AI Transformation in Manufacturing - HPC ASIA
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AI Transformation in Manufacturing

The issue that most commonly stares in the face of the manufacturers is the ever increasing pressure to continue to deliver high quality products and service and in tandem decrease costs. Smart manufacturers are taking a futuristic approach to present day problems of streamlining operations. The off take :-Beautifully and most optimally developed market sensitive products, could be a spike in efficiency, may result in some pleasantly reduced downtime, and all that increasing the value between you and your  customers.

Predict manufacturing design

Today’s Scenario

Even today manufacturing processes,much like in the traditional times too, can require a sizeable investment in creating a prototype and the battery of  tests to have for one self the safest and most  cost-effective assembly solutions one could find.All this comes at a very  expensive cost in terms of wasted materials and in also in the term of valuable man hours of designers and engineers having been consumed, but necessary to prove that a given design will perform and meet client specifications to the last point on the list.

Tomorrows Solution, Today

To find patterns in manufacturing data that can lead to the best possible manufacturing solutions we can be assisted by A.I. By looking at a wide variety of data including materials properties, previous design specifications, test results and more, AI based models can determine which combinations of variables are most likely to produce the dreamed results. Using this information, designers and engineers can pursue avenues that are most lucrative to work and may even find new solutions they had not imaginedearlier. By concentrating on high-probability solutions, manufacturers can saw down costs, speed up time toreach the product to the market and improve quality all in one giant of a masterstroke.

Artificial intelligence is also changing the way we design products by making better products through generative design. One way is to enter a detailed list of specs defined by designers and engineers as input into an AI algorithm mostly a generative design software

The specs provided to the A.I can include anything under the Sun, from data describing restrictions and spelling out various  parameters such as material types, available  methods to produce at hand,  to financial limitations griping time constraints. The algorithm will then sieve through every possible configuration, before homing in on a set of the best solutions and all that and more,sky is the limit .

One if not of the temperament of shooting in the dark can have , The proposed solutions  be tested using machine learning, offering additional insight as to which designs would be the best match for the scenario you have at hand. The process can be repeated until an optimal design solution is reached and matched what you and your team dreamed of.

The greatest advantage to anyone’s’ eye to this approach would most certainly be that AI algorithm is completely objective – it doesn’t default to what a human designer would regard as a logical starting point. No assumptions are taken at face value and everything is tested prior to actual performance testing against a wide range of manufacturing scenarios and conditions, what more can one ask for?

If you would still not like to settle for that , well you have got A.I to help you to adapt to an ever-changing market. AI algorithms can also be used to optimize manufacturing supply chains, helping companies anticipate market changes well before they have initiated many times. This gives management a huge advantage, moving from a reactionary/response mind set, to a strategic goal achieving one.

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Predict maintenance

Today’s Scenario

According the International Society of Automation, a typical factory loses between 5% and 20% of its manufacturing capacity due to downtime and become the reason for delay in reaching the market by some research studies in some as high as 42% of the time. Traditional preventive maintenance processes require machines to be repaired at intervals based on time or usage. These methods, however, still result in significant instances of equipment failure resulting in idle workers, increased scrap rates, lost revenues and upset customers. In addition to the plethora of issues one can find himself having, preventive maintenance may replace parts that still have significant working life, which can be a waste of time and money.

Tomorrows Solution, Today

AI based predictive maintenance uses a wide array of data from IoT (Internet of Things) sensors located in equipment at critical points of breakdown and usage , data from  various manufacturing operations, aspects and  environmental data, and more to determine whichcomponents should be replaced before they actually break down. AI models can look for patterns in data that indicate failure modes for specific components or generate more accurate predictions of the lifespan for a component while keeping a complete consideration of environmental conditions. When specific failure signals manifest themselves, or component usage criteria are met, the components can then be replaced during scheduled maintenance windows. McKinsey and Company found that AI based predictive maintenance typically generates a 10% reduction in annual maintenance costs, up to a 25% downtime reduction and a 25% reduction in inspections costs.

In manufacturing scenario, ongoing maintenance of production line machinery and equipment poses a big expense, having a direct impact on the profitability of any asset-reliant production operation. Moreover, studies have show that unplanned downtime costs manufacturers an estimated $50 billion annually, and that asset failure is the cause of 42 percent of this unplanned downtime ,all this and more while extending the Remaining Useful Life (RUL) of production machines and equipment.

Now all may never go as per plan all the time ,thus during times  where maintenance is unavoidable, technicians are briefed ahead of time on which components need inspection and which tools and methods to use, resulting in very focused repairs that are scheduled in advance as much as they may look situation responding at that moment in time.

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Supply Chain Optimization

 Today’s Scenario

Yesterday and even today to manage a supply chain management one would need to attempt to forecast future demands for resources based on historical data, and to top it all, you shall be lucky if you get all what you ned in one format. A safety stock is added to these levels to prevent stock outs and delays in production ranging anywhere from weeks of extra supply to twice the normal demand depending on the variation in needs for the product. This inventory level supports the overall production plan including stock levels in individual locations and also incorporates transportation plans to meet manufacturing needs. Moving and holding extra inventory is a fair share of expense for manufacturers who are always looking for ways and means to improve the bottom-line.

Tomorrows Solution, Today

An AI enabled  based supply chain optimization can utilize a variety of factors including historical data, environmental data and recent trends to predict optimal resource needs at any stage of production that one may require.AI models can also be used to find deviantbehaviour in current resource utilization and identify areas for further investigation by supply chain managers. In retail scenario, AI models can fix desirable inventory levels by making trade-offs between inventory level against expected sales. You can also update resource plans, reroute inventory where it is needed, and streamline resource requirements to reduce downtime, reduce costs, increase production speed and increase profits from manufacturing operations, all through A.I and a few clicks.

 Transportation Optimization

Today’s Scenario

Depending upon whatever types of goods the manufactures produce, they have to ensure that they arrive in good perceivable condition. According to the Food and Agricultural Organizations of the United Nations, approximately 40% of food is lost on average in post-harvest and processing stages, in the developing countries. This considerably impacts the food manufacturing, processing and transportation companies. Moreover, most industrial manufacturers are sensitive to increases in any transportation cost or even more sensitive to any losses when in times like these very highly perishable products are being transported around the globe for a ,”glocal” consumption pattern omnipresent pan sectors.

Tomorrows Solution, Today

Maintaining quality management of products through the transit is crucial. Manufacturers can now predict the quality of their products under given transit conditions, hence giving them the opportunity to improve refrigeration (for perishable products) or optimize routes (for raw materials and finished goods). AI based transportation optimization leverages route information, weather data, fuel cost and any such factor, to hone into the best possible route as well as to predict the quality of goods as and when they arrive at the destination, much like having a sooth-sayer by your side ,and having the highest prediction rates. Quality management of products through the transit is crucial. Manufacturers can predict the quality of their products under given transit conditions, hence giving them the opportunity to improve refrigeration (for perishable products) or optimize routes (for raw materials and finished goods). AI based transportation optimization leverages route information, weather data, fuel cost and other such factors to arrive at the best possible route as well as to predict the quality of goods at the destination.

 In Conclusion

Today is a time of incredibly short time-to-market deadlines and a rise in the complexity of products.The manufacturing companies are now finding it increasingly harder to maintain high levels of quality and to comply with quality regulations and standards.

On the other hand, customers have come a long way to expect faultless products, pushing manufacturers to up their quality game while understanding the damage that high defect rates and product recalls can do to a company and its brand. All these and the complexities of the modern day market mechanics, one should certainly look into exploring what the world of A.I has to offer.

 

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