howtoimproveprojectschedulesuccessbybeeresmaon: … · 2019-11-27 · aboutus$!...

©2018 Kodukula & Associates, Inc. All Rights Reserved.

How to Improve Project Schedule Success by Be8er Es:ma:on: Applica:on of Next Genera:on Risk Management Tools

Dr. Prasad S. Kodukula, PMP, [email protected]

Yun Zhang, MMF

Kodukula & Associates, Inc. Chicago, Illinois

Presented at Palisade Risk Conference

San Antonio, Texas

November 12 – 13, 2019


Dr. Prasad S. Kodukula, PMP, PgMP !  Speaker, coach, author, entrepreneur, inventor !  30+ years of professional experience !  Spoke in 45 countries !  Coauthor or contribu:ng author of eight books !  Recognized by PMI as “Best of the Best” in project management

•  2016 PMI Eric Jene8 Project Management Excellence Award

•  2010 PMI Dis:nguished Contribu:on Award

!  Founder/CEO, Kodukula & Associates, Inc. !  Cofounder/CEO, NeoChloris, Inc. !  Cofounder/CEO, 2Ci !  Lecturer, University of Chicago, Illinois Ins:tute of Technology

About Us !  Kodukula & Associates, Inc. !  Project, program, pordolio management

!  Coaching, training, consul:ng !  Founded in 1995 in Chicago !  Experience with 40+ Fortune 100 companies

Relevant Books by Dr. Kodukula

Learning Objec:ves !  Develop more realis:c and accurate project schedule es:mates using next

genera:on risk management tools

!  Control schedules using ra:onal risk response/management techniques

including reserves

5 ©2019 Kodukula & Associates, Inc. All Rights Reserved.

Presenta:on Overview !  Introduce a hypothe:cal project !  Start with determinis:c task dura:on es:mates and project schedule

!  Add impact of different types of risks

!  Discuss es:ma:on of reserves

!  Brief model demo

!  Q&A !  (Presenta:on mode & “role play” mode)

©2019 Kodukula & Associates, Inc. All Rights Reserved. 6

Acme Biomedical Corp.: Project Zulu WBS

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Gather requirements

Design prototype

Get execu:ve sign-‐off

Ini:al Design Build

prototype: Trial 1

Test prototype: Trial 1

Revise/test prototype: Trial 2

Proto-‐typing

Finalize design


Final Design


Prepare applica:on

Submit applica:on

Receive approval

Govt. Approval

Select suppliers

Nego:ate/ sign agreements

Procure equipment/ materials

Procure-‐ment


Ramp-‐up produc:on

Perform produc:on tes:ng

Launch

Launch &

Closeout Kick-‐off/plan project

Monitor/control project

Project Manage-‐ment

Project Zulu WBS con:nued

8 ©2019 Kodukula & Associates, Inc. All Rights Reserved. 8

High Level Project Schedule*

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*255 Work days (Approximately one calendar year)


Project Schedule: Gan8 Chart


1. Events Risk !  Caused by uncertain events with a posi:ve or nega:ve effect on project objec:ves

!  Events with nega:ve impact are called threats.

•  Example: Supply chain disrup:ons

!  Events with posi:ve impact are called opportuni:es. •  Example: Prototype successful in the first trial w/o the need for addi:onal

trials


2. Variability Risk !  “Non-‐event” risk due to uncertainty related to es:mates causing actual

dura:ons to differ (higher or lower) from es:mated dura:ons

!  Examples:

•  Actual resource produc:vity happens to be lower (or higher) compared to the

es:mated produc:vity

•  The actual material prices turn out to be higher (or lower) than planned


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How to Account for Variability and Events Risks in Es:ma:ng Project Dura:ons? !  Monte Carlo (MC) simula:on generates numerous possibili:es of project schedule

based on numerous possibili:es of task dura:ons

!  Simula:on tool accounts for impact of risk events in accordance with individual

probabili:es of risk events.

!  For example, if a risk has 60% probability of occurrence, risk impact will be included

in 600 out of the, say, 1,000 itera:ons.

!  Each itera:on of the simula:on (re)calculates the project schedule using the

schedule es:ma:on model (cri:cal path method), and records the result.


“Must-‐Know” Distribu:ons !  Normal

!  Pert

!  Triangular

!  Binomial

!  Bernoulli

!  Poisson


Variability Risk: Input Data PROJECT ZULU

Task Type of Distribu:on

Distribu:on Parameters (days)*

No. Name Minimum Most Likely Maximum 1 Start 0 2 Monitor, Control & Manage Project

3 Kickoff Project/Develop Project Plan 5 4 Gather Requirements Pert 14 15 19 5 Design Prototype Triangular 23 25 31 6 Get Execu:ve Sign-‐off 1 5 7 Build Prototype: Trial 1 Pert 23 25 31 8 Test Prototype: Trial 1 Pert 9 10 13 9 Revise/Test Prototype: Trial 2 Pert 14 15 19 10 Finalize Design Pert 9 10 13 11 Get Execu:ve Sign-‐off 2 5 12 Prepare Applica:on Pert 18 20 25 13 Submit Applica:on 5 14 Receive Approval Triangular 54 60 75 15 Select Suppliers Pert 45 50 63 16 Nego:ate/Sign Agreements Pert 27 30 38 17 Procure Equipment/Materials Triangular 36 40 50 18 Get Execu:ve Sign-‐off 3 5 19 Ramp-‐up for Produc:on Pert 45 50 63 20 Perform Produc:on Tes:ng Pert 14 15 19 21 Launch 5 22 Finish 0

*Minimum: 90% of Most Likely Maximum: 125% od Most Likely


Events Risk (Threats): Input Data

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No. Event Proba-‐bility*

Impact

Descrip:on Distribu:on/Dura:on, days

Min Most Likely

Max

1 Applica:on rejected. 30% Task 23: Resubmit Applica4on (new task) is created.

Pert 9 10 13

2 Prototype test Trial 1 total failure

30% Task 9: Revise/Test Prototype – Trial 2 requires addi:onal :me.

Pert 9 10 13

3 Supply chains disrupted due to hurricane(s)

0.25* Task 17: Procure Equipment/Materials takes longer. New dura:ons are es:mated.

Pert 54 60 75

*Threat 3 follows Poisson distribu:on with a “lambda” of 0.25. Probability of no hurricanes: 77.9%, one: 19.5%, two: 2.4%, three: 0.2%


Events Risk (Opportuni:es): Input Data

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Opportunity Proba-‐bility

Impact

No. Event Descrip:on Distribu:on/Dura:on, days

Min Most Likely

Max

1 Fast applica:on approval 20%

Task 14: Receive Approval – Task dura:on is reduced.

Pert -‐18 -‐20 -‐25

2 Prototype Trial 1 highly successful.

10%

Task 9: Revise/Test Prototype – Trial 2 is no longer required.

No Dist. -‐ 0 -‐


Schedule Es:ma:on Model

Model O

Model for Project Schedule Es:ma:on Cri:cal Path (Microsos Project) I = Input

Task Dura:on Task Dependencies Risk events (probabili:es and impacts)

O = Output Project Schedule


Simula:on Results/Itera:ons* for Risk Events

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Itera:on No.

Threats Opportunities

1 2 3 1 2

Applica:on is rejected. Trial 1 of prototype

tes:ng is a total failure.

Supply chain disrup:ons are caused by a hurricane(s).

Applica:on is approved quickly.

Trial 1 of prototype tes:ng is a total

success.

Task 23: Resubmit Applica4on (new task)

is created.

Task 9: Revise/Test Prototype – Trial 2

requires addi:onal :me.

Task 17: Procure Equipment/Materials

takes longer. New dura:ons are

es:mated.

Task 14: Receive Approval – Task

dura:on is reduced.

Task 9: Revise/Test Prototype – Trial 2 is no longer required.

30% 30% -‐ 20% 10%

1 0 0 67 -‐ 22 23

2 0 15 0 0 24

3 0 0 0 0 0

4 0 0 0 -‐ 21 25

5 0 0 0 0 27

6 16 17 0 0 30

7 15 0 60 0 31

8 0 0 0 0 29

9 15 0 0 0 26

10 0 16 0 0 25

*Only 10 itera:ons shown for illustra:on


PDF with Variability and Events Risks

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Min: 234 d Mean: 272 Max: 440


CDF with Variability and Events Risks


Con:ngency Reserves for Project Zulu

Contingency Reserve

For PM

For Sponsor

Deterministic Estimate

255 d

17 d

10 d Determinis:c Es:mate + PM reserve (P50): 272 d

Determinis:c Es:mate + PM & sponsor reserves (P80): 282 d


Other Risk Types 3.  Ambiguity Risk: Risk related to uncertainty about future. Examples:

•  Evolving requirements

•  Inherent complexity in the project

4.  Emergent Risk: Risk caused by events that can only be recognized aser

they have occurred; events are osen referred to as “unknown-‐unknowns”

or “black swans.” Examples: •  Tsunami

•  Major poli:cal upheavals


Ambiguity Emergent

Variability Events

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Qualita:

ve

Quan:

ta:ve

Non-‐Event Event

Risk Ana

lysis App

roach

Risk Type

Next Generation Risk Analysis and Management

Manage by Using Reserves


Current/NextGen Risk Management

No. Component Current NextGen

1 Project estimates Deterministic Probabilistic

2 Risks (threats/opportunities) Not considered Considered

3 Risk analysis Qualitative Quantitative

4 Reserves Not included Included

5 Reserve estimation Ad hoc Rational


NextGen Project and Pordolio Management !  Two-‐day workshop

!  In Chicago

!  Q1 2020 (dates to be determined)

!  Topics covered:

•  Schedule and cost risk analysis

•  Integra:on of schedule and cost risks

•  Es:ma:on of reserves

•  Risk mi:ga:on

•  Uncertainty analysis of NPV

•  Sensi:vity analysis

•  Pordolio op:miza:on

•  Efficient fron:er

•  Decision analysis

!  For more informa:on, contact: [email protected]


APPROVED

Project Zulu Schedule !  Project team receives 272 days (P50) as the performance measurement

baseline that includes 17 days of con:ngency reserve.

!  Management will keep in mind that P80 will be 282 days.

!  We do not expect any ambiguity risks on the project.

!  Management needs to be aware of emergent risk.


Highlights


1. Ignoring variability and events risks is the most common reason for schedule failures.

•  So… Account for them!!

2. Deterministic schedule estimates are unrealistic and overly optimistic.They lead to project failure.

•  So… Stop using them!

3. Want to increase your chances of project success? Of course!

•  So… START USING NEXT GEERATION RISK MANAGEMENT!!!

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Contact Informa:on !  Dr. Prasad S. Kodukula, PMP, PgMP

!  [email protected]

!  LinkedIn: Kodukula

!  www.kodukula.com

!  www.facebook.com/KodukulaAndAssociates

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howtoimproveprojectschedulesuccessbybeeresmaon: … · 2019-11-27 · aboutus$!...

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