This document discusses using machine learning to analyze IoT data and calculate the return on investment (ROI) for various use cases. It provides an example ROI case study of using machine learning in manufacturing to increase overall equipment effectiveness (OEE) and lower production costs. The summary outlines how machine learning can be used to predict and prevent quality issues, analyze diagnostic data, and plan for higher quality at lower variable costs. It estimates that plant managers could increase OEE by up to 6% and achieve a target ROI of 175% with payback within one year.

1. ROI of Machine Learning
in IoT Use Cases
By: Adj. Prof. Giuseppe Mascarella
giuseppe@valueamplify.com

3. AGENDA
1. How Do I Build an Economic
Justification for ML?
2. ROI Case Study: ML in Manufacturing
What is Machine Learning
It is a branch of Computer Science that,
instead of applying pre-defined logic to
solve problems in explicit, imperative
logic, applies data science algorithms to
discover patterns implicit in the data

4. What Drives Value?

5. What is Value?
An action that generates a Business Performance
Improvement that is aligned with the organization CSF
and that enables the organization to make optimal use
of its resources within the context of acceptable Risks.
REJ is the framework
for effective application of technology
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6. Economic Justification is both a PMI Project Envisioning and CFO Requirement
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Action

7. REJ Is an Engineered Approach To Assess and Plan the Value
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8. Hypothesis Chart
Business
Assessment
Chart
Machine Learning
Data Driven
Decisions

9. Business Assessment: Finding Value Driven Hypothesis
.
OEE
CSF (Critical Success Factor)
Use data to produce high
quality(profitable) level steels
and reduce cost of reworks.

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Operation KPI (Key Performance Indicator)
www.oee.com

11. How Does Analytics Play a Role
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12. Step 2: MAP SOLUTION
Purpose:
• Build a solution aligned with findings from
the Business Assessment Roadmap
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13. Identify the business activity
or process that that affects
the most CSFs
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Identify Factors That Create Obstacles
Pattern 1: Cause and Effect
Opportunity

15. Automate Dis-intermediate Synergy Competency
Pattern 2: Maturity Model Progression
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The Solution Based on Best Practices
Gartner Group Model

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18. Intelligence and Asset OEE
JOURNEY
Phase/
Internal
Labels
1 Reactive 2 Informative 3 Predictive 4 Transformative 5 Game Changer
Vision Manage What You
Know
“Tame the Operation Beast”.
Analyze and Predict
Where You Are Going.
Master the Data, Sensors
and Algorithms To
Discover New Insights
Transform The
Experience with Real
Time Insights and
Continuous Feedback
Loop.
Redevelop the Biz
Model with Your
Digital Ecosystem
(I.e. AMZN)
Strategic
Intent
• Define the operational
modelabd RoB (Rhythm
of the Business),
• Orchestrate reports use
• Meet SLAs
• Warranties conditions
• Compliance requirements.
• Take control of modeling the
action plan for your business
aspirations.
• Become data-driven with
easy access to insights on the
“whys” and the trends.
• Collect asset condition data
for asset-specific
• Manage the VoA ( Voice of the
Asset.)
• Instrument the assets to
provide in real-time all the data
needed for predicting and
scheduling maintenance based
on the DESIRED condition of
the asset.
• Improve the User
Experience
• Predict and perform
maintenance based on the
conditions of the use and
the surrounding
environment.
• Leverage data and
knowledge on the use
of asset to offer value
added services.
• The assets is not
longer a cost center
but a profit generator
opportunity.
+1-5% +5-10% +10-15% +15%

19. Sample Data Driven Scenario: Electricity Usage Optimization
Maximize profitability by dynamically operating well sites based on variable cost structure
• x0% of production costs are electricity
• Smart Grid connects well to customer and utility
• Utility charges real-time rates based on Smart Meter readings
• Price of oil determines well site operational parameters
• Minimal acceptable well pressure maintained at all times
• Pump speed maximized when revenues > costs
Maximize Profitability
0.0
1.0
2.0
3.0
4.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time of Day (24 hour clock)
Optimized Electricity Consumption
Electricity Costs ($/kWh) Pump Speed (x100)
Electricity Cost Forecasting (Real-time Model)
Variables
• Supplier
• Season
• Temperature
• Time of day
• Load
0.00
5.00
1 3 5 7 9 11131517192123
Time of Day (24 hour clock)
Electricity Costs
Ŷ = 𝑏0 + 𝑏1 𝑋1
Variable Energy Consumptions
0
50
100
150
1 3 5 7 9 11 13 15 17 19 21 23
PumpSpeed Time of Day (24 hour clock)
By Time of the Day
Step 4: ComparisonStep 1-2-3: In-Out-Effectivness
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20. Five Steps to Benefit Qualification
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21. Increasing OEE Means Increasing ROI
Step 1-2: Current Input-Output
- Estimated period: 3 years
- Yearly Revenues: $ 800,000,000
- Yearly EBITDA: 6.5%
- Percentage of Revenues which can be affected by data: 18.0%
- Discount Rate: 9.0%
Step 3: Effectiveness
- Source: Bob Hansen, Overall Equipment Effectiveness, pp 47-66; where it is
estimated, for each increase of 10.0% of OEE, an incerase of 21.0% of IFO
(Income from Operations).
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22. Increasing OEE Means Increasing ROI
Modeling ROI Calculations in preparation for customer engagement
Regarding Costs, we estimate, a yearly
total amount of $100k, adding internal
costs related to the data usage and
customization.
The costs have been actualized,
calculating the NPV, using the discount
rate.
Regarding OEE, we estimate the various
improvements along the years, thanks to
the Value Amplify Analytics solution.
Considering the assumptions, we calculate
the effect of data insight solution on IFO,
basing on a conservative approach.
Eventually, considering the NPV of the
impact of data (gain minus costs), we
calculate the ROI, as:
NPV [Gain - Cost (related to data)] /
NPV [Cost (related to data)]
Plant A Year 1 Year 2 Year 3 Total
Cost
XX Solution Package $ 50.000 $ 50.000 $ 50.000 $ 150.000
Azure Units $ 50.000 $ 50.000 $ 50.000 $ 150.000
Customization/Operations $ 20.000 $ 15.000 $ 15.000 $ 50.000
Total Cost $ 120.000 $ 115.000 $ 115.000 $ 350.000
NPV [Total Cost (related to Q3)] $ 120.000 $ 105.505 $ 96.793 $ 322.298
OEE - Start of Period 60,0% 61,2% 62,1%
From 60% to 63%
(approx. +5%)
OEE Improvements (Per Year) 2,0% 1,5% 1,0%
OEE Improvements (Cumulative) 2,0% 3,5% 4,5%
OEE - End of Period 61,2% 62,1% 62,7%
Revenues $ 800.000.000 $ 800.000.000 $ 800.000.000 $ 2.400.000.000
IFO (EBITDA 6,5%) $ 52.000.000 $ 52.000.000 $ 52.000.000 $ 156.000.000
% of "Revenue from product" in scope 18,0% 18,0% 18,0%
IFO influenced by Q3 - Start of Period $ 9.360.000 $ 9.360.000 $ 9.360.000 $ 28.080.000
IFO Improvements using Q3 (%):
IFO Increment = 2,10* OEE Increment
4,2% 7,4% 9,5%
IFO Improvements using xx (%) -
Conservative:
IFO Increment = 1,05* OEE Increment
2,1% 3,7% 4,7%
IFO influenced by Q3 - End of Period $ 9.556.560 $ 9.703.980 $ 9.802.260 $ 29.062.800
Gai $ 196.560 $ 343.980 $ 442.260 $ 982.800
Gain - Cost (related to Q3) $ 76.560 $ 228.980 $ 327.260 $ 632.800
NPV [Gain - Cost (related to Q3)] $ 76.560 $ 210.073 $ 275.448 $ 562.082
ROI (Discount Rate 9,0%): 174,4%

23. Machine Learning Project Proposal
Some of the feature discussed:
 Rich and customizable real time production reports
from furnace to warehouse
 Use of Machine Learning to prevent quality issues and
rework
 Visual and interactive diagnostic on complex problems
 Planning for high quality and lower costs of variables
 No installation required, pay-per-use model
Plant managers that want to increase OEE by up to 6% can use Machine
Learning to lower production costs and prevent rework due to lack of
predictive quality systems.
This project in itself has a target potential of 175% ROI, with a payback in 1 year.

24. By: Prof. Giuseppe Mascarella
giuseppe@valueamplify.com
By: Prof. Giuseppe Mascarella
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www.valueamplify.com