Fabrikam provides an Intelligent Project Prediction (IPP) solution that uses machine learning models and statistical analysis of historical project data to predict project outcomes with over 95% accuracy, identify projects at greatest risk of failing or exceeding budget/schedule, and recommend areas for improvement. The solution analyzes customer data through a common data model and automated data pipelines to generate predictive insights and dashboards to help executives focus resources and enhance decision making throughout a project's lifecycle. Case studies found the solution improved project delivery and forecasting accuracy, identified savings opportunities of up to 18%, and enhanced strategic decision making.

1. FABRIKAM
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CEO & Founder
25 yrs Prog Mgt

2. FABRIKAM
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The Solution - Intelligent Project Prediction (IPP)
High confident predictions and rationale for project outcomes
1. Supports executives to identify where
best to focus attention and resources
2. Provides assurances of which projects
at greatest risk / opportunity
3. Enhances decision making at the
beginning and throughout a project
4. Identifies how to improve chances of
success across the portfolio

3. FABRIKAM
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Motivations
Do I have
robust data?
Can I course
correct?
Can we do
better in the
future?
Are my
suppliers
efficient?
Will new
projects
succeed?
Improved
Project
Delivery
Improved
Project
Forecasting
Improved
Supplier
Profiled
Improved
Strategic
Foresight
Better
Data
Reduced
Cost

4. FABRIKAM
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The Prediction Multiplier
Prediction accuracy and quality data unlocks up to 18% savings
Improvement Toolkit
• Data Health check
• Automated pipelines
• System agnostic
• Top down data set
• Common Data Model
Future
Savings
Data
Quality
Prediction
Accuracy
Case study based evidence
• Dynamic AI predictions
• 1st and average prediction
compared to actual
• Finance 96% accurate
• Schedule within 95% accurate
Enhanced decision making
• Assured remedial priorities
• Feature transformation
• Improved delivery planning
• Project selection and approval
• Resource allocation
Budget
Actual
Predicted
TIME
Finished
Started
Actual vs
Budget
+19%
Predicted
Overspend
accuracy 96%
Potential
Savings 18%

5. FABRIKAM
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Case Study - FMCG
Greater certainty over delivery outcomes to increase project success and provide Exec knowledge where to
focus their limited time. Portfolio of c250 Projects, significant % failing to deliver to time and cost
STEP 1 STEP 2 STEP 3 STEP 4
Undertake regular data quality
health checks and update data
insights and trends
SUPPORT
Communicate and train use of systems and how to
identify the insights. Implement project controls
transformation.
HANDOVER
Identify project data sources ii) create
customer data model iii) benchmark data
quality iv) provide recommendations to
transform project controls
DATA DISCOVERY
Bespoke dashboards based
on current data model
CONFIGURE
Lessons Learned and roadmap
agreed
REVIEW
STEP 5 STEP 6
Develop underlying system
INFRASTRUCTURE BUILD
Challenge
Provision of an up to
date, dynamic Intelligent
Project Prediction tool
that predicts individual
project outcomes and
compares to what is
being reported, and
identifies core drivers for
project success / failure
Solution

6. FABRIKAM
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Case Study – Customer Outcomes
• Level of
exposure
• Savings
opportunities
• Accurate AI
driven
predictions
• Informed
decision
making
Improved
Data
Quality
Assured
Project
Outcomes
Projects at
risk
Sub-
features
driving
risk
Conclusions:
• Prediction can be trusted
• Unlocks savings
opportunties
• Requires process and
people support
• High quality data extends
opportunities
Now:
• Automating within pre-
projects workflow
• Integrating with other.
Lessons learned

7. FABRIKAM
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Contact us
/company/greyfly/
@greyflythinking
www.greyfly.ai
advice@greyfly.ai

8. FABRIKAM
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How does it work?
Prediction accuracy unlocks savings
Dashboards & Insights
User friendly intuitive
visualisations
Statistical and ML
models
Customer
Data
Model
Pipelines
Fast &
repeatable
processes
Top-down data
Current & Historical
Project Data
Example source systems
Analytics
Engine