5 Ways Manufacturers Can Embrace Machine Learning and AI for Business Advantage
With the advent of Information Technology and it's subsequent evolution into Deep Learning, Machine learning, Artificial Intelligence and Internet of Things (IoT), every industry has been trying to incorporate them into their workflow due to the realization that proper capturing of metrics and it's analysis would lead to better business intelligence, advanced automation and better demand forecasting. Unlike other industries, the manufacturing industry needs to utilize ML as the industry is cost intensive and heavily depended on the proper utilization of raw materials.
The below are the ways manufacturers can embrace ML for business advantage:
- Tracking of metrics and using advanced analytics: The key to any efficient operation is to track and record each and every activity as a metric. These metrics are then analysed and fed into the Management Information System (MIS) to study and further improve the efficiency of the workflow. PwC forecasts the integration of APIs, analytics and big data contributing to a 31% growth rate in the next five years for connected factories.
- Reducing dependency on humans for machine calibration: Until recently the MIS team would calculate the metrics using MS Excel or SQL queries to generate reports which have to be analysed by a human to arrive a conclusion and drive the process based on his/her inference. Now with ML and AI, these metrics are fed back into the system to identify what variation in which activity can impact the output. This information is used to instantly calibrate the machines which was earlier depended on humans to study the samples and then make corrections.
- Usage of loop inputs, error reduction and quality improvement: Since the feedback goes back into the system instantly, the AI takes the necessary corrective actions thereby reducing the error and improving the quality of the output/products.
- Forecasting wear and tear and predictive maintenance of the machinery: With AI tracking all the activities in the plant, it can forecast when the machine would reach the break down point, how much wear and tear has occurred etc. Having these information handy helps the AI system to alert the human supervisors for a maintenance of the machinery which would further reduce lead time and reduce loss due to non-operation of a machinery. With machine learning, supply chain forecasting errors can be reduced by 50% and lost sales by 65%.
- Providing a Global Integrated Platform: Since information is in a digital format, it can be shared with anyone in any part of the world. People can monitor the activities live and get quicker forecasts and business predictions.
TrendForce,a world leading market intelligence provider estimates that smart manufacturing is to grow at a rapid rate in three plus years. The firm predicts the market of smart manufacturing to be worth over $200 billion before the year end and grow to $320 billion by 2020. A projected compound annual growth rate of 12.5% is marked.
While the scope of integrating ML and AI into manufacturing is wide, being a capital intensive sector it would take a while to develop tested and verified solutions and gain the trust of industrialists before it's started to get utilized to it's full potential.
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