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MK team

ATRIYA Ltd, London, UK

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AI  in  Manufacturing & Supply Chain 

Predictive Manufacturing Design

Traditional manufacturing processes can require significant investment in prototyping and destructive testing to find safe and cost-effective assembly solutions. This development process is expensive in terms of wasted materials and time consuming for designers and engineers, but necessary to prove that a given design will perform and meet client specifications.

AI can be used to find patterns in manufacturing data that can lead to the best possible manufacturing solutions. By looking at a wide variety of data including materials properties, prior configurations, test results and more, AI based models can determine which combinations of variables are most likely to produce a positive result. Using this information, designers and engineers can pursue avenues that are most likely to work and may even find new solutions they had not thought of before. By focusing on high-probability solutions, manufacturers can reduce costs, speed time to market and improve quality.

Predictive Maintenance

According the International Society of Automation, a typical factory loses between 5% and 20% of its manufacturing capacity due to downtime. 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 angry customers. In addition, preventive maintenance may replace parts that still have significant working life, which can be a waste of time and money.

AI based predictive maintenance uses a variety of data from IoT sensors imbedded in equipment, data from manufacturing operations, environmental data, and more to determine which components should be replaced before they 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 given environmental conditions. When specific failure signals are observed, or component aging 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.

Supply Chain Optimization

The traditional approach to supply chain management attempts to forecast future demand for resources based on historical data. Supply chain managers then add a safety stock to these levels to prevent stockouts and delays in production. These safety levels can be anywhere from weeks of extra supply to twice 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 transportation plans to meet manufacturing needs. Moving and holding extra inventory is a significant expense for manufacturers who are constantly looking for ways to improve profitably.

AI based supply chain optimization can utilize a variety of factors including historical data, environmental data and recent trends to predict optimal resource needs at each stage of production.
AI models can also be used to find anomalous behavior in current resource utilization and pinpoint areas for further investigation by supply chain managers. In retail situations, AI models can determine desirable inventory levels by making tradeoffs between inventory level versus expected sales. AI models can also be used to 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.

Transportation Optimization

Depending upon the types of goods they produce, manufacturers have to ensure that they arrive in good 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 developing countries. This obviously impacts the food manufacturing, processing and transportation companies. Moreover, most industrial manufacturers are sensitive to increases in any transportation cost.

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.

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