Manufacturing trends: Disruptions and innovation

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Manufacturing trends: Disruptions and innovation

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Manufacturing is becoming more smarter, efficient, precise, and sustainable by adopting IIoT, AI, Robots, Blockchain, and 5G for operations optimization. Manufacturing business trends are enabling flexible & transparent supply chains, customer-centric & agile production, ecosystem collaborations, and new business models.

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What’s inside

  1. Key Takeaways
  2. Key drivers shaping trends in manufacturing
  3. Business Trends driving innovation
  4. Technology Trends aiding the business

Key takeaways

The convergence of advanced technologies with labour, supply chain, and demand challenges is driving full automation. Nearly 84% of manufacturers have adopted or are considering smart manufacturing. Manufacturers are exploring tech-enabled ecosystem partnerships, reshoring, and factory-in-a-box model to address supply chain instability. AI, IIoT, Big Data & Analytics, Robotics, 5G & Edge Computing are enabling data collection, pattern identification, and prediction for process optimization and efficiency improvement. AI in manufacturing is expected to reach $115 billion in 2032 globally. Blockchain is ensuring supply chain and ecosystem security. Driven by regulations and environmental commitments, manufacturers are adopting technologies to reduce emissions. On-demand production, mass customization, and subscription-based products are enhancing customer experience. Download Complete Research

Key drivers shaping trends in banking

  • Ensuring supply chain resilience and navigating market demand uncertainty are manufacturers’ main requirements amid natural disasters and geopolitical tensions. AI and Data Analytics are key to overcoming disruptions and predicting demand.
  • Manufacturers are prioritizing automation to deal with skilled labour shortage, manage rising costs and enhance efficiency while maintaining quality.
  • Sustainability is another priority for manufacturers, focusing on responsible materials sourcing, environment-friendly production, and safe waste disposal. Customer preference is changing, and they are seeking sustainable products and processes.

Business trends driving innovation

  • Dark factories fully orchestrate processes using automated machines while operators remotely monitor activities, enhancing performance and resource efficiency.
  • The product-as-a-service business model opens new revenue streams for manufacturers while lowering the upfront costs for customers.
  • Global data exchanges & platforms allow data sharing between ecosystem partners while protecting proprietary data.
  • Green manufacturing is getting propelled using digital technologies and adopting sustainability policies.
  • Reshoring and micro-factories have gathered pace due to wage increases, supply chain disruptions, and the advent of digital technologies.
  • Prescriptive maintenance identifies patterns, performs root-cause analysis, and recommends the best maintenance approach, lowering running costs and downtime.
  • Customized product manufacturing is being enabled by additive manufacturing and flexible manufacturing approaches.
  • Advanced environment-friendly materials are gaining traction resulting in enhanced performance, high wear resistance, and high thermal stability of products.
  • These trends impact various stakeholders, including manufacturers, suppliers, workers, and customers.

Technology trends aiding the business

  • AI is transforming manufacturing through real-time machine operation optimization, problem prediction, demand forecasting, cost modelling, product quality inspection, robot navigation, etc.
  • IIoT is key for prescriptive maintenance, real-time production and machine monitoring. 5G provides high bandwidth for reliable connectivity between IIoT devices, offloading computation-heavy tasks to edge infrastructure.
  • Robots & Cobots enable production line process handling, intelligent material handling, and warehouse automation.
  • Digital Twin enables the convergence of information technology (IT), operational technology (OT), & engineering technology (ET) in manufacturing.
  • Blockchain allows for tracking materials & goods through the supply chain, inventory management, & warranty management.
  • Secure and dedicated private networks, Blockchain, and AI are increasingly used to safeguard critical manufacturing operations from cyberattacks.

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Credits
Lead Authors@lab45: Parag Arora
Contributing Authors@lab45: Hussain S Nayak

Latest stories

Generative AI on the cusp of disruption

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Generative AI on the cusp of disruption

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By acquiring 100+M users in first 3 months of launch, ChatGPT has brought the field of Generative AI (GenAI) into mainstream awareness. The adoption of ChatGPT and similar applications have positioned Generative AI (as well as “Deep Learning”) as the newest disruptive tech after cloud computing.

What's inside

  1. An overview of Generative AI, the market for it and the reasons for the hype
  2. A qualitative assessment of its time to mainstream across various industries
  3. Enterprise use cases and its limitations

What is Generative AI?

Generative AI, a field of Artificial Intelligence, refers to computational models that are trained on massive amounts of input data (300bn words in the case of ChatGPT). They can synthesize data, draw inferences and create new outputs in the form of text, images, video, audio, new data and even code.

Two architectures have made GenAI immensely valuable

  1. Generative Adversarial Networks (GAN), that became popular in 2014 are used for generating images & videos.
  2. Transformer Models, proposed by Google in 2017, are used for generating text.

ChatGPT, for instance, uses the transformer model along with Human Feedback Reinforcement training to generate high quality outputs. Training a model requires intensive computational power (supercomputers were used to train GPT3) and significant investments (OpenAI being a key example). But once a model is trained, it can be optimized for a larger user base. Download Complete Research

The market for Generative AI

The market is expected to grow from $8B in 2022 to $109B in 2030 at a CAGR of 34.6%. Key facts as follows

  • Software segment accounts for 60%, service segment is the fastest growing.
  • Media & entertainment is the biggest user of Gen AI accounting for 18% of revenue,  BFSI is the fastest growing at 36% CAGR.
  • North America is the biggest market with 40% share and APAC is the fastest growing region.

Why GenAI is here to stay?

Need for content synthesis
We generate ~2.5 quintillion bytes of data every day on the internet. This not only makes searching for information tough but also makes inferring tougher, for regular users. GenAI tools can search, synthesize and compose an answer.

Democratization of content creation
We are moving from a search and retrieval economy to an infer and compose economy. People used to prompt algorithms to search and retrieve information but now they can prompt algorithms to infer and compose information.

Instant economy
Digital natives prefer tools that enable instant creation of content e.g. Tik Tok. ChatGPT can generate a word in 350ms after processing database of 300B words.

Access to massive computational power
The ability to instantly process and compose information using cloud computing.

Evolution of deep learning neural networks
Large Language Models have become openly available. These models help organize much of the internet’s information and develop patterns to mimic human decision-making.

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An early assessment of time to market & key GenAI companies.

Industry To Mainstream Gen Ai

Gen Ai Companies Capabilities

Enterprise use cases of Generative AI

GenAI will fundamentally change several functions in enterprises leading to improved productivity and performance of employees. A Few areas of business where it will have the biggest impact are as follows:

Content creation
Gen AI will lead to more automation in content creation. It will not only reduce the cost of content creation but also increase the quality & variety of content created. Generative AI based DIY Apps are expected to emerge for marketing and design functions.

Content personalisation
Marketing touchpoints like newsletter, websites, videos, metaverse etc. will get hyper-personalized. This will improve brand engagement and conversion ratio of the sales funnel.

Drug discovery
Drug Discovery is a time-consuming process that can extend to 5-12 years. Gen AI can help identify potential drug candidates and test their effectiveness using computer simulations, thus saving time in the process. It has already led to tremendous real-world value, when the first mRNA covid vaccines were developed by programming mRNA molecules to express the specific antigen response. By 2025, more than 30% of new drugs and materials could be systematically discovered using GenAI techniques, up from zero today.

Software development
IT products and services could see the biggest impact. Below are some scenarios that may unfold.

  • Reduced time on testing and coding
    Gen AI has the capability to create, test and debug the code in real time. In typical product development cycles, coding and testing takes 30-40% of the time. Gen AI will cut this significantly, thus reducing time to market.
  • Improve programmer performance
    Code can now be generated with a simple prompt command. This will allow even less-tech savvy programmers to generate a better code. Gen AI can also translate the code from one programming language to another. However human intervention will still be needed to customize the code for specific vertical / client use.
  • Automate recurring tasks
    Manhours will be freed from repetitive tasks, as automation is easier with GenAI. Tasks like report generation, log analysis etc will fall in the domain of automation.
  • More secure and reliable IT infrastructure
    Gen AI can track performance and security of IT infrastructure in real time. It can pre-empt any failures, by generating early warning signals and hence improve reliability of operations.

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Credits
Lead Authors@lab45: Siddhant Raizada, Nagendra Singh, Tommy Mehl, Arvind Ravishunkar
Contributing Authors: Aishwarya Gupta, Anindito De

Top trending insights

Future of connected world with AIoT

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Future of connected world with AIoT

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AIoT is a revolutionary blend of AI and IoT that creates a connected world with limitless opportunities. Smart devices can collaborate to make informed decisions without human intervention, transforming various industries. As AI and IoT converge, their applications will become more advanced, presenting new prospects for businesses and consumers.

What's inside

  1. Insights and technology trends
  2. Overview, benefits, and architecture
  3. Use cases, challenges and key players
  4. Enterprise AIoT adoption strategy

Insights and technology trends

  1. AIoT enables devices to become smarter and more autonomous by analyzing massive data streams in real time and making informed decisions based on that data. They are expected to become more personalized and intuitive by implementing self-learning algorithms, providing real-time solutions. 
  2. Fast, reliable connectivity ensures seamless user experience and data exchange, allowing AIoT systems to respond and act promptly.As cybersecurity technology advances, users gain confidence in the security of AIoT systems.
  3. AIoT can contribute to sustainability initiatives through intelligent monitoring and control systems.
  4. Integrating new technologies in AIoT systems has become an essential component in the future of enterprises and individuals. Key Technology Trends include:  Secure Access Service Edge (SASE), Nanotechnology, Multiplicity, Contextual Proactivity, Seamless Multi-Modal Interaction, Brain- Computer Interfaces.

Overview, benefits, and architecture

AIoT combines sensors, AI, data and ambient computing elements to create a responsive, context-aware environment. It uses embedded devices and natural user interfaces to provide services based on detected requirements and user input.

AIoT can revolutionize how users interact with technology, offering greater convenience and seamless connectivity. The benefits include: intuitive and seamless experience without commands, automated decision making, efficiency and convenience.

While creating an AIoT system, a well-balanced architecture is crucial to manage data processing speed and costs. There is a flow of information in the system based on the external inputs, that ultimately results in a response based on analysed data points by AI and ML algorithms. Download Complete Research

AIoT adoption – Connected enterprise strategy

The 5 step enterprise strategy include the following:

  1. Define AIoT Vision, scope, goals and objectives
  2. Assess IoT Capability, identify AIoT devices & systems
  3. Define Connected enterprise System Architecture
  4. AIoT System Development & Pilot implementation
  5. Enterprise - wide implementation

Use cases, challenges and key players

Use Cases for following domains are discussed:

  • Manufacturing: Employee safety, Inventory management, Collaborative robots (Cobots), AI-driven product design
  • Smart Homes: Home Security, Health and Wellness, Child and Elderly Care, Home Automation
  • Healthcare: Predictive Healthcare, Smart Wearables
  • Automobile: Autonomous Vehicles, Smart Traffic Management, Connected Car Services
  • Retail: Smart Shelves, Customer Experience, Ambient Commerce
  • BFSI: Fraud Detection, Personalized Financial Services

Implementing a complex system like AIoT requires careful planning, collaboration, and attention to detail. Data management, privacy concerns, and integration with various systems can pose significant obstacles to successful implementation.

The AIoT space is dominated by key players such as IBM, Microsoft, Siemens, GE, Cisco, Huawei, ABB, Bosch, SAP, and Honeywell. Download Complete Research

Credits
Author@lab45: Anju James
Contributing Authors@lab45: Hussain S Nayak, Nagendra Singh

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