How To Turn Net Zero Ambitions into Real-world Credible Decarbonization?

Industrial “Net Zero” CO2 emission is an ambitious target. It’s one thing to talk about it, and it’s another thing entirely to make it a reality. But AI apps and data-driven decision-making can move decarbonization from just remaining topics of boardroom discussions to control tower actions. Read to learn how!

Decarbonization is the process of reducing emissions of carbon dioxide (CO2) and other greenhouse gases (GHGs) during your manufacturing processes. It’s a daunting task but essential for effectively alleviating the global climate change crisis. 20% of global carbon emissions come from the manufacturing and production sectors.[1] Well-meaning organizations discuss this actively, but what credible actions will help meet net-zero ambitions?

The challenge for the manufacturing industry is to stop the rise in global temperatures and accelerate the transition to a lower-carbon future. The great decarbonization debate is not about whether the manufacturing industry should or shouldn’t do it. It is about how to prioritize and operationalize it. What technology options help with the different aspects of planning and executing a decarbonization strategy?

What IR4 Technologies Help Build a Decarbonization Playbook?

The manufacturing sector utilizes energy resources while converting raw materials into products. The greater the efficiency in energy consumption, the higher the conservation of the resources and the lower the emissions of greenhouse gases and air pollutants. Carbon efficiency will not only reduce the impact on the environment but also lowers production costs and increases productivity. Gearing for decarbonization is thus a win-win for both manufacturers and humanity at large. Technology-based plans enable organizations to determine a resilient framework for realizing decarbonization goals.

Some of the most promising IR4 technologies that can help reduce greenhouse gas emissions by improving resource efficiency, minimizing waste, and reducing emissions throughout the product life cycle are:

  • Data-powered decision-making
  • AI apps
  • Advanced ML algorithms
  • Digital Twin technology

 

The Intelligent Control Tower: A Data-driven Revolution!

Implementing AI/ML technologies powered by integrated data analytics can enhance the capabilities of the Control Tower. Our Digital Twin technology simulates the entire value chain in real-time, allowing 360 degrees of visibility and enabling decision-makers to plan and execute decarbonization strategies.

LIVE visualization of production data allows analysts and production planners to refer to the most precise and current manufacturing carbon footprints. Advanced predictive analytics forecast fuel consumption and facilitate a better understanding of subsequent energy losses.

With prescriptive analytics our AI apps provide actionable insights for adjusting production parameters. Accurate recommendations will help the manufacturers with procurement and to optimize consumption of low-carbon energy resources. Measuring the results against the stipulated benchmarks will help calculate the immediate benefits from fuel optimization and moving toward energy-efficient production.

A McKinsey report has suggested that 30% of total emissions could be reduced through such relatively straightforward decarbonization efforts and developing a playbook for your AI initiatives.[2]

Call-to-Action!

Bridge the gap between your business and a net-zero value chain. It is the need of the hour, especially for those in energy and resource-intensive manufacturing. Let our AI/ML and Digital Twin technologies develop an energy-efficient production ecosystem to accelerate you on the decarbonization pathway while also turning your cost curve around.

For on-demand provisioning of AI/ML environments at your production sites CONTACT US NOW!

References:

[1] World Economic Forum. (2022, March 23). Reducing the carbon footprint of the manufacturing industry through data sharing. World Economic Forum. https://www.weforum.org/impact/carbon-footprint-manufacturing-industry/

[2] Spiller, P. (2021, June 14). Making supply-chain decarbonization happen | McKinsey. Www.mckinsey.com. https://www.mckinsey.com/business-functions/operations/our-insights/making-supply-chain-decarbonization-happen

Subscribe to our Blog


    Posts by Topic

    View All

    Blog Archive

    Blog Tag

    Share

    Aerospace

    Aerospace Manufacturing

    Agile

    AI

    AI

    AI for business

    AI for business growth

    AI for good

    AI for manufacturing

    AI in manufacturing process

    AIML

    Algorithms

    Angel Investors

    apis

    Aviation Industry

    Big Data Analytics

    Blue Collar Workers

    Business Analytics

    Business Intelligence

    Business Intelligence Tools

    Case Study

    Chemical

    Cigarette Manufacturing

    CO2 Reduction

    Company culture

    Company Culture Matters

    Company Values

    Connectivity

    Conversations at Neewee

    Corporate Life

    Corporate Life

    Customer Experience

    Data

    Data Acquisition

    Data analytics

    Data Communication

    Data Driven

    Data experts

    Data Science

    Data Visualization

    de-risking business model

    De-risking strategy

    Decarbonization

    Digital

    Digital Business Transformation

    Digital Revolution

    Digital Transformation

    Digital Transformation

    Digital Twin

    Digitalization

    Digitization

    Discrete Manufacturing

    Edge ai

    Edge Computing

    Empathy

    Empathy at work

    Employee Management

    Employee Satisfaction

    Energy Efficiency

    Failure is part of success

    Flexible Manufacturing

    Food and beverage industry

    food manufacturers

    food quality

    foodmanufacturing

    Forrester Report

    Funding Funnel

    Future

    Future Ready

    Future technology predictions

    General

    Gold Rush

    Graph Data Science

    Growth

    Growth Mindset

    Idea Meritocracy

    iiot

    Improve Efficienty

    Impurities

    Industry 4 point 0

    Innovation

    intelligence

    IoT

    ir4

    IR4 technologies

    Japanese Market

    Latency

    Learning

    Legacy Systems

    lstm

    Machine Learning

    Manufacturing

    Manufacturing AI

    Manufacturing Automation

    Manufacturing Industry

    manufacturing performance

    Manufacturing Process

    Manufacturing Solutions

    Manufacturing Technology

    Maximize Productivity

    Mistakes are lessons

    ml

    Myth Busting

    Myths and Facts

    Neewee ai

    Net Zero

    Net Zero Carbon

    Net Zero Emissions

    Net Zero Future

    Net zero future

    Net Zero Manufacturing

    Net Zero Steel

    Net zero transition

    netzerocarbon

    netzeroemissions

    Operational Excellence

    Operators

    Optimization

    Packaging Safety

    Partnerships

    Performance

    performance monitor software

    performance monitor tool

    Personalization

    Pharma Industry

    Pharmaceutical

    Predictive Analytics

    Predictive Modeling

    Predictive Quality

    Prescriptive Analytics

    Problem Solving

    Process Control

    Process Improvement

    Process Mapping

    Process Optimization

    Product Quality

    Production

    production performance monitoring

    Production performance,

    Production Planner

    Production Planning

    Quality Assurance

    Quality Check

    Quality Control

    Radical Transparency

    Reinforcement learning

    Resilience

    Reskilling

    ROI

    Scheduling

    Shark Tank

    Shop Floor

    Simulation

    sins

    Smart Factories

    Smart Factory

    Smart Manufacturing

    Strategy Implementation

    Talent

    Tech predictions

    Test

    Transparency Matters

    Upskilling

    Use Case

    User Story

    Value in Action

    Venture Capitalists

    what is production performance

    WIP Reduction

    Workforce Development

    workforcedevelopment

    Zero Defect Manufacturing

    Related Blog

    What Is the Right Approach to Resolving Operational Pain Points?

    30% of manufacturers will use AI and customer data to deliver more personalization in 2022. [1] Smart manufacturing not only Read more

    Our 5 Most Popular AI and ML Algorithms and How They Are Used by Businesses Today

    "Machine Learning algorithms are magic!" You have heard that a lot, but what are algorithms, and more importantly, how do Read more

    How to Unlock Connectivity for Holistic Digital Transformation of Manufacturing

    Different generations of assets on manufacturing shop floor increases the challenge of data acquisition, hence creating data silos. The efforts Read more

    How Data and AI Apps Can Help the Blue-Collar Employees on the Shop Floor

    Applying Lean manufacturing methodologies to human resource functions can create employee satisfaction. Happy and self-motivated workers on the shop floor Read more