The manufacturing sector is the cornerstone of the UK’s economy. Last year, the sector saw a remarkable rise in output to £224bn. Employing 2.6 million people and accounting for nearly half of the UK’s exports, the manufacturing sector is in the spotlight for its potential to drive economic growth. AI and the role of other technologies has been critical in this growth. Teodor Stanilov, Principal, and Antti Kautovaara, Partner at Kearney explain further.
However, with manufacturers often struggling with digital integration due to complex systems and infrastructure, developing and implementing effective digital strategies is more important than ever.
Manufacturing’s AI journey
Kearney has witnessed first-hand the changing landscape of discussions on leveraging data and digital in manufacturing. Initially, the focus was on fundamental tasks such as data capture and management. As years passed, attention turned to data visualisation and now, the priority has moved towards harnessing AI to maximise data value.
Organisations are now exploring a wide range of advanced technologies for interconnected systems, from automation and vision systems to digital twins, data visualisation, and additive manufacturing solutions.
These innovations reflect the movement towards smarter factories and highlight the significance of interconnected systems. They particularly underline the essential role of IoT connectivity in creating manufacturing processes that are more efficient and agile.
Complications in AI Deployment
The most significant development in the sector recently has been the advent of generative AI, which has become the latest addition to the suite of digital technologies manufacturers are exploring. In fact, GenAI has kick-started the next wave of AI transformations in manufacturing by promising to integrate disparate technologies to enable companies to reinvent their operations.
However, despite implementing these measures, manufacturers are still having trouble getting the full benefits from the digital solutions they’ve already invested in, let alone something new and powerful like GenAI.
So what’s the issue? Manufacturers face two major challenges in digitalising their operations. The first is overwhelming technology paralysis. The market is flooded with systems and platforms that have undifferentiated features, making it tough for manufacturers to make well-informed decisions about which technologies to adopt. This confusion often leads to missed opportunities to leverage cloud, IoT and AI.
One of the major reasons for this paralysis is that while there is a focus from companies on developing use cases, there is limited understanding that enablement needs to come before use cases in order to make them a sustained reality. This creates a gap between the impact that the use cases promise, and the real-world benefits that many companies are seeing at the moment.
The second, more fundamental challenge is the lack of a clear vision and adoption roadmap. Many organisations don’t thoroughly understand how an AI-driven transformation fits into their overall enterprise strategy and creates sustainable value. This is where AI needs to be approached as a holistic business transformation rather than solely as a technological development.
AI is an exciting advancement, but key steps need to be taken regarding data usability from the outset. These include developing a data strategy, implementing data cleaning and management processes, and setting up data governance frameworks. By implementing these measures, businesses can demonstrate how AI can lead to transformative changes across the organisation.
When manufacturers fail to understand this from the outset, they often implement isolated solutions that address specific pain points but fall short of delivering a comprehensive transformation. So, how can manufacturers fully capitalise on AI?
Unlocking the benefits
Manufacturers looking to capitalise on AI should develop a clear vision and strategy that aligns with their overall business goals. This involves defining precise objectives for AI like enhancing operational efficiency or product quality, and identifying key areas where AI can offer significant value.
To start with, organisations should confirm their AI-enabled manufacturing vision for the next five to ten years. This requires a clear understanding of the current state of their processes, digital infrastructure, and AI maturity – covering data architecture, systems and manufacturing practices – and aligning this with their overall business ambitions.
Following that, it’s important to develop an implementation roadmap with defined priorities and value cases to help the organisation achieve its ambition. This roadmap should clearly show the investment needs, expected return on investment, rollout sequence, necessary capabilities, and enablement requirements.
With the plan in place, the next step is to implement technology and manage the transformation to realise the company’s ambitions.
This ‘plan-first’ strategy can be the difference between market-leading success and abject failure, and it is something that I have seen work first hand, supporting AI pilot implementations with rollout use cases, capture the lessons learned and enhance team capabilities by using data analytics, to elevate the manufacturer’s AI capabilities.
To help companies through this process, Kearney, AWS and Novotek have come together to provide a holistic offering that covers the entire set of capabilities needed to support the AI transformation journey in manufacturing from start to finish. However, despite the potential of AI and GenAI, many in the sector find the benefits hard to grasp. This underscores the critical need for organisations to have a roadmap.
Such a strategy ensures manufacturers can effectively adopt and integrate AI, overcoming current challenges and paving the way for widespread adoption.
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