Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

1

Megaprojects Need New Tools:

Why Current PM Tools Don’t Deliver the Goods

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

2

Overview

• Why a fundamentally new project representation and methodology is needed for Megaprojects

• The Plexus™ representation and methodology

• Experience on real industry projects

• Demonstration

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

3

Fundamental research question

• “Why do large, mature, sophisticated organisations routinely fail to deliver complex new products successfully?”

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

4

Existing Tools

• “project management theory remains stuck in a 1960s time warp and that the underlying theory of project management is obsolete”

Professor Peter Morris,“Rethinking Project Management”

EPSRC Network 2004-2006

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

5

Importance of suitable

representations

• Multiply LXXXVII by LXXIII in your head without converting to decimal!

• The Roman Number system is not suited to arithmetic

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

6

Megaprojects Are Complex

Systems

• Involving large numbers of adaptive entities

• Interacting in multiple dimensions

• With stochastic effects

• And time evolution

– Extant project models are not suited to Complex

systems

– How do we derive an adequate models, then?

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

7

Product and Process Knowledge

Planning

Product Knowledge

Equations, Rules.

Process Knowledge Connections, Sequences

Stochastic data Workflow

Engineering

Domain

Management

Domain

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

8

Planning Paradox• Process Knowledge is the

responsibility of, and is generated within the Management domain

• The Engineering domain has an aversion to planning and treats it as low priority, non-core activity

• Planning data has inadequate input derived from and associated with Product Knowledge.

• Plans are too abstract, unrealistic and are more likely to fail

• Poor execution track record further erodes the Engineering domain’s interest and confidence in planning activities.

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

9

“Post-it Planning”

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

10

• Many, large, international, otherwise sophisticated organisations universally resort to “post-it” note planning

• Such planning sessions require labour intensive follow up activities

“Post-it Planning”

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

11

Iterations

• Complex products (such as in aerospace) are highly refined artefacts

• There construction involves many detailed iterations

• Example aircraft design…

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

12

Iteration in design

Determine

aerodynamic

shape

Span, Chord,

Section

Calculate

structural loads

Load casesDesign Load

bearing

structure

Wing box design,

Material volume, density

Calculate

structural mass

Mass of wing structure,

Systems, fuel

In reality projects have many complex iterative loops…..

Not allowed

In PM tools

Aerodynamicist

Flight Physicsengineer

Structural analyst

Mass properties engineer

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

13

Real Iterations!

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

14

Traditional Project management

networks

• Cannot model

iterations

• “Hard”, content-

free dependencies

• One-dimensional,

unsophisticated

visualization

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

15

The Goal

• Tools with a representation that– Can effectively capture process and

product knowledge in meaningful project models

• From which plans are derived

• And from which knowledge is reused

• And which allows for organic growth and replanning

• And which leads to real buy in from all project participants

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

16

Requirements

• Separate project logic from scheduling logic

• Allow for collaborative input

• Allow for multi-dimensional categorization

• Allow for organic growth and replanning

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

17

Plexus approach

• Detailed, multi-dimensional dependency network

• Collaboratively constructed

• For Detailed Simulation

• Multi-objective optimisation

• Analysis and Execution

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

18

Information Driven Planning

Apply Duration & Resource

Requirements

Publish

Schedule

Execute

Monitor and

Control

Re-schedule /

Re-optimise

Scope

Project

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

19

Experience• Project “war-room”

equipped with a data projector and a number of networked lap-top computers.

• Teams to switch between;– collective review of planning

using data-projector

– intensive sessions where sub-groups concurrently develop and refine the network using individual laptops.

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

20

Software Demo

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

21

Demo: Modelling In Plexus

• The Network View

• Focusing the context

• Adding:

– Hierarchy Elements

– Tasks

– Requirements

– Documenting Dependencies with Content

• Dropping and Satisfying Requirements

(collaborative planning)

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

22

Demo: Automatic Understanding

• Cyclic activity handling

• Automatic options and iterations

• Ignoring = Estimating = Risk

• Dependency Matrix View

• Multiple, user-defined plan hierarchies

• Using the project filter

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

23

Demo: Automatic Scheduling

• The DSM for quick schedules

• The resource model

• Discrete event simulation for non-conflicted resources

• Gantt chart view

• Coordinated, filtered views: Network, Matrix, Gantt

• Critical Path to any milestone

• Smart lookup

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

24

Demo: Optimizing Tradeoffs

and Analysis• The Optimizer and DES

• The Tradeoff Surface

– Views, Filters, Colors, Selections

• Multidimensional Export to MS Project

– With roundtrip

• Stochastic Analysis• And much more…

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

25

Demo: Execution and Replanning

• Roundtrip to MSP

• Updating of actual values and percentages complete

• Rescheduling

• Re-optimization that respects actual values

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

26

In Summary• Plexus provides a shift in the planning representation:

– Dependencies have content

– Collaboration provides appropriate content

– And buy in

– For reusable logic

– Iterations and options effect risk naturally

– Schedules are derived data

– That shows trade offs (risk/cost/time)

• Execution should include comparisons to baseline, switching on the trade off surface, reoptimizationwhen necessary, and remodelling

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

27

Benefits• Allows engineers to declare a detailed

representation of the relevant dependencies including iterations.

• Sophisticated viewing and navigation tools allow deep appreciation of context.

• Health checking shows up missing network data, inconsistencies or unconnected activities.

• Allows very fast generation of realistic, detailed networks

• Diagnostics automatically classify network nodes highlighting critical nodes, deliverables and inputs

• Powerful automatic layout algorithms to arrange the network in a logical, compact and visually appealing format

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

28

Questions?

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

29

Model Optional Activity!

• More (resource) cost, less risk:

• More cost and time, less risk:

Dr R Smith

Professor James Scanlan

Professor Phil Lawrence

30

Model Iterations Appropriately

• If a model has natural iterative refinement cycles, model them

• There are three ways: