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This blog has specifically been written for you – because you run a manufacturing enterprise and would perhaps like to know (in more detail than you already do) how Intelligent Manufacturing (IM):

  • Optimizes resources
  • Enhances process safety
  • Reduces wastage (on the floor as well as in back-office operations)
  • Improves business value

All the above while helping you meet the customized delivery and quality demands of your customers.

Why is Artificial Intelligence critical for intelligent manufacturing?

To quote Forbes, “AI is core to manufacturing’s future because of its ability to enable real-time monitoring which – in turn – provides excellent sources of contextually relevant data that can be used for training machine learning (ML) models.”

In other words, AI allows machines to detect fluctuations in manufacturing processes, helping them respond in real-time to unanticipated/ changing circumstances such as troubleshooting production bottlenecks, tracking scrap rates, ensuring on-time delivery to customers. All of this with minimal human interference.

Factories equipped with such machines are experiencing the much-needed shift from reactively solving a problem to proactively managing equipment, processes and product(s). Also called ‘smart’ factories, these manufacturing setups are using the combined intelligence of people, processes and machines to impact the overall economics of manufacturing. The phenomenon is known as Intelligent Manufacturing.

How is Artificial Intelligence catalyzing intelligent manufacturing?

Manufacturers are leveraging AI-powered intelligent devices, the latest manufacturing execution system (MES) and machine-to-machine communication to analyze, and draw meaningful results from, real-time data to:

  • Improve and expand production processes and lines, facilities and products
  • Sequence and track defects in packaging and labelling
  • Forecast trends and carry out root-cause analysis
  • Prevent defects and recalls
  • Stay ahead of competition

While the above is a list of the advantages manufacturers are observing, the all-important question is ‘how is AI stimulating intelligent manufacturing?’

Here’s how:

  • By ensuring predictive maintenance of systems – Manufacturers are constantly looking for that one sure-shot way of improving operating efficiency and minimizing maintenance expenses. A combination of cognitive AI technology, smart sensors and an interconnected network of machines is that one way because together, they continuously monitor devices on the floor to generate predictive analytics that:
    • Track the condition of equipment leading to them being serviced when actually required instead of at scheduled times, thereby minimizing downtime
    • Set up equipment to evaluate their own efficiency status, order their own replacements/ substitute parts and schedule field technician visits whenever needed
    • Anticipate future failures by leveraging Big Data-based algorithms (i.e., through AI-powered digital twin technology)
    According to a McKinsey study, ‘AI-enhanced predictive maintenance of industrial equipment can generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and a 25% reduction in inspection costs,’ positively impacting manufacturers’ bottom lines near immediately.

    Meanwhile, according to Gartner, half of the large industrial companies will use digital twins by 2021, resulting in those organizations gaining a 10% improvement in effectiveness.
  • By making near-accurate demand forecasting possible – Because AI-based systems deliver continuous monitoring (as mentioned previously), seamless quality control is now no longer a tall ask. This product quality inspection, coupled with intelligent maintenance of equipment, has ushered in a new era of near-accurate demand forecasting and planning which has:
    • Improved planning and coordination across the marketing, sales, account and supply chain management, and finance functions leading to more precise forecasts
    • Facilitated manufacturers’ quickly adapting to new information on product introductions, supply chain disruptions or sudden demand changes.
  • By improving supply chain management – According to McKinsey, AI and ML-based systems ‘will reduce supply chain forecasting errors by 50% and reduce costs related to transport and warehousing and supply chain administration by 5% to 10% and 25% to 40%, respectively.’ The message these figures convey is loud and clear – that now is the right time for manufacturers to opt for automated material procurement. In other words, integrate AI and ML algorithms into their procurement, strategic sourcing and cost management functions.

    Then there’s the aspect of global supply chains producing volumes of information - data that AI can analyze to help manufacturers with insights into optimizing processes and varying market situations so they can adjust rapidly.
  • By making buyer-centric/ hyper-personalized manufacturing a reality – A Deloitte survey has found that 20% of consumers would be willing to pay a 20% premium for personalized products or services. A finding corroborated by brands these days readily personalizing their products to build more trust with their customers.

    AI and ML-based technologies are helping companies to take personalization several notches higher by building smart industrial processes that adjust to customers’ variable demands. One way for enabling this
  • By creating an empowered workforce – Intelligent manufacturing software helps companies streamline workflows, giving employees heightened visibility into processes and products that allows them to work more efficiently. Automating basic tasks helps free up employees’ time, letting them focus on higher-level functions.

    Despite the skills gap the changing nature of manufacturing has created, intelligent manufacturing gives companies the opportunity to close that gap – By reskilling employees through relevant trainings; by leveraging smart connectivity to provide technicians and other staff with team collaboration tools and remote expert assistance capable of guiding them through troubleshooting procedures and best practices.
  • By implementing Artificial Intelligence-driven Quality 4.0 in the manufacturing setup – Artificial intelligence is a key driver of the quality and compliance aspect of manufacturing. However, current machine learning algorithms face a monumental challenge when it comes to existing standards of quality, production, innovation, safety, and conformance. Quality 4.0 – sometimes called the fourth wave in quality and compliance – is a concept that was created to face these new challenges.

    According to the article ‘Quality 4.0: a review of Big Data challenges in manufacturing,’ Quality 4.0’s aim is to leverage Big Data, the Internet of Things, and Artificial Intelligence to meet new demands and solve difficult engineering problems.

In conclusion
Intelligent Manufacturing is about how several little pieces come together and fit each other to create a much larger (and smarter) whole. Together, these pieces make it possible for manufacturers to operate in a proactive instead of reactive manner, thereby focusing their attention and effort in looking ahead.

How can Columbus realize Quality 4.0 for you?


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