1. Material Flow Control, CONWIP and Theory of Constraints 35E00100 Service Operations and Strategy 7 Fall 2015

2. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 2 Contents  CONWIP (Part 1) Principles Mean value analysis model Comparison with MRP and kanban  Shop floor control Design and control aspects Production activity control CONWIP and other pull mechanisms  Key points  Theory of Constraints (Part 2)  Useful material in the textbook:  Hopp, W. & Spearman, M. (2000), Factory Physics, Ch. 10.4-10.6 and 14

3. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 3 Push versus Pull Systems  Push systems Schedule work releases based on demand  No limit for system WIP Inherently due-date driven Performance measurement  control release rate  observe WIP level  Pull systems Authorize work releases based on system status  Deliberately establish a limit on system WIP Inherently rate driven Performance measurement  control WIP level  observe throughput Hopp and Spearman 2000, 339-344

4. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 4 Push and Pull Line Schematics Pure Push (MRP) CONWIP Full containers Authorization signals Pure Pull (kanban) Stock Point... … Stock Point... Stock Point Stock Point... Stock Point Stock Point Hopp and Spearman 2000, 351

5. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 5 Pull Benefits Achieved by WIP Cap  Reduces costs prevents WIP explosions reduces average WIP reduces engineering changes  Improves quality pressure for higher quality improved defect detection improved communication  Improves customer service reduces cycle time variability pressure to reduce sources of process variability promotes shorter lead times and better on-time performance  Maintains flexibility avoids early release less direct congestion less reliance on forecasts promotes floating capacity Hopp and Spearman 2000, 344-349

6. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 6 CONWIP  Mechanics Allow next job to enter line each time a job leaves (i.e., maintain a WIP level of m jobs in the line at all times).  Assumptions 1. Single routing 2. WIP measured in units  Different mechanisms from the modeling perspective MRP – open queuing network CONWIP – closed queuing network Kanban – closed queuing network with blocking... Hopp and Spearman 2000, 349-350

7. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 7 Comparing CONWIP with Pure Push  A CONWIP system has the following advantages over an equivalent pure push system 1) Observability  WIP is observable but capacity is not. 2) Efficiency  A CONWIP system requires less WIP on average to attain a given level of throughput. 3) Variability  For the same TH and customer service level, lead times will be longer in the push system for two reasons: longer mean CT and larger standard deviation of CT. 4) Robustness  A profit function of form Profit = pTH – hWIP is more sensitive to errors in throughput (TH) than in WIP level. Hopp and Spearman 2000, 354-358

8. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 8 Comparing CONWIP with Pure Push 2. CONWIP Efficiency  Equipment data 5 machines in tandem Every machine has capacity of one part/hr (u=TH*t e =TH) Exponential process times (moderate variability)  CONWIP system  Pure push system  How much WIP is required for the push system to match TH attained by CONWIP system with WIP=w? The increase is not always as high as 25 % but it will always take more WIP to get the same TH under a pure push system than under a pull system. PWC formula Five M/M/1 queues Hopp and Spearman 2000, 355-356 Example WIP is always 25% higher for the same TH in push than in CONWIP

9. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 9 Comparing CONWIP with Pure Push 4. CONWIP Robustness  Profit function  CONWIP system  Push system  What happens when we don’t choose optimum values (as we never will)? Need to find “optimal” WIP level Need to find “optimal” TH level (i.e. release rate) Hopp and Spearman 2000, 357-358 Example

10. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 10 Relative Robustness of CONWIP and Pure Push Systems Push CONWIP Optimum Efficiency Robustness Hopp and Spearman 2000, 358 Example

11. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 11 Comparing CONWIP with Pull System  ‘Normal’ pull environment (kanban) provides Less WIP  earlier detection of quality problems Shorter lead times  increased customer response and less reliance on forecasts Less buffer stock  less exposure to schedule and engineering changes  CONWIP provides a pull environment that Has greater throughput for equivalent WIP than kanban Can accommodate a changing product mix Can be used with setups Is suitable for short runs of small lots Is predictable Hopp and Spearman 2000, 359-362

12. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 12 Shop Floor Control  Basic problem To control the flow of work through plant and coordinate with other activities, e.g., quality control and preventive maintenance.  Key issues Customization  SFC is often the most highly customized activity in a plant. Information collection  SFC represents the interface with the actual production processes and is therefore a good place to collect data. Simplicity  Departures from simple mechanisms must be carefully justified. Hopp and Spearman 2000, 453-456 We think in generalities, we live in detail.

13. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 13 Execution Detailed Planning Aggregate Planning Resource planning Aggregate production planning Demand management Master production scheduling Shop floor control Vendor systems Scheduling Material requirements planning Capacity planning PAC in the MPC System Order release Purchase orders Vollmann et al. 1997, 15

14. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 14 Production Activity Control (PAC)  Primary objectives Management of material flows to meet MPC plans  Lead times are not calculated but planned Efficient use of capacity, labour, machine tools, time, or material High material velocity (e.g. JIT and TBC)  Material and capacity plans Information to the SFC and vendor follow-up systems  Feedback to detailed planning is essential Status information Warning signals Vollmann et al. 1997

15. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 15 Planning for Shop Floor Control  Gross capacity control, i.e. match the line capacity to demand through Varying staffing  # of shifts  # of workers per shift Varying length of work week (or work day) Using outside vendors to augment capacity  Bottleneck planning Cost of capacity is the key Bottlenecks can be designed Stable bottlenecks are easier to manage  Span of control Physically or logically decompose system Span of labor management  Max. 10 subordinates

16. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 16 PAC Techniques  Basic concepts (input) Routings Lead time data  Other Gantt charts Priority scheduling rules Finite loading Vendor scheduling and follow-up Lead time management Part D routing Operation Work centerRun timeSetup timeMove timeQueue time Total time Rounded time 1 1011,40,40,324,14,0 2 1091,50,50,32,54,85,0 3 1030,1 0,20,50,91,0 Total lead time 10.0 days 0123456701234567 A B C D E 80 % Waiting is typically over 80 % of total customer LT

17. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 17 Infinite versus Finite Loading Capacity Planned orders Open shop orders “PAC Technique”

18. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 18 Potential Functions of SFC Module  SFC is the process by which decisions directly affecting the flow of material through the factory are made. WIP tracking Throughput tracking Status monitoring Work forecasting Capacity feedback Quality control Material Flow Control Hopp and Spearman 2000, 453-456

19. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 19 Basic CONWIP  The rationale Simple starting point Effective in some environments  Requirements Constant routings Similar processing times (stable bottleneck) No significant setups No assemblies  Design issues Work backlog:How to maintain and display Line discipline:FIFO, limited passing Card counts:WIP = CT  r P initially, then conservative adjustments Card deficits:Violate WIP-cap in special circumstances Work ahead:How far ahead relative to due date? Hopp and Spearman 2000, 461-464

20. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 20 CONWIP Line Controlling WIP with CONWIP Cards Production line Inbound stock Outbound stock CONWIP cards Hopp and Spearman 2000, 462

21. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 21 Tandem CONWIP Loops Basic CONWIP Kanban Multi-loop CONWIP work centerbuffercard flow Hopp and Spearman 2000, 465

22. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 22 Modifications of Basic CONWIP  Multiple product families Capacity-adjusted WIP CONWIP controller  Assembly systems CONWIP achieves synchronization naturally  unless passing is allowed WIP levels must be sensitive to “length” of fabrication lines Hopp and Spearman 2000, 468 card flow buffer material flow Processing times for Line B Processing times for Line A Assembly 1 3233 24 1

23. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 23 Kanban in Comparison with CONWIP  Advantages Improved communication Control of shared resources  Disadvantages Complexity in setting WIP levels Tighter pacing puts pressure on workers, and gives less opportunity for work ahead Part-specific cards cannot accommodate many active part numbers Inflexible to product mix changes Handles small, infrequent orders poorly

24. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 24 Pull From the Bottleneck (PFB)  Problems with CONWIP/Kanban Bottleneck starvation due to downstream failures Premature releases due to CONWIP requirements  Remedies Ignores WIP downstream of bottleneck Launches orders when bottleneck can accommodate them  Main problem Floating bottlenecks B Hopp and Spearman 2000, 472 card flow material flow

25. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 25 Production Tracking and Feedback  Basic problems Signal quota shortfall Update capacity data Quote delivery dates  Short term Statistical Throughput Control (STC) Progress toward quota Overtime decisions  Long term Capacity feedback Synchronize planning models to reality Hopp and Spearman 2000, 475-482

26. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 26 Key Points  Shop floor control SFC is more than material flow control Good SFC requires planning (workforce policies, bottlenecks, management, etc)  CONWIP Simple starting point for advanced pull mechanisms Reduces variability due to lower WIP fluctuations Many modifications possible (kanban, pull-from-bottleneck)  Benefits of pull mechanisms Observability, efficiency and robustness  Statistical throughput control Intuitive graphical display Tool for overtime planning/prediction

27. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 27 Abbreviations Used  CONWIP= constant WIP  MVA= mean value analysis  PAC= production activity control  PFB= pull from the bottleneck  SFC= shop floor control  STC= statistical throughput control

28. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 28 Part 2: Theory of Constraints Contents Theory of constraints (TOC) Principles Drum- buffer- rope system Thinking processes Product mix planning A comparison of TOC, MRP and JIT Useful material in textbook and in course package: Hopp, W. & Spearman, M. (2000), Factory Physics, Chapter 16.3 Goldratt, E. (1990) “Appendix: Two Selected Readings from The Goal” Theory of Constraints, pp. 129-160

29. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 29 The Impact of Measurements Goals Measure ments Actions "Trust is nice as long as there are measurements that serve as a watchdog."

30. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 30 Top management... Middle management... Tell me how you measure me, and I will tell you how I will behave. If you measure me in an illogical way... do not complain about illogical behavior. Measurements Deployed at All Levels Operators...  Return on assets  Net profits  Cash flows, etc.  Inventories  Operating costs  Throughput  Cycle time  On time delivery, etc.  Cycle time  % rework / scrap  Cross-training

31. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 31 What is Good Management? Manage well Manage according to cost world Manage according to throughput world Control cost Protect throughput Goldratt 1997, 99 There is no way to achieve good throughput performance through good local performance everywhere The only way to achieve good cost performance is through good local performance everywhere

32. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 32 Change Performance Measurement!  Cost concept (and local measures) must be replaced with global operational measures  The recommended measures Throughput  The rate at which money is generated by the system through sales Inventory  All the money the system has invested in purchasing; things it intends to sell Operating expenses  All the money that the system spends to turn inventory into throughput  Why these three? Those emphasize total system performance Those measure firm’s ability to make money TP I OE

33. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 33 TOC Principles  Balance the flows – not the capacity Throughput matters  Bottleneck governs both throughput and inventory An hour saved at the bottleneck  an extra hour  The level of utilization of a non- bottleneck resource is not determined by its potential Some other constraint in the system determines it An hour saved at a non- bottleneck  mirage and more idle time  Utilization and activation of a resource are not the same  Process batch  transfer batch Transfer batch may not and in many times should not be equal to the process batch Process batch should be a variable not fixed  Schedules should be established by looking at all of the constraints simultaneously Lead times are the result of a schedule and cannot be predetermined Goldratt 1984

34. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 34 Goldratt has authored many business novels… 1991 1984 1994 2000 1997 1990 1986 1996 1998 1999 2000 1995 1998

35. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 35 Drum-Buffer-Rope A Troop Analogy - Marching Soldiers

36. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 36 A Troop Analogy - Marching Soldiers What if the Physical Condition of the Soldiers Varies? Raw materialFinished goods Work-in-process

37. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 37 A Troop Analogy Put the Slowest Soldier at the Front Expensive? Feasible? Raw materialFinished goods Work-in-process

38. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 38 A Troop Analogy Place a Drummer at the Front to Set the Pace Do efficiencies, incentives & variances allow workers to follow the drumbeat? Raw materialFinished goods Work-in-process

39. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 39 A Troop Analogy Take Load off from the Slowest Raw materialFinished goods Work-in-process

40. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 40 A Troop Analogy Rope the Soldiers Together The invention of Henry Ford: Assembly Line The invention of Dr. Ohno from Toyota: Kanban System

41. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 41 A Troop Analogy Tie the Weakest Soldier to the Front Raw materialFinished goods Work-in-process

42. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 42 Drum-Buffer-Rope Scheduling  Advantages of the system Practical and effective method for achieving synchronous flows Can be applied to complex and dynamic mfg environments  Elements Drum (constraint)  Sets the beat that establishes the production rate  Approach to develop MPS consistent with system constraints Buffer (inventory)  Prevents the constraint from running out of material to work on  Protects the plant performance from disruptions Rope (scheduling)  Pulls necessary raw material in the system by controlling strategic locations  Reduces communication (problems) to non-CCR e.g. Umble & Srikanth 1996 R1 R3 (CCR) R2 Shipping R4

43. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 43 DBR Scheduling  Situation Plant producing 2 items A and B Five resources (each available 480 min/day) Demand, routings, setup times and processing times are given  Strategic placement of time buffers  Drumbeat R1R3R2R5 R4 (CCR) Shipping 3-day buffer Rope Example

44. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 44 Pull From the Bottleneck (PFB)  Problems with CONWIP/Kanban Bottleneck starvation due to downstream failures Premature releases due to CONWIP requirements  Remedies Ignores WIP downstream of bottleneck Launches orders when bottleneck can accommodate them  Main problem Floating bottlenecks B Hopp and Spearman 2000, 472 card flow material flow

45. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 45 Continuous Improvement and Thinking Processes Goldratt ’s books

46. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 46 How to Invent Simple Solutions? Evaporating Clouds B C D Not D A ObjectiveRequirementPrerequisite Goldratt 1990, 39 Conflict B C Some amount of D Some add’l amount of D A ObjectiveRequirementPrerequisite Conflict (limited availability of D)

47. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 47 The Evaporating Cloud Diagram A Typical Problem in Manufacturing Environments Reduce setup cost per unit Reduce carrying cost per unit Large batch Small batch Reduce cost per unit ObjectiveRequirementPrerequisite Goldratt 1990, 43

48. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 48 The Evaporating Cloud Diagram The Goal of a Company Protect current throughput Protect future throughput Keep inventory Reduce inventory To make more money now and in the future ObjectiveRequirementPrerequisite Goldratt 1990,118

49. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 49 Ongoing Improvement Process of TOC 1.Identify the system’s constraints Calculate the capacities of each resource Calculate the loads on capacity Determine the capacity constrained resource (CCR) 2.Decide how to exploit the system’s constraints Calculate the throughput of each product Calculate the throughput per unit of production of the CCR (bang-for-the-buck calculation) Determine how much of each product should be produced Calculate the throughput minus operating expense 3.Subordinate everything else to the previous decision 4.Elevate (remove) the system’s constraint 5.If a constraint is broken, go back to step 1 but do not allow inertia to cause a system constraint Goldratt 1984

50. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 50 Product Ranking Applying TOC Basic Product Data in a Case Company Case example

51. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 51 Contribution per tonContribution per bottleneck hr Product Ranking Applying TOC Two Different Product Rankings Case example

52. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 52 Goldratt 1997, 218 X Completion date X X X X Critical chain Critical Chain will revolutionize project management! Feeding buffer Project buffer Critical path

53. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 53 Comparison of the Philosophies History System Focus on Demand assumed Capacity scheduling Objective of planning Reaction on changes Role of IT Inventory status Coordination Problems JITMRPTOC 60s Push Lead times and customer service - Infinite Raw mat.availability & lead time control Very sensitive Important (Planned) safety stocks Data-based planning Inflexibility, long lead times, inventories 70s Push & Pull Bottlenecks Stable Finite (balancing) Control bottlenecks & maximize profit Sensitive Eases If no bottlenecks, no inventory Knowledge & incentives Defining profit and bottlenecks 50s Pull Quality Stable - Minimum inventories & high quality Quick Not necessary Zero Routine-based Reaction to demand variation, incentives Criteria

54. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 54 Key Points  Understand the link between performance measures and behaviour. Productivity  Efficiency Good portfolio of measures: Throughput, Inventory, and Operating expenses Don’t achieve whatever, achieve the goal.  The ongoing improvement process is important. Identify the system’s constraints Decide how to exploit the system’s constraints Subordinate everything else to the previous decision Elevate (remove) the system’s constraint If a constraint is broken, go back to step 1 but do not allow inertia to cause a system constraint

55. 35E00100 Service Operations and Strategy #7Aalto/BIZ Logistics 55 Abbreviations Used  CCR= capacity constrained resource  DBR= drum, buffer and rope  OPT= optimized production technology  PFB= pull from the bottleneck  TOC= theory of constraints  UDE= undesirable effect