Industry 4.0, also known as the Fourth Industrial Revolution, is set to transform the process manufacturing industry. It brings together a number of technological advances, including machine learning (ML), cloud and edge computing, big data from the Industrial Internet of Things (IIoT), and robotic process automation.

What’s Next for Industry 4.0?

Industry 4.0 takes computing and digitized workflows a step beyond Industry 3.0, which introduced electronic and digital tools. Under Industry 4.0, products and machines communicate with each other, analyze data, and take action independently of human direction. 

Since its beginnings in Germany in 2011, Industry 4.0 has spread across the world. Manufacturers today see Industry 4.0 as transformative, with 90% of respondents in a recent survey agreeing that it will have some or significant impact over the next 5 years, and research by Deloitte concluding that by 2025, 85% of American manufacturing companies will use smart factories to drive competition.

Industry 4.0 is seen as driving financial value within supply chain and demand forecasting, improving operational value by increasing efficiency and uptime, and raising brand value through sales and marketing tactics. 

Industry 4.0 Perception Overly Optimistic

But progress towards Industry 4.0 lags behind the optimistic perception of its potential. In early 2020, only 5% of manufacturers had 1 or more “smart” factories, and just 30% more were implementing smart initiatives. Close to two-thirds had made no headway towards Industry 4.0. There’s a real gap between the acknowledged value of Industry 4.0, and the extent to which plants and factories have implemented these practices. 

This is largely due to problems with connectivity, with many plants struggling to integrate data, break departmental silos, and connect legacy software with smart systems.

Process plants tend to be rich in data, but smart tools can’t analyze it and mine it for insights unless it’s collected in a single repository. Teams typically operate within their own departments, with little crossover between them, which makes it difficult to implement agile business practices and enable productive collaboration. And legacy software doesn’t play well with cutting edge ML-powered analytics. 

Additionally, companies need to change their culture to move beyond proof of concept. McKinsey notes that many plants carry out successful pilots for Industry 4.0, but get stuck when it comes to scaling practices into production processes. Only around 44% of participants were able to move from a pilot to site-wide implementation, and just 38% were considering horizontal integration beyond plant walls. Plants that focus only on the tech, without implementing a digital culture across the organization, find that their digital transformation efforts go nowhere. 

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COVID-19 Accelerated Digital Transformation

COVID-19 radically transformed attitudes towards Industry 4.0, machine learning, and smart systems. If previously these were seen as nice-to-have, the pandemic pushed them to the top of the priority list. According to McKinsey, around 25% of industry leaders accelerated plans for robotic automation programs in 2020, to make up for the need to keep factory employees at safe distances.

Smart factory tools like remote monitoring, predictive maintenance, and predictive analytics became crucial to manage plants from a distance. COVID-19 also shook up customer demands and disrupted global supply chains, further emphasizing the need for integrated data and ML-based analytics that can optimize supply chains and forecast demand.

The need for a robust, reliable predictive maintenance system like Precognize was thrown into relief during the pandemic. Plants that received early alerts about emerging issues were able to deal with them quickly with a skeleton maintenance crew, and avoid them snowballing into serious issues and provoking unplanned shutdowns.

With a drastically smaller number of employees on-site at any given time during pandemic-related lockdowns, and many people working remotely, there have been fewer eyes on the ground to spot anomalies and correct them before they grow, making it even more imperative to use ML and IIoT to fill in the gaps. 

But Gaps Persist Across the Industry

The economic crisis provoked by COVID-19 caused financial trouble for many businesses, leading them to cut back on expenditures. Many companies avoided investing both capital and the time to reorganize the plant, reimagine plant workflows, purchase new technologies, and train employees in the new competencies needed for Industry 4.0.

McKinsey reports a noticeable gap between industry leaders who have embraced Industry 4.0, and those who are still holding back from making bold changes. However, the laggards have no choice but to act fast to catch up. Only those companies that are agile, integrated, and digitally transformed will remain competitive in the post-COVID world. 

In addition, the rush to Industry 4.0 highlighted gaps in infrastructure, culture, and workforce structure that are a prerequisite to success. Many plants took hasty steps to implement smart tools and are reaping the benefits, but they need to consolidate these moves to drive value over the long term.

Digitizing individual manual processes may have improved productivity and reliability during the pandemic, but now they need to be connected to a central system. In the middle of a pandemic, with many employees working from home, isn’t an ideal time to work on changing company culture, so it needs to be top of the to-do list when employees can return to work. 

Precognize’s Machine Learning Helps Bridge the Gaps

Precognize’s SAM GUARD solution uses Industry 4.0 principles of big data and machine learning to produce a small number of targeted alerts every day. The system learns which anomalies could be serious and which are simply background noise, and filters out those which are not critical to reduce the number of alerts from hundreds per day down to a handful.

It’s quick to implement, taking just 2 weeks to become operational, making it an ideal way to tap into Industry 4.0 practices quickly and at a reasonable budget. By offering predictive maintenance as a service, with analytical monitoring engineers on site or working remotely to enable the predictive maintenance, Precognize makes it accessible even to plants which aren’t yet in a position to implement a digital culture that can run such a solution independently. 

COVID-19 altered the playing field for process plants. Industry 4.0 is no longer an interesting concept on the distant horizon, but a vital tactic for ensuring survival. Solutions based on machine learning like Precognize are crucial for success in 2021. 

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