Manufacturing and Machine Learning: 10 Improvements

Machine learning platforms, algorithms, and applications have helped manufacturers optimize manufacturing operations to the shop floor level. They have also helped create new business models and improve the quality of the product.

The most important thing for manufacturers worldwide is finding new ways to prosper and achieve top product quality while taking less time to produce and ship products to customers. New business models always affect the existing systems of Customer Relationship Management (CRM), Product Lifecycle Management (PLM), and Enterprise Resource Management (ERP) by trying to maintain the relationship of creating new products and improving time-to-customer performance. The production of new products is rapidly increasing, and the delivery window is decreasing. Manufacturers are now using machine learning to improve their performance and find the best solution for the problem.

Machine learning has been revolutionizing manufacturing since 2008. According to McKinsey, 50% of companies with integrated machine learning and artificial intelligence in their manufacturing businesses have a greater chance of doubling their cash flow in the next five to 7 years. Another recent survey by Deloitte has estimated that 15 – 30% of unplanned machinery downtime has been reduced in the manufacturing industry, production throughput has increased by 20%, maintenance costs have decreased by 30%, and quality has risen 35%. This has all been achieved with the help of integrating machine language technology.

It is a great and wondrous change if you compare it with a time when machine learning in the manufacturing field was just theoretical. The ten most important ways of machine learning have improved the manufacturing industry with the help of machine learning are described as follows:

  1. By improving semiconductor manufacturing with machine learning, profits increase up to 30%, scrap rates are drastically decreased, and fab operations are optimized. There are three main fields where machine learning is expected to improve semiconductor manufacturing. These fields include using machine-learning-based root-cause analysis to reduce scrap rates, using AI (Artificial Intelligence) optimization to reduce testing costs, and decreasing yield loss in semiconductor manufacturing by up to 30%. McKinsey, the advanced manufacturing industry action group which is expert in giving accurate advice to different organizations, has stated that using Artificial Intelligence technology for predictive maintenance of the industrial equipment will lead to a 10% decrease in annual maintenance cost, almost 20% downtime reduction and 25% decrease in inspection costs.
  2. Machine learning, artificial intelligence, and IoT adoption in manufacturing are focusing majorly on the areas of management such as the management of assets, management of supply chain, and the management of inventory. The World Economic Forum (WEF) and A.T. Kearney, the global management consulting firm, joined forces to study the future of production. They found that the manufacturers are really interested in combing machine learning, artificial intelligence, and IOT in their technologies. If you have unsold or returned goods, you will have to pay storage and other costs, which may seem miniature at first, but they cause considerable disruption in the cash flow. They believe that this will help them improve supply chain visibility, the accuracy of asset tracking, and inventory optimization.
  3. PricewaterhouseCoopers, the global professional services network, has estimated that there will be a 38% increase in the adoption of analytics and machine learning for the improvement of maintenance based upon prediction technologies. It is estimated that MI-driven process, analytics, and quality optimization will grow by 35% in the future. Similarly, automation and process visualization is estimated to increase by 34%. PwC has foreseen that the integration of APIs, analytics, and big data will contribute to a growth rate of 31% for the many connected factories in the coming five years.
  4. McKinsey has predicted that machine learning will help reduce forecasting errors occurring in supply chains by 50% and lost sales by 65%. It will also help in increasing product availability. The essential constituent of any business is the supply chain. A single error or unplanned interruption of service can result in the loss of millions of dollars. With machine learning, it is expected that the costs of transport and warehousing will reduce by 5 – 10%, and that of supply chain administration will decrease by 25 – 40%. It is also anticipated that the overall inventory costs will reduce by 20 – 50%.
  5. Machine learning is responsible for uncovering price sensitivity and price elasticity by using machine learning. It will improve the demand for forecast accuracy and will rapidly reduce negative price variances and energy costs. Honeywell, the international conglomerate company whose one of the main four focuses is manufacturing, focuses its efforts on integrating machine learning algorithms and artificial intelligence to achieve strategic sourcing, procurement, and cost management.
  6. Machine learning has done a great deal of work in automating inventory optimization. This has resulted in a significant improvement in the service levels by approximately 16% and has simultaneously increased inventory turns by at least 25%. The constraint-based algorithms and models of machine learning and artificial intelligence have made scale inventory optimization across all the different locations around the globe possible. They do this by considering and taking care of many external and independent factors that affect the demand and time to customer delivery performance.
  7. The combination of machine learning and real-time monitoring has brought significant changes and has optimized shop floor operations. It has also provided excellent insight and information about machine-level loads and production schedule performance. It is always beneficial to know how the overall production performance schedule affects the load level of each individual machine. The obtained information helps in making better and more profitable decisions about managing each production run. Machine learning has now made it possible to optimize and improve machines’ best possible set for a given production run.
  8. Suppose you use machine learning to improve the accuracy and efficiency of detecting performance decrease costs across several manufacturing and production scenarios. In that case, the prices are sure to decrease by 50% or more. Many machine learning apps have been created recently. These machine learning apps need data to accurately determine the behaviors of cost in various manufacturing scenarios. This data is obtained by using multiple kinds of real-time monitoring technologies. These technologies are efficient in creating several data sets. They do this by recording the various inventory velocity, costing, and other related variables.
  9. A manufacturer created a project and used machine learning in that project. He used accurate prediction of calibration and test results and successfully achieved a 35% reduction in calibration and test time. The project’s main aim was to reduce the calibration and testing time during the production of mobile hydraulic pumps. The basic methodology behind the work was to use several machine learning models that can speculate the tests’ outcomes and rapidly learn things over time. The process workflow that was finally created was able to isolate the bottlenecks while efficiently using the calibration and testing time.
  10.  The forces of Overall Equipment Effectiveness (OEE) and machine learning were combined. The result was an improvement in yield rates, increased preventative maintenance accuracy, and a decrease in the asset’s workloads. OEE is a prevalently used matrix that is commonly used in manufacturing. It can combine performance, availability, and quality to define how effectively the production is proceeding accurately. It can also discover factors that affect the manufacturing performance the most and the least after combining it with other matrices. Today, one of the fastest-growing fields of analytics and manufacturing intelligence is integrating OEE and different kinds of datasets in machine learning, where models learn quickly with iterations’ help.           


Manufacturing and Machine Learning: Working Together

This goes to show how much machine learning has helped the manufacturing industry. Michael Dukakis Institute for Leadership and Innovation (MDI) has stated that machine learning and artificial intelligence is an irreplaceable tool in the manufacturing industry for the government and society. It has helped to solve important cases such as SDGs. It has helped people escape from the limitation of resources, the haphazard and inflexible rules and regulations, and the imprisonment of complex processes.