Projects. Powered

Projects. Powered.Empowering organizations with project intelligence.

PIPMG Webinar SeriesMarch 18, 2021

▸ Hello from Prof. Paul Boudreau

▸ Make this an interactive Zoom call together, respectfully

▸ Use the chat box to submit your questions

▸ Be interactive with others during breakout sessions

▸ Present yourself on the chat box and include your LinkedIn profile

▸ Ex: Edward Ko, Co-Founder at Mely.ai, https://www.linkedin.com/in/edwardkks/

▸ We will provide links for:

▸ Our company website

▸ LinkedIn company page

▸ How to book in my office hours

A bit of housekeeping

Introduction

https://www.dropbox.com/s/v72twmo7c8dj3ee/Pharma%20intro2.mp4?dl=0

https://www.linkedin.com/in/edwardkks/

How might Intelligent Document Extraction, Comprehensive Search Engine, Computer Vision and Virtual Assistance be relieving some common pain points from Pharmaceutical Project Management?

Let’s Get Started

Mely.ai™: Succeed with Project Intelligence | © Mely.ai™ 2021, All Rights Reserved

1. Company Presentation

▸ Problem Statement, Mission, Value Proposition

▸ Team and History

2. Mely.ai™ Technology – Product Demo and Industrial Use Cases

▸ AI-Powered Solution and Value Proposition

▸ Use Case 1 – Using Computer Vision on Construction design drawings

▸ Use Case 2 – Smart Document Management in Supply Chain and Logistics

▸ Use Case 3 – Intelligent Knowledge Engine in Project Management

▸ Use Case 4 – Virtual Assistance with Sentiment Analysis, for all verticals

3. Discussion

▸ Breakout session to discuss Pharma needs, issues, and potential for AI

▸ Recap to the full group, best practice sharing

Agenda


1. Mely.ai™ Company Presentation


Problem Statement

Enterprise project management focuses on delivering organization’s overall business objectives.

However, minimum of 1 project out of 3 fails and this ratio has been steady for the past 6 years¹.

Source: Pulse of the Profession 2020, Project Management Institute ¹

3 reasons why business objectives are not met:

o Inability to leverage past accumulated knowledge

o Difficult to predict project success

o Misalignment between business objectives and projects49%

44%

34%

Late

Over budget

Goals not met


https://www.pmi.org/learning/library/forging-future-focused-culture-11908

What we have learned from project management professionals and from the market.

• It is the right time

• Computing power is accessible and AI algorithm has advanced significantly, lowering the computing

• Cloud storage has been advanced and PMO are digitalizing and uploading previous project data, making it accessible

• All project management events in 2020 has a topic in AI

• The interest is high

• Project management professions, enterprise innovators and strategists show interest

• Curiosity is present

• Waiting for the leading solution to emerge

Artificial Intelligence


Mission

At Mely.ai™,We leverage all past knowledge to provide enterprise project intelligence to executives and project managers so they can make informed decisions to achieve enterprise business objectives.


► Make Everyone An Expert

Synthesize all past work from multiple sources into searchable knowledge for everyone in the organization

► Project Success Predictor

Our engine predicts the outcome of current projects and helps you plan future projects

► Real-Time Data Visualization

Modern and customizable dashboards available with live data for executives and project managers

Value Proposition

Succeed with Project Intelligence


Company History

Jan 2020

• Admitted to incubator District 3, focused on business model, helping startups to harness emerging tech to create businesses with global impact

•Interviewed over 100 project managers to confirm pain points and value proposition

March 2020

• Admitted to Next AI Montreal, accelerator and founder development program for artificial intelligence-based ventures

• Runner-up of BMO Award at the end of cohort, as the Chief Data Officer sees real-life application of our technology in project management of banking services

July 2020

• Industrial Research Project started, supported by The National Research Council of Canada Industrial Research Assistance Program (NRC IRAP), Canada's leading innovation assistance program for small and medium-sized businesses

Nov 2020

• Admitted to Creative Destruction Lab, is the country’s largest program for seed-stage science-based businesses with high potential for growth

Dec 2020

• Won the IEEE Startup Pitch Competition – Innovations amid COVID-19

• People’s choice on the demonstration of resilience during difficult time of COVID-19 and most potential to achieve product-market fit

• MVP ready for commercialization

Jan 2021

• Laureate to Foundation Montreal Inc.

• Connecting innovative and ambitious businesses through startup grants, access to a prestigious business network, and the expertise of seasoned volunteer coaches.


https://district3.co/

https://www.hec.ca/en/executive-education/next-ai/next-ai-montreal-en-us.html

https://nrc.canada.ca/en/support-technology-innovation/about-nrc-industrial-research-assistance-program

https://www.hec.ca/en/executive-education/creative-destruction-lab/index.html

https://site.ieee.org/scv-yp/2020/10/27/ieee-startup-pitch-innovations-amid-covid-19/

https://www.montrealinc.ca/en/home/

Our Tech Team At Your Service

Edward Ko, CEO and Co-FounderProject Management Professional, Green Belt Lean 6 Sigma15 years of PM experience, implemented 2 PMOs

Hoang Ngo, CTO and Co-FounderAzure Cloud Expert, Professional Scrum Master 17 years of PM experience in the SaaS industry

Laurie Huang, Lead Data ScientistMaster’s thesis on Computer Vision and Deep LearningImplemented text analysis and text generation projects

Bahar Sateli, AI Technical AdvisorManager in AI & Analytics at PwC CanadaCo-Founder at Knowly.ai, Forbes Women in AI to Watch 2020

Jai Yadav, Data EngineerImplemented data pipeline in AWS projectsLed anomaly detection projects in the finance industry

Nathalie Houde, Branding and Marketing AdvisorEx-VP Branding at Cossette CommunicationsFounder at iF::créativité collaborative, FourSight certified


https://www.linkedin.com/in/edwardkks/

https://www.linkedin.com/in/hoangngo1/

https://www.linkedin.com/in/laurie-huang/

https://www.linkedin.com/in/jai-yadav-23804297/

https://www.linkedin.com/in/bahar-sateli/

https://www.linkedin.com/in/nhoude/

2. Mely.ai™ Technologies


► Knowledge

► Prediction

► Visibility

Value Proposition

Succeed with Project Intelligence


Mely.ai™ Core Technology uses artificial intelligence to extract key information from documents of all formats.

► Our technology includes the use of:

► Natural Language Processing (NLP)

► Machine Learning (ML)

► Computer Vision (CV)

► Optical Character Recognition (OCR).

► It extracts keywords from complex project management documents of any formats:

► PDF, excel spreadsheets, word documents, JPEG/PNG images

► The Core Technology is designed in such architecture that it can be trained with a specific set of training documents to then find relevant keywords on-demand, within your working environment.

Mely.ai™ Core Technology Overview

How?


Use Case 1 – Construction


Use of Computer Vision


Background

► In every design-build construction project, there is a large volume of documents to be reviewed.

► Manual analysis takes up too much time and creates too many errors.

Pain Point: Manual Effort and Low Value Work

► Lengthy and repeated manual work

► Unnecessary repeated work due to different document delivery times

► Manual data re-entry to other systems


Scope of the challenge applies mostly in the Pre-Construction process:

1. Information Breakdown from Client Mandate to Conceptual Planning and Preliminary Design

▸ Generally one-time process at the start of the project

2. Extract information for Estimation and Bid

▸ Generally one-time quantity extraction for estimation purposes

3. RFI, Directives, Major Change Requests

▸ Occurs frequently in the project, but the volume of documents are significantly less compared to the initial documents

5. Data Entry to Documentation Systems – during Construction Kickoff phase

▸ Information needs to be entered to other systems such as: CMIC, Procore, Oracle Aconex, Smartuse, Autodesk Bim360, Box, Sage Timberline, iTWO costX, local servers and more

Design-Build Construction Process Overview

4. Quality Control process after each extraction to look

for errors / contradictions

1

2

3

5

4

Project ApplicationClient

mandate

1

2

3

5

4


https://cmicglobal.com/

https://www.procore.com/en-ca

https://www.oracle.com/ca-en/industries/construction-engineering/aconex-project-controls/

https://smartuse.com/

https://www.autodesk.com/bim-360/

https://www.box.com/

https://www.sage.com/en-ca/products/sage-estimating/

https://www.rib-international.com/en/costx/products/

Leverage Computer Vision Capability to Read Technical Engineering Drawings and to Extract Key Information

► Add Computer Vision capability to read information from plans, drawings, functional and technical diagrams

► Enhance OCR capability to read handwriting comments, mostly in comment sheets

► Extracted information can be reused for Knowledge Engine (searchability) or Business Intelligence (Analytics)

► KPI Expectation: 50% time saved by automating the key info extraction and comparison between mandate specs and technical plans

Proposed Solution


POC Demonstration


Use Case 2 – Supply Chain and Logistics


Background

► Each year ocean freight forwarders alone exchange in excess of 12 billion documents that come in a variety of formats

Pain Point: Manual Effort and Low Value Work

► Lengthy and repeated manual work

► Unnecessary repeated work due to different document delivery times

► Manual data re-entry to other systems

► Visibility of current shipment status

Use of Natural Language Processing



Many companies are still using manual data entry to process business documents.

This approach is error-prone, time-consuming, and expensive.

Manual Entry To Process Freight Documents

Problem Statement

Each year ocean freight forwarders alone exchange in excess of 12 billion

documents that come in a variety

of formats

Document Input Read and Understand Documents

Open Operating System

Back-and-Forth Manual Entry and Audit Cycles

Store in Different Document

Repositories

© Mely.ai™ 2021, All Rights Reserved

Solution: Automatic Data Extraction Process

Document Input Data Capture Audit Workflow Output

Email entry

PDF conversion and enhancement

Extraction by AI-OCR

Deep Learning Model (DL-NER) to perform

Key-Value Pairs

Human Audit of Data Extraction

Feedbacks used to retrain models

Rule-Based Automation

No Code Workflow Designer

Structured Data ready to be sent to other systems via

API

Continuously-Trained Machine Learning from manual interventionto refine the bounding box definition and the key-value pair taxonomy



Leverage Natural Language Processing, Computer Vision and Optical Character Recognition to extract accurately complex information from Freight Documents.

► Named Entity Recognition to pair extracted information to a specific taxonomy vocabulary built for Supply Chain and Logistics

► Enhance OCR capability to read handwriting comments, mostly in comment sheets

► Enable Workflow and other automation based on the extracted data

► KPI Expectation: 95% time saving and 85% cost saving from current business process

Proposed Solution


Product Demo


Use Case 3 – Project Management


Background

► Project Management Office is the keeper of historical project knowledge

► Project Managers are less than equipped to succeed in project

Pain Point: Lack of Data-Driven Project Intelligence

► Knowledge is not well shared or not searchable

► No prediction tool available

► Lack of Insights and Prescriptive Analytics

Use of Machine Learning



AI-Powered Real-Time Insights

Our solution uncovers impactful insights and lets leaders make informed project decisions.

Unifying Data Actionable Recommendations

Systems-agnostic solution interacts with all project

management platforms and document repositories

Capable of processing high volume of highly complex data

to reveal live actionable insights

Expert recommendations and augmented intelligence to make

every decision based on data

1 2 3



Leverage a combination of System Integration, Machine Learning to provide an Insight Engine and to perform Prescriptive Analytics

► Integrate systems to structured knowledge from previous Microsoft Project plans, SharePoint site with previous project management documentation

► Knowledge to search for previous knowledge by relevance

► Look for insights through previous and current projects

► Prescriptive Analytics to run future project scenarios

► Expectation: Maximize project success rate

Proposed Solution


Product Demo


Use Case 4 – Better Communication


Background

► There are too many channels of communication

► Still, most issues in Project Management are caused by a bad communication within the Project Team

Pain Point: Too Much/Too Little Communication

► Asynchronous communication

► Text vs Voice, did we get the message across?

► Difficult to determine the emotion behind the message

► Is Chatbot my only option out there?

Use of Virtual Assistance



Leverage a combination of Natural Language Processing and an Integration to Communication Channels to start a conversation and to collect responses.

► Integrate the virtual assistance communication system directly to your project management platform

► The communication can be in SMS or email

► Sentiment Analysis score provided for each response

► Automate communication based on triggering keyword or events

► Expectation: Streamlined Communication

Proposed Solution



Questions/Comments/Feedbacks?

Breakout Room Discussions

• Construction / Computer Vision (CV)

• How to apply CV into clinical trials end point measurement?

• Any other applications?

• Supply Chain and Logistics / Data Extraction

• Any value from extracting data from heavy documents in clinical documentation, regulatory dossiers?

• Project Management / Searchability of Knowledge

• In which areas does the knowledge management would benefit the most?

• Regulatory, clinical, manufacturing?

• Any value from collecting automatically historical data into a searchable database?

• Communication / Virtual Assistance (VA)

• How would you use VA to manage your projects?

Sample Breakout Room Openers


Group Recap

Feedbacks from Group Discussions


Group 1

• Identify Key documents to be extracted

• How to use AI, how to choose AI vendors: performance, privacy

• Use AI in team meetings? Bring knowledge to the table during meeting?

• Knowledge resides with companies, need to share into a Data Trust to feed AI models



Group 2

• Quality of data is the concern, using supervised learning to cleanse the data

• Predict the length of projects, resources required – prescriptive analytics

• Predictive early warnings – cost, risk

• Data extraction of regulatory documents

• Regulators to use AI to review documents? – disruptive



Group 3

• Make sure to have readable inputs – the way the documents are put together (format)

• Mining from standard documentation

• Quantity: how smaller size company can benefit from AI (or lack of data compared to larger companies)

• Data Trusting, information sharing

• Technical information, insights

• Looking at process not equal to looking at outcomes

• Transforming data into knowledge, wisdom

Your feedbacks are important to us.

Please fill up this 2-min survey with 5 questions:

https://mely-ai.typeform.com/to/C5qh47Qb

Looking forward to connect with you again soon!

• Company website: www.mely.ai

• LinkedIn page: www.linkedin.com/company/mely-ai

• Office hours: https://calendly.com/mely-ai/

Thank you for attending this Webinar


https://mely-ai.typeform.com/to/C5qh47Qb

http://www.mely.ai/

http://www.linkedin.com/company/mely-ai

https://calendly.com/mely-ai/

Thank you.

/company/mely-ai

[email protected]

Edward Ko, [email protected]

mailto:[email protected]