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MMM341/1
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Manufacturing Systems III
Chris Hicks MMM Engineering
Email: [email protected]
MMM341/2
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Assessment
• End of year examination
• 2.5 hours duration
• Answer 4 questions from 6
MMM341/3
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Manufacturing Systems III
• Manufacturing Strategy• JIT Manufacturing• Manufacturing Planning and
control• Company classification• Modelling & Simulation• Queuing theory (CFE)
MMM341/5
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Reference
• Hill, T (1986),”Manufacturing Strategy”, MacMillan Education Ltd., London. ISBN 0-333-39477-1
MMM341/6
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Manufacturing Strategy
• Long term planning• Alignment of manufacturing to satisfy
market requirements
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© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/8
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Manufacturing Strategy
• 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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© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Market Requirements
Price
Quality
Delivery
CofOwn
Customisation
Other factors
Unimportant V Imp.
MMM341/14
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Current Performance
Price
Quality
Delivery
CofOwn
Customisation
Other factors
Unimportant V Imp.
MMM341/15
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Price
Quality
Delivery
CofOwn
Customisation
Other factors
Market requirement
Achieved performance
Unimportant V Imp.
MMM341/16
© Dr. C.Hicks, MMM EngineeringUniversity 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?
MMM341/17
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
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
MMM341/25
© Dr. C.Hicks, MMM EngineeringUniversity 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
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© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
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
MMM341/27
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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
MMM341/28
© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/29
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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
MMM341/30
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
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
MMM341/34
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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?
MMM341/35
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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
MMM341/39
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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.
MMM341/40
© Dr. C.Hicks, MMM EngineeringUniversity 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.
MMM341/41
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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.
MMM341/42
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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
MMM341/46
© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/47
© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/48
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Pull Systems
Implementations vary• Visual / audio signal• “Chalk” square• One / two card Kanban
MMM341/49
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Material Requirements Planning / JIT
• Stable Master Production Schedule• Flat bills of materials• Backflushing• Weekly MRP quantities with “call off” ,
a common approach
MMM341/50
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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
MMM341/51
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
JIT Purchasing
• Controls and reduces inventory• Reduces space• Reduces material handling• Reduces waste• Reduces obsolescence
MMM341/52
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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
MMM341/53
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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
MMM341/54
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
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
MMM341/55
© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/56
© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/58
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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
• 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”,Addison-Wesley, UK, ISBN 0-201-17820-6
MMM341/63
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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
MMM341/64
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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
MMM341/69
© Dr. C.Hicks, MMM EngineeringUniversity 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)
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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)
MMM341/71
© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/72
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
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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© Dr. C.Hicks, MMM EngineeringUniversity 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
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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
MMM341/79
© Dr. C.Hicks, MMM EngineeringUniversity 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
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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
MMM341/82
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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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© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Causes of Nervousness
• Demand uncertainty• Product structure characteristics• Incorrect lot-sizing rules
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© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/90
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
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
MMM341/91
© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/93
© Dr. C.Hicks, MMM EngineeringUniversity 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 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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© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/96
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Plossl Bath Tub
Planned
input
Output(demonstrated capacity)
Ratedcapacity
Backlog / load
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© Dr. C.Hicks, MMM EngineeringUniversity 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.
MMM341/98
© Dr. C.Hicks, MMM EngineeringUniversity 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
MMM341/99
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
Capacity Planning Approaches
• Infinite loading: assume infinite capacity, disregarding capacity constraints
• Finite loading: work to capacity constraints
MMM341/100
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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)
MMM341/103
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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
MMM341/104
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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
MMM341/106
© Dr. C.Hicks, MMM EngineeringUniversity of Newcastle upon Tyne
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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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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DEPTH OF PRODUCT STRUCTURE
FLOWDEEPSHALLOW
MANU
FACT
URING
PROC
ESS
SUBCONTRACTBATCH
MAIN PRODUCTSPARES
JOBBING
COMPANY TYPE "A"
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DEPTH OF PRODUCT STRUCTURE
FLOWSHALLOW
CDEEP
MANU
FACT
URING
PROC
ESS
SUBCONTRACTMINI BUSINESSDIGGER CABSELECTRIC MOTORSVALVES & PUMPS
BATCH MAIN PRODUCTSPARES
E
JOBBINGCOMPANY TYPE "B"
VP
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DEPTH OF PRODUCT STRUCTURE
FLOWDEEPSHALLOW
MANU
FACT
URING
PROC
ESS
JIT
BATCH
MRPMRP+JIT
JOBBING
COMPANY TYPE "A"PROJECT
PLANNING
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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