Smart Factory Software: Best Path To A Smarter Manufacturing

Over the next ten years, the smart factory has the ability to triple labour productivity.  By merging quickly evolving technologies like industrial IoT, big data analytics, cloud environments, artificial intelligence, M2M communication, and machine learning, digitalization of manufacturing is gaining traction.

Businesses that are slow to adjust to the new realities brought about by the IIoT acceleration may find Industry 4.0 tough. The manufacturing sector is able to bring the digital and physical worlds closer together by combining modern digital technologies thanks to the exceptional value of insights gained from data collected throughout the supply chain lifecycle.

Let's delve deeper and explain each technology that can be used to improve operations, procedures, and assets.

What is a Smart Factory?

It's important to note that the terms intelligent manufacturing, industrial Internet of Things, Industry 4.0, and the digital factory can all be used as synonyms for "smart factories." All of these terms refer to fully automated, digitalized, and IoT-driven solutions that aim to gather production data, analyse it to produce useful insights, and then supplement human intelligence with ML algorithms to ensure adaptable, effective, and streamlined processes at every stage of the manufacturing lifecycle.

The production activities are supported by IIoT in order to attain the ultimate objective of a comprehensive view of the processes. Let's move on and discuss the advantages and uses of the IoT, cloud computing, and big data analytics, as well as how these technologies might truly affect your organisation.

Streamlined Business Operations

Asset usage is optimised and automatically maintained when data is tracked in real-time. That implies that the machinery can be automatically altered and is continuously under control. This facilitates the function-streamlining of manufacturers. Due to remote monitoring of vital assets, IoT-enabled data systems reduce the number of inventory activities. Employee manual engagement, which could be more important for strategic company decisions, is subsequently reduced as a result of this.

Business operations that are more efficiently run result in fewer mistakes, less downtime and need for spare components, better quality control, and more productive workers. Businesses can raise their ROI and grow their profitability with such optimised efficiencies. The basic rule is to determine what data points need to be acquired and examined while estimating the existing state of your legacy systems.

Asset downtime prevention with predictive maintenance

Poor maintenance practises, according to Deloitte, contribute to unscheduled downtime, which harms industrial productivity, user satisfaction, and revenue. The annual cost to businesses is $50 billion. The maximisation of asset availability raises a conundrum. Businesses sometimes struggle with the trade-off between replacing potentially good parts in advance or running equipment until they break. Predictive maintenance (PdM), which uses data from many touchpoints linked with ML, has enormous potential for manufacturers. They are able to foresee failures with accuracy and take preventative measures.

Enhanced Labor Management with Smart Robotics

Robots with artificial intelligence (AI) are said to be "smart" if they can gather and evaluate environmental data and use it to produce more advanced results than humans. Smart robots can perform not only monotonous jobs but also produce advanced business-critical judgments in partnership with people based on past experience and learning models thanks to their great learning and perceptual abilities. Smart robots enable manufacturers to enhance supply chain and logistics through the integration of IoT sensors and machine learning algorithms.

Autonomous guided vehicles are one of the smart robotics technologies being used in smart factories (AGV). By handling the heavy lifting, they may greatly speed up logistical procedures on the factory floor, which has a favourable effect on workers' productivity and safety.

Conclusion

It is not sufficient to use connected technology in order to start the digital transformation of your industrial operations. It's a step-by-step process that begins with figuring out which of your regions are the most important for data-driven upgrading.

You must first evaluate your legacy systems and the degree to which they can be merged with systems driven by the Industrial IoT. Additionally, decide which assets should be closely watched and managed. The connectivity network design should then be implemented to complete this move seamlessly.