As raw materials become increasingly expensive or difficult to source process manufacturers are under pressure to find new ways to maximize productivity growth. Historically companies have relied on outsourcing and cost-cutting to increase productivity but the gains from these methods are shrinking. As Asia continues to develop rapidly the middle class is growing and wages are rising. A study conducted by BCG found that the total cost of production for manufacturers will only be between 10% and 15% less than in the US within the next five years. With these tried and true methods becoming less profitable, industrial companies are taking an inward look at their operations to see where gains can be made.
Lost Industrial Data
Data collection, data analysis, and data science are becoming a major part of the business landscape but there’s still a long way to go before it becomes ubiquitous in the industrial industry. Sensors have played a significant role in manufacturing for several years and have undoubtedly helped improve production in varying capacity but it’s widely understood that industrial manufacturing companies are not using them to their full potential.
The data these sensors collect give an insight into the manufacturing operation and can be used to set guidelines for improvement, but many companies lack the internal infrastructure to interpret this data. According to Fero Labs, an industrial machine learning application company, manufacturing companies discard 98% of collected data due to lack of operational analytics capabilities .
In 2017 the Economist published a report called “The world's most valuable resource is no longer oil, but data” detailing how valuable data will continue to become in the modern world. If industrial manufacturing companies want to optimize their processes, it is vital that they begin using their data or risk being left behind.
The Future: AI and Predictive Maintenance
Relying only on planned maintenance and condition-based maintenance is no longer acceptable if a company wants to increase productivity which is why predictive maintenance is becoming popular. Predictive maintenance systems can be used in conjunction with AI to predict machine failures ahead of time which lowers costs associated with downtime and repairs. Major cost savings can be made by utilizing predictive maintenance because advanced machinery is very expensive and also depreciates relatively quickly leading to financial loss. Predictive maintenance systems measure a variety of data such as temperature, vibration, pressure, and rotation speeds to detect when conditions are falling outside the normal range and may be putting the machine under increased pressure.
While AI has become commonplace across a hoard of industries, it is still lagging behind in heavy asset industrial industries where reliance on highly skilled operators is still common. These operators are typically tasked with manually monitoring the data on the sensors and making judgment calls. Not only is this inefficient and prone to human error, but it also makes replacing these operators incredibly difficult if they retire or leave the workplace.
AI can transform the industry in many ways, for example with smart sensors. Smart sensors can increase operational efficiency and reduce maintenance costs by collecting and analyzing data and making decisions without the need for an operator. AI algorithms can also detect faults quicker than operators or planned maintenance and allow for an increase in quality. If companies can identify where they can make jumps in product quality then they are not just cutting costs but providing a more competitive product.
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