MMM Engineering
University of Newcastle upon Tyne