MFS605/EE605 Systems for Factory Information and Control

1. MFS605/EE605Systems for Factory Information and Control Lecture 8: Production Control: Continued Fall 2005 Larry Holloway Dept. of Electrical Engineering and Center for Manufacturing

2. Production Control: Task of taking orders and forecasts for final product and converting them into orders for production operations and orders for raw materials. Orders Forecasts Production Control Materials Product Shipments Capabilities Impacts lead time, WIP

3. MRP Review • MRP: Materials Requirement Planning • computerized approach to translating orders for final goods into orders for operations and raw materials • Key idea: Blow up bill of materials • expand to subcomponents • factor in lead times • determine when operations and orders should be done

4. Positive aspects of MRP • Better coordination of orders for dependent demand items… • May reduce WIP by ordering component products only as needed for final product. • But… • Production Planning: Helps determine peaks and valleys • Purchasing and Finance: tells needs over the horizon. • Sales: MRP helps sales by giving estimates of lead times… • But…

5. Issues with MRP • MRP is useful for high-level planning. • Generally seen as a problem for low-level shop-floor control • Issues: • buckets are too course for shop-floor control • Lead times treated independent of demand or batch size • Inflation of lead times or safety stock-- no method to see if these are too big, no method to encourage reduction • Commonly gives large lead times and large WIP • Assumes “Transfer quantity = order quantity”

6. Transfer Quantity vs. Order Quantity • Example: 50 components in batch, 10 minutes per part.

7. Pull System • A production system driven by actual consumption and controlled by synchronized replenishment signals. • Orders for product pulled from end of line, rest of line then responds to replace removed product Supplier order CUSTOMER Raw materials Product Manufacturing Facility

8. Just-in-time manufacturing • Just-in-time manufacturing: “Lean Manufacturing” • Key Concept: Continuous improvement to eliminate waste of all forms. • Goals: • Minimum zero inventories • zero lead time • zero set up time • lot size of 1 • zero defects • total elimination of waste Is this realistic?

9. We will just study the production control, but concept is much bigger. Affects: • transportation • supplier relations • quality • setup • employee responsibilities • …all aspects of organization…

10. Background: • Toyota -- postwar Japan • Taichi Ohno: Chief production engineer. • Not really noticed by US until 1970’s Why so slow for West to notice or catch on?

11. JIT/Lean • JIT/Lean is holistic approach • keep tackling all parts of system • different aspects affect each other • example: lot sizing and setup time • keep striving for continuous improvement

12. Overview • Production Control: Push vs. Pull • Kanban • Tool for Pull • Tool to drive lean • Issues in using signal kanbans • Importance of Leveling

13. Example Bill of Materials for Truck Seat Seat assemble Seat Frame Padding Cover Material weld parts Frame Parts cut and shape material Raw Stock

14. Push System • Customer orders and forecasts are fed into beginning of line Forecasts CUSTOMER Raw materials Product Manufacturing Facility

15. The Pull System A production system driven by actual consumption and controlled by synchronized replenishment signals. Tool to: • lower inventory • reduce lead-time Supplier order CUSTOMER Raw materials Product Manufacturing Facility

16. Effect on order lead times • Push system: order sequences through all stations • Pull system: order pulls only from final station (assumes WIP available) Actual lead time for push system typically long since other orders waiting in the pipeline.

17. Effect on Work-in-Process (WIP) Push system: • longer lead times mean more orders in process • example: 3 weeks of orders vs. 1 week • longer lead times encourage larger and less frequent customer orders • demand more irregular • larger batches

18. Little’s Law “Little’s Law”: WIP = Production Rate x Throughput Time or Throughput Time = WIP / Production Rate To reduce throughput time, either reduce WIP or increase production rate

19. Effect on Quality Benefits of Pull system • Each workstation is “supplier” to preceding station. • Low WIP means faster detection of problems • Low WIP has no room for poor quality • Pull requires quality Push System: Batch orders inflated as quality safety, leads to expectation and acceptance of problems

20. INVENTORY HIDES WASTE LABOR & MATERIAL IN PRODUCTS OUT SEA OF INVENTORY BAD HOUSEKEEPING POOR WORK BALANCING INSUFFICIENT COMMUNICATION EXCESSIVE SETUP TIMES NON-PRODUCTIVE MAINTENANCE POOR QUALITY ABSENTEEISM

21. How to reduce need for stock? Minimize impact of disruptions: • shorten lead time -- more responsive to demand • improve quality -- eliminate defects • preventive maintenance • reduce setup times • improve organization and communication • improve supplier reliability

22. Kanban: A production authorization and parts replenishement signal based on consumption • fixed number circulate between producer&consumer stations • tool to limit build-up of inventory • tool to supply right parts at right time • tool to drive lean manufacturing improvements: • lower inventories make wastes more apparent

23. empty kanbans parts (with kanbans)

24. Types of Kanban Systems • Withdrawal Kanbans • 1-card and 2-card systems • Signal Kanbans • Emergency Kanbans and others

25. Withdrawal Kanbans • Multiple cards circulating • example: each is <1/10 daily demand • Supplier processes don’t have significant setup costs • “2-card system used when distance between stations: • “Production Kanbans” and “Withdrawal Kanbans” • Allows for additional delay in circulation

26. Choosing initial number of Kanbans # of kanbans = (units daily demand) x (order cycle time) x safety / lot size Safety = 1.00 means no safety. Safety = 1.3 means 30% safety Example: # of kanbans = 120 x (6hrs/8hrs) * 1.00 / 30 = 3 # of kanbans = 120 * (6hrs / 8hrs) * 1.30 / 30 =3.9 --> 4 In practice: keep reducing kanbans as much as possible.

27. Kanban limitations • large demand fluctuations cause problems. • Real benefits only when constrained variation in product. • Toyota: kanbans on feeder lines, not for final production

28. 2-card Kanban Production cards Move cards A A Warehouse B B A B A A A B B Producing Station Consuming Station A A A A A

29. Signal Kanbans • Used only when supply process has large setup cost • examples: stamping, forming, molding • should continue setup reduction • Kanban signals inventory below threshold.

30. Signal Kanban System Multiple parts (k = 1,2,…K) for one producing machine Production for part k authorized when WIP falls below rk Fixed Batch Policies:run stops after completing fixed number of parts. Fixed Fill Policies: run stops when fixed level reached Parts consumed Inventory for part k Parts produced Fill level fk Producing Machine Signal level rk Consuming Process Signal Kanbans

31. Buffer levels for two parts buffer levels r t Time from signal to production includes kanban queue time and setup time dk Issue: In practice, sometimes kanban waits in queue too long, resulting in parts shortages.

32. Comparison of Fixed-batch and Fixed-fill • Investigations of each for: • deterministic case • variation in demand • disruptions in production • imbalance in batch-size/fill-size among parts Simulations with Matlab for two-part systems for both policies.

33. Fixed-Batch Fixed-Batch Policy: production run continues until given number of parts produced in run. rfixbatch(x0,y0,d0,[rx,sx,ry,sy],[trigx,trigy], [batchx,batchy],[rangex,rangey],[probfreq,probeffect],xsetup,n) x0, y0 initial inventory of product x and product y d0initial state: idle (d0 = 0), producing x (d0 = 1), or y (d0=2). rx, ry nominal consumption rates sx, sy nominal production rates [rangex,rangey] range of variation in demand (+/- %) [probfreq,probeffect] frequency and effect of random drops in production. trigx,trigy signal level batchx,batchy batch sizes xsetup relative setup time n steps of simulation

34. Fixed-Batch Policy -- Deterministic Case Shows repeated cycle: (no production)/(produce x)/(no production)/(produce y) Periodic behavior with no parts shortages. rfixbatch(30,100,0,[4,10,4,10],[20,20],[80,80],[1,1],[0,0],10,100)

35. Fixed-Batch Policy -- Deterministic Case Shows repeated cycle: (no production)/(produce x)/(no production)/(produce y) Periodic behavior with no parts shortages. rfixbatch(30,100,0,[4,10,4,10],[20,20],[80,80],[1,1],[0,0],10,100)

37. Fixed-batch - not ideal (30,100,0,[4,10,4,10],[20,20],[80,80],[1,1],[3,10],10,100) (30,100,0,[4,10,3.8,10],[20,20],[80,80],[1,1],[0,0],10,50) Mismatched demand parameters: inventories cycle down (long periodicity) Occasional disruptions in production: inventories cycle down

38. Fixed-batch - not ideal (30,100,0,[4,10,4,10],[20,20],[80,80],[1,1],[3,10],10,100) (30,100,0,[4,10,3.8,10],[20,20],[80,80],[1,1],[0,0],10,50) Mismatched demand parameters: inventories cycle down (long periodicity) Occasional disruptions in production: inventories cycle down

39. Fixed-batch: Long term periodicity Inventories spiral down until both signals present, then spiral up

40. Fixed-Fill Fixed-Batch Policy: production run continues until inventory reaches specified level rfixfill(x0,y0,d0,[rx,sx,ry,sy],[trigx,trigy],[fullx,fully], [rangex,rangey],[probfreq,probeffect],xsetup,n) x0, y0 initial inventory of product x and product y d0initial state: idle (d0 = 0), producing x (d0 = 1), or y (d0=2). rx, ry nominal consumption rates sx, sy nominal production rates [rangex,rangey] range of variation in demand (+/- %) [probfreq,probeffect] frequency and effect of random drops in production. fullx,fully signal level batchx,batchy batch sizes xsetup relative setup time n steps of simulation

41. Fixed-Fill Policy -- Deterministic Case Repeated cycle: (noproduction)/(produce x)/(noproduction)/(produce y) Periodic behavior with no parts shortages. Initial conditions don’t matter rfixfill(40,100,0,[4,10,4,10],[20,20],[100,100],[1,1],[0,0],10,100)

42. Fixed-Fill Policy -- Deterministic Case Repeated cycle: (noproduction)/(produce x)/(noproduction)/(produce y) Periodic behavior with no parts shortages. Initial conditions don’t matter rfixfill(40,100,0,[4,10,4,10],[20,20],[100,100],[1,1],[0,0],10,100)

43. Fixed-fill - disturbed (40,100,0,[4,10,4,10],[20,20],[100,100],[1,1],[3,10],10,100) (30,100,0,[4,10,3.5,10],[20,20],[100,100],[1,1],[0,0],10,50) Mismatched demand parameters: Inventories find new cycle and stabilize. Occasional disruptions in production: Inventories return to regular cycle.

44. Fixed-fill - disturbed (40,100,0,[4,10,4,10],[20,20],[100,100],[1,1],[3,10],10,100) (30,100,0,[4,10,3.5,10],[20,20],[100,100],[1,1],[0,0],10,50) Mismatched demand parameters: Inventories find new cycle and stabilize. Occasional disruptions in production: Inventories return to regular cycle.

45. Signal Kanban Summary • Simulation of policies under two-part system. • Poorly configured or disrupted fixed-batch policy has long-period behavior which slowly cycles down inventory levels. • problem: continuous improvement activities for reducing threshold at begining of long-period may lead to subsequent parts shortages • Fixed-fill is robust to configuration and disruptions. • requires real-time buffer level info during fill • consistency of behavior allows much lower thresholds

46. Other Kanban signals • Requests for die setup • Requests for die change • Requests for maintenance • etc.

47. Kanban Limitations • Large demand fluctuations cause problems • Will there be enough cards in system to keep it running and responsive? • Kanban quantities or sizes adjusted • Real benefits only when limited variation: • Limited variety: • Toyota: kanbans on feeder lines -- not final product. • Limited fluctuations: • Demand leveling • Limited disruptions • Preventive maintenance, setup reduction, problem-solving workforce

48. Preconditions to using Kanbans Implement: • focused factory and cellular production • visual management and standardized work • kaizen and problem solving • setup reductions • demand leveling

49. Kanban on Feeder Lines Final Assembly -- wide variety Feeder lines -- limited variety

50. Leveling • Leveling: smooth production • Spread production of each product over periods • Smoother production at supplying stations • Allows response to late-period changes A B A B A B A A A B B B