AI use cases for the Manufacturing Vertical

2 mn read

AI Use cases in Industrial Vertical

Lots of news and talk about AI Usecases  in Technology, retail consumer and health care verticals.  I feel we need more focus on manufacturing vertical which can get enormous benefits from this exciting new technology. 

After large amount of shifting manufacturing to low cost countries, now rich/developed countries are moving back factories back in to their home country due to voter’s concern on job losses.  However, the same voters are not willing to pay higher prices for their goods and services.  Hence the focus is on use of AI technologies to the fullest to manage the jobs and cost conundrum.

Andrew NG, AI Guru, who is focusing on effective deployment of this new technology in enterprises.  They are promoting Domain Specific Visual Models targeting at manufacturing, electronics, food & beverages verticals. 

We at @AiThoughts.Org agree with Landing.Ai approach and started advocating use of Vision solutions to solve many problems in the manufacturing vertical.  We debated with our experts on manufacturing and identified a sample e of few usecases worth consideration.

  1. Quality control of incoming supplier materials at the receiving dock or even at shipping dock at the supplier’s premises.  With the complete eco system in the manufacturing life cycle at extreme margin pressures, short cutting of quality of the materials supplied is a real concern and any fast , mostly automated solution will be a great solution.  Bad quality material will eventually the manufacturers end product bad and increase customer returns or customer complaints. In many cases, this may even disrupt the assembly line process causing unplanned shut downs costing $$s.
  2. Quality control of sub-assemblies and final product.  Landing.Ai’s first product launch for based on this use case.   Customer returns or customer service requests post shipment are huge concerns of any manufacturing company.  Any solution where large amount of sub-assembly and end products can be quality checked by AI models will be very useful.  In many cases, this may not be a human replacement solution but human enhanced solution.
  3. With factories becoming more automated with conveyor belts, and human-Robot work flow assemblies, employee safety in factory premises becomes a major concern.  AI visual solutions to ensure humans are safe at all times, provide ample warnings to humans to stay away from risk areas and even shut down systems such as conveyor belts, robot assembly etc. to save a human worker from harm are needed.
  4. I can go on with other solutions as I am very passionate about Manufacturing and spent first 15 years of my IT career helping customers in the Manufacturing verticals.

As a bonus, there are problems in the global supply chain which we still need to be solved using AI.  The optimization of JIT vs safe inventory both for raw  materials and finished products is a long standing problem.  Even large enterprise software providers have not found any new solutions or new algorithms to attack this problem. The industry needs a holistic AI based solution taking in to consideration the end to end life cycle from supplier production, global shipping, port loading/unloading work flows, road transportation, quality rejections, own factory scheduled maintenance , customer orders and host of other employee union/strike related issues at supplier side and own side …  The problem is very complex and needs a good AI solution.

Hoping that this post will create more interest in the manufacturing vertical amongst the T community and some solutions will come along.  Ai.Thoughts@org is available for any help in this regard.

More later.

L Ravichandran

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