MFS605/EE605
Systems for Factory Information and Control
Lecture 8: Production Control: Continued
Fall 2005
Larry Holloway
Dept. of Electrical Engineering and
Center for Manufacturing
1
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
Capabilities
Product
Shipments
Impacts lead time, WIP
2
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
3
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…
4
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”
5
Transfer Quantity vs. Order Quantity
• Example: 50 components in batch, 10 minutes per part.
6
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
CUSTOMER
Supplier
order
Raw
materials
Manufacturing Facility
Product
7
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?
8
• We will just study the production control, but concept is much
bigger. Affects:
– transportation
– supplier relations
– quality
– setup
– employee responsibilities
– …all aspects of organization…
9
• 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?
10
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
11
Overview
• Production Control: Push vs. Pull
• Kanban
– Tool for Pull
– Tool to drive lean
– Issues in using signal kanbans
• Importance of Leveling
12
Example
Bill of Materials for Truck Seat
Seat
assemble
Seat Frame
Padding
Cover Material
weld parts
Frame Parts
cut and shape
material
Raw Stock
13
Push System
•
Customer orders and forecasts are fed into
beginning of line
Forecasts
CUSTOMER
Raw
materials
Manufacturing Facility
Product
14
The Pull System
A production system driven by actual consumption and
controlled by synchronized replenishment signals.
Tool to:
• lower inventory
• reduce lead-time
CUSTOMER
Supplier
order
Raw
materials
Manufacturing Facility
Product
15
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.
16
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
17
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
18
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
19
INVENTORY HIDES WASTE
LABOR &
MATERIAL IN
SEA OF INVENTORY
POOR WORK
BALANCING
BAD
HOUSEKEEPING
EXCESSIVE
SETUP
TIMES
NON-PRODUCTIVE
MAINTENANCE
POOR
QUALITY
PRODUCTS
OUT
INSUFFICIENT
COMMUNICATION
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
29
2-card Kanban
Production cards
Move cards
A
A
A
B
B
Warehouse
A
A
B
Producing
Station
A
A
B
A
B
A
Consuming
Station
A
A
30
Signal Kanbans
•
Used only when supply process has large setup cost
– examples: stamping, forming, molding
– should continue setup reduction
•
Kanban signals inventory below threshold.
31
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 produced
Producing
Machine
Fill level
fk
Signal level
rk
Inventory for part k
Parts
consumed
Consuming
Process
Signal Kanbans
32
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.
33
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.
34
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
d0
initial state: idle (d0 = 0), producing x (d0 = 1), or
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
y (d0=2).
35
Fixed-Batch Policy -- Deterministic Case
rfixbatch(30,100,0,[4,10,4,10],[20,20],[80,80],[1,1],[0,0],10,100)
Shows repeated cycle: (no production)/(produce x)/(no production)/(produce y)
Periodic behavior with no parts shortages.
36
Fixed-Batch Policy -- Deterministic Case
rfixbatch(30,100,0,[4,10,4,10],[20,20],[80,80],[1,1],[0,0],10,100)
Shows repeated cycle:
(no production)/(produce x)/(no production)/(produce y)
Periodic behavior with no parts shortages.
37
Title: Figure1adx1.eps (modified)
Creator: MATLAB, The Mathworks, Inc.
CreationDate: 02/25/97 12:35:49
38
Fixed-batch - not ideal
Title: Figure1adx1.eps (modified)
Creator: MATLAB, The Mathworks, Inc.
CreationDate: 02/25/97 12:35:49
Title: Figure1adx1.eps (modified)
Creator: MATLAB, The Mathworks, Inc.
CreationDate: 02/25/97 12:35:49
(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)
Occasional disruptions in production:
inventories cycle down
Mismatched demand parameters:
inventories cycle down
(long periodicity)
39
Fixed-batch - not ideal
(30,100,0,[4,10,4,10],[20,20],[80,80],[1,1],[3,10],10,100)
Occasional disruptions in production:
inventories cycle down
(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)
40
Fixed-batch: Long term periodicity
Title: MATLAB graph
Creator: MATLAB, The Mathworks, Inc.
CreationDate: 02/26/97 22:40:31
Inventories spiral down until both signals present, then spiral up
41
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
d0
initial state: idle (d0 = 0), producing x (d0 = 1), or
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
y (d0=2).
42
Fixed-Fill Policy -- Deterministic Case
rfixfill(40,100,0,[4,10,4,10],[20,20],[100,100],[1,1],[0,0],10,100)
Title: Figure2ad.eps
Creator: MATLAB, The Mathworks, Inc.
CreationDate: 02/26/97 10:58:40
Repeated cycle: (noproduction)/(produce x)/(noproduction)/(produce y)
Periodic behavior with no parts shortages.
Initial conditions don’t matter
43
Fixed-Fill Policy -- Deterministic Case
rfixfill(40,100,0,[4,10,4,10],[20,20],[100,100],[1,1],[0,0],10,100)
Repeated cycle: (noproduction)/(produce x)/(noproduction)/(produce y)
Periodic behavior with no parts shortages.
Initial conditions don’t matter
44
Fixed-fill - disturbed
Title: Figure2ad.eps
Creator: MATLAB, The Mathworks, Inc.
CreationDate: 02/26/97 10:58:40
Title: Figure2ad.eps
Creator: MATLAB, The Mathworks, Inc.
CreationDate: 02/26/97 10:58:40
(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)
Occasional disruptions in production:
Inventories return to regular cycle.
Mismatched demand parameters:
Inventories find new cycle and stabilize.
45
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)
Occasional disruptions in production:
Inventories return to regular cycle.
Mismatched demand parameters:
Inventories find new cycle and stabilize.
46
Signal Kanban Summary
•
Simulation of policies under two-part system.
•
Poorly configured or disrupted fixed-batch policy has longperiod 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
47
Other Kanban signals
• Requests for die setup
• Requests for die change
• Requests for maintenance
• etc.
48
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
49
Preconditions to using Kanbans
Implement:
– focused factory and cellular production
– visual management and standardized work
– kaizen and problem solving
– setup reductions
– demand leveling
50
Kanban on Feeder Lines
Final Assembly -- wide variety
Feeder lines -- limited variety
51
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
AA A
B
A
B
B B
52
Importance of Leveling
• Leveling: even consumption of parts
– by month, by week, by hour
– coordination of sales, marketing, production
– some finished goods inventory
• Critical for success of Kanban system
– no excess inventory to handle significant swings in
consumption
– kanban quantities depend on replenishment period
53
Pull/Kanban Summary
•
Kanban is implementation of Pull system
•
Simplified production control / visual tool
– Reduced lead time
– Reduced WIP
•
Improved Communication
– faster recognition of problems
– faster response to changes in demand
•
Improved operator responsibility (Quality)
•
Limitations: Requires limits on disruptions and variations.
•
Can be tool to expose problems and focus on continuous
improvement.
54
• OPT and review of production control…
55
Review
• Push system (MRP)
– designed for scheduling individual order/forecast batches
– Good: helps explode requirements for end items into timephased orders for components and raw materials
– Bad:
• typically associated with large WIP, long lead times
• buckets too course for low-level floor control
56
Review
• Pull system (Kanban, just-in-time)
– part of larger philosophy of lean manufacturing
– Allows tight control of WIP
– production is keyed to consumption (pulled from end of line)
– Good:
• can be used as tool for reducing WIP and Leadtime and
identification of problems needing improvement
--> tool for assisting and achieving continuous improvement
• responsive to demand
• less reliance on forecasts
– Drawbacks:
• assumes leveling
• assumes limited variety of products on the kanban line
57
• What if we don’t have level demand or constrainted variety of
parts for kanban?
– Use kanban on feeder lines (example: Toyota)
– Use kanban on restricted sets of products
• focused factory within factory
• cellular manufacturing
58
OPT
•
OPT: “Optimized Production Technology”
– philosophy (and software) aimed between MRP and Kanban
– Developed by Eliyahu Goldrat
– Popularized through a novel called “The Goal”
• philosophy taught through a story
– Many good points in philosophy -• points are adopted and used even when software is not
– Also goes by:
• Theory of constraints
• Drum-Buffer-Rope (DBR)
59
• The goal of the firm: To make money
• How to measure the goal:
– net profit per product
– return on investment
– cash flow
• Measures on the plant floor that drive the global
measurements:
– Throughput (DBR: rate for selling finished products)
– Inventory
– Operating Expense
60
• Increase throughput:
– profit per good up
– ROI up
– cashflow up
• Reduction in operating expenses:
– profit per good up
– ROI up
– cash flow up
• Reduction in inventory: reduces operating expenses :
– ROI up
– cashflow up
– profit per good unchanged
– also: improves responsiveness, quality, price:
• more competitive
61
• DBR philosophy tells how to manage throughput, operating
expense, and inventory to move toward goals:
• Philosophy summarized in 9 (sometimes 10) “rules of OPT”
– underlying theme: manage your constraints (bottlenecks)
• use them to dictate your schedule and your activities
62
• Rule 1: balance flow, not capacity:
• Key idea: we shouldn’t worry about capacity, as long as we
have enough.
– Instead, we should schedule our line to maintain flow of
product, regardless of how much capacity is being used
63
• Rule 2: “Level of utilization of a non-bottleneck is determined
not by its own potential, but by other constraints in the
system”
• All parts of mfg. System are either a bottleneck or a nonbottleneck
– bottleneck: a point or storage in process that limits the
amount of product that a factory can produce
• Example:
64
• Types of bottleneck relationships
65
• Rule 3: Utilization and activation of a resource are not
synonomous.
Doing work regardless of whether it is needed:
keeping machines busy
vs.
Doing required work only
“Don’t confuse being busy with being productive”
Being busy can lead to inventory buildup which cannot be
absorbed by bottleneck or market share
66
• Rule 4: An hour lost at a bottleneck is an hour lost for the
total system
• Thus: strive to use bottlenecks at 100%
– implication:
• an hour gained at a bottleneck means ganins throughout
system
• This focuses setup reduction on bottlenecks
• Also encourages large lots on bottlenecks
67
• Rule 4: An hour saved at a non-bottleneck is a mirage
• saving that time may be just increasing idle time
• Thus: can use small lot sizes on NB
68
• Rule 6: Bottlenecks govern both throughput and inventory in
the system
• Bottlenecks constrain throughput, but also:
– stocks buildup to keep bottlenecks busy
– lot sizes large at bottlenecks to reduce setup impact
69
• Rule 7: The transfer batch may not and often should not be
equal to the process batch
• Rule 8: Process batch size should be variable, not fixed
– dynamically determine lot size for each operation
70
• Rule 9: Lead times should be variable and not fixed.
• Rule : Consider all constraints when establishing schedules
• Rule: Sum of local optima is not global optimum.
71
Drum-Buffer-Rope Approach
• Combine push and pull.
• The bottleneck becomes the drum which drives the system
rate
• The following stations are operated in push mode:
– Orders are released from the bottleneck and pushed (flow)
at their own speed in the system
• The preceding stations are operated in a pull mode:
– There is a “rope” tying the entry operation to the
bottleneck – thus allowing new parts to enter only as the
bottleneck releases others.
72
Drum-Buffer-Rope (DBR/TOC) examples
73