How AI is Changing the Art & Science of CPM Scheduling

Transcription

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

2 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

3 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

4 The Shortcomings of CPM It s not execution 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

5 The Original Gantt Chart 1795 Harmonogram

6 Artificial Intelligence Any device that perceives its environment & takes actions that maximize its chance of success at some goal. AI is simply the next step in the evolution of computing power

7 Examples of AI How many of us use AI in our personal lives? Siri & Alexa GPS Guidance Waze or Google Maps Uber and Lyft Machine learning to optimize driver 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 What about in our professional lives? IoT & Connected Devices Fleet Management Equipment Uptime Optimization Cybersecurity Why aren t we using it to build a more realistic plan?

8 Artificial or Augmented Artificial Intelligence: ability for computer to perform tasks that normally require human intelligence Augmented Intelligence: supplements human thinking rather than replacing it Makes our lives easier by performing tasks faster and with greater efficiency Still requires human intelligence, reasoning, and expertise What a computer is to me is it s the most remarkable tool that we ve ever come up with, and it s the equivalent of a bicycle for our minds.

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

10 What about all that data? 63% Average percentage of organizations that believe 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

11 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 Expert System Knowledge Library Facts BASIS Planner or Team-member Learning Weighted Inputs BASIS Neural Network Hidden Layer Updated Weights Inference Engine Expertise Expert (Knowledge-Based) Systems Knowledge base + inference engine Rule-based, e.g., IF AND THEN Domain-specific, e.g., planning Neural Networks Learn by example/pattern, e.g., face recognition Not task (domain) specific Requires history or supervised learning

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

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

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

15 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

16 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

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

18 Human Intelligence Input Plan Validation Team Member Markup Markup Review & Plan Consensus Sandbox (markup layers) Determine buy-in & consensus Consensus is key 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: 35d TM2 Markup: 60d Planned Duration: 40d TM1 Markup: 60d TM2 Markup: 60d

19 Demonstration

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

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

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

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

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

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