manufacturing systems iii

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© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne

Manufacturing Systems III

Chris Hicks MMM Engineering

Email: [email protected]

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Assessment

• End of year examination

• 2.5 hours duration

• Answer 4 questions from 6

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Manufacturing Systems III

• Manufacturing Strategy• JIT Manufacturing• Manufacturing Planning and

control• Company classification• Modelling & Simulation• Queuing theory (CFE)

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Manufacturing Strategy

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Reference

• Hill, T (1986),”Manufacturing Strategy”, MacMillan Education Ltd., London. ISBN 0-333-39477-1

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• Long term planning• Alignment of manufacturing to satisfy

market requirements

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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

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• Make / buy• Process choice• Technology• Infrastructure, systems, structures &

organisation• Focus• Integration with other functions

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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

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Identifying Market Requirements

• Order Qualifying criteria• Order winning criteria• Order losing criteria

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Manufacturing Influences

• Costs• Delivery• Quality• Demand flexibility• Product range• Standardisation / customisation

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Profile Analysis

• Assess match between market requirements and current performance

• Identify changes required to manufacturing system

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Market Requirements

Price

Quality

Delivery

CofOwn

Customisation

Other factors

Unimportant V Imp.

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Current Performance

Price

Quality

Delivery

CofOwn

Customisation

Other factors

Unimportant V Imp.

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Price

Quality

Delivery

CofOwn

Customisation

Other factors

Market requirement

Achieved performance

Unimportant V Imp.

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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?

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Manufacturing Structure

• Layout: functional or cellular?• MTS / MTO• Flexibility of workforce• Organisation, team working etc.• Breakdown of costs• HRM issues

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Products

• Relative importance, present and future

• Mix• Complexity

– Product structure– Concurrency– Standardisation / customisation

• Contribution

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Measures of performance

• What are they?• Frequency of measurement• Comparison with plan.• Orientation: product / process /

inventory• Integration with other functions

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Infrastructure

• Manufacturing planning & control• Sharing information / knowledge• CAD / CAM• Accounting systems• Quality systems• Performance measurement

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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?

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“Manufacturing is a business function rather than a technical function. The

emphasis should be on supporting the market” Terry Hill (1996)

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Just-in-Time Manufacturing

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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”,Addison-Wesley, UK, ISBN 0-201-17820-6

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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

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Just-in-Time

• 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

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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

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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

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Benefits of JIT

• Reduced costs• Waste elimination• Inventory reduction• Increased flexibility• Raw materials / parts reduction• Increased quality• Increased productivity• Reduced space requirements• Lower overheads

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Just-in-Time

JIT links four fundamental areas• Product design• Process design• Human / organisational issues• Manufacturing planning and control

Vollmann et al 1992

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Product design

Processdesign

Human /organisation

Planning &control

JIT

Elements of Just-in-time

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Product Design

• 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

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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

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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?

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Organisational Elements

• Little distinction between direct / indirect labour

• Activity Based Cost (ABC) accounting• Visible team performance

measurement• Communication / information sharing• Joint commitment

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JIT Techniques

• Manufacturing techniques• Production and material control• Inter-company JIT• Organisation for change

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Manufacturing Techniques

• Cellular manufacturing• Set-up time reduction• Pull scheduling• Smallest machine concept• Fool proofing (Pokayoke)• Line stopping (Jikoda)• I,U,W shaped material flow• Housekeeping

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Group Technology / Cellular Manufacturing

• Improved material flow• Reduced queuing time• Reduced inventory• Improved use of space• Improved team work• Reduced waste• Increased flexibility

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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.

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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.

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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.

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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

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Separating Internal and External Set-up

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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

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JIT Material Control

• Pull scheduling• Line balancing• Schedule balance and smoothing

(Heijunka)• Under capacity scheduling• Visible control• Material Requirements Planning• Small lot & batch sizes

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“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

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Pull Systems

Implementations vary• Visual / audio signal• “Chalk” square• One / two card Kanban

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Material Requirements Planning / JIT

• Stable Master Production Schedule• Flat bills of materials• Backflushing• Weekly MRP quantities with “call off” ,

a common approach

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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

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JIT Purchasing

• Controls and reduces inventory• Reduces space• Reduces material handling• Reduces waste• Reduces obsolescence

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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

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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

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JIT Flexibility

• Set-up time reduction• Small transfer batch sizes• Small lot sizes• Under capacity scheduling• Often labour is the variable resource• Smallest machine concept

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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

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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

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Implementation of JIT

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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

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JIT Circle

JIT

StandardisationDesign - focus

TPM

TQM

Set-upreduction

PlantLayout

Small machines

Multi-skillWorkforce

Pull scheduling

Visibility

JIT Purchasing

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JIT Limitations

• Stable regular demand• Medium to high volume• Requires cultural change• Implementation costs

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Computer Aided Production Management

Systems (CAPM)

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References


(Earlier editions just as good!)• Browne J., Harhen J, & Shivnan J.

(1988),“Production Management Systems: A CIM Perspective”,Addison-Wesley, UK, ISBN 0-201-17820-6

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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

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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

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CAPM Systems

• Planning• Control• Performance measurement

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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

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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

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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)

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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)

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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

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ResourcePlanning

ProductionPlanning

DemandManagement

MasterProductionScheduling

DetailedMaterialPlanning

Timed-phasedrequirement

(MRP) records

Materialand capacity

plans

VendorSystems

Detailedcapacityplanning

Shop floorsystems

FRONT END

ENGINE

BACK END

Figure 3 Manufacturing Planning and Control Systems (Vollman et. al. 1989)

Bill ofMaterials

RoutingFile

InventoryStatusData

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MRP Record Card

PeriodGross RequirementsScheduled receiptsProjected availablebalancePlanned order releasesLead time = 1 periodLot size = 50

4 50

1 2 3 4 510 40 10

44 44 4 44

50

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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

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A

B C

Simple Product Structure

Representation of Product

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Linked MRP Cards

PeriodGross RequirementsScheduled receiptsProjected availablebalancePlanned order releasesLead time = 1 periodLot size = 50

4 50

1 2 3 4 510 40 10

44 44 4 44

50

PeriodGross RequirementsScheduled receiptsProjected availablebalancePlanned order releasesLead time = 2 periodsLot size = 100

9 9

1 2 3 4 550

9 9 59 59

100

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Backwards Scheduling

A

B C

Due Date

(2 days)

(1 day) (3 days)

2

1

3

Work back from Due Date

Backwards Scheduling

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Forwards Scheduling

A

B C

(2 days)

(1 day) (3 days)

Work forwards from start time

2

1

3

Slack

Star

t tim

e

Due

Tim

e

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MRP Domain

• Steady state systems• Low levels of uncertainty• Shallow / medium or deep product

structure• Stable demand• Predominantly make to stock• Manufacturing orientation

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MRP Parameters

• Planning horizon• Size of time bucket• Lot sizing rules• Regeneration vs.. net change

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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

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Typical Control Parameters

• Safety stock• Safety lead time• Yield• Order quantity category• Min/max order levels• Max. days supply• Min. days between orders

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Lot sizing

• Lot-for-lot• Economic Order Quantity (EOQ)• Complex optimisation algorithms

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Uncertainties in MRP

• Environmental uncertainty– Customer orders– Suppliers

• System uncertainty– Product quality– Scrap / rework– Process times– Design changes

• MRP nervousness / instability

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Dealing with uncertainty in MRP

• Safety stocks• Safety lead times• Safety due date• Hedging• Over-planning• Yield factors

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Appropriate approaches

• Timing uncertainty: safety lead time• Quantity uncertainty: safety stock

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MRP Nervousness

• Significant changes in plans due to minor changes in high level plans

• Frequent changes in plans make the MRP system lose crdibility

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Causes of Nervousness

• Demand uncertainty• Product structure characteristics• Incorrect lot-sizing rules

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Nervousness: Solutions

• Stable MPS• Carefully change any parameter

changes• Use different lot sizing rules at the high

and low levels of the product structure

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MRP Problems

• 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

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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

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Capacity Planning

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References


• Plossl G.W. & Wight O.W. (1973), “Capacity Planning and Control”, Production and Inventory Management, 3rd quarter 1973 pp31-67

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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

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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

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Plossl Bath Tub

Planned

input

Output(demonstrated capacity)

Ratedcapacity

Backlog / load

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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.

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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

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Capacity Planning Approaches

• Infinite loading: assume infinite capacity, disregarding capacity constraints

• Finite loading: work to capacity constraints

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Infinite LoadingLo

ad

Period

Capacity

0 1 2 3 4 5

Bac

klog

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Finite LoadingLo

ad

Period

Capacity

1 2 3 4 5 6

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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)

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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

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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

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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

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ResourcePlanning

Rough-cutCapacityPlanning

CapacityRequirements

Planning

FiniteLoading

Input/OutputAnalysis

Shop FloorControl(SFC)

VendorFollow-upSystems

DemandManagement

ProductionPlanning

MasterProductionScheduling

(MPS)

MaterialRequirements

Planning(MRP)

Figure 4 Capacity Planning (Vollmann et al 1989)

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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

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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

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Company Classification

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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

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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

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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

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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)

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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

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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

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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

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Organisational Structure

• Type of layout (process / cellular)• Management style• Company culture• Flexibility

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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

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Chris Voss (1987)

DEPTH OF PRODUCT STRUCTURE

FLOWDEEPSHALLOW

MANU

FACT

URING

PROC

ESS

JIT

BATCH MRP+JITMRP

PLANNINGPROJECTJOBBING

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FLOWDEEPSHALLOW

MANU

FACT

URING

PROC

ESS

SUBCONTRACTBATCH

MAIN PRODUCTSPARES

JOBBING

COMPANY TYPE "A"

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FLOWSHALLOW

CDEEP

MANU

FACT

URING

PROC

ESS

SUBCONTRACTMINI BUSINESSDIGGER CABSELECTRIC MOTORSVALVES & PUMPS

BATCH MAIN PRODUCTSPARES

E

JOBBINGCOMPANY TYPE "B"

VP

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FLOWDEEPSHALLOW

MANU

FACT

URING

PROC

ESS

JIT

BATCH

MRPMRP+JIT

JOBBING

COMPANY TYPE "A"PROJECT

PLANNING

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Modelling & Simulation

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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

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Modelling

• Systems identification• System representation• Model design• Model coding• Validation

(last two points relate to simulation modelling)

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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

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• Induction: “deducing a general principle from particular instances”

• Deduction: “deducing a particular instance from a general law”

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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

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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

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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

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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

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Simulation Methodology• System identification• System Representation• Model design• Data collection and parameter

estimation• Program design• Program implementation• Program verification• Model validation• Experimentation• Output analysis

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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)

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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

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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

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“It is necessary to abstract from the real system all those components (and their interactions that are considered to be important”Mitrani 1982

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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

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Types of Simulation Model

• Monte Carlo• Quasi-continuous• Discrete event• Combined simulation

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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

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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

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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

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Types of Discrete Event Simulation

• Event scheduling• Process interaction• Object orientated• Activity scanning

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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”

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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

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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

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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

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Types of Simulation

• Deterministic - no random component• Stochastic - represents uncertainties

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Stochastic Simulation

• Sampling experiments• Standard statistical approaches such

as design of experiments used• Random processes based upon

pseudo random number generators

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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

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X

C D F(x)

0

1

P seudo-random num ber p icked inrange 0 to 1

1

2V alue o f X determ ined fromC um ula tive D istribu tion function asshown

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Validation

ComputerModel

Analysis

Progr

amm

ing

Computer

Simulation

CONCEPTUAL

MODELREALITY

Model qualification

Modelverification

Modelvalidation

manufacturing systems iii

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