Industrial processes can be expensive and time-consuming for companies that don’t have the right tools in place for product development. As of today, machine learning has become more prevalent in the manufacturing industry as it accelerates the development cycle and reduces time-to-market. Besides, the use of machine learning technology significantly reduces labor costs, eliminates product defects, and prevents unplanned downtimes. Using ML in assembly processes is termed as smart manufacturing, wherein robots perform assembly functions with surgical precision.
This blog post focuses on the applications of machine learning in manufacturing processes and how it is revolutionizing the manufacturing industry.
Predictive Maintenance
Most often, maintenance is performed only when a problem arises. Taking equipment offline for troubleshooting leads to manufacturing overheads. In such circumstances, business managers face challenges in making decisions. Either they have to take the equipment offline for maintenance which causes temporary disruptions or face even greater risks down the line.
Companies can proactively monitor maintenance processes if they use ML to predict the potential breakdowns of manufacturing equipment. Predictive maintenance ensures that an equipment is serviced before a problem arises. Besides, it eliminates errors that cause equipment failure, reduces downtime, and human-capital costs with minimal manpower requirements.
Minimizing Equipment Failures
Determining the right time to conduct maintenance of equipment might be a difficult task. Each time a machine is taken out for maintenance, it affects business productivity and causes downtime. According to Uptake, worldwide business losses incurred due to equipment downtime adds up to $647 billion annually. That being said, the average worldwide business losses due to equipment downtime is $5,600 per minute. The use of ML algorithms can significantly reduce these losses by eliminating unplanned equipment outages. ML algorithms can predict and determine optimal repair time by identifying potential errors that may cause equipment failure. Besides, ML algorithms use historical data to identify patterns of previous equipment failure instances and determine the right time for maintenance.
Supply Chain Optimization
Robust and reliable supply chain equipment is an essential requirement for a seamless manufacturing process. A shift in weather or hike in fuel prices produces cascading effects throughout the supply chain with a significant impact on business operations. ML takes these factors into account and optimizes each element of your supply chain in response. It includes calculating shipment time or determining shipping location, based on weather patterns and other potential challenges. ML algorithm analyzes and processes multiple factors to take important business decisions for the supply chain.
Inventory Optimization
Apart from supply chain optimization, machine learning algorithms are equally beneficial for inventory optimization. According to Waspbarcode.com, the cost of storing inventory can add up to 20-30% of the entire product cost. Nevertheless, a small reduction in inventory holding costs (say 10%) can reduce per-unit costs by 2-3%. Holding unsold or undelivered products unnecessarily increases storage costs and have a negative impact on cash flows. Here, ML algorithms can be used to determine the right time to hold and sell inventory for enhanced business productivity. This is done by monitoring the supply chain elements including market prices, holding costs, and production capacity.
Quality Control
Quality control is an essential part of the manufacturing process, where products are filtered based on several quality metrics. It enables machines to identify defective items and remove them from inventory. The process of identifying the root causes is time-consuming and often requires multiple disciplines to collaborate. Using machine learning techniques, machinery and product data can be monitored throughout the manufacturing life-cycle to predict potential failures before they arise. Integrating ML into quality management processes can reduce quality issues, reduce manufacturing costs, and minimize product recalls.
Read More: Transforming CRM Operations With Artificial Intelligence
Concluding Thoughts
Maintaining efficiency in industrial processes is essential as it directly affects the top and bottom lines. The use of ML and AI enables performance optimization at all levels to eliminate potential challenges including hardware management. Integrating ML algorithms into quality control processes can minimize product defects to reduce unnecessary production costs. From predictive maintenance to supply chain optimization and quality control, machine learning can improve multiple industrial processes.
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