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Blair Williams is a chartered engineer from London and has an MBA from Loyola University of Chicago. Williams was Director of Materials Management and Manufacturing Engineering at AT&Ts Submarine Systems factory in New Jersey. He is now an industry professor at the Polytechnic Institute of New York University. Currently, he serves on the DSP curriculum and certification committee. He is a past president of the Princeton Southern Jersey APICS Chapter and has presented numerous papers at national and international conferences.

If You Want To Be Lean, Control Variability

Blair Williams, C.Eng (Lon) CFPIM, Jonah, CSCPIndustry Professor

NYU - Polytechnic University, NY

blairrw@att.net

OverviewThe Enemy of Flow - Variability

The ideal process is one of flowIn operations efficiency means improving flow, and this means reducing variability

• Objectives of presentation: 1. To understand variability and its effect on operations

and flow.2. To examine systems for controlling variability.3. To learn practices to improve flow.4. To provide insights to how these concepts are applied,

specifically to services.

Presentation Dynamics• Please ask questions as i go along, because

– I will be presenting some new concepts– If you have not noticed, i have an accent!

• I have a habit of asking questions – my classroom habit - and I will!– Learning comes from your understanding of the subject matter

• Please be prepared to participate in a couple of exercises– Everyone needs a sheet of paper and a pencil (and eraser!)

• First half somewhat tedious – formulas, calculations etc – then we will get descriptive

• Ok? Lets go

Operations• Operations applies to all processes that include either a

product or a service• Operations will include:

– All manufacturing– All services

• Hospitals• Banks• Grocery stores• Services – financial; quality; purchasing• Government services• Your household

• Today, services are particularly dysfunctional and need some formalized models of efficiency

• The well developed BOK for manufacturing needs to be applied, Mutatis Mudandis, to services

Basics• Ideal system – product flows

Operations cycle efficiencyOCE = Time worked on x 100 = ?%

Time in system• Reality ? • OCE is 10% to 20%• What consumes most of the time ?• queues – people; product; files• Why do queues form?

Variability• Queues lead to increased time in the process (lead

time) and generally increased cost and inefficiency.

Effects of Reducing Variability• Reducing variability, reduces queues and lead time

and has the following effects: • Customer delivery response time and reliability improved.

• Quality defects are detected earlier• Design improvements are implemented• Inventory is reduced• Space utilization is improved

• Product cost is reduced as, overhead is spread over shorter period of time

• Total cost reduced for all above reasons

Variability Terms• C2 = Variability• WIP = Work in progress• LT = Average lead time (or throughput time)• PR = Process rate• Model of operations

Every Process consists of products/files/people arriving and being transformed/dealt with (Input and transformed output)λa. = Average arrival rateλs = Average service or processing rate

λa. / λs = Utilization = ρ (Capacity utilization)• ( = Service time/inter arrival time)

•As long as arrival rate less than service rate• Or, since time is the reciprocal of rate•As long as service time is less than arrival time arrival time)

NO QUEUE

Deterministic Case: C2 = 0No variation in arrival or service times

1.00

Queue build Without limits for

ρ > 1

ρ = Utilizationρ ≡ Utilization = arrival rate/service rate = λa/λs or service time/ arrival time

• As long as service time is less than arrival time - no queue• But as they get close, small increases in service time, or, reductions in

inter arrival time – ρ gets closer to 1 • This is variability – lead to queues

Throughouttime

Servicetime

Examples: Formation of QueuesDeterministic Case

Arrival Rate Service Rate Queue5 per hour 4 per hour ?3 per hour 4 per hour ?

Variable Rates CaseArrival Rate Service Rate Queue1 ± 1 per hour 2 ± 1 per hour finite3 ± 2 per hour 4 ± 2per hour finiteWhich case likely to have more queues ? Why?

• Congestion or queues form when utilization high - small variation • Congestion forms when significant variability in arrival rates and service rates• Queues lead to increased LT and/or WIP.

C2≡variance

mean2

variance = N

Σi =1

(xi -x)2

N

LT & WIP increase with increase in C2

If something is important is must be measuredC2 is the measure of Variability

Where the variance is that of the service process or the arrival process and is defined as follows:

Example --Calculating C2 =

---------------------------

(MEAN)2

Consider the processing of three lots where the service time varies. Calculate C2

Observation Service time1 3

2 8

3 4

(Service time- mean) (Service time- mean)2

-2 4

3 9

-1 1

average service time = 3 + 8 + 43

= 153

= 5

variance = 4 + 9 + 13

= 143

= 4.6

C2 = variancemean 2 = 4.6

25= 0.18

N

Σi =1

(xi -x)2

N

EFFECTS OF VARIABILITY–FUNDAMENTAL RULES

To prevent queuesRule 1: If lead time is kept constant, as variability increases, utilization level is reducedRule 2: If utilization is kept constant, as variability increases, lead time increases

Arrival rate /Service rate

highvariance

lowvariance

Averagethroughput

time[lead time]

novariance

0.80.5

•Can we identify with this in a daily life?–tolls; checkout.

What two rules can you derive?

Variation in Average TT for low and high values of the utilization, ρ

02468

1012141618

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

C2 = 1

ΔTT

ΔTT

ΔρΔρ

Operations tend to operate herewhere small changes in arrival Or service rate produce big changes in waiting timeAverage

throughputtime

[ time]

utilization ≡ ρ = λa

λs= average arrival rate

average service rate

Taken from‘Manufacturing forSurvival’Blair R Williams– Pearson Pub

Pause and Reflect• What have we learned so far• Queues form when?

– Service time exceeds arrival time (arrival rate exceeds service rate)

– There is variability in service and/or arrival times– Most processes are at the high end of capacity

utilization• Queues lead to increase in lead time• To prevent queues forming

– Keep service time less than arrival time– Reduce variability– Increase flexibility (adding capacity)– Work at reduced capacity levels

• Ok? Lets look at types of variability and what we can do

Forms of Variability

• Arrival variability – types?• Types

– Batching or large lot sizes (a busload arriving at McDonalds). The most damaging form of variability

– Uneven scheduling – hot lots and other rush orders. – System created peaks – start and finish– Uneven flow between work stations–poor layouts or

poor dispatch lists – Nervous master or Mrp schedules leading to frequent

schedule changes– Customers coming in groups, evening and holidays – Customers coming in late

• Any others?

Demand Management StrategiesSmoothing variable or lumpy demand

• Pricing strategies– Price to increase or decrease demand

• Restricted service at peak times– Only standard services available

• Specialist channel services– Separate channel for non standard or specific

requirements• Advertising and promotion

– Increase or decrease• Toll / surcharge at peak times

– Limit demand• Off peak reductions of fees/tolls

Forms of Variability• Service variability – types

• Machine downtime (excl. Planned maintenance)

• Design or process not manufacturable• Defective work. Poor quality leading to

rework or repair• Defective material• Lack of material –shortages• Untrained or unskilled operators• Lack of operator availability.

• Tardy, insufficient, inflexible

Service or Execution Management• Key machines or personnel must be used only for the

critical process• Empowering front office/contact personnel• Minimize paperwork

– Visit the details required on forms • Deal with one issue or customer at a time• Have a specialist to deal with complicated issues• Must have a routine for ending transaction

– Prevent excess socialization• Ensure multi trained personnel – flexibility• Availability of reserve resources• Make sure there is follow up where needed

Sequencing the Allocation of Capacity

• First in first out – simple & perceived as fair

• Last in first out – occasionally like truck loading

• Most valuable customer first

• Most critical first (time/work)

• Least work first

• Most work first

Relating Queues, Capacity and Lead Time

Little’s law: WIP = LT x PR

• Conditions:

– System in equilibrium, no parts removed except by exiting

– Applies to single or multi stage process

– Applies to all queuing systems regardless of distribution

• Little’s law modified: WIP=LT x PR x VF

– Where VF = variability factor

EXAMPLE (WIP = LT x PR x VF)• If WIP = 120, production Rate is 10 pieces per hour, and Variability factor is 20%

Then WIP = 120 = 10 x 1.2 x Lead Time, OR,

Lead Time = 10 hours

• If we reduce WIP to 60 and keep PR and VF constant what happens?

• 60 = 10 x 1.2 x LT or LT = 5 hours. We have halved LT (the essence of Kanban)

• If we reduce variability to zero

• 60 = PR x 5 or PR = 12 pcs per hour. By reducing variability, we have increased Production Rate (throughput)

• IF PR is 10 pcs per hour, and lead time is 6 hours. What is WIP?

• WIP = 10 x 6 = 60 pcs

• For above production, IF VF is 1.2 what is the WIP?

• WIP = 60 x 1.2 = 72pcs

• Little’s law allows us to relate WIP, PR, LT and variability

Improving Resource Utilization• Yield management

– Particularly important in services where capacity is perishable –hotels; airlines etc

• Building flexibility - forms– Product mix flexibility - options– Delivery flexibility – Volume flexibility – chase strategy

• Building flexibility – approaches– Employment contracts – flex times; on demand– Overtime– Short-term outsourcing– Subcontractors– Menu driven service – standardization– Team work

• Use of customers– Self check outs

Improving Resource Utilization

• Reducing capacity leakage– Sickness and absenteeism– Labor underperformance– Scheduling losses– Complexity– Quality failures

• Organizational support for resource utilization– Importance of culture of everyone serves the

customer– Understanding good and poor practices– Training on how to best use resources– Managing internal interfaces

Process of Dealing with Variability1. Observe the work centers where queues

form consistently. These are likely to be constraints

2. Determine the type of variability these constraints are subjected to (arrival or service or both)

– For arrival variability: record the times when new work orders arrive and the length of the queue

– For service variability: record the operation time and reasons for delay

– Calculate the arrival and service variability

Process For Dealing With Variability

3. Reduce the arrival variabilityReduce lot size– Uniform scheduling– Apply Kanban to provide consistent flow

between work stations– Ensure proper work flow between work

stations– Do not disrupt work that is being processed

with hot orders etc.– Control demand by pricing/promotion

Process For Dealing With Variability

4. Reduce the service variability– Allocate capacity– Schedule maintenance– Train operators– Develop flexible operators– Ensure good quality (spc) – hi yield– Limit or eliminate material shortages– Ensure work station has required

instructions, tools, guages5. Reobserve and recalculate the arrival and

service variability

Process For Dealing With Variability

6. Based on the level of variability and the criticality of lead time - decide on a utilization level

7. Understand the trade off between utilization and lead time

8. Measure and understand the actual lead time

9. Repeat the process. As Dr. Goldratt says “ don’t let intertia become the constraint”

Managing The Bottleneck• Identify the constraint or bottleneck?

• Ensure only essential work passes through the bottleneck

– Medical, grocery store

• Be ruthless in taking away non essential work from the bottleneck

• Ensure the bottleneck produces only quality, ‘right first time’ work

• Overcompensate operators or other

– Subsidiary resources to ensure bottleneck is not idle

• Devote specific management attention to the working of the bottleneck

• Track throughput on a continuous basis

– Establish communication to react to any issues with bottleneck

Customer Induced Variability in Services*• Service operations have much more built in

customer variability• Classification of customer variability

– Arrival variability• Arrive at different rates

– Request variability• Have different preferences

– Capability variability• Particularly when customer a participant

– Effort variability• Particularly when customer required to interact

– Subjective preference variability• Customers opinion of level of service they received

* HBR – Nov. 2006. Breaking the trade-off between Efficiency and Service

Strategies For Managing Customer Induced Variability*

• Accommodation– Attempt to meet customer induced variability– More costly, but higher customer satisfaction

• Classical accommodation - have excess employees on hand to meet variability

• Low cost accommodation – low cost labor, outsource and/or create self service options

• Reduction– Attempt to limit customer options, hence variability– Lower cost, but lower customer experience

• Classical reduction – process limits options and/or requires morecustomer skill/effort

• Uncompromised reduction –– Target customers on the basis of their equests/capability.– Increase customers influence on outcome or persuade

customers to adjust preferences* HBR – Nov. 2006. Breaking the trade-off between Efficiency and Service

If Queues Cannot Be Avoided In Services-How Are They Best Managed

• Unoccupied time feels longer they occupied time

• Pre-process wait feels longer than in-process waits

• Anxiety makes the wait seem longer

• Uncertain waits are longer than known, finite waits

• Unexplained waits seem longer than explained waits

• Unfair waits are longer than equitable waits

• The more valuable the service the more customers will wait

• Solo waits seem longer than group waits

• Uncomfortable waits feel longer than comfortable waits

• New or infrequent users feel they wait longer than frequent user

Johnston and Clark

Summary• Understood how queues form and learned the nomenclature of

queues• Defined variability, its types and learned how it can be calculated• Examined variability and its relationship to utilization (capacity)

and throughput• Highlighted common forms of arrival variability• Highlighted common forms of service variability• Reviewed little’s law and the relationship of WIP, lead time,

production rate and variability• Developed a process for dealing with variability• Looked at improving resource utilization and bottleneck

management• Briefly reviewed customer induced variability and strategies to

manage it

QUESTIONS ?

IF YOU NEED A COPY OF SLIDES PLEASE SEND ME AN EMAIL

BLAIR WILLIAMSblairrw@att.net

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