Manufacturing Systems III Chris Hicks MMM Engineering Email

Manufacturing Systems III Chris Hicks MMM Engineering Email: Chris. Hicks@ncl. ac. uk MMM 341/1 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Assessment • End of year examination • 2. 5 hours duration • Answer 4 questions from 6 MMM 341/2 © 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) MMM 341/3 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Manufacturing Strategy MMM 341/4 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Reference • Hill, T (1986), ”Manufacturing Strategy”, Mac. Millan Education Ltd. , London. ISBN 0 -333 -39477 -1 MMM 341/5 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Manufacturing Strategy • Long term planning • Alignment of manufacturing to satisfy market requirements MMM 341/6 © 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 MMM 341/7 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Manufacturing Strategy • • Make / buy Process choice Technology Infrastructure, systems, structures & organisation • Focus • Integration with other functions MMM 341/8 © 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 MMM 341/9 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Identifying Market Requirements • Order Qualifying criteria • Order winning criteria • Order losing criteria MMM 341/10 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Manufacturing Influences • • • Costs Delivery Quality Demand flexibility Product range Standardisation / customisation MMM 341/11 © 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 MMM 341/12 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Market Requirements Unimportant V Imp. Price Quality Delivery Cof. Own Customisation Other factors MMM 341/13 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Current Performance Unimportant V Imp. Price Quality Delivery Cof. Own Customisation Other factors MMM 341/14 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Market requirement Achieved performance Price Unimportant V Imp. Quality Delivery Cof. Own Customisation Other factors MMM 341/15 © 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? MMM 341/16 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Manufacturing Structure • • • Layout: functional or cellular? MTS / MTO Flexibility of workforce Organisation, team working etc. Breakdown of costs HRM issues MMM 341/17 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Products • Relative importance, present and future • Mix • Complexity – Product structure – Concurrency – Standardisation / customisation • Contribution MMM 341/18 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Measures of performance • • What are they? Frequency of measurement Comparison with plan. Orientation: product / process / inventory • Integration with other functions MMM 341/19 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Infrastructure • • • Manufacturing planning & control Sharing information / knowledge CAD / CAM Accounting systems Quality systems Performance measurement MMM 341/20 © 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? MMM 341/21 © 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) MMM 341/22 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Just-in-Time Manufacturing MMM 341/23 © 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 (3 rd 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 MMM 341/24 © 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 MMM 341/25 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Just-in-Time • • • Arose in Toyota, Japan in 1960 s 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 MMM 341/26 © 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 MMM 341/27 © 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 MMM 341/28 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Benefits of JIT • • • Reduced costs Waste elimination Inventory reduction Increased flexibility Raw materials / parts reduction Increased quality Increased productivity Reduced space requirements Lower overheads MMM 341/29 © 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 MMM 341/30 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MMM 341/31 © Dr. C. Hicks, MMM Engineering Vollmann et al 1992 University of Newcastle upon Tyne

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 MMM 341/32 © 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 MMM 341/33 © 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? MMM 341/34 © 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 MMM 341/35 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

JIT Techniques • • Manufacturing techniques Production and material control Inter-company JIT Organisation for change MMM 341/36 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

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 MMM 341/37 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Group Technology / Cellular Manufacturing • • Improved material flow Reduced queuing time Reduced inventory Improved use of space Improved team work Reduced waste Increased flexibility MMM 341/38 © 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. MMM 341/39 © 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. MMM 341/40 © 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. MMM 341/41 © 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 MMM 341/42 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Separating Internal and External Set-up MMM 341/43 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MMM 341/44 © 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 MMM 341/45 © 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 MMM 341/46 © 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 MMM 341/47 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Pull Systems Implementations vary • Visual / audio signal • “Chalk” square • One / two card Kanban MMM 341/48 © Dr. C. Hicks, MMM Engineering University 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 MMM 341/49 © 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 MMM 341/50 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

JIT Purchasing • • • Controls and reduces inventory Reduces space Reduces material handling Reduces waste Reduces obsolescence MMM 341/51 © 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 MMM 341/52 © 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 MMM 341/53 © Dr. C. Hicks, MMM Engineering University 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 MMM 341/54 © 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 MMM 341/55 © 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 MMM 341/56 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Implementation of JIT MMM 341/57 © 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 MMM 341/58 © 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 MMM 341/59 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

JIT Limitations • • Stable regular demand Medium to high volume Requires cultural change Implementation costs MMM 341/60 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Computer Aided Production Management Systems (CAPM) MMM 341/61 © 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 (3 rd 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 MMM 341/62 © 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 MMM 341/63 © 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 production decisions using algorithms MMM 341/64 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MMM 341/65 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

CAPM Systems • Planning • Control • Performance measurement MMM 341/66 © 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 MMM 341/67 © 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 MMM 341/68 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Material Requirements Planning (mrp) “Material requirements plannning originated in the 1960 s 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) MMM 341/69 © 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) MMM 341/70 © 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 MMM 341/71 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MMM 341/72 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MRP Record Card MMM 341/73 © 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 MMM 341/74 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Representation of Product MMM 341/75 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Linked MRP Cards MMM 341/76 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Backwards Scheduling MMM 341/77 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Forwards Scheduling MMM 341/78 © 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 MMM 341/79 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MRP Parameters • • Planning horizon Size of time bucket Lot sizing rules Regeneration vs. . net change MMM 341/80 © 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 MMM 341/81 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Typical Control Parameters • • Safety stock Safety lead time Yield Order quantity category Min/max order levels Max. days supply Min. days between orders MMM 341/82 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Lot sizing • Lot-for-lot • Economic Order Quantity (EOQ) • Complex optimisation algorithms MMM 341/83 © 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 MMM 341/84 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Dealing with uncertainty in MRP • • • Safety stocks Safety lead times Safety due date Hedging Over-planning Yield factors MMM 341/85 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Appropriate approaches • Timing uncertainty: safety lead time • Quantity uncertainty: safety stock MMM 341/86 © 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 MMM 341/87 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Causes of Nervousness • Demand uncertainty • Product structure characteristics • Incorrect lot-sizing rules MMM 341/88 © 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 MMM 341/89 © Dr. C. Hicks, MMM Engineering University 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 MMM 341/90 © 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 MMM 341/91 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Capacity Planning MMM 341/92 © 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 (3 rd Edition)”, Irwin, USA. ISBN 0 -256 -08808 -X • Plossl G. W. & Wight O. W. (1973), “Capacity Planning and Control”, Production and Inventory Management, 3 rd quarter 1973 pp 3167 MMM 341/93 © 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 MMM 341/94 © 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 MMM 341/95 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Plossl Bath Tub MMM 341/96 © 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. MMM 341/97 © 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 MMM 341/98 © 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 MMM 341/99 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Backlog Infinite Loading MMM 341/100 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Finite Loading MMM 341/101 © 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) MMM 341/102 © 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 MMM 341/103 © 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 MMM 341/104 © 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 MMM 341/105 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MMM 341/106 © 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 MMM 341/107 © 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 MMM 341/108 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Company Classification MMM 341/109 © 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) pp 311 -327 MMM 341/110 © 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) pp 203 -22 MMM 341/111 © 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 MMM 341/112 © 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) MMM 341/113 © 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 MMM 341/114 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Market / Customer Environment • Make to stock v/s make to order • Marucheck & Mc. Clelland (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 MMM 341/115 © 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 MMM 341/116 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Organisational Structure • • Type of layout (process / cellular) Management style Company culture Flexibility MMM 341/117 © 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 MMM 341/118 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Chris Voss (1987) MMM 341/119 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MMM 341/120 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MMM 341/121 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MMM 341/122 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Modelling & Simulation MMM 341/123 © 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 MMM 341/124 © 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) MMM 341/125 © 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 MMM 341/126 © 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” MMM 341/127 © 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 MMM 341/128 © 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 MMM 341/129 © 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 MMM 341/130 © 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 MMM 341/131 © 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 MMM 341/132 © 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) MMM 341/133 © 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 MMM 341/134 © 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 MMM 341/135 © 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 MMM 341/136 © 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 MMM 341/137 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Types of Simulation Model • • Monte Carlo Quasi-continuous Discrete event Combined simulation MMM 341/138 © 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 MMM 341/139 © 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 MMM 341/140 © 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 MMM 341/141 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Types of Discrete Event Simulation • • Event scheduling Process interaction Object orientated Activity scanning MMM 341/142 © 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” MMM 341/143 © 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 MMM 341/144 © 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 MMM 341/145 © 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 MMM 341/146 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Types of Simulation • Deterministic - no random component • Stochastic - represents uncertainties MMM 341/147 © 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 MMM 341/148 © 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 MMM 341/149 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

MMM 341/150 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne

Validation Model qualification CONCEPTUAL MODEL Analysis am gr Pr o r te io at ul pu m Co m Si n Model validation m ing REALITY Model verification Computer Model MMM 341/151 © Dr. C. Hicks, MMM Engineering University of Newcastle upon Tyne