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

Material Flow Control, CONWIP and

Theory of Constraints

35E00100 Service Operations and Strategy

7 Fall 2015

35E00100 Service Operations and Strategy #7 Aalto/BIZ Logistics2

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


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


Push and Pull Line Schematics

Pure Push (MRP)

CONWIP

Full containers

Authorization signals

Pure Pull (kanban)StockPoint. . .

…

StockPoint

. . . StockPoint

StockPoint

. . .StockPoint

StockPoint

Hopp and Spearman 2000, 351


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



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).

Assumptions1. Single routing2. WIP measured in units

Different mechanisms from the modeling perspective MRP – open queuing network CONWIP – closed queuing network Kanban – closed queuing network with blocking

. . .



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.




2. CONWIP Efficiency

Equipment data 5 machines in tandem Every machine has capacity of one part/hr (u=TH*te=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.

41)(

0

w

wr

Ww

wwTH b

TH

TH

u

uTHw

15

15)(

PWC formula

Five M/M/1 queues

4

5

))4/((1

))4/((5

4

w

ww

ww

w

ww


Example

WIP is always 25% higher for the same TH in push

than in CONWIP



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

hwpTH Profit

hww

wp

4Profit(w)

TH

THhpTH

1

5Profit(TH)

Need to find “optimal” TH level

(i.e. release rate)


Example


Relative Robustness of CONWIP and Pure Push Systems

Push

CONWIP

Optimum

Efficiency

Robustness


Example

= marginal profit per job 100

= cost for each unit of WIP 1

p

h


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



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.


We think in generalities, we live in detail.


Execution

DetailedPlanning

AggregatePlanning

Resource planning

Aggregate production planning

Demand management

Master production scheduling

Shop floorcontrol

Vendor systems

Scheduling

Material requirementsplanning

Capacity planning

PAC in the MPC System

Order releasePurchase orders

Vollmann et al. 1997, 15


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


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


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 center Run time Setup time Move time Queue time Total time Rounded time

1 101 1,4 0,4 0,3 2 4,1 4,02 109 1,5 0,5 0,3 2,5 4,8 5,03 103 0,1 0,1 0,2 0,5 0,9 1,0

Total lead time 10.0 days

0 1 2 3 4 5 6 7

A

B

C

D

E

80 %Waiting is typically over 80 % of total

customer LT


Infinite versus Finite Loading

Capacity

Finite load capacity profile

01020

30405060

7080

1 2 3 4 5 6 7 8 9 10

Hours

Wee

ks

Planned orders

Open shop orders

CRP profile - infinite loading

0

20

40

60

80

100

120

140

160

Pastdue

1 2 3 4 5 6 7 8

Hours

We

ek

s

“PAC Technique”


Potential Functions of SFC Module

SFC is the process by which decisions directly affecting the flow of material through the factory are made.

WIPtracking

Throughputtracking

Statusmonitoring

Workforecasting

Capacityfeedback

Qualitycontrol

MaterialFlow

Control



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 = CTrP initially, then conservative adjustments Card deficits: Violate WIP-cap in special circumstances Work ahead: How far ahead relative to due date?



CONWIP LineControlling WIP with CONWIP Cards

Production line

Inboundstock

Outboundstock

CONWIP cards



Tandem CONWIP Loops

Basic CONWIP

Kanban

Multi-loop CONWIP

work center buffer card flow Hopp and Spearman 2000, 465


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, 468card flowbuffer material flow

Processing timesfor Line B

Processing timesfor Line A

Assembly

1

3233

2 4

1


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


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, 472card flow material flow


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



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


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


Part 2: Theory of ConstraintsContents

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


The Impact of Measurements

GoalsMeasure

ments

Actions

"Trust is nice as long as there are measurements that serve as a watchdog."


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 assetsNet profitsCash flows, etc.

Inventories Operating costsThroughputCycle timeOn time delivery, etc.

Cycle time% rework / scrapCross-training


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


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


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


Goldratt has authored many business novels…

1991

1984

1994

2000

1997

1990

1986

1996

1998

1998

1999

2000

19951998


Drum-Buffer-Rope

A Troop Analogy - Marching Soldiers


A Troop Analogy - Marching SoldiersWhat if the Physical Condition of the Soldiers Varies?

Raw material Finished goods

Work-in-process


A Troop AnalogyPut the Slowest Soldier at the Front

Expensive?

Feasible?


Work-in-process


A Troop AnalogyPlace a Drummer at the Front to Set the Pace

Do efficiencies, incentives & variances allow workers to

follow the drumbeat?


Work-in-process


A Troop AnalogyTake Load off from the Slowest


Work-in-process


A Troop AnalogyRope the Soldiers Together

The invention of Henry Ford: Assembly LineThe invention of Dr. Ohno from Toyota: Kanban System


A Troop AnalogyTie the Weakest Soldier to the Front


Work-in-process


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 ShippingR4


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, 472card flow material flow


Continuous Improvement and Thinking Processes

Goldratt ’s books

Questions of continuous improvement

Thinking process tools

UDEsCurrent Reality TreeEvaporating Cloud DiagramFuture Reality TreePrerequisite TreeTransition TreeNegative Branches6 Steps to Buy-In

What to change?

To what to change to?

How to cause the change?


How to Invent Simple Solutions?Evaporating Clouds

B

C

D

Not D

A

Objective Requirement Prerequisite

Goldratt 1990, 39

Conflict

B

C

Some amount of D

Some add’l amount of D

A


Conflict (limited

availability of D)


The Evaporating Cloud DiagramA Typical Problem in Manufacturing Environments

Reduce setup cost per unit

Reduce carrying cost per unit

Large batch

Small batch

Reduce cost per unit


Goldratt 1990, 43


The Evaporating Cloud DiagramThe Goal of a Company

Protect current throughput

Protect future throughput

Keep inventory

Reduce inventory

To make more money now and

in the future


Goldratt 1990,118


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 decision4. Elevate (remove) the system’s constraint5. If a constraint is broken, go back to step 1 but do not allow inertia to cause a

system constraint

Goldratt 1984


Product Dimensions Time required Contribution (diameter x wall thickness) on the bottleneck (sales - direct materials)

(mm) (min) (index) A 10,00 X 0,80 65,48 156 B 12,00 X 1,00 48,98 84 C 15,00 X 1,00 46,61 78 D 18,00 X 1,00 44,25 100 E 15,00 X 1,20 35,76 132 F 22,00 X 1,00 26,33 77 G 28,00 X 1,20 24,27 80 H 22,00 X 1,50 24,10 127 I 28,00 X 1,50 15,99 125

Product Ranking Applying TOCBasic Product Data in a Case Company

Caseexample


Ranking Product Contribution per ton (sales - materials) (index)

1. A 153 2. E 132 3. H 127 4. I 125 5. D 100 6. B 84 7. G 80 8. C 78 9. F 77

Ranking Product Contribution per bottleneck hour (index)

1. I 470 2. H 315 3. E 221 4. G 196 5. F 174 6. A 139 7. D 135 8. B 103 9. C 100

Contribution per ton Contribution per bottleneck hr

Product Ranking Applying TOCTwo Different Product Rankings

Caseexample


Goldratt 1997, 218

X

Completion date

X

X

X

X

Critical chain

Critical chain

Critical chain

Critical Chain will revolutionize project management!

Feeding buffer

Project buffer

Critical path


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

JITMRP TOC

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

EasesIf no bottlenecks, no inventoryKnowledge & 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


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


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

Documents

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