Post on 27-Nov-2015
description
transcript
Introduction
Prof. Christian Terwiesch
Operations in a Restaurant
Prof. Christian Terwiesch
Operations in an Emergency Room
Prof. Christian Terwiesch
Operations from the Perspective of the Customer
Prof. Christian Terwiesch
Four Dimensions of Performance
Cost▪ Efficiency
Quality▪ Product quality (how good?)
▪ Process quality (as good▪ Process quality (as good as promised?)
Time▪ Responsiveness to
demand
Variety▪ Customer heterogeneity
Important for- Performance measurement- Defining a business strategy
Prof. Christian Terwiesch
Four Dimensions of Performance: Measurements for a Sandwich Store
Cost▪ Efficiency
Quality▪ Product quality (how good?)
▪ Process quality (as good as promised?)
Time▪ Responsiveness to demand
p )
Variety▪ Customer heterogeneity ▪ Responsiveness to demand▪ Customer heterogeneity
Prof. Christian Terwiesch
IntroductionEfficient Frontier
Prof. Christian Terwiesch
Four Dimensions of Performance: Trade-offs
Cost▪ Efficiency▪ Measured by:
Quality▪ Product quality (how good?)
=> Price▪ Process quality (as good- cost per unit
- utilization
▪ Process quality (as good as promised?)=> Defect rate
Time▪ Responsiveness to
Variety▪ Customer heterogeneity p
demand▪ Measured by:
- customer lead time- flow time
Customer heterogeneity▪ Measured by:
- number of options- flexibility / set-ups
make to order
Prof. Christian Terwiesch
- flow time- make-to-order
What Can Ops Management (This Course) Do to Help? Step 1: Help Making Operational Trade-Offs
ResponsivenessHigh
Very short waiting times,Comes at the expense ofFrequent operator idle time
Trade-off Long waiting times,
yet operators are almostfully utilized
Example: Call center of a large retail bank
Labor Productivity(e.g. $/call)
Low
Low laborproductivity
High laborproductivity
y
Example: Call center of a large retail bank- objective: 80% of incoming calls wait less than 20 seconds - starting point: 30% of incoming calls wait less than 20 seconds- Problem: staffing levels of call centers / impact on efficiency
Prof. Christian Terwiesch
OM helps: Provides tools to support strategic trade-offs
What Can Ops Management (This Course) Do to Help?Step 2: Overcome Inefficiencies
Responsiveness
HighCurrent frontier
Eliminate inefficiencies
In the industry
Competitor A
Low
Competitor C
Competitor B
Labor Productivity(e.g. $/call)
Low laborproductivity
High laborproductivity
Competitor B
Example:• Benchmarking shows the pattern above• Don’t just manage the current system… Change it!
Provides tools to identify and eliminate inefficiencies => Define Efficient Frontier
Prof. Christian Terwiesch
Types of inefficiencies:-Poor process design- Inconsistencies in activity network
What Can Ops Management (This Course) Do to Help?Step 3: Evaluate Proposed Redesigns/New Technologies
Responsiveness
HighHigh
Redesignprocess
Current frontierNew frontier
LowIn the industry
Labor Productivity( $/ )
Low labor High labor
Example:• What will happen if we develop / purchase technology X?
Better technologies are al a s (?) nice to ha e b t ill the pa ?
(e.g. $/call)productivity productivity
Prof. Christian Terwiesch
• Better technologies are always (?) nice to have, but will they pay?
OM helps: Evaluates system designs before they occur
Example: The US Airline Industry
Prof. Christian Terwiesch
Example: The US Airline Industry
Prof. Christian Terwiesch
IntroductionFormat of the course
Prof. Christian Terwiesch
Course Outline / Grading / Homework
Objective of the course: Understanding and improving business processes
Performance measuresHow-to
Mix of industries: healthcare restaurants automotive computers call centers banking etcMix of industries: healthcare, restaurants, automotive, computers, call centers, banking, etc
Course OutlineIntroduction (0.5 weeks)1. Process analysis (1.5 weeks)2. Productivity3. Product variety 4. Responsiveness 5. Quality
Requirements / Prerequisites: There are no prerequisites for the course
Some modules require statistical knowledge (standard deviation, normal distribution)
Homework assignmentsOne large assignment after each module (five assignments); 10% each
Final exam with questions from all modules; 50%
Prof. Christian Terwiesch
q ;
Text Book
Course book Cachon, Gerard, Christian Terwiesch, Matching Supply with Demand: An Introduction to Operations Management, 3rd edition, Irwin - McGraw Hill, 2012 (ISBN 978-0073525204, 507 pages)
Prof. Christian Terwiesch
Personal IntroductionMBA core course: Operations Management: Quality and Productivity
Taught ~ 60 times ~ 4000 MBA students
McKinsey Ops Practice ~ 500 new associates
Research: Operations Management, focus on Healthcare Management
Innovation tournaments and contests
Christian Terwiesch terwiesch@wharton.upenn.edu
Andrew M. Heller Professor at the Wharton SchoolSenior Fellow Leonard Davis Institute for Health Economics
573 Jon M. Huntsman Hall
Prof. Christian Terwiesch
Philadelphia, PA 19104.6366
Process AnalysisI d i / Th hIntroduction / The three measures
Prof. Christian Terwiesch
Subway – Sitting in Front of the Store
Prof. Christian Terwiesch
Subway – Sitting in Front of the Store
25 Minutes later….
Prof. Christian Terwiesch
Subway – Sitting in Front of the Store
Prof. Christian Terwiesch
Processes: The Three Basic Measures
• Flow rate / throughput: number of flow units going through the process per unit of time
• Flow Time: time it takes a flow unit to go from the beginning to the end of the process
• Inventory: the number of flow units in the process at a given moment in time
• Flow Unit: Customer or SandwichFlow Unit: Customer or Sandwich
Prof. Christian Terwiesch
Process Analysis: The Three Measures
Immigration department Champagne MBA program Auto company
Applications
Approved or rejected cases
Processing time
Bottle of champagne
Bottles sold per year
Time in the cellar
Student
Graduating class
2 years
Car
Sales per year
60 daysProcessing time
Pending cases
Time in the cellar
Content of cellar
2 years
Total campus population
60 days
Inventory
Prof. Christian Terwiesch
Summary
When observing a process always aim to understand the three process measuresWhen observing a process, always aim to understand the three process measures
• Flow rate / throughput: number of flow units going through the process per unit of time
Flow Time: time it takes a flow unit to go from the beginning to the end of the process• Flow Time: time it takes a flow unit to go from the beginning to the end of the process
• Inventory: the number of flow units in the process at a given moment in time
In the next session we will discuss what drives these measuresIn the next session, we will discuss what drives these measures
We will then find out that the three measures are related to each other
Prof. Christian Terwiesch
Process AnalysisFinding the bottleneck
Prof. Christian Terwiesch
Process Analysis
In this session, we will take you INSIDE the black box
Specifically, you will learn how to:
1. Create a process flow diagram
2. Find the bottleneck of the process and determine the maximum flow rate
3 Conduct a basic process analysis3. Conduct a basic process analysis
Prof. Christian Terwiesch
Subway – Inside the Store
Prof. Christian Terwiesch
Drawing a Process Flow Diagram
Prof. Christian Terwiesch
Drawing a Process Flow Diagram
Customers Station 1 Station 2 Station 3
Symbols in a process flow diagram
Difference between project management and process management
Prof. Christian Terwiesch
Basic Process Vocabulary
• Processing times: how long does the worker spend on the task?
• Capacity=1/processing time: how many units can the worker make per unit of timeIf there are m workers at the activity: Capacity=m/activity time
• Bottleneck: process step with the lowest capacity
• Process capacity: capacity of the bottleneck
• Flow rate =Minimum{Demand rate, Process Capacity)
• Utilization =Flow Rate / Capacity
• Flow Time: The amount of time it takes a flow unit to go through the process
• Inventory: The number of flow units in the system
Prof. Christian Terwiesch
Inventory: The number of flow units in the system
Process AnalysisLabor productivity measures
Prof. Christian Terwiesch
Labor Productivity MeasuresTi
me
a2
a4
Bottleneck=Idle Time =Processing time
a1
Pro
cess
ing
a• Cycle time CT= 1/ Flow Rate
Di t L b C t t
Labor Productivity Measures
P a3 Direct Labor Content=p1+p2+p3+p4If one worker per resource:
Direct Idle Time=(CT-p1) +(CT-p2) +(CT-p3)
A l b tili ti1 2 3 4
• Capacityi =
Review of Capacity CalculationsResources ofNumber i
time idle direct content labor content labor
• Average labor utilization
Capacityi
• Process Capacity=Min{Capacityi}
• Flow Rate = Min{Demand Capacity}
iTime Processing
timeofunitperRateFlow time of unit perwages Total
• Cost of direct labor
Prof. Christian Terwiesch
Flow Rate Min{Demand, Capacity}
• Utilizationi=iCapacity
Rate Flow
p
Example: Assembly Line with Six Stations
3 min/unit 5 min/unit 2 min/unit 3 min/unit 6 min/unit 2 min/unit
Prof. Christian Terwiesch
Insert Excel analysis of Subway line here
Prof. Christian Terwiesch
100%
The Role of Labor Costs in Manufacturing: The Auto Industry
70%
80%
90%
100%
QualityWarrantyOverheadOther
30%
40%
50%
60%
Purchasedparts andassemblies
Parts andmaterialcosts Logistics costs
Assembly and otherLabor costs
0%
10%
20%
30%
Fi l I l di I l di R ll d
Material costs
Final Assembler’s cost
IncludingTier 1Costs
IncludingTier 2Costs
Rolled-upCosts over~ 5 Tiers
• While labor costs appear small at first, they are importantlook relative to value added- look relative to value added
- role up costs throughout the value chain
• Implications
Prof. Christian Terwiesch
- also hunt for pennies (e.g. line balancing) - spread operational excellence through the value chain
Source: Whitney / DaimlerChrysler
Process AnalysisLittle’s Law
Prof. Christian Terwiesch
Processes: The Three Key Metrics
Prof. Christian Terwiesch
Little’s law: It’s more powerful than you think...
What it is: Inventory (I) = Flow Rate (R) * Flow Time (T)
How to remember it: - units
Implications:• Out of the three fundamental performance measures (I,R,T), two can be chosen by
management, the other is GIVEN by nature• Hold throughput constant: Reducing inventory = reducing flow time
Given two of the three measures, you can solve for the third:• Indirect measurement of flow time: how long does it take you on average to respond to an email?
You write 60 email responses per dayYou have 240 emails in your inbox
Prof. Christian Terwiesch
Examples for Little’s Law Applications
In a large Philadelphia hospital, there are 10 births per day.80% of the deliveries are easy and require mother and baby to stay for 2 days20% of the cases are more complicated and require a 5 day stay
What is the average occupancy of the department?
Prof. Christian Terwiesch
Source: Graves and Little
Little’s law: Some remarks
Not an empirical law
Robust to variation, what happens inside the black box
Deals with averages – variations around these averages will exist
Holds for every time window
Shown by Professor Little in 1961
Prof. Christian Terwiesch
Process AnalysisInventory Turns / Inventory costs
Prof. Christian Terwiesch
Inventory Turns
Cost of Goods sold: 25,263 mill $/yearInventory: 2,003 mill $
Cost of Goods sold: 20,000 mill $/yearInventory: 391 mill $Inventory: 391 mill $
Inventory TurnsComputed as: COGSComputed as:
Based on Little’s law
Inventory COGS
Inventory turns=
Prof. Christian Terwiesch
Based on Little s lawCareful to use COGS, not revenues
Inventory Turns At Dell
90
100
60
70
80
40
50
60
10
20
30
0
10
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Prof. Christian Terwiesch
Inventory Turns in Retailing and Its Link to Inventory Costs
Inventory Cost Calculation
Compute per unit inventory costs as:
P it I t t = costsinventory AnnualPer unit Inventory costs=turnsInventory
y
Example:Example:
• Annual inventory costs=30%• Inventory turns=6
Per unit Inventory costs= %5year per turns 6
year per 30%
Prof. Christian Terwiesch
Source: Gaur, Fisher, Raman
Process AnalysisBuffer or Suffer
Prof. Christian Terwiesch
Simple Process Flow – A Food TruckFood Truck Every five minutes:
- You get 0, 1, or 2 orders with equal probability- You have a capacity of 0, 1, or 2 with equal probability- It is not possible to make a sandwich before the order - Customers are not willing to wait
=> How many sandwiches will you sell per five minute slot?
Prof. Christian Terwiesch
Variability Will Be a Key Factor in Waiting Time
Why variability does not always average itself out
Buffer-or-suffer strategy
Prof. Christian Terwiesch
Buffering is easier in production settings than in services (make to order vs make to stock)Preview two different models: Queue and Newsvendor
Difference Between Make-to-Order and Make-to-StockMcDonald’s
1. Make a batch of sandwiches2. Sandwiches wait for customer orders3 Customer orders can filled immediately
Subway1. Customer orders2. Customer waits for making of sandwich3 Customer orders can filled with delay3. Customer orders can filled immediately
=> Sandwich waits for customer3. Customer orders can filled with delay
=> Customer waits for sandwich
Which approach is better?Which approach is better?
Make-to-Stock advantages include:+ Scale economies in production+ Rapid fulfillment (short flow time for customer order)+ Rapid fulfillment (short flow time for customer order)
Make-to-Order advantages include:+ Fresh preparation (flow time for the sandwich)+ Allows for more customization (you can’t hold all versions+ Allows for more customization (you can t hold all versions
of a sandwich in stock)+ Produce exactly in the quantity demanded
Prof. Christian Terwiesch
Examples of Demand Waiting for Supply
Service Examples ER Wait Times: 58-year-old Michael Herrara of Dallas died of a heart attack
after an estimated 19 hours in the local Hospital ERSome ER’s now post expected wait times online / via Apps
It takes typically 45 days do get approval on a mortgage; Strong link between wait times and conversionW iti ti f d i th h t M D ld’ 159 d L Waiting times for drive-through at McDonald’s: 159 seconds; Long queues deter customers to join
Production ExamplesProduction Examples• Buying an Apple computer • Buying a Dell computer
=> Make-to-order vs Make-to-Stock> Make to order vs Make to Stock
Prof. Christian Terwiesch
http://www.minyanville.com/businessmarkets/articles/drive-thrus-emissions-fast-food-mcdonalds/5/12/2010/id/28261
Five Reasons for Inventory
Pipeline inventory: you will need some minimum inventory because of the flow time >0
Seasonal inventory: driven by seasonal variation in demand and constant capacity
Cycle inventory: economies of scale in production (purchasing drinks)
Safety inventory: buffer against demand (Mc Donald’s hamburgers)
Decoupling inventory/ buffers: buffers between several internal steps
Prof. Christian Terwiesch
Source: De Groote
Process AnalysisMultiple flow units
Prof. Christian Terwiesch
Processes with Multiple Flow Units
Contact faculty/other persons
Foreign Dep.m=2
20 min/app
3 cases per hour11 cases per hour4 cases per hour EZ form
Regular
Foreign acc.
File
Contact prioremployers Confirmation
Filem=1
3 min/app Print invoicem=1
Department 1m=3employers
Benchmarkgrades
Confirmationletter2 min/app
m 315 min/app
Department 2m=2
8 min/app8 min/app
Prof. Christian Terwiesch
Approach 1: Adding-up Demand Streams
Prof. Christian Terwiesch
Approach 2: A Generic Flow Unit (“Minute of Work”)
Prof. Christian Terwiesch
Steps for Basic Process Analysis with Multiple Types of Flow Units
1. For each resource, compute the number of minutes that the resource can produce
2. Create a process flow diagram, indicating how the flow units go through the processthe process
3. Create a table indicating how much workload each flow unit is consuming at each resource
4 Add up the workload of each resource across all flow units4. Add up the workload of each resource across all flow units.5. Compute the implied utilization of each resource as
The resource with the highest implied utilization is the bottleneck
Prof. Christian Terwiesch
Note: you can also find the bottleneck based on calculating capacity for each step and then dividing the demand at this resource by the capacity
Processes with Attrition Loss
500 ideas70/500 20/70 6/20 2/6
Where is the Bottleneck?
Pitches Scripts Pilots New Series
Showsper year
Processing time 2 days 10 days 30 days 70 days 200 daysProcessing time 2 days 10 days 30 days 70 days 200 days
Resources 5 judges 3 script writers 2 pilot teams 2 Series crews 1 Main crew(250 days per year)
Prof. Christian Terwiesch
ProductivityIntroduction
Prof. Christian Terwiesch
Productivity as a Major Challenge
“The conservation of our national resources is only preliminary to the larger question of ti l ffi i [ t b US id t]”national efficiency. [quote by a US president]”
Who is the president quoted here?
I thi d l S b + Ai liIn this module: Subway + Airlines
Prof. Christian Terwiesch
Introduction to Productivity
Published in 1911
Opens with a discussion of Theodore Roosevelt’s address about improving national efficiency and making more productive use of limited resources
“We can see and feel the waste of material things. Awkward, inefficient, or ill-directed movements of men, however, leave nothing visible or tangible behind”
“Employers derive their knowledge of how much of a given class of work can be done in a day from eitherEmployers derive their knowledge of how much of a given class of work can be done in a day from either their own experience, which has frequently grown hazy with age, from casual and unsystematic observation of their men, or at best from records [..]”
“This work is so crude and elementary in its nature that the writer firmly believes that it would be possible to t i i t lli t ill t b ffi i t i i h dl th b ”train an intelligent gorilla so as to become a more efficient pig-iron handler than any man can be”
Often, 3x productivity improvements were obtained through waste reduction, picking the right men/tool for the job, and setting the ride incentives
Prof. Christian Terwiesch
Formal Definitions
Basic definition of productivityBasic definition of productivityProductivity = Units Output produced / Input used
Example: Labor productivityL b d ti it 4 it l b h (l k l t lik i ti )Labor productivity = 4 units per labor hour (looks a lot like an processing time)
Multifactor productivityProductivity = Output / (Capital$ + Labor$ + Materials$ + Services$ + Energy$)y p ( p $ $ $ $ gy$)
Waste and InefficienciesOutput: productive time; input: total timeSome measures of productivity have natural limits (e g labor time energy)Some measures of productivity have natural limits (e.g. labor time, energy)What reduces productivity?
Prof. Christian Terwiesch
ProductivityEfficient Frontier
Prof. Christian Terwiesch
The Efficient Frontier
Responsiveness
HighCurrent frontier
Eliminate inefficiencies
In the industry
Competitor A
Low
Competitor C
Competitor B
Competitor D
Labor Productivity(e.g. $/call)
Low laborproductivity
High laborproductivity
Competitor B
There exists a tension between productivity and responsiveness
Efficient frontier
Prof. Christian Terwiesch
Example: The US Airline Industry
Prof. Christian Terwiesch
Example: The US Airline Industry
Prof. Christian Terwiesch
ProductivityThe Seven Sources of Waste
Prof. Christian Terwiesch
OverproductionTo produce sooner or in greater quantities than ExamplesTo produce sooner or in greater quantities than what customers demand
• Overproduced items need to be stored (inventory) and create further waste
• Bad for inventory turns
p
81.6 kg of food are trashed by the average
Bad for inventory turns• Products become obsolete / get stolen / etc
g y gGerman
61% of the trashing happens by households
Large package sizes is the main reasonLarge package sizes is the main reason
Match Supply with Demand
Prof. Christian Terwiesch
TransportationExamplesUnnecessary movement of parts or people pUnnecessary movement of parts or people
between processesExample: Building a dining room and kitchen at opposite ends of a house, then keeping it that way
• Result of a poor system design and/or layout• Can create handling damage and cause
production delays
Crabs fished in the North Sea
Shipped 2,500km South to Morocco
Produced in MoroccoProduced in Morocco
Shipped back to Germany
R l tRelocate processes, then introduce standard sequences for transportation
Prof. Christian Terwiesch
ReworkExamplesRepetition or correction of a process pRepetition or correction of a process
Example: Returning a plate to the sink after it has been poorly washed
• Rework is failure to meet the “do it right the first time” expectationtime expectation
• Can be caused by methods, materials, machines, or manpower
• Requires additional resources so that normal production is not disrupted
Readmissions to the ICU in a hospital (also called “Bounce backs”)
Readmissions to the hospital afterReadmissions to the hospital afterdischarge (major component of AffordableCare Act)
Analyze and solve rootAnalyze and solve root causes of rework=> More in quality module
Prof. Christian Terwiesch
Over-processingExamplesProcessing beyond what the customer requires pProcessing beyond what the customer requires
Example: Stirring a fully mixed cup of coffee
• May result from internal standards that do not reflect true customer requirements
• May be an undesirable effect of an operator’s pride inMay be an undesirable effect of an operator s pride in his work
Keeping a patient in the hospital longer than what is medically required
Provide clear, customer-driven standards for every process
Prof. Christian Terwiesch
MotionExamplesUnnecessary movement of parts or people within pUnnecessary movement of parts or people within
a process
Example: Locating (and keeping) a refrigerator outside the kitchen
• Result of a poor work station design/layout• Focus on ergonomics
Ergonomics
Look at great athletes
Arrange people and parts around stations with work content that has been standardized to
i i i ti
Prof. Christian Terwiesch
minimize motion
InventoryExamplesNumber of flow units in the system pNumber of flow units in the system
• “Product has to flow like water”• For physical products, categorized in: raw material,
WIP, or finished productsWIP, or finished products • Increases inventory costs (bad for inventory turns)• Increases wait time (see above) as well as
the customer flow time• Often times, requires substantial real estate
Loan applications at a bank
=> the BIGGEST form of waste
I d tiImprove production control system and commit to reduce unnecessary “comfort stocks”
Prof. Christian Terwiesch
WaitingExamplesUnderutilizing people or parts while a process pUnderutilizing people or parts while a process
completes a work cycleExample: Arriving an hour early for a meeting
Labor utilizationIdle timeIdle time
Note: - Waiting can happen at the resource (idle time)- But also at the customer level (long flow time)
Often, the time in the waiting room exceedsthe treatment time by more than 5x
Understand the drivers of waiting; more in Responsiveness module
Prof. Christian Terwiesch16
Wasteful vs LeanThe IMVP Studies
General Motors Framingham Assembly Plant Versus Toyota Takaoka Assembly Plant, 1986
GM Framingham Toyota TakaokaGross Assembly Hours per Car 40.7 18Assembly Defects per 100 Cars 130 45Assembly Space per Car 8.1 4.8Inventories of Parts (average) 2 weeks 2 hours
Gross assembly hours per car are calculated by dividing total hours of effort in the plant by the total number of cars producedDefects per car were estimated from the JD Power Initial Quality Survey for 1987Assembly Space per Car is square feet per vehicle per year, corrected for vehicle sizeInventories of Parts are a rough average for major parts
Prof. Christian Terwiesch
Source: Womack et al
Understand Sources of Wasted Capacity
Poor use of capacity Waste of the Resource’s time
Overproduction Transportation Over-processing MotionRework
Poor use of capacity – Waste of the Resource s time
The seven sources of waste (Muda)
Potential eighth source of waste: The waste of intellect
WaitingInventory
Not “orthogonal to each other”
Poor flow – Waste of Customer’s time
• Taichi Ohno Chief Engineer at Toyota• Taichi Ohno, Chief Engineer at Toyota• The first five sources are RESOURCE centric (and correspond to capacity): • Ask yourself: “What did I do the last 10 minutes? How much was value-add?”
Look around at the work-place (360 degree) – what percentage of people are working?• The last two sources are FLOW UNIT centric (and correspond to Flow Time and Inventory)
Prof. Christian Terwiesch
The last two sources are FLOW UNIT centric (and correspond to Flow Time and Inventory)• Ask yourself: “Did I really have to be here that long?”
ProductivityLink to Finance
Prof. Christian Terwiesch
Revisiting the Process Flow Diagram at Subway
Customers Station 1 Station 2 Station 3
Processing Time 37 sec/cust 47 sec/cust 37 sec/cust
Prof. Christian Terwiesch
Subway – Financial Importance of Operations
Prof. Christian Terwiesch
ProductivityKPI trees
Prof. Christian Terwiesch
Subway – EBIT tree
Prof. Christian Terwiesch
ProductivityOEE F k / Q ilOEE Framework / Quartile Analysis
Prof. Christian Terwiesch
Overall Equipment Effectiveness
100
55
100
Improve-ment potential
30
5545 > 3x
Net opera-ting time
Idlingand minorstop
Re-ducedspeed
OEEDefects Start-upAvail-able time
Break-down
Change-overs*
Total planned up-time
timestop-pages
Downtime lossesAvailability rate55 %
Speed lossesPerformance rate82 %
X X = OEE30 %
Quality lossesQuality rate67 %
Prof. Christian Terwiesch
55 % 82 % 30 %67 %
Source: McKinsey
OEE of an Aircraft
65*2
4h
t gat
e or
in
aint
enan
ce
3 At ma
book
ed
axi a
nd la
ndin
g
Not
bTa
Total timeIn a year
Block time Seat isIn the air
Value add(about 30%)
Prof. Christian Terwiesch
n
Overall People Effectiveness
Vaca
tion
Sic
k
Tim
e no
t bo
oked
Can
cela
tions
ents
that
don
’t e
to s
ee M
D
that
don
’t be
don
e by
MD
C
Pat
ieha
ve
Act
iviti
es
have
to b
Total paid time Time in practice Time booked For appointments
Time withpatients
True valueadd time
Prof. Christian Terwiesch Source: Marcus, Terwiesch, Werner
ProductivityLi b l i / iLine balancing / capacity sizing
Prof. Christian Terwiesch
Staffing / Capacity Sizing
So far: we started the process analysis with the process flow diagram / capacities
Often, demand can change over timeAt Subway: More customers at noon than at 3pm
Typical situation in practice – Given are:Demand (forecasts)Activities that need to be completed
Decision situation: how to build a staffing plan?
Two strategies:Production smoothing (pre-produce)Staff to demand
Prof. Christian Terwiesch
Line Balancing and Staffing to Demand
4545
Time46
Time
45
30
Takt45
3737
1 2 3
Operator
1 2 3
Operator
Labor content: 120 seconds / unit
3,600 sec/hourTakt: 3,600sec / 80 units=45 sec/unit
Target manpower= 120 sec/unitLabor content: 120 seconds / unitDemand: 80 units per hour
Target manpower=
= 2.67 => round up
St ff t d d t t ith th t kt ti d d i th f th
45 sec/unit
Prof. Christian Terwiesch
=> Staff to demand: start with the takt time and design the process from there
What Do You Do When Demand Doubles?Ideal Case Scenario
Time
22.5Takt
1 2 3
Operator
3,600 sec/hourT kt 3 600 / 160 it 22 5 / it
4 5 6
Labor content: 120 seconds / unitDemand: 160 units per hour
Takt: 3,600sec / 160 units=22.5 sec/unit
Target manpower=
= 5 33 => round up
120 sec/unit22.5 sec/unit
Prof. Christian Terwiesch
= 5.33 => round up
Balancing the Line
Determine Takt time
Assign tasks to resource so that total processing times < Takt time
Make sure that all tasks are assignedg
Minimize the number of people needed (maximize labor utilization)
What happens to labor utilization as demand goes up?
Difference between static and dynamic line balancing
Prof. Christian Terwiesch
Line Balancing and Staffing to DemandActual DemandVolume
Time
60
30
Takt time 2 minutes
Step1
Step2
Step3
Step4
Step5
Step6
Leveled DemandVolume
60 60Takt time 1 minute
S S S S S S
Takt time*Takt
30
Step1
Step2
Step3
Step4
Step5
Step6
Takt
1 1
2
Volume flexibilityAbility to adjust to changing demands
Resource planningManpower
6 6
Ability to adjust to changing demands
Often implemented with temporary workers
Keeps average labor utilization high
Prof. Christian Terwiesch
3
ProductivityQ il l i /Quartile analysis / Standardization
Prof. Christian Terwiesch
Call Center Example
Two calls to the call center of a big retail bank
Both have the same objective (to make a deposit)
Different operatorsDifferent operators
Take out a stop watch
Time what is going on in the calls.
Prof. Christian Terwiesch
Beyond Labor Utilization: Quartile Analysis
Bi t d ti it diff f k l d i t t k
Prof. Christian Terwiesch
Biggest productivity differences for knowledge intense tasks
Source: Immaneni and Terwiesch
Example: Emergency Department
Analyzed data for over 100k patients in three hospitals
80 doctors and 109 nurses
Up to 260% difference between the 10th %-tile and the 90th %-tile
=> Dramatic productivity effects
Prof. Christian Terwiesch
Source: McCarthy, Ding, Terwiesch, Sattarian, Hilton, Lee, Zeger
ProductivityProductivity Ratios
Prof. Christian Terwiesch
Basic definitions of productivity
Productivity = Output units produced / Input used
Problems:Output is hard to measure=> often times, use revenue insteadMultiple input factors (Labor, Material, Capital) => use one cost category
Example:Labor productivity at US Airways 1995: Revenue: $6.98B Labor costs: $2.87B2011: Revenue: $13.34B Labor costs: $2.41B
Labor productivity at SouthWest1995: Revenue: $2.87B Labor costs: $0.93B2011: Revenue: $13.65B Labor costs: $4.18B
Prof. Christian Terwiesch
Basic definitions of productivity
But WHY is one firm more productive than the other?
The ratio alone does not tell! Use the following trick:
Airline example:Revenue / labor costs = Revenue/RPM * RPM/ASM * ASM / Employee * Employees/Labor costs
Revenue/Cost= Revenue/Output * Output/Capacity * Capacity/Cost
Operational yield Transformationefficiency
1/unit cost of capacityefficiency capacity
Prof. Christian Terwiesch
Labor Productivity Comparison between Southwest and US Airways
Prof. Christian Terwiesch
Do Calculations in Excel
ProductivityReview Session
Prof. Christian Terwiesch
Tom and JerryTom and Jerry run an ice cream business out of their condo in Solana Beach, CA. They have purchased a fully automated ice cream making machine from Italy (at a $30k price tag) that they put in their basement. T i lli i d J t th i k Oft ti h th t f iTom is selling ice cream and Jerry operates the ice cream maker. Often times, however, they run out of ice cream and so Jerry suggested purchasing a second ice cream maker.
Tom, however, wants to first look at the usage of the current ice cream maker and suggests an Overall Equipment Effectiveness (OEE) analysis. Preliminary data suggests that:q p ( ) y y gg• Jerry is not particularly skilled at programming the machine, which needs to be done when a new
batch of ice cream gets made. Instead of spending a negligible time per set-up, he presently spends 20 minutes. A batch of ice cream takes 1h in the machine, once the machine is set-up.
• A new batch is only started if there exists sufficient time to complete the batch the same day before 7pm (including the 20 minute set up and the 1h production)7pm (including the 20 minute set-up and the 1h production)
• Since Jerry started dating a woman from the WWF, he is fascinated by energy efficiency. So he turns the machine off when he goes home at 7pm. As a result of this, the next morning, the machine has to be cooled down to its desired operating temperature, which takes from 7am to 8am.
• Jerry is also not particularly diligent at following the recipe that Tom’s aunt in Italy had sent them. So roughly one quarter of the produced ice cream has to be thrown away.
• Every other Friday, Jerry prefers to go surfing rather than showing up for work. On those days, the business has to stay closed.
TJ1: How many good batches of ice cream are produced each day Jerry comes to work?TJ1: How many good batches of ice cream are produced each day Jerry comes to work?TJ2: What is the OEE of the ice cream maker? (use 12h per day as the available time)
Prof. Christian Terwiesch
Preliminary data suggests that:• Jerry is not particularly skilled at programming the machine, which needs to be done when a new
batch of ice cream gets made. Instead of spending a negligible time per set-up, he presently spends 20 i t A b t h f i t k 1h i th hi th hi i t20 minutes. A batch of ice cream takes 1h in the machine, once the machine is set-up.
• A new batch is only started if there exists sufficient time to complete the batch the same day before 7pm (including the 20 minute set-up and the 1h production)
• Since Jerry started dating a woman from the WWF, he is fascinated by energy efficiency. So he turns the machine off when he goes home at 7pm. As a result of this, the next morning, the machine has to g p gbe cooled down to its desired operating temperature, which takes from 7am to 8am.
• Jerry is also not particularly diligent at following the recipe that Tom’s aunt in Italy had sent them. So roughly one quarter of the produced ice cream has to be thrown away.
• Every other Friday, Jerry prefers to go surfing rather than showing up for work. On those days, the business has to stay closedbusiness has to stay closed.
TJ1: How many good batches of ice cream are produced each day Jerry comes to work?
TJ2: What is the OEE of the ice cream maker? (use 12h per day as the available time)
Prof. Christian Terwiesch
Penne PestoPenne Pesto is a small restaurant in the financial district of San Francisco. Customers order from a variety of pasta dishes. The restaurant has 50 seats and is always full during the four hours in the evening. It is not possible to make reservations at Penne; most guests show up spontaneously on their way home from work. p ; g p p y yIf there is no available seat, guests simply move on to another place. On average, a guest spends 50 minutes in the restaurant, which includes 5 minutes until the guest is seated and the waiter has taken the order, an additional 10 minutes until the food is served, 30 minutes to eat, and 5 minutes to handle the check-out (including waiting for the check, paying, and leaving). It takes the restaurant another 10 minutes to clean the table and have it be ready for the next guests (of which there are always plenty) The averageclean the table and have it be ready for the next guests (of which there are always plenty). The average guest leaves $20 at Penne, including food, drink, and tip (all tips are collected by the restaurant, employees get a fixed salary).
The restaurant has 10 waiters and 10 kitchen employees, each earning $90 per evening (including any preparation, the 4 hours the restaurant is open, and clean-up). The average order costs $5.50 in materials, including $4.50 for the food and $1 for the average drink. In addition to labor costs, fixed costs for the restaurant include $500 per day of rent and $500 per day for other overhead costs.
The restaurant is open 365 days in the year and is full to the last seat even on weekends and holidaysThe restaurant is open 365 days in the year and is full to the last seat even on weekends and holidays. There is about $200,000 of capital tied up in the restaurant, largely consisting of furniture, decoration, and equipment.
Define the return on invested capital as the ratio of the profits (PER YEAR) and the invested capital. You can O C S O C “ ” fdraw an ROIC tree in the same way that we drew a KPI tree in class. Simply have the ROIC as “the root” of
the tree instead of profits. Then answer the following questions.
a. How many guests will the restaurant serve in one evening?b. What is the Return on Invested Capital (ROIC) for the owner of the restaurant?
Prof. Christian Terwiesch
b. What is the Return on Invested Capital (ROIC) for the owner of the restaurant? c. Assume that you could improve the productivity of the kitchen employees and free up one person who would be helping to clean up the table. This would reduce the clean-up to 5 minutes instead of 10 minutes. What would be the new ROIC?
Assign Tasks to WorkersConsider the following six tasks that must be assigned to four workers on a conveyor-paced assembly line (i.e., a machine-paced line flow). Each worker must perform at least one task.
Time to Complete Task (seconds / unit)Task 1 30Task 2 25Task 3 35Task 4 40Task 5 15Task 6 30
The current conveyor-paced assembly line configuration assigns the workers in the following way:The current conveyor paced assembly line configuration assigns the workers in the following way:• Worker 1: Task 1• Worker 2: Task 2• Worker 3: Tasks 3, 4• Worker 4: Tasks 5, 6
a. What is the capacity of the current line?b. Now assume that tasks are allocated to maximize capacity of the line, subject to the conditions that (1) a worker can only perform two adjacent operations and (2) all tasks need to be done in their numerical order. What is the capacity of this line now?p yc. Now assume that tasks are allocated to maximize capacity of the line and that tasks can be performed in any order. What is the maximum capacity that can be achieved?d. After focusing on capacity in questions a-c, you now want to factor in demand in questions d-e. Demand is 50 units per hour. What is the takt time?e What is the target manpower?
Prof. Christian Terwiesch
e. What is the target manpower?f. How many workers will you need?
Prof. Christian Terwiesch
Customer ChoiceIntroduction
Prof. Christian Terwiesch
Customer Choice for HP DeskJet Printers
How many HP printers are there on Amazon?
Why are there so many?
HP Deskjet printer (a look at Amazon)1000 line2000 line3000 line
3050 Printer Series3050 All-in-One3050A Wireless All-in-One
4000 line5000 line6000 line
=> A printer for every day of the year…
Prof. Christian Terwiesch
Customer Choice in Henry Ford’s Days
Henry Ford: “You can have any color of a car, as long as it is black”
Why did Ford not offer color?Actually, he didProduction reasons to keep the cars (a) in one color (b) black
In this module, we discuss different types of product variety; we discuss the benefits, butalso explore the costs associated with variety
End of intro lecture
Prof. Christian Terwiesch
Forms of Variety - Fit Based Variety
Customers differ in shirt sizes
Each customer has a unique utility maximizing shirt size
The further you go away (in either direction) from that point, the lower the utility
Hotelling’s linear city
Example: sizes, locations, arrival times
Source: Ulrich
Prof. Christian Terwiesch
Forms of Variety - Performance Based Variety
Each customer prefers the high end model
Customers differ in their valuation of quality (performance) and/or their ability to pay
Vertical differentiation
Example: screen resolutions, mpg, processor speeds, weight
Source: Ulrich
Prof. Christian Terwiesch
Forms of Variety - Taste Based Variety
Customers differ in their preferences for taste
Often times, these preferences vary over time
Rugged landscape
Example: taste for food, music, artists
Prof. Christian Terwiesch
Economic Motives for Variety
Heterogeneous preferences of customers Price discrimination Variety seeking by consumers Avoiding price competition in channel Channel self space Niche saturation and deterrence to market entry
Source: Ulrich
Prof. Christian Terwiesch
Customer ChoiceImpact on process capacity
Prof. Christian Terwiesch
Ordering Custom Shirts
Custom shirts ordered online
Large variety of styles
Basically infinitely many sizes
Four weeks lead time
Minimum order: 5 shirts
Prof. Christian Terwiesch
Cutting DepartmentThe pattern is programmed into a machine and/or a cutting template is created. This takes a certain amount of set-up time IRRESPECTIVE of how many shirts will be produced afterwards.
Sewing DepartmentSewing Section – Cut pieces of fabric are sewn together and inspected Assembly Section - Responsible for assembling shirts and measuring the size.
Finishing DepartmentResponsible for ironing shirts before folding, packaging and delivery to customers.
Custom Tailored Shirts: Production Process
Source: http://hosting.thailand.com/MWT00255/process1.htm
Prof. Christian Terwiesch
• Example: Cutting Machine for shirts20 minute set-up time (irrespective of the number of shirts)4 minute/unit cutting time15 Shirts in a batch
• Capacity calculation for the resource with set-up changes:
Batch SizeSet-up time + Batch-size*Time per unit
Capacity given Batch Size=
Process Analysis with Batching
Prof. Christian Terwiesch
Example Calculations
Cutting Section 1 Section 2 Finishing
Set-up time: 20 minutes - - -Processing time: 4 min/unit 40 min/unit 30 min/unit 3 min/unitResources: 1 machine 8 workers 5 workers 1 worker
What is the capacity of the cutting machine with a batch size of 15?
Prof. Christian Terwiesch
Capacity 1/p
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
10 50 90
130
170
210
250
290
330
370
410
450
490
530
570
610
650
Batch Size
Large Batches are a Form of Scale Economies
Prof. Christian Terwiesch
Customer ChoiceChoosing a good batch size
Prof. Christian Terwiesch
Production with large batches Production with small batches
CycleInventory
End ofMonth
Beginning ofMonth
CycleInventory
End ofMonth
Beginning ofMonth
Produce Sedan
Produce Station wagon
Production with large batches Production with small batches
CycleInventory
End ofMonth
Beginning ofMonth
CycleInventory
End ofMonth
Beginning ofMonth
Produce Sedan
Produce Station wagon
Production with large batches Production with small batches
CycleInventory
End ofMonth
Beginning ofMonth
CycleInventory
End ofMonth
Beginning ofMonth
Produce Sedan
Produce Station wagon
Production with large batches Production with small batches
CycleInventory
End ofMonth
Beginning ofMonth
CycleInventory
End ofMonth
Beginning ofMonth
Produce Sedan
Produce Station wagon
• Large batch sizes lead to more inventory in the process• This needs to be balanced with the need for capacity• Implication: look at where in the process the set-up occurs
If set-up occurs at non-bottleneck => decrease the batch sizeIf set-up occurs at the bottleneck => increase the batch size
The Downside of Large Batches
Prof. Christian Terwiesch
Example Calculations
Cutting Section 1 Section 2 Finishing
Set-up time: 20 minutes - - -Processing time: 4 min/unit 40 min/unit 30 min/unit 3 min/unitResources: 1 machine 8 workers 5 workers 1 worker
Prof. Christian Terwiesch
- one cart every 10 seconds- 2 sec boarding time per passenger- 2 sec exit time per passenger- 2 minutes to go down the elevator
Batch Size120sec + Batch-size*4sec
Capacity given Batch Size=
Batch Size120sec + Batch-size*4sec
1/10 [units/sec] =
Batch Size = 20 units
How to Set the Batch Size – An Intuitive Example
Prof. Christian Terwiesch
• Batching is common in low volume / high variety operations• Capacity calculation changes:
• This reflects economies of scale (similar to fix cost and variable cost)• You improve the process by:
Setting the batch size:(a) If set-up occurs at the bottleneck => Increase the batch size(b) If set-up occurs at a non-bottleneck => Reduce the batch size(c) Find the right batch size by solving equation
Process Analysis with Batching: Summary
Batch SizeSet-up time + Batch-size*Time per unit
Capacity given Batch Size=
Prof. Christian Terwiesch
Customer ChoiceUnderstanding the Diseconomies of Scale Extra inventory
Prof. Christian Terwiesch
Production with large batches Production with small batches
CycleInventory
End ofMonth
Beginning ofMonth
CycleInventory
End ofMonth
Beginning ofMonth
Produce Sedan
Produce Station wagon
Production with large batches Production with small batches
CycleInventory
End ofMonth
Beginning ofMonth
CycleInventory
End ofMonth
Beginning ofMonth
Produce Sedan
Produce Station wagon
Production with large batches Production with small batches
CycleInventory
End ofMonth
Beginning ofMonth
CycleInventory
End ofMonth
Beginning ofMonth
Produce Sedan
Produce Station wagon
Production with large batches Production with small batches
CycleInventory
End ofMonth
Beginning ofMonth
CycleInventory
End ofMonth
Beginning ofMonth
Produce Sedan
Produce Station wagon
• Large batch sizes lead to more inventory in the process• This needs to be balanced with the need for capacity
The Downside of Large Batches
Prof. Christian Terwiesch
General Definition of a BatchProduct A: Demand is 100 units per hourProduct B: Demand is 75 units per hour
The production line can produce 300 units per hour of either product
It takes 30 minutes to switch the production line from A to B (and from B to A)
How would you set the batch size?
Prof. Christian Terwiesch
Introducing a Third Product into the Product LineConsider a company that has two products, product A and product B.
Product A: Demand is 100 units per hourProduct B: Demand is 75 units per hour
The production line can produce 300 units per hour of either product (takt time: 12 sec/unit)
It takes 30 minutes to switch the production line from A to B (and from B to A)
How would you set the batch size?
Batch SizeSet-up time + Batch-size*Time per unit
Required Flow Rate =
Batch Size1 hour+ Batch-size/300 hour
175 units per hour =
Batch size = 420
Batch size for A= 420 * 100 / (100+75) = 240Batch size for B=420-240=180
Prof. Christian Terwiesch
Introducing a Third Product into the Product LineNow, the Marketing folks of the company add a third product. Total demand stays the same.
Product A1: Demand is 50 units per hourProduct A2: Demand is 50 units per hourProduct B: Demand is 75 units per hour
How would you set the batch size?
Prof. Christian Terwiesch
Introducing a Third Product into the Product LineNow, the Marketing folks of the company add a third product. Total demand stays the same (maybe they dothis because they can raise prices). Say they offer product A in two colors.
Product A1: Demand is 50 units per hourProduct A2: Demand is 50 units per hourProduct B: Demand is 75 units per hour
How would you set the batch size?
Batch SizeSet-up time + Batch-size*Time per unit
Required Flow Rate =
Batch Size1.5 hour+ Batch-size/300 hour
175 units per hour =
Batch size = 630
Batch size for A1= 630* 50 / (50+50+75) = 180Batch size for A2= 630* 50 / (50+50+75) = 180Batch size for B= 630* 75 / (50+50+75) = 270
Prof. Christian Terwiesch
Customer ChoicePooling Effects / Demand Fragmentation
Prof. Christian Terwiesch
Demand Fragmentation
You have 3 products (different shirt sizes)
Demand for each product could be 1, 2, or 3 with equal (1/3) probability
How good is your forecast FOR YOUR OVERALL SALES?
Prof. Christian Terwiesch
Customer ChoiceBuilding Flexibility: SMED / Heijunka
Prof. Christian Terwiesch
The 6-stage SMED approach
Source: McKinsey Ops Training Material
Before/after shutdown During shutdownStage
Measure total changeover time
1
Determine internal and external activities
2
Move external activities to before or after the shutdown
3
Improve the internal activities
4
Improve the external activities
5
ExternalInternal
Standardize procedures6
Reduce set-up so that you can change models as often as needed => Mixed model production (Heijunka)
Prof. Christian Terwiesch Source: Jordan and Graves
Full Flexibility
Prof. Christian Terwiesch Source: Jordan and Graves
Flexibility vs Chaining
Prof. Christian Terwiesch Source: Moreno and Terwiesch
Pooling vs ChainingFord’s manufacturing network Nissan’s manufacturing network
Chaining is a form of partial flexibility (“pooling” light)Does not require full flexibility, but relies on a clever product-to-plant assignment
Prof. Christian Terwiesch
Customer ChoiceStrategies to deal with variety / Investing in flexibility
Prof. Christian Terwiesch
Design for supply chain performance
Source: Ulrich
Prof. Christian Terwiesch
Design for supply chain performance
Source: Ulrich
Prof. Christian Terwiesch
Isolate the variable elements of the product
vs.
Source: Ulrich
Prof. Christian Terwiesch
Customer ChoiceLimits to customization
Prof. Christian Terwiesch
Introduction
Design Variables Performance Specifications User Needs/ utility function
User Utility
processor
display
memory
package
XGA / SXGA / UXGA
video card
hard drive
portability
gaming performance
Informationon screen
View from distance
MS-office performance
affordability
Resolution
price
Physical dimensions
Frames per second
HD capacity
Viewable area
RAM
Instructions per second (MIPS)
Data storage potential
Integrated devices
Design Variables Performance Specifications User Needs/ utility function
User Utility
processor
display
memory
package
XGA / SXGA / UXGA
video card
hard drive
portability
gaming performance
Informationon screen
View from distance
MS-office performance
affordability
Resolution
price
Physical dimensions
Frames per second
HD capacity
Viewable area
RAM
Instructions per second (MIPS)
Data storage potential
Integrated devices
Source: Randall, Terwiesch, Ulrich
Prof. Christian Terwiesch
Introduction
Prof. Christian Terwiesch
Customer ChoiceReview Session
Prof. Christian Terwiesch
Window BoxesMetal window boxes are manufactured in two process steps: stamping and assembly. Each window box is made up of three pieces: a base (one part A) and two sides (two part Bs).
The parts are fabricated by a single stamping machine that requires a setup time of 120 minutes whenever switching between the two part types. Once the machine is set up, the processing time for each part A is one minute while the processing time for each part B is only 30 seconds.
Currently, the stamping machine rotates its production between one batch of 360 for part A and one batch of 720 for part B. Completed parts move from the stamping machine to the assembly only after the entire batch is complete.
At assembly, parts are assembled manually to form the finished product. One base (part A) and two sides (two part Bs), as well as a number of small purchased components, are required for each unit of final product. Each product requires 27 minutes of labor time to assemble. There are currently 12 workers in assembly. There is sufficient demand to sell every box the system can make.
a. What is the capacity of the stamping machine?
b. What is the capacity of the overall process?
c. What batch size would you recommend for the process?
Prof. Christian Terwiesch
(Gelato) Bruno Fruscalzo decided to set up a small production facility in Sydney to sell to local restaurants that want to offer gelato on their dessert menu. To start simple, he would offer only three flavors of gelato: fragola(strawberry), chocolato (chocolate), and bacio (chocolate with hazelnut). Demand is 10kg/hour for Fragola, 15 for chocolate, and 5 for Bacio.
Bruno first produces a batch of fragola, then a batch of chocolato, then a batch of bacio and then he repeats that sequence. After producing bacio and before producing fragola, he needs 45 minutes to set up the ice cream machine, he needs 30 minutes to change to Chocolato and 10 minutes to change to Bacio.
When running, his ice cream machine produces at the rate of 50 kg per hour no matter which flavor it is producing (and, of course, it can produce only one flavor at a time).
a. Suppose Bruno wants to minimize the amount of each flavor produced at one time while still satisfying the demand for each of the flavors. (He can choose a different quantity for each flavor.) If we define a batch to be the quantity produced in a single run of each flavor, how many kilograms should he produce in each batch?
b. Given your answer in part (a), how many kilograms of fragola should he make with each batch?
Prof. Christian Terwiesch
SmartPhoneApfel is a German company selling smart-phones. Presently, the company is only selling a 64GB model. The marketing department recently proposed to add a 128 GB model. Preliminary data suggests that (a) the margins for the product will increase (b) the total sales will remain the same and will be split 50:50 between the two models (c) there exists a mild positive correlation in the demand between the two models.
Consider the following statements:1. The coefficient of variation of the 64GB phone will go down2. The coefficient of variation of the 64GB phone will stay constant3. The coefficient of variation of the 64GB phone will go up4. It would be nice for the production and distribution process if the memory component, which is
the only difference between the two models, would be inserted early in the process.5. It would be nice for the production and distribution process if the memory component, which is
the only difference between the two models, would be inserted late in the process.
Which of the above statements is true?
1+41+52+42+53+43+5
Response TimeIntroduction
Prof. Christian Terwiesch
ExamplePhysician office
- Patients arrive, on average, every five minutes- It takes ten minutes to serve a patient- Patients are willing to wait
What is the implied utilization of the barber shop?
How long will patients have to wait?
Prof. Christian Terwiesch
ExamplePhysician office
- Patients arrive, on average, every five minutes- It takes four minutes to serve a patient- Patients are willing to wait
What is the utilization of the barber shop?
How long will patients have to wait?
Prof. Christian Terwiesch
A Somewhat Odd Service Process
Patient
ArrivalTime
ServiceTime
1 0 4
2
3
4
5
10
15
4
4
44
5
6
15
20
25
4
4
4
7
8
9
30
35
40
4
4
4
10
11
12
45
50
55
4
4
4
Prof. Christian Terwiesch
7:00 7:10 7:20 7:30 7:40 7:50 8:00
12 55 4
A More Realistic Service Process
Patient
ArrivalTime
ServiceTime
1 0 5
Patient 1 Patient 3 Patient 5 Patient 7 Patient 9 Patient 11
Patient 2 Patient 4 Patient 6 Patient 8 Patient 10 Patient 12
1
2
3
0
7
9
5
6
7
Time
7:10 7:20 7:30 7:40 7:50 8:007:00
4
5
6
12
18
22
6
5
2 3
7
8
9
25
30
36
4
3
4
2
case
s
9
10
11
36
45
51
4
2
20
1
Num
ber o
f
Prof. Christian Terwiesch
12 55 3 2 min. 3 min. 4 min. 5 min. 6 min. 7 min.
Service times
PatientArrivalTime
ServiceTime
Variability Leads to Waiting Time
Service time
Patient
1234
07912
5676
Wait time
5678
18222530
5243
7:00 7:10 7:20 7:30 7:40 7:50 8:00
89101112
3036455155
34223 7:00 7:10 7:20 7:30 7:40 7:50
5
4
8:0012 55 3
Inventory
3
2
1
Prof. Christian Terwiesch
y(Patients atlab) 0
7:00 7:10 7:20 7:30 7:40 7:50 8:00
The Curse of Variability - Summary
Variability hurts flowWith buffers: we see waiting times even though there exists excess capacity
Variability is BAD and it does not average itself outy g
New models are needed to understand these effects
Prof. Christian Terwiesch
W i i i d l ThResponse TimeWaiting time models: The need for excess capacity
Prof. Christian Terwiesch
Modeling Variability in Flow
OutflowN l iti l
Flow RateMinimum{Demand, Capacity} = Demand = 1/a
ProcessingBuffer
No loss, waiting onlyThis requires u<100%Outflow=Inflow
InflowDemand process is “random”
Look at the inter-arrival timesProcessingp: average processing time
a: average inter-arrival timeSt Dev(inter arrival times)
TimeIA1 IA2 IA3 IA4 Same as “activity time” and “service time”
CVp = St-Dev(processing times)
Average(processing times)
CVa =
Often Poisson distributed:CVa = 1Constant hazard rate (no memory)
St-Dev(inter-arrival times)Average(inter-arrival times) Can have many distributions:
CVp depends strongly on standardizationOften Beta or LogNormal
Prof. Christian Terwiesch
Exponential inter-arrivals
Difference between seasonality and variability
Average flowtime T
Flow rate
The Waiting Time Formula
Inflow Outflow
Inventorywaiting Iq
Increasing VariabilityEntry to system DepartureBegin Service
Theoretical Flow Time
Utilization 100%
Time in queue Tq Service Time p
Flow Time T=Tq+pUtilization 100%
Waiting Time Formula
22 CVCVnutilizatio
Variability factor
21
pa CVCVnutilizatio
nutilizatioTimeActivity queue in Time
Prof. Christian Terwiesch
Service time factor
Utilization factor
Example: Walk-in Doc
Newt Philly needs to get some medical advise. He knows that his Doc, Francoise, has a patient arrive every 30 minutes (with a standard deviation of 30 minutes). A typical consultation lasts 15 minutes (with a standard deviation of 15 minutes). The Doc has an open-access policy and does not offer appointments.
If Newt walks into Francois’s practice at 10am, when can he expect to leave the practice again?
Prof. Christian Terwiesch
Summary
Even though the utilization of a process might be less than 100%, it might still require long customer wait time
Variability is the root cause for this effect
As utilization approaches 100%, you will see a very steep increase in the wait time
If you want fast service, you will have to hold excess capacity
Prof. Christian Terwiesch
M W i i i d l /Response TimeMore on Waiting time models / Staffing to Demand
Prof. Christian Terwiesch
Inventory
Waiting Time Formula for Multiple, Parallel Resources
Inflow Outflow
Inventorywaiting Iq
in service Ip
Inflow OutflowFlow rate
E t t t D tB i S iEntry to system DepartureBegin Service
Time in queue Tq Service Time p
Flow Time T=Tq+p
221)1(2 m CVCVnutilizatiotimeActivity
Waiting Time Formula for Multiple (m) Servers
Prof. Christian Terwiesch
21pa CVCV
nutilizationutilizatio
mtimeActivityqueue in Time
Example: Online retailer
Customers send emails to a help desk of an online retailer every 2 minutes, on average, and the standard deviation of the inter-arrival timeminutes, on average, and the standard deviation of the inter arrival time is also 2 minutes. The online retailer has three employees answering emails. It takes on average 4 minutes to write a response email. The standard deviation of the service times is 2 minutes.
Estimate the average customer wait before being served.
Prof. Christian Terwiesch
ServerFlow unitUtilization (Note: make sure <1)
Summary of Queuing Analysis
amp
pmau 1*
1
Inventory
Utilization (Note: make sure <1)
CVCV
p
221)1(2
Inventorywaiting Iq
in service Ip
Time related measures
q
pam
q
pTT
CVCVu
umpT
21
221)1(2
Inflow Outflow
TI *1Inventory related measures (Flow rate=1/a)
qp
p
III
muI
Ta
I
*
*
Entry tosystem
DepartureBeginService
Prof. Christian Terwiesch
qpWaiting Time Tq Service Time p
Flow Time T=Tq+p
Staffing Decision
Customers send emails to a help desk of an online retailer every 2 minutes, on average, and the standard deviation of the inter-arrival timeminutes, on average, and the standard deviation of the inter arrival time is also 2 minutes. The online retailer has three employees answering emails. It takes on average 4 minutes to write a response email. The standard deviation of the service times is 2 minutes.
How many employees would we have to add to get the average wait time reduced to x minutes?
Prof. Christian Terwiesch
What to Do With Seasonal DataMeasure the true demand data Apply waiting model in each sliceApply waiting model in each slice
Slice the data by the hour (30min, 15min)Slice the data by the hour (30min, 15min)
Level the demandAssume demand is “stationary” within a slice
Prof. Christian Terwiesch
Service Levels in Waiting Systems
0.8
1Fraction of customers who have to wait xseconds or less Waiting times for those customers
h d t t d i di t l
90% of calls had to wait 25 seconds or less
0.4
0.6
who do not get served immediately
Fraction of customers who get served
0
0.2
0.4 Fraction of customers who get served without waiting at all
00 50 100 150 200
Waiting time [seconds]
• Target Wait Time (TWT)• Service Level = Probability{Waiting TimeTWT}• Example: Big Call Center
- starting point / diagnostic: 30% of calls answered within 20 seconds
Prof. Christian Terwiesch
starting point / diagnostic: 30% of calls answered within 20 seconds- target: 80% of calls answered within 20 seconds
Response TimeCapacity Pooling
Prof. Christian Terwiesch
I d d t R
Managerial Responses to Variability: PoolingIndependent Resources
2x(m=1) Example:Processing time=4 minutesInter-arrival time=5 minutes (at each server)m=1 Cva=CVp=1m 1, Cva CVp 1
Tq =
Pooled Resources(m=2) Processing time=4 minutes
Inter-arrival time=2.5 minutesm=2, Cva=CVp=1
Tq =Tq =
Prof. Christian Terwiesch
Managerial Responses to Variability: Pooling
Waiting Time Tq
50.00
60.00
70.00
m=1
30.00
40.00
m=2
0.00
10.00
20.00m=5
m=10
0.0060% 65% 70% 75% 80% 85% 90% 95%
Utilization u
Prof. Christian Terwiesch
Pooling: Shifting the Efficient Frontier
Prof. Christian Terwiesch
Summary
What is a good wait time?
Fire truck or IRS?
Prof. Christian Terwiesch
Limitations of Pooling
Assumes flexibility
Increases complexity of work-flowIncreases complexity of work flow
Can increase the variability of service time
I t t th l ti hi ith th t / f t th tInterrupts the relationship with the customer / one-face-to-the-customer
Group clinicsGroup clinics
Electricity grid / smart grid
Flexible production plants
Prof. Christian Terwiesch
The Three Enemies of Operations
Additional costs due to variability in demand and activity times
Is associated with longer wait times
Use of resources beyond what is needed to meet customer requirements• Not adding value to the productIs associated with longer wait times
and / or customer loss
Requires process to hold excess capacity (idle time)
Variability
Not adding value to the product, but adding cost
• Reducing the performance of the production system
• 7 different types of waste
Waste
capacity (idle time) yp
Inflexibility
WasteWork Value-adding
WasteWork Value-adding
C tAdditional costs incurred because of supply demand mismatches• Waiting customers or• Waiting (idle capacity)
Capacity
Customerdemand
Prof. Christian Terwiesch
Waiting (idle capacity)
Response TimeScheduling / Access
Prof. Christian Terwiesch
Managerial Responses to Variability: Priority Rules in Waiting Time Systems
• Flow units are sequenced in the waiting area (triage step)• Flow units are sequenced in the waiting area (triage step)
• Provides an opportunity for us to move some units forwards and some backwards
• First-Come-First-Serve- easy to implement- perceived fairness- lowest variance of waiting timelowest variance of waiting time
• Sequence based on importance- emergency cases
id tif i fit bl fl it- identifying profitable flow units
Prof. Christian Terwiesch
Managerial Responses to Variability: Priority Rules in Waiting Time Systems
Service times:A: 9 minutesB: 10 minutesB: 10 minutesC: 4 minutesD: 8 minutesA
B9 min. D
C
4 min.
D
C19 min.
23 min.
Total wait time: 9+19+23=51min
B
A12 min.
21 min.
Total wait time: 4+13+21=38 minTotal wait time: 9+19+23=51min Total wait time: 4+13+21=38 min
• Shortest Processing Time Rule - Minimizes average waiting time- Problem of having “true” processing times
Prof. Christian Terwiesch
Appointments
•Open Access•Open Access
• Appointment systems
Prof. Christian Terwiesch
Response TimeRedesign the Service PProcess
Prof. Christian Terwiesch
Reasons for Long Response Times (And Potential Improvement Strategies)
Insufficient capacity on a permanent basis=> Understand what keeps the capacity low
Demand fluctuation and temporal capacity shortfallsUnpredictable wait times => Extra capacity / Reduce variability in demandPredictable wait times => Staff to demand / Takt timePredictable wait times > Staff to demand / Takt time
Long wait times because of low priority=> Align priorities with customer valueg p
Many steps in the process / poor internal process flow (often driven by handoffs and rework loops)=> Redesign the service process
Prof. Christian Terwiesch
http://www.minyanville.com/businessmarkets/articles/drive-thrus-emissions-fast-food-mcdonalds/5/12/2010/id/28261
The Customer’s Perspective
20 minutes
How much time does a patient spend on a primary care encounter?
Driving Parking Check‐in Vitals Waiting PCP Appt. Check out Labs Drive home
20 minutes
Two types of wasted time:Auxiliary activities required to get to value add activities (result of process location / lay-out)Wait time (result of bottlenecks / insufficient capacity)
Total value add time of a unitFlow Time Efficiency (or %VAT) =
Prof. Christian Terwiesch
Total time a unit is in the processFlow Time Efficiency (or %VAT) =
Process Mapping / Service Blue Prints
Customer actions
Walk into the branch / talk to agent
Customer supplies more data
Customer supplies more data
Sign contracts
Line of interaction
Onstageactions
Collect basic information
Request for more data
Request for more data
Explain final documentact o s
Line of visibility
BackstagePre Approval
t
data
Pre Approval tBackstage
actions
Line of internal interaction
process; set up workflow / account responsibility
process; set up workflow / account responsibility
Supportprocesses
Run formal credit scoring model
Prof. Christian Terwiesch Source: Yves Pigneur
Process Mapping / Service Blue PrintsHow to Redesign a Service Process
Move work off the stageExample: online check-in at an airport
Reduce customer actions / rely on support processesy pp pExample: checking in at a doctor’s office
Instead of optimizing the capacity of a resource, try to eliminate the step altogetherExample: Hertz Gold – Check-in offers no value; go directly to the car
Avoid fragmentation of work due to specialization / narrow job responsibilitiesExample: Loan processing / hospital ward
If customers are likely to leave the process because of long wait times, have the wait occurlater in the process / re-sequence the activities
Example: Starbucks – Pay early, then wait for the coffee
Have the waiting occur outside of a lineExample: Restaurants in a shopping malls using buzzersExample: Restaurants in a shopping malls using buzzersExample: Appointment
Communicate the wait time with the customer (set expectations)Example: Disney
Prof. Christian Terwiesch
Response Time
L M d lLoss Models
Prof. Christian Terwiesch
Different Models of Variability
Waiting problemsUtilization has to be less than 100%Impact of variability is on Flow Time
Loss problemsDemand can be bigger than capacityImpact of variability is on Flow Rate
Pure waitingproblem, all customersare perfectly patient.
All customers enter the process,some leave due totheir impatience
Customers do notenter the process oncebuffer has reached a certain limit
Customers are lostonce all servers arebusy
Same if customers are patient Same if buffer size=0
S if b ff i i t l lSame if buffer size is extremely large
Variability is always bad – you pay through lower flow rate and/or longer flow time
Prof. Christian Terwiesch
Buffer or suffer: if you are willing to tolerate waiting, you don’t have to give up on flow rate
Analyzing Loss Systems Resources3 trauma bays (m=3)y ( )
Ambulances / Helicopters
Trauma center moves to diversion status once all servers are busy
Demand Process Service Processy
incoming patients are directed to other locations
One trauma case comes in every 3 hours
(a=3 hours)
Patient stays in trauma bayfor an average of 2 hours
(p=2 hours)(a 3 hours)
a is the interarrival time
Exponential interarrival times
(p 2 hours)
p is the service time
Can have any distribution
Prof. Christian Terwiesch
Exponential interarrival times Can have any distribution
What is Pm, the probability that all m resources are utilized?
Analyzing Loss Systems: Finding Pm(r)
• Define r = p / a1 2 3 4 5
m
• Example: r= 2 hours/ 3 hoursr=0.67
0.10 0.0909 0.0045 0.0002 0.0000 0.00000.20 0.1667 0.0164 0.0011 0.0001 0.00000.25 0.2000 0.0244 0.0020 0.0001 0.00000.30 0.2308 0.0335 0.0033 0.0003 0.00000.33 0.2500 0.0400 0.0044 0.0004 0.0000
• Recall m=3
• Use Erlang Loss Table
0.33 0.2500 0.0400 0.0044 0.0004 0.00000.40 0.2857 0.0541 0.0072 0.0007 0.00010.50 0.3333 0.0769 0.0127 0.0016 0.00020.60 0.3750 0.1011 0.0198 0.0030 0.00040.67 0.4000 0.1176 0.0255 0.0042 0.00060.70 0.4118 0.1260 0.0286 0.0050 0.0007
r = p / a
• Find that P3 (0.67)=0.02550.70 0.4118 0.1260 0.0286 0.0050 0.00070.75 0.4286 0.1385 0.0335 0.0062 0.00090.80 0.4444 0.1509 0.0387 0.0077 0.00120.90 0.4737 0.1757 0.0501 0.0111 0.00201.00 0.5000 0.2000 0.0625 0.0154 0.0031
Given Pm(r) we can compute:• Time per day that system has to deny access
Prof. Christian Terwiesch
Time per day that system has to deny access• Flow units lost = 1/a * Pm (r)
Implied utilization vs probability of having all servers utilized: Pooling Revisited
Probability 0.6
utilized: Pooling Revisited
Probabilitythat all serversare utilized
0.4
0.5
m=1m=2
m=5 m=100.2
0.3
m=3 m 10
m=20
0
0.1
Implied utilization0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1
Prof. Christian Terwiesch
Erlang Loss Tablem
1 2 3 4 5 6 7 8 9 100.10 0.0909 0.0045 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.20 0.1667 0.0164 0.0011 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.25 0.2000 0.0244 0.0020 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.30 0.2308 0.0335 0.0033 0.0003 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.33 0.2500 0.0400 0.0044 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000.40 0.2857 0.0541 0.0072 0.0007 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000
Erlang Loss Table0.40 0.2857 0.0541 0.0072 0.0007 0.0001 0.0000 0.0000 0.0000 0.0000 0.00000.50 0.3333 0.0769 0.0127 0.0016 0.0002 0.0000 0.0000 0.0000 0.0000 0.00000.60 0.3750 0.1011 0.0198 0.0030 0.0004 0.0000 0.0000 0.0000 0.0000 0.00000.67 0.4000 0.1176 0.0255 0.0042 0.0006 0.0001 0.0000 0.0000 0.0000 0.00000.70 0.4118 0.1260 0.0286 0.0050 0.0007 0.0001 0.0000 0.0000 0.0000 0.00000.75 0.4286 0.1385 0.0335 0.0062 0.0009 0.0001 0.0000 0.0000 0.0000 0.00000.80 0.4444 0.1509 0.0387 0.0077 0.0012 0.0002 0.0000 0.0000 0.0000 0.00000.90 0.4737 0.1757 0.0501 0.0111 0.0020 0.0003 0.0000 0.0000 0.0000 0.00001.00 0.5000 0.2000 0.0625 0.0154 0.0031 0.0005 0.0001 0.0000 0.0000 0.00001 10 0 5238 0 2237 0 0758 0 0204 0 0045 0 0008 0 0001 0 0000 0 0000 0 00001.10 0.5238 0.2237 0.0758 0.0204 0.0045 0.0008 0.0001 0.0000 0.0000 0.00001.20 0.5455 0.2466 0.0898 0.0262 0.0063 0.0012 0.0002 0.0000 0.0000 0.00001.25 0.5556 0.2577 0.0970 0.0294 0.0073 0.0015 0.0003 0.0000 0.0000 0.00001.30 0.5652 0.2687 0.1043 0.0328 0.0085 0.0018 0.0003 0.0001 0.0000 0.00001.33 0.5714 0.2759 0.1092 0.0351 0.0093 0.0021 0.0004 0.0001 0.0000 0.00001.40 0.5833 0.2899 0.1192 0.0400 0.0111 0.0026 0.0005 0.0001 0.0000 0.00001.50 0.6000 0.3103 0.1343 0.0480 0.0142 0.0035 0.0008 0.0001 0.0000 0.00001.60 0.6154 0.3299 0.1496 0.0565 0.0177 0.0047 0.0011 0.0002 0.0000 0.00001.67 0.6250 0.3425 0.1598 0.0624 0.0204 0.0056 0.0013 0.0003 0.0001 0.0000 Probability{all m servers busy}= 1.70 0.6296 0.3486 0.1650 0.0655 0.0218 0.0061 0.0015 0.0003 0.0001 0.00001.75 0.6364 0.3577 0.1726 0.0702 0.0240 0.0069 0.0017 0.0004 0.0001 0.0000
r = p/a 1.80 0.6429 0.3665 0.1803 0.0750 0.0263 0.0078 0.0020 0.0005 0.0001 0.00001.90 0.6552 0.3836 0.1955 0.0850 0.0313 0.0098 0.0027 0.0006 0.0001 0.00002.00 0.6667 0.4000 0.2105 0.0952 0.0367 0.0121 0.0034 0.0009 0.0002 0.00002.10 0.6774 0.4156 0.2254 0.1058 0.0425 0.0147 0.0044 0.0011 0.0003 0.00012.20 0.6875 0.4306 0.2400 0.1166 0.0488 0.0176 0.0055 0.0015 0.0004 0.00012.25 0.6923 0.4378 0.2472 0.1221 0.0521 0.0192 0.0061 0.0017 0.0004 0.00012.30 0.6970 0.4449 0.2543 0.1276 0.0554 0.0208 0.0068 0.0019 0.0005 0.0001
y{ y}
!)( 21 rrrmr
rP m
m
m 2.30 0.6970 0.4449 0.2543 0.1276 0.0554 0.0208 0.0068 0.0019 0.0005 0.00012.33 0.7000 0.4495 0.2591 0.1313 0.0577 0.0220 0.0073 0.0021 0.0005 0.00012.40 0.7059 0.4586 0.2684 0.1387 0.0624 0.0244 0.0083 0.0025 0.0007 0.00022.50 0.7143 0.4717 0.2822 0.1499 0.0697 0.0282 0.0100 0.0031 0.0009 0.00022.60 0.7222 0.4842 0.2956 0.1612 0.0773 0.0324 0.0119 0.0039 0.0011 0.00032.67 0.7273 0.4923 0.3044 0.1687 0.0825 0.0354 0.0133 0.0044 0.0013 0.00032.70 0.7297 0.4963 0.3087 0.1725 0.0852 0.0369 0.0140 0.0047 0.0014 0.00042.75 0.7333 0.5021 0.3152 0.1781 0.0892 0.0393 0.0152 0.0052 0.0016 0.00042.80 0.7368 0.5078 0.3215 0.1837 0.0933 0.0417 0.0164 0.0057 0.0018 0.00052 90 0 7436 0 5188 0 3340 0 1949 0 1016 0 0468 0 0190 0 0068 0 0022 0 0006
!...
!2!11
mrrr
2.90 0.7436 0.5188 0.3340 0.1949 0.1016 0.0468 0.0190 0.0068 0.0022 0.00063.00 0.7500 0.5294 0.3462 0.2061 0.1101 0.0522 0.0219 0.0081 0.0027 0.00083.10 0.7561 0.5396 0.3580 0.2172 0.1187 0.0578 0.0249 0.0096 0.0033 0.00103.20 0.7619 0.5494 0.3695 0.2281 0.1274 0.0636 0.0283 0.0112 0.0040 0.00133.25 0.7647 0.5541 0.3751 0.2336 0.1318 0.0666 0.0300 0.0120 0.0043 0.00143.30 0.7674 0.5587 0.3807 0.2390 0.1362 0.0697 0.0318 0.0130 0.0047 0.00163.33 0.7692 0.5618 0.3843 0.2426 0.1392 0.0718 0.0331 0.0136 0.0050 0.00173.40 0.7727 0.5678 0.3915 0.2497 0.1452 0.0760 0.0356 0.0149 0.0056 0.00193.50 0.7778 0.5765 0.4021 0.2603 0.1541 0.0825 0.0396 0.0170 0.0066 0.0023
Prof. Christian Terwiesch
3.60 0.7826 0.5848 0.4124 0.2707 0.1631 0.0891 0.0438 0.0193 0.0077 0.00283.67 0.7857 0.5902 0.4191 0.2775 0.1691 0.0937 0.0468 0.0210 0.0085 0.00313.70 0.7872 0.5929 0.4224 0.2809 0.1721 0.0960 0.0483 0.0218 0.0089 0.00333.75 0.7895 0.5968 0.4273 0.2860 0.1766 0.0994 0.0506 0.0232 0.0096 0.00363.80 0.7917 0.6007 0.4321 0.2910 0.1811 0.1029 0.0529 0.0245 0.0102 0.00393.90 0.7959 0.6082 0.4415 0.3009 0.1901 0.1100 0.0577 0.0274 0.0117 0.00464.00 0.8000 0.6154 0.4507 0.3107 0.1991 0.1172 0.0627 0.0304 0.0133 0.0053
Response Time
R iReview
Prof. Christian Terwiesch
(My-law.com) My-law.com is a recent start-up trying to cater to customers in search of legal services online. Unlike traditional law firms, My-law.com allows for extensive interaction between lawyers and their customers via telephone and the Internet This process is used in the upfront part of the customer interaction largely consisting of answeringthe Internet. This process is used in the upfront part of the customer interaction, largely consisting of answering some basic customer questions prior to entering a formal relationship. In order to allow customers to interact with the firm’s lawyers, customers are encouraged to send e-mails to my-lawyer@My-law.com. From there, the incoming e-mails are distributed to the lawyer who is currently “on call.” Given the broad skills of the lawyers, each lawyer can respond to each incoming request.
E-mails arrive from 8 A.M. to 6 P.M. at a rate of 10 e-mails per hour (coefficient of variationfor the arrivals is 1). At each moment in time, there is exactly one lawyer “on call,”that is, sitting at his or her desk waiting for incoming e-mails. It takes the lawyer, on average,5 minutes to write the response e-mail The standard deviation of this is 4 minutes5 minutes to write the response e mail. The standard deviation of this is 4 minutes.
a. What is the average time a customer has to wait for the response to his/her e-mail, ignoring any transmission times? Note: This includes the time it takes the lawyer to start writing the e-mail and the actual writing time.
b. How many e-mails will a lawyer have received at the end of a 10-hour day?
c. When not responding to e-mails, the lawyer on call is encouraged to actively pursuecases that potentially could lead to large settlements. How much time on a 10-hour daycan a My-law.com lawyer dedicate to this activity
Prof. Christian Terwiesch
Jim’s ComputerJim wants to find someone to fix his computer. PC Fixers (PF) is a local service that offers such computer repairs. A new customer walks into PF every 10 minutes (with a standard deviation of 10 minutes). PF has a staff of 5 computer technicians Service times average around 40 minutes (with a standard deviation of 40staff of 5 computer technicians. Service times average around 40 minutes (with a standard deviation of 40 minutes).
JC1. If Jim walks into PF, how long must he wait in line before he can see a technician? (Only include the waiting time, not any service time)
JC2. How many customers will, on average, be waiting for their computer to be fixed?
Prof. Christian Terwiesch
Real ComputeRealCompute offers real-time computing services. The company owns 4 supercomputers that can be accessed through the internet. Their customers send jobs that arrive on average every 4 minutes (inter-arrival times are exponentially distributed and, thus, the standard deviation of the inter-arrival times is 4 minutes). p y , , )
Each job takes on average 10 minutes of one of the supercomputers (during this time, the computer cannot perform any other work). Customers pay $20 for the execution of each job. Given the time-sensitive nature of the calculations, if no supercomputer is available, the job is redirected to a supercomputer of a partner company called OnComp which charges $40 per job to Real Compute (OnComp always has supercomputer capacitycalled OnComp, which charges $40 per job to Real Compute (OnComp always has supercomputer capacity available).
RC1. What is the probability with which an incoming job can be executed by one of the supercomputers owned by RealCompute?
RC2. How much does RealCompute pay on average to OnComp (in $s per hour)?
Prof. Christian Terwiesch
ContractorA contractor building houses and doing renovation work has currently six projects planned for the season. Below are the items, and the estimated times to complete them:
New construction at Springfield - 60 daysBathroom remodeling at Herne - 10 daysTraining time for solar roof installation - 2 daysUpdate web-site - 6 daysyRenovation of deck at Haverford - 8 daysNew kitchen at Rosemont - 20 days
Suppose the contractor starts immediately with the first project, no other projects get added to this list, and the contractor sequences them so as to minimize the average time the project waits before it gets started What willcontractor sequences them so as to minimize the average time the project waits before it gets started. What will the contractor be doing in 30 days from the start date of the first project?
Prof. Christian Terwiesch
Call CenterConsider a call center that has a constant staffing level. Because of increased demand in the morning, the call center has a very high utilization in the morning and a very low utilization in the afternoon. Which of the following will decrease the average waiting time in the call center?
(a) Add more servers(b) Decrease the service time coefficient of variation(c) Decrease the average service time(d) Level the demand between the morning hours and the afternoon hours(d) Level the demand between the morning hours and the afternoon hours (e) All of the above
Prof. Christian Terwiesch
Prof. Christian Terwiesch
QualityIntroduction
Prof. Christian Terwiesch
Quality Introduction
I said that the worst thing about healthcare would be waiting, not true; worst thing are defects
Two dimensions of quality: conformance and performance
Our focus will be on conformance quality
Motivating example: the sinking ship / swiss cheese logic
Prof. Christian Terwiesch
Assembly Line Defects
Assembly operations for a Lap-top
9 Steps
Each of them has a 1% probability of failure
What is the probability of a defect?
Prof. Christian Terwiesch
The Duke Transplant Tragedy
Source: http://www.cbsnews.com/2100-18560_162-544162.html
17 year old Jesica Santillan died following an organ transplant (heart+lung)
Mismatch in blood type between the donor and Jesica
Experienced surgeon, high reputation health system
About one dozen care givers did not notice the mismatch
The offering organization did not check, as they had contacted the surgeon with another recipient in mind
The surgeon did not check and assumed the organization offering the organ had checked
It was the middle of the night / enormous time pressure / aggressive time line
A system of redundant checks was in place
A single mistake would have been caught
But if a number of problems coincided, the outcome could be tragic
Prof. Christian Terwiesch
Swiss Cheese Model
Source: James Reason
Barriers
Example:
3 redundant steps
Each of them has a 1% probability of failure
What is the probability of a defect?
Prof. Christian Terwiesch
The Nature of Defects
Assembly line example: ONE thing goes wrong and the unit is defective
Swiss cheese situations: ALL things have to go wrong to lead to a fatal outcome
Compute overall defect probability / process yield
When improving the process, don’t just go after the bad outcomes, but also after the internal process variation (near misses)
Prof. Christian Terwiesch
QualityDefects / impact on flow
Prof. Christian Terwiesch
Impact of Defects on Flow
5 min/unit
4 min/unit50% defectScrap 6 min/unit
Prof. Christian Terwiesch
Impact of Defects on Flow
5 min/unit
4 min/unit30% defectRework 2 min/unit
Prof. Christian Terwiesch
Impact of Defects on Variability: Buffer or Suffer
Processing time of 5 min/unit at each resource (perfect balance)
With a probability of 50%, there is a defect at either resource and it takes 5 extra min/unit at the resource to rework
=> What is the expected flow rate?
Prof. Christian Terwiesch
The Impact of Inventory on Quality
Inventory takes pressure off the resources (they feel buffered): demonstrated behavioral effects
Expose problems instead of hiding them
Inve
ntor
y in
pro
cess
Buffer argument:“Increase inventory”
Toyota argument:“Decrease inventory”
Prof. Christian Terwiesch
Operations of a Kanban System: Demand Pull
• Visual way to implement a pull system• Amount of WIP is determined by
number of cards
• Kanban = Sign board • Work needs to be authorized by demandAuthorize
productionof next unit
Prof. Christian Terwiesch
QualitySix sigma and process capability
Prof. Christian Terwiesch
Gurkenverordnung:http://de.wikipedia.org/wiki/Verordnung_(EWG)_Nr._1677/88_(Gurkenverordnung)
Failure of a pharmacy
Intro: two types of variability
Prof. Christian Terwiesch
M&M Exercise
A bag of M&M’s should be between 48 and 52g
Measure the samples on your table:Measure x1, x2, x3, x4, x5Compute the mean (x-bar) and the standard deviationNumber of defects
All data will be compiled in master spread sheetYield = %tage of units according to specificationsHow many defects will we have in 1MM bags?
Prof. Christian Terwiesch
Process capability measure
• Estimate standard deviation in excel• Look at standard deviation relative to specification limits
3
Upper Specification Limit (USL)
LowerSpecificationLimit (LSL)
X-3A X-2A X-1A X X+1A X+2 X+3A
X-6B X X+6B
Process A(with st. dev A)
Process B(with st. dev B)
6LSLUSLC p
x Cp P{defect} ppm
1 0.33 0.317 317,000
2 0.67 0.0455 45,500
3 1.00 0.0027 2,700
4 1.33 0.0001 63
5 1.67 0.0000006 0,6
6 2.00 2x10-9 0,00
Measure Process Capability: Quantifying the Common Cause Variation
Prof. Christian Terwiesch
Not just the mean is important, but also the variance
Need to look at the distribution function
The Concept of Consistency:Who is the Better Target Shooter?
Prof. Christian Terwiesch
QualityTwo types of variation
Prof. Christian Terwiesch
Common Cause Variation (low level)
Common Cause Variation (high level)
Assignable Cause Variation
• Need to measure and reduce common cause variation• Identify assignable cause variation as soon as possible• What is common cause variation for one person might be
assignable cause to the other
Two Types of Variation
Prof. Christian Terwiesch
M&M Exercise
Analysis of new sample in production environment
=> Show this in Excel
Prof. Christian Terwiesch
Time
ProcessParameter
Upper Control Limit (UCL)
Lower Control Limit (LCL)
Center Line
• Track process parameter over time- average weight of 5 bags- control limits- different from specification limits
• Distinguish between- common cause variation
(within control limits)- assignable cause variation
(outside control limits)
Detect Abnormal Variation in the Process: Identifying Assignable Causes
Prof. Christian Terwiesch
Statistical Process Control
CapabilityAnalysis
ConformanceAnalysis
Investigate forAssignable Cause
EliminateAssignable Cause
Capability analysis • What is the currently "inherent" capability of my process when it is "in control"?
Conformance analysis• SPC charts identify when control has likely been lost and assignable cause
variation has occurred
Investigate for assignable cause• Find “Root Cause(s)” of Potential Loss of Statistical Control
Eliminate or replicate assignable cause• Need Corrective Action To Move Forward
Prof. Christian Terwiesch
QualityDetect / Stop / Alert
Prof. Christian Terwiesch
71
2345
68
ITAT=7*1 minute
3
1
2
4
ITAT=2*1 minute
Good unit
Defective unit
Information Turnaround Time
Inventory leads to a longer ITAT (Information turnaround time) => slow feed-back and no learning
Assume a 1 minute processing time
Prof. Christian Terwiesch
Cost of a Defect: Catching Defects Before the Bottleneck
What is the cost of a defect?
Defect detected before bottleneck
Defect detected after bottleneck
Bottleneck
Buy pasta / ingredients for $2 per meal
Prepare Cook ServeServe food for $20 per meal
Prof. Christian Terwiesch
Detecting Abnormal Variation in the Process at Toyota: Detect – Stop - Alert
Source: www.riboparts.com, www.NYtimes.com
JidokaIf equipment malfunctions / gets out of control, it shuts itself down automatically to prevent further damageRequires the following steps:
DetectAlertStop
Andon Board / Cord A way to implement Jidoka in an assembly line
Make defects visibly stand out
Once worker observes a defect, he shuts down the line by pulling the andon / cord
The station number appears on the andonboard
Prof. Christian Terwiesch
Detect, stop, alert
Jidoka
Andon cord
Root-cause
problem-solving
Ishikawa Diagram
Kaizen
Avoid
Poka Yoke
Build-in quality
Two (similar) Frameworks for Managing Quality
Toyota Quality System
CapabilityAnalysis
ConformanceAnalysis
Investigate forAssignable
Cause
EliminateAssignable
Cause
Six Sigma System
Some commonalities:Avoid defects by keeping variation out of the process If there is variation, create an alarm and trigger process improvement actionsThe process is never perfect – you keep on repeating these cycles
Prof. Christian Terwiesch
QualityProblem solve / improve
Prof. Christian Terwiesch
Root Cause Problem Solving
Ishikawa Diagram A brainstorming technique of what might have contributed to a problem
Shaped like a fish-bone
Easy to use
Pareto ChartMaps out the assignable causes of a problem in the categories of the Ishikawa diagram
Order root causes in decreasing order of frequency of occurrence
80-20 logic
Prof. Christian Terwiesch
The Power of Iterative Problem-solvingM
odel
sR
ealit
y
Prof. Christian Terwiesch
Root Cause Problem Solving
Ishikawa Diagram A brainstorming technique of what might have contributed to a problem
Shaped like a fish-bone
Easy to use
Pareto ChartMaps out the assignable causes of a problem in the categories of the Ishikawa diagram
Order root causes in decreasing order of frequency of occurrence
80-20 logic
Prof. Christian Terwiesch
ConclusionLean Operations
Prof. Christian Terwiesch
The Ford Production System
Influenced by Taylor; optimization of work
The moving line / big machinery => focus on utilization
Huge batches / long production runs; low variety
Produced millions of cars even before WW2
Model built around economies of scale=> Vehicles became affordable to the middle class
Prof. Christian Terwiesch
The Toyota Production System
Toyota started as a maker of automated looms
Started vehicle production just before WW2
No domestic market, especially following WW2
Tried to replicate the Ford model (produced about 10k vehicles)
No success due to the lack of scale
Around 1950, TPS was born and refined over the next 30 years Systematic elimination of waste Operating system built around serving demand
Prof. Christian Terwiesch
Introduction
19031st car
19081st Model
T
1911F.W.
Taylor
19131st
movingline
19232.1
millionvehicles/
yearCost USD/unit
19161904 1926
950
360 290Key idea of TPS: systematic elimination
of non-value-adding activities
1933Founded
1946Major strike
1950Start of
TPS
1960sSupplierdevelop-
ment
1980sTrans-plants
Mass production driven by economies of scale impossible– Low production volume (1950):
GM 3,656,000 – Toyota 11,000– Low productivity (Japan 1/9 of US)– Lack of resources
Taylorism: Standardized parts and workpatterns (time studies)
Moving line ensuring working at same paceProcess driven by huge, rapid machinery
with inflexible batch production
Source: McKinsey
Key idea of Ford: cost reduction throughcheap labor and economies of scale
Prof. Christian Terwiesch
Zero non-value added activities (muda)
Production flow synchronized with demand (JIT)One-unit-at-a-time flow
Mixed model production (heijunka)Match production demand based on Takt time
Pull instead of pushSupermarket / KanbanMake-to-order
Quality methods to reduce defectsFool-proofing (poka-yoke) and visual feed-backDetect-stop-alert (Jidoka)
Defects at machines (original Jidoka)Defects in assembly (Andon cord)
Flexibility
Standardization of work
Worker involvementQuality circles (Kaizen)Fishbone diagrams (Ishikawa)Skill development / X-training
Reduction of VariabilityQuartile AnalysisStandard operating procedures
Adjustment of capacity to meet takt-time
Reduce inventory to expose defects
Toyota Production System: An Overview
Prof. Christian Terwiesch
The Three Enemies of Operations
Is associated with longer wait times and / or customer loss
Requires process to hold excess capacity (idle time)
Buffer or suffer
Often times: quality issues
Variability
Use of resources beyond what is needed to meet customer requirements• 7 different types of waste• OEE framework• Lean: do more with less
WasteWork Value-adding
WasteWork Value-adding
Waste
Inflexibility
Additional costs incurred because of supply demand mismatches• Waiting customers or• Waiting (idle capacity)
Capacity
Customerdemand
Source: Reinecke / McKinsey
Prof. Christian Terwiesch
QualityReview Questions
Prof. Christian Terwiesch
Pharmacy Medication ErrorA pharmacy in a Philadelphia suburb wants to investigate the likelihood of making a medication error. There are two ways in which a patient can end up with the wrong medication:- In about 2% of the cases, the doctor fills out the prescription incorrectly. Nobody in the pharmacy catches
these errors- In about 1% of the cases, the pharmacist makes a mistake in picking the medication according to the
prescription. The pharmacy has an internal quality inspection process that catches about 97% of the errors made by the pharmacist.
Another source of quality control is the patients. The pharmacy estimates that about half of the errors made by the physician are recognized by the patient. However, the patient is only able to recognize 10% of the mistakes done at the pharmacy.
What is the likelihood that the patient is presented with a wrong medication?
What is the likelihood that the patient leaves the pharmacy with the wrong medication?
Prof. Christian Terwiesch
Four Step Process with Rework and Scrap Consider the following four step assembly operation with quality problems. All resources are staffed with one operator. - The first resource has a processing time of 4 minutes per unit - The second resource has a processing time of 3 minutes per unit. This process suffers from a high yield
loss and 50% of all products have to be scrapped after this step.- The third resource also suffers from quality problems. However, instead of scrapping the product, the third
resource reworks it. The processing time at the third resource is 5 minutes per unit. In the 30% of the products in which the product needs to be reworked, this extends to a total (initial processing time plus rework) processing time of 10 minutes per unit. Rework always leads to a non-defective unit.
- No quality problems exist at the first and final resource. The processing time is 2 minutes per unit.
For every unit of demand, how many units have to flow through the third step in the process?
Where in the process is the bottleneck?
What is the process capacity?
Prof. Christian Terwiesch
Chicken EggsA farmer focusing on the production of eco-friendly chicken eggs collects the following data about his output. In a sample of 50 eggs, the farmer finds the average egg to weigh 47 grams. The standard deviation of the egg weight is 2 grams and the distribution of weights resembles a normal distribution reasonably closely.
The farmer can sell the eggs to a local distributor. However, they have to be in the interval between 44 grams and 50 grams (i.e., the lower specification limit is 44 grams and the upper specification limit is 50 grams).
What is the capability score of the eco-friendly chicken egg operation?
What percentage of the produced eggs fall within the specification limits provided by the local distributor?
By how much would the farmer have to reduce the standard deviation of the operation if his goal were to obtain a capability score of Cp=2/3 (i.e., get 4.5% defects)?
Prof. Christian Terwiesch
Process capability measure
• Estimate standard deviation in excel• Look at standard deviation relative to specification limits
3
Upper Specification Limit (USL)
LowerSpecificationLimit (LSL)
X-3A X-2A X-1A X X+1A X+2 X+3A
X-6B X X+6B
Process A(with st. dev A)
Process B(with st. dev B)
6LSLUSLC p
x Cp P{defect} ppm
1 0.33 0.317 317,000
2 0.67 0.0455 45,500
3 1.00 0.0027 2,700
4 1.33 0.0001 63
5 1.67 0.0000006 0,6
6 2.00 2x10-9 0,00
Measure Process Capability: Quantifying the Common Cause Variation
Prof. Christian Terwiesch
Toyota Word MatchingPlease write the letter corresponding to the most appropriate example or definition from choices (a – k below) on the blank line next to each word below.a) Examples of this include: workers having to make unnecessary movements (i.e. excessive reaching or walking to get tools or parts), working on parts that are defective and idle time.b) A system that enables a line worker to signal that he or she needs assistance from his or her supervisor, for example in the case of a defect. Used to implement the Jidoka principle.c) A brainstorming technique that helps structure the process of identifying underlying causes of an (usually undesirable) outcome d) As an example of this philosophy, workers at Toyota often times make suggestions for process improvement ideas. e) A method that controls the amount of work-in-process inventory f) If an automotive assembly plant used this technique, the adjacent cars on an assembly line would be mixed models (e.g. Model A with sunroof, Model A without sunroof, Model B, Model B with sunroof), in proportions equal to customer demand.g) Making production problems visible and stopping production upon detection of defects
Please only add ONE LETTER to each of the following terms:
Kanban ____Muda ____Heijunka ____ Andon cord ____ Kaizen ____ Ishikawa ____ Jidoka ____