Capability Overview - DataTheta
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DataTheta 1 www.datatheta.com www.datatheta.com Capability Overview R&D Teams February 2020
Transcript of Capability Overview - DataTheta
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www.datatheta.com
Capability Overview R&D Teams
February 2020
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Our DNA
What is in our DNA?
MATH ALGORITHM
We are mathematicians, data engineers, data analysts and data scientists. We know which question to ask to your data and get actionable answers that would benefit your business.
TECHNOLOGY
Technology is our middle name. Data need to be cleaned, sorted, displayed and applied math on it. We are canny with tech stacks that handle the data at any scale, speed and structure.
Why DataThetaexist?
BUSINESS
We understand your business and your problems. We exist to bring technology and data science to your table to ask the right questions to solve your business problems.
DECISION SUPPORT
We exist to fortify your business decisions using scientific techniques. Your intuition is powerful. Our decision science and your intuition would add fire-power to your business decisions.
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Digital R&D Accelerator Model
The R&D function is transforming in the digital age. Organizations that are nimble are reaping the benefit. Differentiate the Digitization Opportunity and Hype.
Today’s R&D environment
Records on Excel sheets and databases silos
Closed innovation approach
Static dashboards that decipher the past
Partners are considered outsiders and termed as “vendors”
Records are shared and collaborative
Open innovation approaches
Dynamic learning systems that predict the future
Partners are considered as catalysts and acceleration agents
Digital R&D environment
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What we bring to the table?
How are changing when the customer priority changes so fast? Where to start?
Technology Driven strategic advice, Implementing Data Driven decision making and Partnering in producing results that matters.
High
HighLow
Customer Preference Shift
Competition Shift
R&D Digitization Impact
Long term opportunities
1. Deduce the product creation life cycle that wins the market
Unmissable opportunities
1. Scout for new products2. Create prototypes, MVPs and test market
Not in Priority
1. Focus on patent management and other tactical needs (Anything that is not having direct implication on customer delight)
Taste immediate wins
1. Tailor Product based on customer needs
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Our CraftsData to Decision approach
Algorithm Development for custom needs
We help you to use ML and AI models for your targeted needs. Your problem is unique, and the algorithms should be created on the merit of the problem. Efficient algorithm is the first step for reaching the optimal solution
Lab-to-Market AccelerationThe product development takes longer time due to more experiments and testing. Machine Learning models can help to conduct the best experiments. The models can be a soft instrument to pre-screen your samples before taking to original testing.
Product Quality Monitoring & ControlThe off-specification product generation is mainly due to uncontrolled input variability. Thanks to Machine Learning models that can learn the process and prescribe the input and process variables for maintaining the end product quality.
Elucidate Product adaptability to marketThe past experimental data and the customer feedback is a treasure trove for finding the Product – Market fit for your next product. ML and AI models can inferentially measure the product adaptability to the marketplace based on the product success rule set and adjacent product templates.
Soft Sensors CreationThere are variables in your business that could not be measured with a sensor. The viscosity, fineness, etc., can be inferentially measured by mathematical models. This will allow you to have better control over the process.
Self-service Data LabsWe build your Data Lab to enable your analysts and data scientists to figure out the value hidden in your data. The end result is monetizing your data and experience.
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Illustrative Solutions built on Math algorithms
Business Problem Conventional Solutions How can AI/ML solve? Benefit
Target property achieving systems (Quality
systems and New product development)
Statistical Process Control,
Design of experiments and
Taguchi method
• Quantitative Model based
methods
• Qualitative models and
search strategies
• History based methods
Prescription of input variables for
achieving the target property
Soft Sensor for inferential measurement of
variable (Wear of material after 10 years of
use, Viscosity of material during processing)
Heuristics (by visual
examination or intuition)Reduces the lab-to-market time.
Expert Machine Learning Systems for
building information storehouse (Inferring
from past experiments without or reduced
experiments)
Not applicable
Leveraging data from disparate
sources to make confident
decisions
Product quality variability due to input
variability or unascertained disturbances
(Quality systems in production)
Statistical Process Control,
Heuristics
Predict the output quality of the
end product
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Human View Vs Machine ViewWhy can math algorithms do a better job?
Measurement Space + Feature SpaceThe machine combines these measurements and make multiple mappings such as Temp + Pressure or Temp + Pressure + Percentage Composition. This is stored in the feature space (such features are non-existent for a rational human mind with limited computation capacity even the IQ is 200+)
Decision Space + Class SpaceThe decisions made are suitably stored in the class space, which is the mapping all past results for different combination of success, failure or unable to decide results. This suitably updates the feature space for next best results.
Where we measure the variables such as temperature, pressure, percentage composition, shear stress, etc.,
Based on the measurement, its thresholds and apriori knowledge we decide on it. Eg: the body temperature is above 99 deg C (measurement) so the person has mild fever (Decision based on apriori knowledge)
Human View
Measurement Space
Machine View
DecisionSpace
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Clientele
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Algorithms in action
How it Works?Data. Declutter. Display. Dissect. Decide.
Data FromSources
DataEngineering
DecisionScience
BusinessDecision
AdvancedAnalytics
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Technology landscape
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Thermal Fluid property monitoring for a Chemical Manufacturer
Business situation
Our customer is an US Chemical manufacturer having a vast product portfolio. They manufacture thermal fluids for heat transfer applications. They intend to predict the quality of the fluid and life time of operation to give better value to the customer.
Solution
Created a mixed model with the historical data for the thermal fluid. The variables used includes process conditions, chemical and rheological properties of the fluid and variability of the chemical and rheological properties with respect to time. The polynomial based structure made the computation fast and efficient.
Implementation
The Datawarehouse and Datamart are built on AWS. ETL was performed using Talend. Custom and proprietary segmentation and classification algorithms were executed on Python.
Value creation
The data was acquired from different sources, cleaned and populated in the Datawarehouse. Created a descriptor for quality benchmark with different process operating conditions. Based on the mixed model the expected thermal fluid properties were predicted.
Tools used
Algorithm:
Support Vector Regression
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Product blend optimization for a Chemical Manufacturer
Business situation
Our customer is a paint manufacture based out of US. The coating materials contains binders, pigments, etc. Our customer is creating a composition for military application with certain end use properties. They want to test various compositions and its functionality and durability by experimentation.
Solution
We created a dimensionality fixed multimodal model with the targets in place. The target property variation to the ingredient variation is predicted with the Machine Learning methods. The optimal blend candidates were identified from the cohort and qualified for experimentation and master batches.
Implementation
The experimental physio chemical characteristics data of the ingredients were used create the model. Based on the model, few experiments were performed to predict the possible outcomes for validation of the model. The results whereconfirmed with the empirical evidences.
Value creation
The model-based approach has reduced the time of experimentation in the product development lifecycle. This has allowed the product team to downsize the experimentations by 28% initially on implementation. The lab to market time has significantly reduced by 26%.
Tools used
Algorithm:
Principal Component Regression
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Data Lab for inventory intelligence for an automobile Co.
Business situation
A Japanese customer wanted to maximize the return on storage of core parts for a specific model of car. Build a platform around their "Kai" principles to a dynamic demand-driven inventory framework.
Solution
Created Datawarehouse, Datamart and visualization. The BOM data were used to create an ARIMA model that could predict the requirement for the next 5 years in different geographies. The inventory generation and storage was planned accordingly.
Implementation
The ETL was performed using Talend and the model was built to arriving at replenishment strategy. Based on the prediction the replenishment strategy was created.
Value creation
The optimal part storage and supply for the different geographies was arrived. This has led to an additional cash influx of $ 2 M. The platform was created in tandem to the ERP system.
Tools used
Algorithm:
ARIMA Models
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Self-Service Analytics for a healthcare provider
Business situation
Higher Opioid prescription by the providers due to lack of integrated signaling and monitoring system. The provider was unable to achieve the operational excellence goal.
Solution
Created Datawarehouse, Datamart and visualization console to self monitor the real-time opioid prescription and administration for every patient.
Implementation
The Datawarehouse and Datamart are built on AWS. ETL was performed by Pentaho. Real-time visualization and signaling created on Tableau.
Value creation
The Opioid prescription rate was reduced by 16% in the first month of implementation. The technology helped the customer to achieve its operational excellence goal in 6 months.
Tools used
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Team
EASTER PRINCE J
Chief Solutions Officer
NANCY PAULRAJ
Senior Data Engineer
SATISH HARKAL
Data Engineering Lead
ABHISHEK KESHAV
Chief Client Partner
Contact Us
+1 (908) 312 -2323
+91 981 028 8768
datatheta.com
