Deploying AI on the road to the Fourth Industrial Revolution (4IR) – entrepreneurship presents unprecedented opportunities for manufacturers. Lighthouse production are pioneers to apply 4IR technology on a large scale in their factories.
These industries have sustainably leveraged the capabilities of AI to enable manufacturing beacons to make predictions and decisions, realizing a variety of competitive, financial, and operational advantages and efficiencies.
For example, predictive maintenance is already possible increase asset productivity up to 20%. With AI providing so much scope for growth in the manufacturing sector, what is stopping businesses from adopting the Industrial Internet of Things (IIoT)?
While AI technology is driving the revolution in manufacturing, human intelligence is the biggest deciding factor between success and failure. Increasingly, The company realizes they will need more advanced technical, cognitive, social and emotional skills. You’ll go even faster if you get more understanding, purchasing, and collaboration from your people as your business transitions to the 4IR.
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Lighthouse makers share an approach to change management that includes human capital at all stages of the digital maturity journey. As that path moves toward digital transformation, pervasive barriers, such as miscommunication, lack of purchasing power, lack of critical skills, and rigid company culture can be broken down by people-first approach.
Miscommunication
Many manufacturers operate with a traditional communication style, where silos and management chains don’t connect seamlessly. However, there is a need for seamless communication during complex change management.
In AI-driven projects, large amounts of information are communicated and analyzed among different stakeholder groups. When that information is properly collected and categorized, a broad view of activity emerges that can be seen and understood by everyone.
Santhosh Shetty, a technical sales engineer specializing in AI for manufacturing, says this enterprise-wide view enables new insights connecting silos and hierarchies.
“What’s happening is the teams on the ground, at the factory, they’re working in the bunkers. They are responsible for a particular process and the tooling of that particular process,” said Shetty. “While in reality, the plant’s systems are connected and include many different processes.
“What we are doing is allowing businesses to see the entire factory in a single perspective and highlight the operating modes of the plant in a separate perspective. We can tell customers where they are operating in both good and bad quality areas and how long they have been operating in that area. That is valuable to our customers because they have never seen a factory depicted in such a holistic fashion before — across the entire manufacturing plant and all connected processes at the same time. time, in a perspective. And once people see this, they suddenly get on the same page and start talking about what could happen.”
Lack of buying
Maximizing buy-in at all levels of the business increases the likelihood of projects getting support and on target. For example, as well as providing guidance and resources, how a sponsor is involved in a project determines how seriously the people of the business take the project. From factory preliminary to IT, management, and C-suite, everyone has to see the value for the business and themselves, along with what the digital maturity journey will look like.
People are naturally resistant to change, especially if previous change projects underperform or fail, which statistically many have done.
In a 2019 article at The Innovator, Chief Digital Officer at Michelin, Eric Chaniot, spoke of the company’s success in digital transformation, that only 5% depends on technology. The remaining 95% of success is winning over the people you need to make the new environment work.
“No one tells you “no,” says Chaniot, “but you can see in their eyes that they feel like saying it. “
One of the key reputation factors in successful AI projects is data integrity. Modern data science methods provide more transparency to the AI pipeline and provide the ability to transform raw data into what the machine learning model needs to make rules for optimization. Even before data is shared, explaining the rationale and methods that models use to plant engineers and operators builds greater confidence in the business insights gained.
Lack of key skills
The road to digital maturity is just beginning for most producers — but so is the influx of talent needed to design and execute digital maturity programs. AI projects require multi-skill teams of data scientists, business intelligence analysts, machine learning engineers, and software architects. These roles are complex and require diverse capabilities to integrate critical technologies.
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McKinsey report that “manufacturing C-suites are well aware that talent shortages are the biggest barrier to digital transformation: 42% of industrial companies say they are experiencing a shortage of skilled workers.” 4IR and only 32% feel prepared for the potential impact of 4IR on roles and skills. “
Naturally, this shortfall makes the transition more difficult across the manufacturing landscape.
The AI consultants you appoint will have experience in solving change management issues, especially the more systemic challenges in the early stages of digital transformation. Invite these experts to share their views on the change from leading other manufacturers along the way.
In addition to being able to assess plant readiness, they will bring knowledge that helps guide strategists, project leaders, and stakeholders through the human aspects of change that will necessary, needs.
Rigid company culture
The long-held belief that “the old way is the right way” is difficult to change, especially in the manufacturing sector. The manufacturing industry is characterized by tradition, and tradition is a pillar of culture that is not easily changed.
As operations transition to more digital and data-driven, organizational dynamics in the factory can shift toward confusion, misunderstanding, and resistance.
Forbes reports that “Digital transformation doesn’t start with technology. What we see are the companies that succeed and lead the way in the transition, the ones that can adapt to their culture.”
The change you want to see must be envisioned and implemented by leaders who can influence behavior change and facilitate greater productivity through digital transformation.
This means creating a culture where individuals at all levels know how to interpret data and act on it. A data-driven culture allows its members to discern and understand facts, ignore biases, identify problems, and seize opportunities.
For example, one feature of lighthouse manufacturing is the imperative to have executive patronage at the top, ensuring that the corporate culture changes, so a 4IR disruption can be successful.
People Strategies for Change Management in Manufacturing
Lighthouse makers know the barriers to digital maturity will decrease when risks are minimized through careful change management. That means putting the organization’s human resources at the center.
This approach integrates different perspectives and behaviors, empowers employees, and encourages a culture of continuous improvement through integrated change and innovation. However, it is important to first determine the extent to which 4IR technology is facilitating this change, especially when connecting silos and hierarchies will form a holistic view. maybe about a factory.
Currently, this is a challenge faced by most manufacturers in diverse verticals. Work closely with your AI partners on 4IR change projects, and you’re more likely to exploit the complexity of IIoT sooner.
Taking all of your people with you on the journey will result in a smoother transition to AI-driven digital maturity, meaning you’ll spend less time fighting fires. This saves everyone’s attention for a new era of manufacturing excellence.
Nicol Ritchie, technical writer at DataProphet, instructions for creating written content for DataProphet. He has extensive technical corporate experience in long-form writing in a variety of areas — including financial services, digital consulting, and corporate social responsibility. Nicol holds a master’s degree in both Applied Linguistics and Creative Writing.
