How Manufacturers Can Amplify Intelligence with AI
Manufacturers struggling to scale value from investments in digital technologies and AI need to take a more holistic approach that ensures C-suite buy-in, addresses new skills requirements, and creates an AI-friendly culture for the future
For many manufacturing companies, the potential of artificial intelligence (AI) is easier to envision than the steps needed to integrate it into their business. Leaders can see the transformative power of the technology as it continues to evolve and they begin to imagine what it would mean for their business, from end-to-end supply chain visibility to powerful new insights from predictive analytics to the ability to respond to sudden demand shifts more quickly. It’s all out there, and yet, the strategy and execution needed to bring these tools to life continue to be elusive.
As companies struggle to scale the value from their investments in digital technology, many often find themselves in a state of pilot purgatory. They experiment with AI and machine learning, the Internet of Things (IoT), augmented reality, virtual reality, and other solutions that may have no clearly defined purpose. It’s a fundamental flaw that keeps many manufacturing companies from leveraging the benefits that new technology can bring to the table. They could have the best data scientists in the world and a whiteboard full of bold ideas that would forever reshape the company’s future. But if they don’t have an empowering business vision tied to clear business outcomes and a viable business case, it’s going to be very difficult to find success.
Leaders need the right mindset, the right skill set, and the right culture to effectively scale digital innovation in a company and create a functioning unit that can maximize the advantages that it brings to an organization. This effort requires time and careful planning, and there will, no doubt, be setbacks along the way. Do it right, however, and the payoff for the company and its customers can be substantial.
Opportunities and Hurdles
It’s easy to see why manufacturers are enthusiastic about AI. In factories, smart sensors, IoT, and AI enable predictive maintenance¹ to save costs and extend the lifespan of important assets. Then there are digital twins², which are virtual replicas of a product, process, or piece of equipment, which they can use in simulations to help make supply chains more resilient³. AI can play a role on the other side of the value chain as well by enabling chatbots to respond to inquiries quickly through text analysis. Cybersecurity intrusion identification is a popular response as well.
Other use cases are more nascent, but also powerful. For example, AI can help forecast customer demand and manage inventory for seamless fulfilment. Analytics4 can also drive better decision-making and more effective utilization of labor, and AI visual analytics can be used in maintenance for faster inspections and verifications.
One key is understanding that doing AI is not just a matter of implementing the technology. A focused approach on business outcomes first, followed by a robust data quality and governance process, are critical to drive business value at scale. Hurdles, such as how data must be collected and cleansed to be easily connected to AI solutions in production, must be addressed. Too often, siloed functions and unintegrated platforms don’t forge the links needed to make AI effective.
EY recently worked with one manufacturer that saw an opportunity to make a bigger version of its signature product. The company had spent $100 million on process improvement and assembled a top-notch team of data scientists but struggled to develop a cohesive plan of attack. The company’s CFO had become very skeptical about the value of digitization through this effort. Every time the company tried to change its product mix, throughput dropped significantly, and the production schedule was disrupted.
“Without an engaged C-suite, it will be a struggle to have a dialogue about how best to use AI, how to allocate resources, and how to set priorities across all business units and functions.”
The problem was planning and execution. The CFO needed a baseline understanding of throughput across the company’s factories, including when new products were introduced. He also needed a way to simulate how the plan might work before any capital allocation decisions were made. The solution was to create a business case and pilot a digital twin capability that enabled the plan and led to significant savings for the company.
The AI Road Map
Shaping, accelerating, and optimizing an AI journey requires six steps, regardless of whether a company is just starting out or trying to strengthen its plan to make it holistic.
1. Acknowledge AI potential
Without an engaged C-suite, it will be a struggle to have a dialogue about how best to use AI, how to allocate resources, and how to set priorities across all business units and functions. It’s a good idea to pick company AI agents who know about the potential of the technology and will keep it on the agenda by helping to hone robust business cases, develop metrics for a proof of concept and then move any AI solutions into production. Without leadership from the top, AI initiatives can get lost in the shuffle amid other priorities and disruptions in the market.
2. Transform and plan
An agile and open culture is a baseline need for the business to be able to effectively leverage new technologies, not just AI. A plan should include key performance indicators aligned with the organization’s business strategy, and finance allocations should be clearly set. A data unit should be established, working in tandem with AI agents and a digital committee or center of excellence, to address requirements in the current state and support the journey to the future state around items such as data collection and cleansing.
3. Data foundation and structure
The data unit or owner is vital for asserting oversight across all of the data points across the supply chain, involving many customers and processes. Non-digital data must be converted, other data sources should be cleaned, and structure should be added to boost the quality of the data, and ultimately, its effectiveness in the AI solution. Data storage through databases such as data lakes help guide the data flow and strengthen the ability to perform analytics. Data governance, processing, explainability, and transparency are all components of a successful solution that should be addressed up front.
4. External partnership ecosystem
Manufacturing companies have developed many talented resources with varied skill sets, but AI know-how can be in short supply. Thankfully, a robust ecosystem of external parties, including start-ups, academia, consultancies, and other tech leaders, can be tapped, adding perspective to the company’s understanding of the business and use cases.
5. In-house AI expertise
Even with partners, the existing workforce will need to learn new skills and fulfil new responsibilities. AI experts, data scientists, and engineers are crucial personnel to hire, but an understanding of data science must be spread throughout the organization. Corporate cultures that have become rigid and narrowly focused on the needs of today rather than the possibilities of the future must be challenged, because AI works only when skills and experiences from many disciplines unite.
6. Architecture and infrastructure
Algorithms are a core part of AI solutions. In fact, these rarely pose a struggle for most organizations to build. The complexity arises when the time comes to integrate them with the technological architecture. Smaller modules with clear guidelines and principles make this process simpler for running proofs of concept and scaling the solutions. And standardized infrastructure service offerings on the market provide agile and robust ways to enable these AI solutions with flexibility.
With great challenges come even greater opportunities. Manufacturers that create an AI-friendly culture today are positioning themselves to boost customer and employee satisfaction as costs decline and helping drive their competitive edge in a challenging and complex moment for businesses across the world. M
Footnotes:
1 How preventive maintenance can backfire and harm your assets, https://www.ey.com/en_gl/consulting/how-preventive-maintenance-can-backfire-and-harm-your-assets
2 Can a supply chain digital twin make you twice as agile? https://www.ey.com/en_gl/advanced-manufacturing/can-a-supply-chain-digital-twin-make-you-twice-as-agile
3 How to navigate supply chain disruption with digital process mining and digital twins, https://www.ey.com/en_gl/advanced-manufacturing/how-to-navigate-supply-chain-disruption-with-digital-process-mining-and-digital-twins
4 How forward-thinking organizations are becoming data-driven, https://www.ey.com/en_us/consulting/how-forward-thinking-organizations-are-becoming-data-driven
5 Why contactless field service presents an opportunity beyond COVID-19, https://www.ey.com/en_gl/consulting/why-contactless-field-service-presents-an-opportunity-beyond-covid-19
6 Bridging AI’s trust gaps: Aligning policymakers and companies, https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/ai/ey-bridging-ais-trust-gaps-report.pdf
7 Is AI the start of the truly creative human? https://www.ey.com/en_us/ai/is-ai-the-start-of-the-truly-creative-human
The views expressed by the authors are not necessarily those of Ernst & Young LLP or other members of the global EY organization.
Data supporting the content of this article is derived from the Microsoft and Ernst & Young LLP report to explore how AI can transform manufacturing: https://www.ey.com/en_gl/advanced-manufacturing/how-manufacturers-can-amplify-intelligence-with-ai
