Manufacturers can leverage AI to enhance data visibility, assess supply chain health, and analyze supplier performance to improve outcomes.
By Jim Brown, Logility
The manufacturing sector is predicated on precise coordination and predictability. But supply chains don’t always cooperate.
Decision-makers need to know the right materials will be in the right place at the right time to serve the right consumer market. This has become more difficult as economic uncertainty, geopolitical conflicts, sustainability initiatives, and growing pressure for transparency make coordination and predictability more difficult to achieve.
Technology has become central to solving these challenges.
As Deloitte’s 2024 Manufacturing Industry Outlook report notes, “Technology is poised to play a significant role in supporting manufacturers in taking on the challenges they may face in 2024.”
Increasingly, that means leveraging generative artificial intelligence (GenAI) to make supply chains more capable of predicting changes and developing responsive, resilient strategies. This year, nearly 60 percent of manufacturing leaders are increasing spending on AI. It’s not a small line item. Seventy percent of manufacturing companies opt for more expensive commercial AI models, making it even more important to earn a return on their investment.
That’s why an effective AI-first manufacturing strategy isn’t just a branding exercise. It must deliver tangible ROI that makes manufacturers more agile, efficient, and responsive to market demands. Here are three immediate and tangible examples of “smart manufacturing” – using AI in the manufacturing sector right now.
Modern manufacturers are awash in data. Theoretically, this should make them better decision-makers, disruption anticipators, and operational optimizers.
In reality, the opposite is true.
One executive survey found that 85 percent of business leaders suffer from decision distress and 91 percent report that growing data volumes limit their organization’s success. In total, nearly three-quarters of executives said that data volume is preventing or enabling better decisions.
AI simplifies complicated data, giving executives insights into their supply chain and inventory management. These insights will help leaders understand whether assets are constrained and identify root causes for dropped demand.
At the same time, it clarifies constrained supply planning and visibility of:
As a result, AI products can surface insights into the questions leaders might not even know to ask, making data useful and impactful by elevating data visibility and making it actionable.
The COVID-19 pandemic was a stark reminder for global manufacturers that supply chain consistency and predictability aren’t guaranteed. This reality has remained unabated ever since.
That’s why businesses regularly assess the health of their supply chains, looking to stay ahead of disruption and mitigate potential risks through proactive strategies and increased resilience.
AI introduces flexible platform analytics, enabling proactive visibility of:
In this case, AI isn’t solving supply chain problems, but it allows manufacturing leaders to understand their supply chain’s health and identify solutions accordingly.
The recent push towards onshoring/nearshoring manufacturing gives manufacturers more control of the process. This is another risk mitigation, sitting between supply chain health and supplier performance.
Not every supplier is the same. They differ on key factors such as quality, reliability, cost, and delivery time, all of which can significantly impact your operations and bottom line.
The effects on manufacturers can be profound.
Moody’s analysis ranked poor supply performance as the top supply chain risk last year. The solution is simple but difficult to implement.
As Moody’s notes, “The most important way to protect your company against this potential risk is to use predictive analytics to identify a supplier’s declining financial health before it impacts yours.”
AI allows manufacturers to take this assessment further by surfacing data on fulfillment performance and excess, deficit, and inactive inventory, allowing manufacturers to take necessary actions to improve performance and availability.
This includes constrained supply planning and accurate, proactive visibility of fulfillment performance/shortages. Additionally, AI unlocks integrated business planning scenario analysis to explore the impact of alternative demand prioritization strategies and disruptions/constraints on volume and finances.
More specifically, AI-powered manufacturing can surface sourcing analytics to identify vulnerabilities related to critical raw materials based on their usage/profitability. Manufacturers can leverage this information to produce vendor scorecards that provide transparency of supplier risk based on individual supplier historical performance.
Understandably, manufacturers are investing heavily in AI. It’s the more straightforward path to modernizing operations, increasing efficiency, and outpacing the competition.
It’s also still in the early stages for many businesses.
To see an immediate return on investment, manufacturers should look to leverage the technology to enhance data visibility, assess supply chain health, and analyze supplier performance.
The strategic integration and deployment of AI will enable manufacturers to streamline processes, anticipate disruptions, and make data-driven decisions that significantly improve operational outcomes and profitability.
About the Author:
Jim Brown serves as Senior Vice President of Business and Solution Consulting for Logility, a leader in AI-first supply chain planning software.
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