How AI is Changing the Art & Science of CPM Scheduling Dr. Dan - PowerPoint PPT Presentation

How AI is Changing the Art & Science of CPM Scheduling Dr. Dan Patterson, PMP BASIS CEO

BASIS Legacy • Two decades of analytics: bettering project plan integrity • Evolved CPM through risk-adjusted scheduling & critiquing • The next step, driving plan realism • We’ve actually been implementing AI for a long time

Critical Path Method • 1956 – CPM invented • Dupont/Remington • UNIVAC-1 computer • Today - same algorithm • 15 lines of code • Generates dates & float • Dates are NOT inputs

The Shortcomings of CPM It It’s not exe xecution that is letting us down… • CPM plans are overly optimistic, best case • Don’t encourage sound use of building blocks • Industry breeds schedulers not planners • Gantt chart has not evolved in 100 years

The Original Gantt Chart 1795 Harmonogram

Artificial Intelligence “A “Any device e that per percei eives es its en environmen ent & ta takes acti ctions ons th that t maxim imiz ize its its ch chanc nce of of suc success ss at so some me goal.” AI is simply the next step in the evolution of computing power

Examples of AI How many of us use AI in our personal lives? What about in our professional lives? • Siri & Alexa • IoT & Connected Devices • GPS Guidance • Fleet Management • Waze or Google Maps • Equipment Uptime Optimization • Uber and Lyft • Machine learning to optimize driver • Cybersecurity delivery • Commercial Airlines • Only 7 minutes of a flight on a Boeing airplane involves ‘human-steered’ flight • Credit Card Fraud Protection • Uses a neural net to predict fraudulent transactions Wh Why aren’t we using it to build a more realistic plan?

Artificial or Augmented “What at a computer • Ar Artificial Intelligence : ability for is t is to m me is is it it’s t the computer to perform tasks that normally require human intelligence most re mo rema mark rkable tool that at we’ve • Au Augmented Intelligence : supplements ev ever come up with, human thinking rather than replacing it an and it’s s the • Makes our lives easier by performing eq equivalen ent of a tasks faster and with greater efficiency • Still requires human intelligence, bi bicycle e for our reasoning, and expertise mi mind nds.”

Knowledge-Driven Planning with BASIS Artificial Human Augmented Intelligence Intelligence Consensus-Based Incorporate team expertise Augmented plan building Achievable Plan Consensus analysis Active benchmarking Knowledge capture & re-use Review cycle & plan commitment

What about all that data? Average percentage of organizations that believe 63 63% they do NOT effectively utilize the data they capture to drive business value That begs the question(s)… 1. Does AI Planning really need ‘big data’ to be effective? 2. Can I trust the suggestions made? Source: PwC 2017 Global Digital IQ Survey

BASIS AI Approach • Uses Expert System to make suggestions, Neural Network to learn • Makes planning suggestions based on rules • Automatically adjusts ‘weighting’ of each rule • Doesn't require ‘big data’ BASIS BASIS BASIS Expert System Planner or Neural Network Team-member Weighted Hidden Layer Updated Inputs Weights Facts Knowledge Learning Library Expertise Inference Engine Expert (Knowledge-Based) Systems Neural Networks Knowledge base + inference engine Learn by example/pattern, e.g., face recognition Rule-based, e.g., IF…AND…THEN… Not task (domain) specific Domain-specific, e.g., planning Requires history or supervised learning

BASIS Software Introduction What is it? Wh BA BASIS Appr pproach • Knowledge-driven planning Sketch • Top-down • Guided plan creation/validation • Timelines • Analyzes realism • Incorporates team consensus Consolidate Plan • Consensus • Detailed plan • Buy-in • CPM-based Result: A more achievable plan Markup • Feedback • Expert Opinion All four modules designed together to make planning behavior be better r & more re efficient

Building Schedules in BASIS A Smarter, More Natural Way to Plan 1) 1) Sketch ch 2) Plan 2) • Top-down planning • Detailed planning • Planning packages: timelines • Work packages & tasks • Set deliverable/contract dates • Detailed logic/calendars • WBS dictionaries/templates • Full CPM analysis A.I. used to suggest A.I. used to detail timelines & benchmarks schedules

Building Schedules in BASIS A Smarter, More Natural Way to Plan 3) 3) M Markup 4) Con 4) onsoli olidate • Unique markup layers • Review contribution • Simple experience • Analyze consensus • Durations, dates, risks, etc. • Flatten into plan • Visualize impact • Visualize impact H.I. used to capture H.I. used to drive expert opinion consensus

How Does BASIS Offer Suggestions? BASIS Inference Engine • Offers real-time suggestions • Uses powerful inference engine • More than just a search engine • Understands context • Progressive emphasis • Confidence score in assessment

How Does BASIS Offer Suggestions? BASIS Inference Engine • Offers real-time suggestions • Uses powerful inference engine • More than just a search engine • Understands context • Progressive emphasis • Confidence score in assessment

How Does BASIS Learn? Artificial Intelligence Se Self Learning • Progressive emphasis • Confidence impacted by emphasis Hu Human Tea eaching • Influence suggestions through rules • Project or corporate rules • Establish patterns without natural matches Both drive a more natural planning approach

Human Intelligence Input Plan Validation Team Member Markup Te Ma Markup Review & Plan Consensus • ‘Sandbox’ (markup layers) Planned Duration: 40d • Determine buy-in & consensus TM1 Markup: 35d • Consensus is key TM2 Markup: 60d • It’s okay for team to push back as long as there is consensus on changes • Buy-in without consensus reflects ‘chaos’ in your project Planned Duration: 40d TM1 Markup: 60d TM2 Markup: 60d

Demonstration

Step 1: Sketch Interrogate the Knowledge Library to import predefined scope, planning packages, & benchmarks. Use Waypoint analysis to benchmark your plan. Drag/drop to create logic links between planning. Automatically Manually add new create multiple planning packages. planning packages in sequence or in parallel.

Step 2: Build Plan BASIS indices track alignment, detail, & how continuous activities are over the duration of the project. Define planning windows, e.g., Track plan time windows alignment when specific through scope or work BASIS must be dates. completed. Manually create activities and Drag/drop to create milestones. logic links between activities and milestones.

Step 3: My Markup Drag/drop activities that you believe need resequencing or different durations. ‘Accept’ or ‘Change’ Understand the Identify risks, each line item that you impact of your issues, action have been asked to markup. items, etc. review. Markup BASIS will individual activities or suggest work packages as a common risks, whole. etc., as you review.

Step 4: Consolidate Markup Analysis determines how much buy-in versus push back & how team member opinion impacts the plan. Review all Determine the contributions for impact on each activity & durations and choose either the dates. consensus or a specific team member’s opinion to commit to the plan. If team members Consensus analysis have changed start shows how much dates, these can be alignment there is modified by either a between team constraint (default) members. or lags. Lags will give a more free-flowing schedule than constraints.

So How Does Intelligent Planning Help? A Smarter, More Natural Way to Plan A.I. A. H.I. H. • Access: Organization’s knowledge • Ownership: Promotes buy-in & is accessible & useful plan acceptance • Speed: Plan creation is • Feedback: Quantify team accelerated contribution & inclusion • Quality: Plans are based upon • Closed-Loop: Refine Knowledge standards, benchmarks & history Library with validated plans • Completeness: Plans inclusive of • Efficiency: Minimizes need/time total of scope required for interactive planning sessions

For the latest updates & BASIS content subscribe @ www.basisplanning.com/subscribe