The factory reset that global manufacturing needs

Trade Arabia

13-03-2025

As manufacturers contend with onshoring policies and the impact of extreme weather events, industrial AI applications will be essential to maintaining costs, unlocking production efficiencies and staying competitive, says Jim Chappell, Global Head of AI and Advanced Analytics, AVEVA
Visiting a car manufacturer in Dearborn, Michigan, Japanese executive Eiji Toyoda came away with a startling insight to stockpiled inventory: What if automobile parts were produced just in time for use?
That brainwave was first adopted at the Toyota Motor Corporation in the fifties – a company Toyoda went on to lead. In the wake of the 1973 oil crisis, the concept became a defining feature of the Japanese car industry. By minimising waste while improving quality, the production system garnered Japanese cars a reputation for reliability and affordability.
Just-in-time assembly, often called lean manufacturing, has since become a bedrock of global management canon . Its adoption in the seventies and eighties occurred amid periods of economic stagnation and high inflation, as companies battled to improve business margins and slash production costs while doing more with less. From 1973 to 1984, Toyota alone grew production from 170,046 units to 3.4 million units, the company's records show. The rest of the sector was quick to follow suit.
Risks cloud forecast for manufacturing
Manufacturers around the world face a similarly transformative moment today — although the world is in a very different place. Covid-linked supply chain issues may have eased over the past few months. However, new risks cloud forecasts not just for the immediate term, but well into the next decade, as the World Economic Form noted in its rather bleak Global Risks Report 2025.
Geopolitical turbulence and the emergence of a multipolar world are increasing demand for onshoring production, while new trade tariffs will have their own impact. Extreme weather events, meanwhile, will continue to weigh on the supply of raw materials, including both ingredients and mineral resources.
But when the going gets tough, to reframe the old adage, the tough turn to new tools. In every economic crisis, manufacturing has leveraged cutting-edge technologies and processes to buck economic headwinds and transform business outcomes. The technology for our times is industrial AI, and the true gamechanger will be its ability to generate real-time insights.
Or just-in-time insights, to borrow from Eiji Toyoda.
Leading from the front with business transformation
From Henry Ford's embrace of the assembly line in the early 1900s to General Motors' Unimate and Toyota's just-in-time and Kanban systems, the manufacturing sector has led the pack in terms of innovation and business transformation.
The advent of Industry 4.0 has speeded up digitalization and helped launch autonomous plants. But as manufacturers face unprecedented disruptions, first-mover advantage in the current climate appears to rest with those embracing the role of industrial data and related technologies in the artificial intelligence (AI) family, such as machine learning, predictive analytics and generative AI.
Together, they are critical to providing the insights needed to keep manufacturing operations lean and efficient. It's fair to say the real battle won't be offshore vs onshore—it'll be between smart factories and outdated operating models.
That's something production teams at Barry Callebaut have experienced firsthand. The Swiss-Belgian manufacturer began taking a digital approach to making chocolate seven years ago, using advanced industrial software products to create a smart factory that brings together people, processes and technology.
By integrating enterprise visualization with a next-generation manufacturing execution system (MES), the chocolatier improved traceability and boosted productivity. With this connected digital backbone, it has eliminated data silos and empowered staff with real-time insights from across the value chain. Alongside, predictive models have revealed optimization opportunities for instantaneous adjustments, increasing production capacity by 10%, while system-wide efficiencies have cut energy use and put the consumer products leader on track to achieve its net-zero goals without sacrificing output.
To stay with the chocolate industry, Nestlé used advanced AI-infused analytics and real-time data in the cloud to ensure consistent flavours across each jar of its Nesquik family drink, while cutting wasted powder by 10%. At 101g saved per 1kg jar, that's 10 extra cups of Nesquik. The company now wants to expand that success to more plants and to other products such as Ovaltine.
In the US, meanwhile, New Belgium Brewing Co. has gone from craft brewery to national leader with a digital-centric approach, using a MES platform together with advanced operations control software. Thanks to improved operational scheduling, process visualization, advanced AI analysis, and better collaboration and digital knowledge sharing, the Colorado-headquartered company has streamlined production while boosting efficiency and quality. Within just two years, overall equipment effectiveness rose from 45% to 65%, while downtime dropped 50%. Further, despite local variables, beverages produced at different facilities across the USA now taste identical.
Majority of manufacturers demand new technologies
As more companies look to unlock such transformative gains, 58% of manufacturers say the need for new technology to empower their workforce is a top business challenge, according to the AVEVA Industrial Intelligence Report 2024. Indeed, the overwhelming majority (97%) believe industrial AI solutions and other digital technologies are required more than ever to remain competitive in today's challenging landscape.
Yet, the majority of digital transformation projects – 78% according to Capgemini research – fail to deliver their promised benefits because of poor alignment with business outcomes, limited visibility across end-to-end operations and sub-optimal insights.
The answer lies in implementing flexible and open systems that bring together distributed enterprise and operations teams around a comprehensive digital data thread, where they can view just-in-time business insights at the scale they need.