Manufacturing analytics: How it improves plant efficiency


The widespread automation of factory machines in the 1970s and 80s is often called the Third Industrial Revolution — a change so massive, it shifted the workflow of nearly every factory around the country. Analysts say we’re experiencing a Fourth Revolution today, triggered by considerable advances in data collection and artificial intelligence. Some call it Industry 4.0.

AI and Internet of Things (IoT) sensors make it possible to receive and analyze massive quantities of data from the factory floor in real time. According to a 2016 study, 67% of manufacturers are already investing in big data analytics technology. Many even consider this data the most valuable asset of the modern manufacturer. However, data is just a tool, not a business plan.

So, how are manufacturers using that data to improve plant efficiency?

Predictive Maintenance

Downtime can be a major cost for factory owners, reducing overall productivity by as much as 20%. Preserving automated machines is critical in keeping the production process moving. However, it can be hard to notice machine breakdowns before they occur. The telltale changes in vibration, temperature or timing can be too subtle for human operators to notice.

But with the right data, AI can see failure coming.

AI-powered data analytics is exceptional at detecting abnormal states. Wireless sensors can relay second-to-second information about temperature, vibration, power usage and instrument timing to an AI. When dangerous changes or patterns of changes occur that would be undetectable to a person, the AI can recognize these patterns and alert operators of possible or imminent failure. This ability gives manufacturers time to reroute production, acquire replacement parts or otherwise prepare for maintenance.

Optimizing Machine Efficiency

When left idle or running at inefficient settings, automated machines can become a serious resource sink. Inefficiencies in compressed air systems, for example, cause manufacturers to lose $3.2 billion annually. Proper compressed air system auditing and monitoring can help these manufacturers keep track of performance and efficiency.

Sensors set to continuously monitor these systems will provide manufacturers with a data set they can use to search for inefficiencies in the system. Does a 25% increase in power do the same work as a 5% increase? Is a machine just as efficient when it’s running at normal temperatures versus extreme temperatures? The data gathered by the sensors can answer pressing questions like these.

Variables like instrument power are often left completely in the hands of workers on the floor. These employees don’t have access to the data needed to see how certain power levels or tool settings might be inefficient. With big data collection and analysis, the manufacturer can look for patterns and relationships between variables like instrument power and plant efficiency and pass that information on to workers, thus reducing energy waste and saving resources.

Demand Forecasting and Supply Source Analytics

Demand forecasting is one of the areas that has benefited the most from the rise of AI analytics. Warehouses and retail outlets most often use this analysis, but it can also benefit manufacturers and factories.

Demand forecasting is the use of historical sales data to predict how much of a product a company should order — or, in this case, produce. While the concept isn’t new, traditional methods relied on strict formulas simplified for frequent use. It’s the kind of situation AI is perfect for — huge amounts of data and no one good way to find patterns in that data.

It’s sometimes considered too costly to halt production just because there are no orders — but at the same time, overproduction can be a huge waste of resources, time and money. Instead, factory owners can use predictive AI to determine how much of a certain product they can safely produce based on previous order history.

Similarly, manufacturers can also use AI to:

  • Tighten supply sourcing by determining which supply sources are providing the best deals over time
  • Analyze historical purchasing data to prepare for fluctuations in the market
  • Track correlations between variables like times of the year or major events and the price of raw materials and components, thus allowing them to better plan for sudden cost increases

How Analytics Makes Industry 4.0 Possible

With AI and IoT sensors, it’s possible to receive and analyze massive quantities of data from the factory floor every second. Analysts can then use this data to prevent machine failure or make tools more efficient. Further, they can use information about purchasing and order history to tighten materials sourcing and avoid overproduction.

AI is excellent at seeing failure coming, using subtle patterns in huge amounts of data to prevent the worst. For this reason, data is one of manufacturers’ most valuable assets. You can never know the future — but with enough data, you can see many things coming.

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