ROI of Machine Learning In IoT
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By: Prof. Giuseppe Mascarella [email protected]
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Transcript of ROI of Machine Learning In IoT
By: Prof. Giuseppe Mascarella
Download summary at:
www.valueamplify.com
What Drives Value?
What is Value?
A Business Performance Improvement that is alignedwith 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 [email protected]
What’s the Difference of Project vs Product?
Value Propositions Structure:
The opportunity to make a business improvement of CSFwith What (brand and description of product)
will improve “What” ( CFS relevant)
for “Who” (target audience/stakeholders).
Contact [email protected]
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.
Step 2: MAP SOLUTION
Purpose:
• Build a solution aligned with findings from
the Business Assessment Roadmap
Contact [email protected]
Contact [email protected]
Identify Factors That Create Obstacles
Pattern 1: Cause and Effect
Opportunity
Automate Dis-intermediate Synergy Competency
Pattern 2: Maturity Model Progression
Contact [email protected]
Contact [email protected]
Sample Data Driven Scenario: Electricity Usage OptimizationMaximize 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 + 𝑏1𝑋1
Variable Energy Consumptions
0
50
100
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1 3 5 7 9 11 13 15 17 19 21 23Pu
mp
Sp
eed
Time of Day (24 hour clock)
By Time of the Day
Step 4: ComparisonStep 1-2-3: In-Out-Effectivness
Contact [email protected]
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).
Contact [email protected]
Increasing OEE Means Increasing ROI
Modeling ROI Calculations in preparation for customer engagement
Regarding Costs, we estimate, a yearlytotal amount of $100k, adding internalcosts related to the data usage andcustomization.The costs have been actualized,calculating the NPV, using the discountrate.
Regarding OEE, we estimate the variousimprovements along the years, thanks tothe Value Amplify Analytics solution.
Considering the assumptions, we calculatethe effect of data insight solution on IFO,basing on a conservative approach.
Eventually, considering the NPV of theimpact of data (gain minus costs), wecalculate 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.
By: Prof. Giuseppe Mascarella
Download summary at:
www.valueamplify.com
By: Prof. Giuseppe Mascarella
