The first industrial revolution was a result of steam engines and their abilities to run big machines for mass production. Until now, we have seen three industrial revolutions, and all of them slowly paved a path for the fourth, which is undoubtedly going to be driven by information, data, and analytics.

Today, every industrialist is looking for a way to disrupt the market at the first chance, and those who have not yet realized the potential of data and real-time manufacturing analytics for doing so are the ones who would not make it to the fourth revolution. Now with support in various areas from companies such as novae-group.com, for example, this sector is able to move into a new era, but denying that help is going to stifle things.

I might have sounded a little melodramatic with my words, but nothing today in the market is as simple as it used to be. It is going through extensive digital transformation and taking new shapes. If you can’t adapt to these changes, you should better brush up Darwin’s Theory chapter once again.

What’s makes data analytics a must for manufacturing businesses?

The manufacturing segment has been one of the most prominent beneficiaries of information technology and its applications. Be it a small mill, a mid-sized factory, or a chain of giant manufacturing plants, all of them discharge data at every point of their operation. We have seen manufacturing businesses utilizing that data to not only boost the operational efficiency but also predict demand and regulate inputs accordingly. Companies like Ververica (ververica.com) provide real-time stream processing services that help businesses stay in sync with their industry.

This is just a scratch on the surface of data; real-time manufacturing analytics is going to re-shape everything from supply-chain, manufacturing, and logistics to retail of the products. As per a recent study by Capgemini, 76% of the global manufacturing companies have already undertaken initiatives to formulate IoT-based smart factories. Out of them, 56% admit to having already invested more than $100 million in this plan.

To best utilize the technologies at disposal and improve overall productivity, manufacturers are adopting digital transformations in a speed never seen before. Many manufacturers are using Enterprise Resource Planning (learn more about ERP for Manufacturing) to help them get their businesses at the best it can be, so understanding the digital elements are part of this. At the same time, they are complementing their digital transformation with innovative applications of data analytics in flexibility, demand prediction, real-time manufacturing monitoring, risk analysis, operational security, and smart maintenance of the machines and equipment.

What started as a way to automate the manufacturing process is now the biggest asset for manufacturing corporations. Today, the market has 50 times more digital content than it had in 2017. Ask those who are already utilizing real-time manufacturing analytics in their business process, they will tell you how it’s no more an option but a necessity for businesses to collect, analyze, and practice data.

Productivity Enhancement –

Not just the product manufacturing but also the behind-the-scene such as product designing and streamlining of development processes have become faster, error-free, and efficient with real-time manufacturing analytics. Using software that can be found at sites like https://www.cbxsoftware.com/solutions/plm/, it is now possible to streamline manufacturing processes such as conception, design, manufacturing, quality, compliance, and support.

Not only this but by using the behavioral data emanating in real-time, we have been able to rationalize the operational downtime, as we can now track the connected machinery, whether that be 3D printing or augmented reality. This means that we can use their data to predict maintenance requirements before potential malfunctions. With this data it can be easier to predict when an equipment or machinery is going to require maintenance or a replacement part like these raymond forklift parts, for instance. By analyzing real-time data from these components, we can identify the wear and tear patterns, schedule timely repairs, and reduce unexpected breakdowns. This approach ensures that critical equipment remains operational, further optimizing the overall input to output ratio significantly in the manufacturing sector.

Production Quality –

Cutting-edge sensors are monitoring production parameters at every step throughout the production lines. So yes, real-time manufacturing analytics has not only helped us optimize productivity but also ensure quality assurance simultaneously. Plus, combining it with something like a sheet thickness sensor for achieving higher quality materials, production can continue seamlessly. The machine learning algorithms running in a connected environment across every piece of equipment and machinery within a plant decipher and put the production data to the best possible use. We are not only automatically diagnosing the root causes of manufacturing defects in real-time but also minimizing waste generation to an optimal scale. In addition, the integration of CNC machines (such as those available at this CNC machine shop) has further refined the precision of our manufacturing processes, allowing for seamless, high-accuracy production that meets the stringent demands of modern industry.

Cost of Production –

Netting and studying data generate across all levels of production, QA, maintenance, logistics, and transportation, let us identify innovative ways to reduce the cost of production. Based on the predictive analysis, we can forecast the demand and thus, plan the need for raw materials, machinery, fuel consumption, inventory, logistics, and transportation accordingly. With innovative use of the same data, we can stay prepared to meet the potential demands more accurately and upscale or downscale the production in advance.

Flexibility & Customization –

There is no doubt that mass production has been a significant beneficiary of real-time manufacturing analytics.However, there is a massive market for customization and tailored products too. These products sell on a comparatively higher price than mass-produced products, as manufacturers need to customize their manufacturing lines each time. Since customized products are made strictly on-demand, the time taken to produce a limited amount of customized products is not less than their mass production versions.

Through the digitized transformation of the manufacturing lines, we can accommodate customization lines right into the mainstream processes without disturbing the mass production lines. Unlike analog setups, digitized production lines can go through changes much faster and revert to the mass production mode automatically. It not only allows the manufactures to produce customized products economically but also to accommodate more customization options for the customers. Ultimately, allowing the manufactures to offer competitive pricing on customized products as well.

Workforce Safety –

With lesser manual intervention, it automatically reduced the risk of accidents. Moreover, real-time manufacturing analytics also keeps a track of dangerous environments and predicts operational hazards much in advance through dedicated sensors fitted right into every machine and equipment. A more innovative application of data, machine learning, and real-time analytics can also let manufacturers place AI robots to handle extremely dangerous jobs.

Everything is Data

There is no doubt that the digital transformation of the manufacturing units will bring the next industrial revolution. However, this transformation is only possible through the best utilization of data. Data is the building block of digital transformation. Every automation, optimization, and prediction uses legacy and real-time data to meet the expectations. Even the AI robots can’t function properly without different kinds of sensors installed on them to collect data in the first hand.

What do you think of your operational efficacy now? Almost every manufacturing business has either already digitized or has started digitizing their process, where do you stand in this timeline? Are you still in the thinking process?