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Can AI be a core enabler for Industry 4.0?- AI and the Manufacturing Sector

Case: A fortune 500 manufacturing company (discreet) utilized machine learning for inventory optimization and improving analyst performance. This implementation alone resulted in 28-52% savings in inventory holding costs resulting in US$100-200 million economic value.1

Unbelievable, isn’t it?

Truth be told, manufacturers across the world are launching projects and constantly testing to determine if AI, Machine Learning and IoT can benefit their operations by automating processes, reducing energy consumption and increasing profits.2

According to NASSCOM’s latest report on Uncovering the true Value of Artificial Intelligence, AI will act as a core enabler for manufacturing units aiding in transformation towards industry 4.0 and helping them overcome burning challenges like:

  • Creating quality products at a faster pace.
  • Replacing redundant systems in the manufacturing process
  • Optimizing machine utilization, and
  • Improving batch quality

Image by Franck V

 

The Manufacturing Sector may consider AI as a complex and expensive affair. However, they cannot deny higher efficiency of labourers and smoother production workflow if AI and IoT are adopted and implemented.

A few areas that will flourish with the adoption of AI adoption in the manufacturing sector:

1.Real Time Optimization of Inventory– across supply chains and monitoring progress real time along with resource allocation optimisation through ML algorithms.

2. Prediction Analysis– Using ML to predict energy consumption and automate energy management functions dynamically. Also predicting faults or issues and detecting leaks beforehand will be a key touchpoint of AI.

3. Autonomous Manufacturing– leading to increased efficiency and productivity rise thereafter.

4. Scaling and Simulating – becomes way simpler with AI taking over core processes. A new tool in CAD technology, for example, uses AI to create generative design taking specific inputs related to raw materials, design and even cost from the customer.3

5. Blending Man and Robot– Integrating industrial robots will be a major game changer w.r.t adoption of Ai in the manufacturing sector. According to the International Federation of Robotics, there are more than 1.3 million industrial robots across factories all over the world (2018) and this number is only going to increase as robots eventually take over advanced positions like design and programming4.

We’re living in unprecedented times and the challenges are phenomenal for manufacturing industries especially after being hit by the COVID-19. Despite this, a few other challenges will have to be dealt with before adopting AI within the sector.

1. Lack of Availability and Poor Governance– of data will be one of the major challenges posing a great risk especially while industries are scaling up.

2. Lack of infrastructure– in technology to support AI along with the shortage of skilled resources could be a major concern.

3. Delay in cloud computing infrastructure deployment has significantly impacted the pace at which AI is deployed in the sector.

4. Lack of Consistency– especially when strategizing the usage of AI within organizations of OSCM problems (Operational and Supply Chain Management).

While there have been inhibitions to deploy AI within the manufacturing sector, AI technologies have made tangible improvements to supply chain and administrative functions. That said, the sector continues to rely on experience, intuition and judgement of their skilled workforce5 for numerous activities and needs to be addressed especially when production must be scaled.

This is exactly where manufacturers must think of ways in which the combined expertise of both humans and technology, in other words, the human-robot collaboration can work.

Read the full report on Uncovering the true Value of Artificial Intelligence.

Would you pick human experience over AI powered production at scale or would you care to explore the deadly combination possibilities?

Tell us in the comments below.

References:

  1. AI in production: A game changer for manufacturers with heavy assets. (n.d.). McKinsey & Company. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ai-in-production-a-game-changer-for-manufacturers-with-heavy-assets
  2. How to fit artificial intelligence into manufacturing. (2019, September 19). Machine Design. https://www.machinedesign.com/automation-iiot/article/21838147/how-to-fit-artificial-intelligence-into-manufacturing
  3. Kushmaro, P. (n.d.). 5 ways industrial AI is revolutionizing manufacturing. CIO. https://www.cio.com/article/3309058/5-ways-industrial-ai-is-revolutionizing-manufacturing.html
  4. Technologies, K. (2019, November 12). What is the role of IoT and AI in manufacturing industry?https://medium.com/@KNOWARTH/what-is-the-role-of-iot-and-ai-in-manufacturing-industry-84ee5fc62977
  5. Uncovering the true value of AI – Executive AI playbook for enterprises. (2019, December 27). NASSCOM. https://www.nasscom.in/knowledge-center/publications/uncovering-true-value-ai-executive-ai-playbook-enterprises

 

 

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