Zero Dosing Error Zero Manpower Zero Yield Loss
AI-Driven Manufacturing
Defence to FMCG
Built by researchers from
IIT
Bombay
IIM
Bangalore
Slides
use-cases
AI for Defence and Mondelez Manufacturing
Explosives losses to be reduced by 80%. Chocolate losses to be reduced to 0.1%. Savings in Millions dollars. Payback less than 1 year.
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Zero Yield Loss
Closed-feedback loop AI Manufacturing
AI-controlled Product Overweight and giveaways, Rework, Chemical-reaction Yield conversion. Guarantees Savings from Day 1.
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Zero Dosing Error
AI-Controlled Dosing. Zero Batch Variability.
Eliminate overdose and underdose of powders and liquids. Our AI continuously learns material behavior and dynamically adjusts cutoff.
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Zero Manpower
Transform from Human to AI-Controlled operation
Deep Learning Neural Network AI Model built to completely take decisions Autonomously.
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Use Cases
Zero Yield Loss
Zero Dosing Error
Zero Manpower
Problem Statement
Universal challenge faced across Defence, FMCG, Pharma, and manufacturing industries due to Weight or Volume declaration on packaging. Example below illustrates this:
Overweight Issue
Extra grams of chocolate given to consumer above declared Wt. to avoid legal non-compliance.
Legal Requirement
In India, for 51 g chocolate, avg. Wt. of 125 randomly picked bars from one shift manufacturing must exceed 51 g.
Operational Set-Point
Hence Chocolate depositor is often set to deposit 52 g of chocolate in moulds, leading to excess.
Cost of Overweight
It is significant and a major margin eroder.

Current Industrial Reality

Despite massive investments in Digital Dashboards, Quality Systems, quality Lab instruments, process SCADA systems, and sophisticated Field sensors, most manufacturing plants struggle to convert this expensive Data into Yield loss reduction.

Current Industrial Reality

Measured. Logged. Forgotten.

Plant Quality teams operate sophisticated analytical instruments and collect samples every shift, day and night, to meet R&D-defined specifications. Does this expensive data remain trapped and vanish with their registers instead of driving operational excellence?

Measured Logged Forgotten

Sensors Generating Data, Not Intelligence.

Hundreds of sophisticated field sensors generate massive data every milli-second to control real-time production. But are we truly leveraging this data for a closed feedback loop—or simply letting it auto-delete from hard drives?

Sensors Generating Data

Judgement-Led Manufacturing

Depending on years of experience, educational background, analytical capabilities, and motivation level for on-floor settings tuning of chocolate Depositor, liquid bottle Filler, liquid tetrapak Filler, biscuit Rotary Moulder, biscuit Rotary Cutter, Multihead Weigher, noodle Cutter, chocolate Enrober, and many more?

Judgement Led Manufacturing

The Solution

Deep Learning AI Model

Built from first principles of Engineering and Mathematics (see ellipse diagram), a cutting-edge Neural network designed to precisely control Weight or Volume of product in Closed feedback loop AI Manufacturing process. Following example illustrates it:

Vectors & Matrices First, Second, and Third order Partial Differential Equations Stochastic Probability Distributions Fluid Mechanics Heat Transfer Thermodynamics Chemical Reaction Engineering Electrical & Control Systems GPU Computation
10 sensors 6 sensors 33 sensors 6 parameters
42 sensors 4 sensors 12 sensors 11 sensors 9 parameters
84 parameters
Every 15 mins Multi-Output Deep Neural Network Model Paste Mixer (mixes milk powder, cocoa butter, sugar) Refiner (reduces mix particle size by crushing) Conch (develops chocolate flavors by Mallard Chemical reactions) Quality lab data Auto-correction of Depositor settings Chocolate Storage tanks Chocolate Service tank Temperer (Nucleation & growth of Form-5 Crystals) Depositor (deposits liquid chocolate on plastic moulds) Quality lab data Cooling Tunnel (only chocolate bars sample collection at exit) Quality lab data

Data made Anonymous
& Secured

  • We use only Client's Cloud/Data-Center or Top three Cloud Computing companies GPU engines built for regulated corporate environments.
  • We do not use any AI models provided by these cloud vendors. Our AI model is custom-built from first principles of engineering and applied mathematics, developed entirely in-house. These platforms are used only for high-performance GPU computing infrastructure, not for pre-built or third-party AI models.
  • Raw data never leaves your plant. Data is anonymized, recalculated, and de-labeled before transmission.
  • Our custom-built firewalls enforce strict control and regulate data flow, ensuring bank-grade security.
Microsoft Azure Amazon Web Services Google Cloud Platform
🔒 Bank-Grade
Data Security