Transforming Operations Excellence in Pharma and Manufacturing with AI/ML

As Chemical and Pharma Manufacturing industries grow in volume and complexity, they face challenges like:

  • Product Quality
  • Delivery Delays
  • Cost Optimization
  • First to Market (R&D processes)
  • Lack of digital systems
  • Life Cycle Management of Products

Pandemic Covid-19 has also shown how unprepared the industries can find themselves in combating the disruption, especially with the availability of talent that can help sustain the above mentioned org goals.

It’s no secret that to do things faster, a greater number of organisations are adopting AI/ML alongside the knowledge base that they have been able to build over all these years. [Reference: https://hbr.org/2023/11/how-ai-fits-into-lean-six-sigma] refers to application AI/ML/Automation to all stages of DMAIC]

At <1team.ai / Xtreme> we offer a unique combination of decades of expertise in Applied AI/ML, Data/Process Mining to the Lean and Six Sigma methodology used in Operations Excellence projects in Pharma and Chemical Manufacturing. 

We have developed proprietary methodologies of investigation by applying AI/ML to the Lean and Six Sigma processes generally followed in large pharma and manufacturing organisations. Through these we have been able to shorten the time of the Measure and Analyze phases of the DMAIC of the Six Sigma. 

Define

Ref: https://asq.org/quality-resources/dmaic 

Measure

Ref: https://asq.org/quality-resources/dmaic 

Plus:

  • We use Automation, AI to collect data from Digital or Hard Copy artefacts

  • We use AI and Process Mining to discover and confirm the “process map” shared by the SMEs

  • We use AI for Genealogy Analytics

  • We use Automation to Measure and Baseline

Analyze

Ref: https://asq.org/quality-resources/dmaic 

Plus:

  • Create Hypotheses 

  • Run Deep Analytics using Machine Learning and AI tools for CPP/CQA Analysis 

  • Run advanced models, create simulations for testing Hypotheses

  • Communicate findings of RCA using models’ results

  • Recommend the corrective actions

Improve

Ref: https://asq.org/quality-resources/dmaic 

Control

Ref: https://asq.org/quality-resources/dmaic 

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