1 mfs605/ee605 systems for factory information and control lecture 8: production control: continued...

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MFS605/EE605Systems for Factory Information and Control

Lecture 8: Production Control: Continued

Fall 2005

Larry Holloway

Dept. of Electrical Engineering and

Center for Manufacturing

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Production Control: Task of taking orders and forecasts for final product and converting them into orders for production operations and orders for raw materials.

ProductShipments

OrdersForecasts

Impacts lead time, WIP

Capabilities

Materials

Production Control

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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

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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…

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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”

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Transfer Quantity vs. Order Quantity

• Example: 50 components in batch, 10 minutes per part.

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Supplierorder

Raw materials Product

CUSTOMER

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

Manufacturing Facility

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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?

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

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• 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?

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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

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Overview

• Production Control: Push vs. Pull• Kanban

– Tool for Pull – Tool to drive lean– Issues in using signal kanbans

• Importance of Leveling

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Bill of Materials for Truck Seat

Raw Stock

Frame Parts

Seat Frame Padding Cover Material

Seat

cut and shapematerial

weld parts

assemble

Example

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CUSTOMER

Push System

• Customer orders and forecasts are fed into beginning of line


Forecasts

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Supplierorder


CUSTOMER

The Pull SystemA production system driven by actual consumption and controlled by synchronized replenishment signals.

Tool to:

• lower inventory• reduce lead-time


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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.

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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

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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

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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

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INVENTORY HIDES WASTE

LABOR & MATERIAL IN SEA OF INVENTORY

ABSENTEEISM

POOR QUALITY

EXCESSIVE SETUP TIMES

NON-PRODUCTIVE MAINTENANCE

POOR WORK BALANCING

INSUFFICIENT COMMUNICATION

PRODUCTS OUT

BAD HOUSEKEEPING

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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

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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

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empty kanbans

parts (with kanbans)

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Types of Kanban Systems

• Withdrawal Kanbans– 1-card and 2-card systems

• Signal Kanbans

• Emergency Kanbans and others

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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

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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.

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Kanban limitations

• large demand fluctuations cause problems.

• Real benefits only when constrained variation in product. – Toyota: kanbans on feeder lines, not for final production

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2-card Kanban

Production cards Move cards

ProducingStation

ConsumingStation

A

A A A

A

A A

B B

B B

BA

AA

A

Warehouse

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Signal Kanbans

• Used only when supply process has large setup cost

– examples: stamping, forming, molding– should continue setup reduction

• Kanban signals inventory below threshold.

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Signal Kanban SystemSignal 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

Fill level k

Signal level rk

Producing Machine

Parts produced

Signal Kanbans

Inventory for part k Parts

consumed

ConsumingProcess

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Buffer levels for two partsBuffer levels for two parts

Time from signal to production includes kanban queue time and setup time k

buffer levels

r

t

Issue: In practice, sometimes kanban waits in queue too long, resulting in parts shortages.

Issue: In practice, sometimes kanban waits in queue too long, resulting in parts shortages.

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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.

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Fixed-BatchFixed-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 yd0 initial state: idle (d0 = 0), producing x (d0 = 1), or y (d0=2).rx, ry nominal consumption ratessx, 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 sizesxsetup relative setup timen steps of simulation

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Fixed-Batch Policy -- Deterministic CaseFixed-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)

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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)

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Title: Figure1adx1.eps (modified)Creator: MATLAB, The Mathworks, Inc.CreationDate: 02/25/97 12:35:49

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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

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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

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Fixed-batch: Long term periodicity

Title: MATLAB graphCreator: MATLAB, The Mathworks, Inc.CreationDate: 02/26/97 22:40:31

Inventories spiral down until both signals present, then spiral up

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Fixed-FillFixed-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 yd0 initial state: idle (d0 = 0), producing x (d0 = 1), or y (d0=2).rx, ry nominal consumption ratessx, 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 sizesxsetup relative setup timen steps of simulation

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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)

Title: Figure2ad.epsCreator: MATLAB, The Mathworks, Inc.CreationDate: 02/26/97 10:58:40

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Fixed-Fill Policy -- Deterministic CaseFixed-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)

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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.



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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.

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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

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Other Kanban signals

• Requests for die setup

• Requests for die change

• Requests for maintenance

• etc.

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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

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Preconditions to using Kanbans

Implement:– focused factory and cellular production– visual management and standardized work– kaizen and problem solving– setup reductions– demand leveling

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Kanban on Feeder Lines

Final Assembly -- wide variety

Feeder lines -- limited variety

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Leveling

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

A A A B B B

A B A B A B

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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

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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.

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• OPT and review of production control…

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Review

• Push system (MRP)– designed for scheduling individual order/forecast batches– Good: helps explode requirements for end items into time-

phased orders for components and raw materials– Bad:

• typically associated with large WIP, long lead times• buckets too course for low-level floor control

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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

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• 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

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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)

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• 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

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• 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

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• 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

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• 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

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• 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 non-bottleneck– bottleneck: a point or storage in process that limits the

amount of product that a factory can produce

• Example:

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• Types of bottleneck relationships

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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.

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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.

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Drum-Buffer-Rope (DBR/TOC) examples

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