AI in Manufacturing: A Practical Guide to Getting Started

In This Article

Part 1 of AI in Manufacturing discusses the importance of a growth mindset and B.E.S.T. Practices for AI maturity. In this article, Lori Borg, MCA Connect SVP, Manufacturing Intelligence and Chief Growth Officer, shares how lifting a fog of silos empowers manufacturers to transform operations with data and AI.

The Fog of Silos

Many manufacturers struggle with data trapped in isolated systems, scattered across multiple departments and locations. This “fog of silos” isn’t just inconvenient. It blinds manufacturers to valuable insights that can fuel growth, innovation, and efficiency.

A fog of silos:

Sound familiar? You’re not alone. A recent survey revealed that 74% of manufacturers struggle with data silos that hinder growth potential. Erroneous data, poor data quality, and difficulties with data integration are also common.

Lift the Fog to Reveal a Constellation of Insights

Here’s how visibility challenges come together in an AI-enabled manufacturing scenario:

A series of mergers left one leading manufacturer with a global network of siloed data systems. Leaders had no way of seeing the big picture or making sense of all their data. Instead of making data-driven decisions, they relied on gut feelings or made decisions in silos. As a result, the manufacturing company couldn’t make the most of its acquisitions.

To solve this complex challenge, the manufacturer adopted a growth mindset and followed B.E.S.T Practices for AI maturity. They also implemented MCA Connect Inspire Platform™ to connect their data and leveraged AI to drive their manufacturing processes. By lifting the fog of silos, the manufacturer uncovered a constellation of insights that transformed their business in three key ways.

Manufacturing AI Use Cases

Boost Production with AI Workflows

The company’s production lines were a complex network of machinery, each with its own rhythm and pace. Acting as a global conductor, AI transformed production by analyzing real-time sensor data from equipment across various locations. It identified bottlenecks and imbalances, then redistributed tasks to create a smooth workflow. This maximized utilization of existing machinery, leading to a significant production increase without additional capital investment.

Eliminate Downtime with Predictive Maintenance

AI also empowered the manufacturer to shift from reactive to proactive maintenance. By continuously monitoring equipment health and analyzing historical data on maintenance and failures, AI algorithms learned to identify subtle warning signs of wear and tear and predict potential breakdowns. This empowered the company to schedule maintenance during planned downtime, eliminating disruptions caused by unexpected equipment failures. As a result, AI’s predictive capabilities extended machinery lifespan, reduced repair costs, and ensured a steady production flow.

Predict Demand and Streamline Inventory with AI Forecasting

The manufacturing company also transformed inventory management from a static process to a dynamic one. By analyzing years of sales data, real-time sales trends, and supplier lead times, AI predicted future demand for each location with high accuracy. Leveraging these forecasts, the company automatically adjusted inventory levels, optimizing stock for both in-demand items and slow-moving inventory. This approach improved resiliency, reduced storage costs, and minimized unsold goods. Additionally, AI empowered the company with agility by automating the replenishment process based on real-time demand and inventory data across locations.

In each of these manufacturing use cases, AI served as the linchpin, turning data into actionable insights. The company not only solved its immediate business issues but also set a new standard for efficiency and responsiveness.