A targeted AI approach to maximizing value in manufacturing

AI-powered scheduling and optimization

Traditional production schedules rely on static rules and offer limited visibility into machines, lines and processes. When variability or disruptions occur, teams must manually replan, creating bottlenecks, idle time and waste that undermine throughput and schedule adherence.

AI-powered scheduling engines eliminate much of this friction. They dynamically prioritize production orders based on real-world constraints—across machines, lines and shifts—to keep operations flowing efficiently. Layered on top, agentic AI strengthens shift planning and resource allocation by analyzing production logs to detect deviations, identify root causes and recommend corrective actions. Digital twins extend these capabilities further, simulating production flows, testing optimization scenarios and helping teams make better decisions before changes hit the factory floor.

Together, these technologies create autonomous planning systems that monitor production in real time, detect issues as they arise and trigger rapid adjustments. One manufacturer reduced scrap by up to 10% per ton of production using this approach. This was achieved through AI‑enabled scheduling and monitoring, which streamlined set‑ups, cut downtime and strengthened forecasting accuracy—improving equipment effectiveness by up to 5%.^[2]

More broadly, this intelligence transforms scheduling from a manual control exercise into a self-adjusting, data-driven capability that continuously aligns operations with business demand.

Predictive maintenance

Most factories still rely on reactive maintenance, which leads to over‑servicing, unexpected failures and costly downtime. Siloed sensor data and static thresholds limit early detection, driving excess labor, higher parts usage and lost production hours. Manufacturers can move beyond this by combining AI and the Internet of Things (IoT) to predict and prevent equipment failures. AI models use IoT sensor data to trigger maintenance only when it is needed, eliminating routine over‑servicing. Agentic AI enhances this by continuously analyzing performance data and adjusting the parameters that determine when maintenance should occur—improving prediction accuracy and reducing cost.

The results can be significant. AI-powered Intelligent Asset Management improves asset reliability through self-optimizing operations, reducing downtime by up to 15%. In parallel, Agentic AI–enabled predictive maintenance can lower labor costs by 5–10% in asset-intensive industries such as energy, mining and utilities.^[3] These advances shift maintenance from reactive cost center to proactive source of reliability and long-term value.

Advanced automation

Manual, repetitive tasks and inconsistent processes drive up labor costs, extend cycle times and compromise quality. Heavy reliance on human intervention limits flexibility and scale, leaving production vulnerable to inefficiency and inconsistency.

Companies are addressing these challenges by deploying lightweight collaborative robots (COBOTs) for loading, unloading and material handling and by integrating vision systems for autonomous automation. These technologies increase throughput and accuracy across assembly lines while creating scalable, flexible production models where people and machines work side by side.

The impact is clear. Deploying robotic solutions at scale to automate operations can boost productivity by up to 20% while reducing costs by a similar margin.^[3] Research from the International Federation of Robotics (IFR) highlights that Yokoyama Kogyo, a Japanese manufacturer of car seat frames and automotive components, achieved a 35% cost reduction through robotic automation while maintaining worker safety and product consistency.^[4]

Predictive quality control

Quality control often suffers from fragmented data, inconsistent supplier standards and limited real-time visibility. Variability in processes and weak traceability make proactive quality management difficult, while rework, scrap and warranty claims erode profitability. AI-powered visual inspection systems combine speed with precision in large‑scale checks, while predictive analytics identify defect patterns early and prevent issues before they occur.

The impact is measurable across industries. Foxconn for example, cut inspection time by 30% while increasing accuracy by 80%. GE reduced inspection time by 25% and manufacturing costs by 30%.^[5] LG Innotek achieved a 99.99% defect detection rate.^[6] And in the food sector, FreshTrack reduced waste by 30% through AI-enabled smart packaging.^[7] Beyond these metrics, predictive quality has become a strategic differentiator, helping manufacturers deliver precision, consistency and sustainability at scale.

From isolated gains to integrated intelligence

Taken together, advances in AI-driven scheduling, predictive maintenance, robotics and quality management are transforming manufacturing from a collection of discrete improvements into an intelligent, interconnected system.

A global equipment manufacturer with $8 billion in revenue faced major operational challenges as supply chain disruptions reduced throughput, delayed deliveries and drove up production costs. Missed builds increased production costs and siloed data blocked visibility and slowed cross-functional collaboration.

To address these issues, the company, partnered with Accenture, to launch start a transformation program to lower material costs, recover supplier warranties, cut IT costs and streamline processes through automation. It deployed AI‑powered, cloud‑native platform, rolling out new modules across global facilities with integrated program management, change management and implementation support.

The company then built an operations system that linked connected enterprise and manufacturing applications, provided real‑time visibility and enabled faster, more accurate decisions. By integrating smart, autonomous supply chain capabilities, cross-domain data and a live digital twin, the company optimized production and synchronized planning—activating AI-powered scheduling, predictive maintenance and quality control and advanced automation across operations.

The program delivered more than $30 million in annual savings, increased productivity by up to 50% and reduced cycle times by 20 to 30%, strengthening operational efficiency across the value chain.

Visit Making self-funding supply chains real: Where to start and scale for autonomous, end-to-end growth, for the full view of how manufacturing contributes to an integrated, end-to-end supply chain transformation.