ROI of Machine Learning In IoT

By: Prof. Giuseppe Mascarella
giuseppe@valueamplify.com

Download summary at:
www.valueamplify.com

What is Value?
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 it
Contact giuseppe@valueamplify.com

PMI Project Envisioning and CFO Requirement

What’s the Difference of Project vs Product?
Value Propositions Structure:
The opportunity to make a business improvement of CSF
with What (brand and description of product)
will improve “What” ( CFS relevant)
for “Who” (target audience/stakeholders).

REJ Is an Engineered Approach To Assess and Plan the Value

Hypothesis
Chart
Business
Assessment
Chart

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.

Value Based Project KPI (Key Performance Indicators)
www.oee.com

Step 2: MAP SOLUTION
Purpose:
• Build a solution aligned with findings from
the Business Assessment Roadmap

Identify the business activity
or process that that affects
the most CSFs

Identify Factors That Create Obstacles
Pattern 1: Cause and Effect
Opportunity

Automate Dis-intermediate Synergy Competency
Pattern 2: Maturity Model Progression

The Solution Based on Best Practices
Gartner Group Model

Five Steps to Benefit Qualification

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

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%
- We adopt a conservative approach, estimating for each OEE increase of
10.0%, an IFO increase of 10.5%.
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).

How Does Analytics Play a Role

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%

XXProject Proposal Summary
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
Production managers that want to increase OEE by up to 6% use Project XX 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.

Download summary at:
www.valueamplify.com
giuseppe@valueamplify.com

ROI of Machine Learning In IoT

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