Business Model Canvas vs Lean Canvas vs One-Page Lean Startup
Lean Startup Experiment Canvas
Transcript of Lean Startup Experiment Canvas
Lean Experiment CanvasA Framework for Continuous Process
Improvement & Experimentation
Chad Cote T-2
*Adapted from the Business Model Canvas by Alexander Osterwalder (https://strategyzer.com/) &
the Lean Canvas by Ash Maurya (https://leanstack.com/blog/)
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Lean Experiment Canvas
AssumptionsPredictionFalsifiableHypothesis
ResultsofExperiment
Risks
KeyStakeholdersKey Metrics
ExperimentalSteps
NextObstacles
Timeframe
TheQuestion
Background
CreatedbyChad CoteLeanExperimentCanvasisadaptedfromTheBusinessModelCanvas(www.strategyzer.com)undertheCreativeCommonsAttribution-Share
“Important issues should be presented in writing. Nothing so sharpens the thought process as writing down one’s arguments. Weaknesses overlooked in oral discussion rapidly become painfully obvious on the written page.” - Admiral Rickover
Think Like a Scientist, Start-up Founder, and Engineer
Ideas (are cheap)
Execution (is everything)
How do I get from hereto there?
Ideas (are cheap)
Execution (is everything)
How do I get from hereto there?
Validated Learning
Validated Learning“the process of demonstrating
empirically that a team has discovered valuable truths
about a startup’s present and future business prospects” -
Eric Ries
Ideas (are cheap)
Execution (is everything)
How do I get from hereto there?
Validated Learning
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ExperimentTitle:
Lean Experiment Canvas
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FalsifiableHypothesis
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Lean Experiment Canvas
Ideas (are cheap)
Execution (is everything)
Operations / Management / Perseverance / Luck / Timing / etc..
Validated Learning
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ExperimentTitle:
Lean Experiment Canvas
Assumptions
Prediction
FalsifiableHypothesis
ResultsofExperiment
Risks
KeyStakeholders
Key Metrics
ExperimentalStepsNextObstacles
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TheQuestion
Background
Lean Experiment Canvas
Focus: Understanding Reality
“Toyota Kata” - Mike Rother
“Lean Startup” - Eric Ries
“Certain to Win” - Chet Richards
OODA Loop
PDSA & BML loops are simple manifestations of a
much larger thought process contained in the OODA
“loop”
Experiments are the “Genes” of Innovation & Continuous Process Improvement
PDSAs are a process of probing and assessing your environment to uncover
better methods/ideas
Experiment Loop = Learning
Most Basic Building Block of Improvement Good Experiments lead to learning which is passed down to the organization over
time
Minimizebeing wrong * time
Maximal Interestingess
#1 #2
…. #n
Experiment Loop = Learning
Time
Drive Experimentation in the Direction of Maximal Interestingness
OOHyTOOHyT
OOHyT
Directed Opportunism
Observe - Orient - Hypothesis - Test: OOHyT
Maximal Interestingess
#1 #2
….
#nTime
….
Speed != Agility
Goal: Maintain Accurate Representation of Reality
The “faster” you learn the more accurate your orientation
AgilityOOHyT
OOHyT
OOHyT
OOHyT
Directed Opportunism
Maximal Interestingess
The direction of maximal interestingness can be very rapidly updated to reflect new information, by evolving the rough consensus. …. It is enough to figure out experimental next steps. This ability to reorient and adopt new mental models quickly (what military strategists call a fast transient) is at the heart of agility. - “Breaking Smart” http://breakingsmart.com/en/season-1/
Maximize Agility Through Experimentation Loops
Plan
Do
Study
Act “Natural” pathway to fill out the canvas
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Lean Experiment Canvas
Assumptions(leapsoffaith)Prediction (expectation)FalsifiableHypothesis(If{Idothis},then{this} willhappen)
ResultsofExperiment (validatedlearning)
Risks
KeyStakeholders(connections)Key Metrics(process, outcomes)
Experimental Steps
NextObstacles
Timeframe(prepare, test)
TheQuestion
Background(goandsee)
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Plan
Do
Study
Act DoWhere is the “Do” stage?
“Boots on the ground”Get out of the office!
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Lean Experiment Canvas
Assumptions(leapsoffaith)Prediction (expectation)FalsifiableHypothesis(If{Idothis},then{this} willhappen)
ResultsofExperiment (validatedlearning)
Risks
KeyStakeholders(connections)Key Metrics(process, outcomes)
Experimental Steps
NextObstacles
Timeframe(prepare, test)
TheQuestion
Background(goandsee)
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“No plan survives contact with the enemy.” - Helmuth von Moltke
“The thinking–doing loop is kept as short as possible so as to reduce uncertainty and increase tempo.” - The Art of Action
Example Used Throughout the Presentation Call Center Wait Times
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Lean Experiment Canvas
AssumptionsPredictionFalsifiableHypothesis
ResultsofExperiment
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KeyStakeholdersKey Metrics
ExperimentalSteps
NextObstacles
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TheQuestion
Background
Call Center Wait Times 3/7/16 4/22/16 Chad Cote & Call Center Supervisor
After creating and implementing a call center dashboard we realized we had substantial wait times occurring in the early am hours. Wait times were up to 14 minutes on Monday mornings and we had high average wait times on a daily basis.
How can we reduce average wait times in the early am hours?
-By moving labor hours from the pm to the am we can shorten wait times w/o large risk to pm wait times
-We have enough staff to handle the current volume
-We are betting on the relationship between utilization and cycle time to hold
-We have staff who are ok with changing their work schedules
-We have a good call center script that we believe works to handle call volume
If we bring in one staff member earlier in the morning and allow them to leave earlier in the afternoon
then we can reduce average wait time in the am hours which will also drive down wait times on average.
-Drive down wait times in the early am
-Wait times in pm hours won't be negatively affected
-Afternoon wait times could rise substantially creating supply and demand mismatches
-Staff actually don't want to change working hours and this may hurt morale
-We could be short on total staff and moving staff hours around will only shift the constraint
-Call center operators
-Department Leadership
-Patients who call the call center
-Downstream departments who will be the "receivers" of new appointments made by patients in the am
1. Talk to staff members who would be most likely to come in the early am hours
2. Change hours unofficially for two weeks to test the change
3. Run the test for two weeks and analyze the results on a weekly basis
4. If successful, officially change the staffing hours through the appropriate means
Average wait times in the am hours
Total average wait times
Call center wait time complaints
Test: 2 weeks
-Huge success driving down wait times in the early am hours from 14 minutes to 5 minutes-Successful in driving down overall total wait times (see graph)-Staff actually prefer working the am hours -Wait times in the afternoon hours were not negatively affected confirming the assumption of the relationship between CT and utilization-Extended the test phase a few weeks to ensure process stability
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-Morning am times are still the constraint even though we made huge gains
-We found out that staff spend some time performing their annual trainings during this time frame and that should be eliminated. This might be a "Just Stop It" next step
-Look for new bottlenecks, such as lunch and be aware that with more employees arriving at the same time lunch hours will have to be managed appropriately
Go & SeeSeeing the right problem and defining it
accurately
Know your customers
Understand the greater context of what you are trying to accomplish
What is the “as-is” condition? What is the impetus to change? Why would anyone care?
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Lean Experiment Canvas
AssumptionsPredictionFalsifiableHypothesis
ResultsofExperiment
Risks
KeyStakeholdersKey Metrics
ExperimentalSteps
NextObstacles
Timeframe
TheQuestion
Background
After creating and implementing a call center dashboard we realized we had substantial wait times occurring in the early am hours. Wait times were up to 14 minutes on Monday mornings and we had high average wait times on a daily basis.
“One good question can give rise to several layers of answers,
can inspire decades-long searches for solutions, can
generate whole new fields of inquiry, and can prompt changes in entrenched thinking. Answers,
on the other hand, often end the process.” - Stuart Firestein
How can we reduce average wait times in the early am hours?
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CreationDate: UpdateDate: Owner:ExperimentTitle:
Lean Experiment Canvas
AssumptionsPredictionFalsifiableHypothesis
ResultsofExperiment
Risks
KeyStakeholdersKey Metrics
ExperimentalSteps
NextObstacles
Timeframe
TheQuestion
Background
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Uncertainty
PrioritizeUncertainyReductionPrioritize Uncertainty Reduction
If {I do this}, then {this will happen}
Must be able to be proven false (i.e. not astrology)
If we bring in one staff member earlier in the morning and allow them to leave earlier in the afternoon
then we can reduce average wait time in the am hours which will also drive down wait times on average.
What you think will happen
Looking for cause and effect relationships
Perform experiments that explicitly test your “stories”
Never say, “Let’s Just See What Happens”
-Drive down wait times in the early am
-Wait times in pm hours won't be negatively affected
Expose your beliefs and assumptions about the underlying
problem
What do you believe internally that makes you believe this hypothesis and experiment is the right one?
Your assumptions inform and shape your hypothesis
-By moving labor hours from the pm to the am we can shorten wait times w/o large risk to pm wait times
-We have enough staff to handle the current volume
-We are betting on the relationship between utilization and cycle time to hold
-We have staff who are ok with changing their work schedules
-We have a good call center script that we believe works to handle call volume
Your experiment is more than likely biased and based on beliefs and assumptions
that are wrong
There is potential for negative results. Write them
down.
-Afternoon wait times could rise substantially creating supply and demand mismatches
-Staff actually don't want to change working hours and this may hurt morale
-We could be short on total staff and moving staff hours around will only shift the constraint
Process Metrics&
Outcome MetricsTight feedback loops
Action/Information need to be tightly coupled
Clear cause & effect
**Call centers have very clear metrics which makes testing easier
Average wait times in the am hours
Total average wait times
Call center wait time complaints
Understand who will be influenced by
experimental changes
Beware of externalities
-Call center operators
-Department Leadership
-Patients who call the call center
-Downstream departments who will be the "receivers" of new appointments made by patients in the am
Be explicit about the steps of the
experiment
1. Talk to staff members who would be most likely to come in the early am hours
2. Change hours unofficially for two weeks to test the change
3. Run the test for two weeks and analyze the results on a weekly basis
4. If successful, officially change the staffing hours through the appropriate means
How long will it take to prepare?
How long will it take to run the experiment?Test: 2 weeks
“An experiment that successfully proves a
hypothesis is a measurement; one that doesn’t is a
discovery. A discovery, an uncovering— of new
ignorance.” - Enrico Fermi
-Huge success driving down wait times in the early am hours from 14 minutes to 5 minutes-Successful in driving down overall total wait times (see graph)-Staff actually prefer working the am hours -Wait times in the afternoon hours were not negatively affected confirming the assumption of the relationship between CT and utilization-Extended the test phase a few weeks to ensure process stability
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0.50
1.00
1.50
2.00
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3.50
4.00
2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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2015
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AverageWaitTimeinCallCenter
AvgMinutesonHold Series2 Series3 Series4
Looking for confirmatory data,
and more importantly contradictory data
Risks
• Observe• Orient• Decide• Act
-Morning am times are still the constraint even though we made huge gains
-We found out that staff spend some time performing their annual trainings during this time frame and that should be eliminated. This might be a "Just Stop It" next step
-Look for new bottlenecks, such as lunch and be aware that with more employees arriving at the same time lunch hours will have to be managed appropriately
Start Experimenting
for a copy of the Lean Experiment Canvas email me
at:leanmeanimprovingmachine@
gmail.com