How AI Accelerates MES/MOM Systems Implementations

In a competitive and ever-evolving manufacturing environment, businesses are consistently searching for strategies to improve production processes through increased throughput, improved quality and reduced scrap, while maximizing both labor and machine utilization. Manufacturing Execution Systems (MES) and Manufacturing Operations Management (MOM) sit at the forefront of these organizational strategies, serving as digital threads that connect the shop floor to business operations.

Alone, MES and MOM systems automate shop-floor operations and provide manufacturers with real-time data insights used to monitor and enhance production activities, establishing the building blocks for operational change. Infusing Artificial Intelligence into MES and MOM, accelerates the transformation curve, by unlocking unprecedented levels of visibility, control, and efficiency, while empowering the manufacturing workforce with real-time insight and actionable intelligence.

Seven ways AI accelerates MES/MOM value

MES and MOM systems traditional face challenges such as connectivity, date cleansing, KPI identification/implementation, process integration, and end- user adoption challenges. MES/MOM implementation are time, resource and asset- intensive, with benefit realization often delayed due to network, machine and business process complexity.

For those questioning the value of Artificial Intelligence in MES/MOM deployments, it is more than fair to be skeptical, given past and current software hype curves that have failed to meet even practical expectations in a realistic timeframe.

The good news for manufacturing companies is that AI-enabled MES/MOM systems are not just proof-of-concept projects but production-ready systems ready to be challenged by the toughest critics. Key pain points from a design and deployment perspective are readily embraced by AI emerging technology.

1. Automate connectivity with shop floor ecosystems, from Level 0 (Sensors), Level 1 (PLC), Level 2 (Scada), Level 3 (MES/MOM) through Level 4 (ERP). \
2. Integrate and cleanse variable data sources within a unified platform without manual data mapping and validation steps.
3. Rapidly identify and define KPIs by industry, manufacturing company, business process, and target audience through templated formats and real-time date inquiries.
4. Integrate business processes and automate workflow processes, creating actionable intelligence to impact production processes in real-time.
5. Increase user adoption, minimize training requirements, and empower manufacturing personnel with conversational AI tools and accelerators.
6. Deliver successful implementations in weeks, not months, as business operations solves critical business challenges, quickly realizing a return on investment.
7. Reduce total cost of MES/MOM ownership, as the need for specialized programmers, data scientists, and connectivity experts decreases, as the platform becomes self-sufficient with end-user-enabled tools to build and maintain their own landscape.

AI does have the potential to transform manufacturing operations by automatically adjusting production schedules, schedule maintenance work orders, and pushing information across different departments to keep stakeholders informed. Instead of manually observing each department after a production issue, AI can automate key stakeholder communication by identifying the departmental impact through trained algorithms.

While AI in MES/MOM systems is not perfect and does require subject matter expertise and intervention, as the system learns subtle nuances of the manufacturing operations, the potential to transform the manufacturing environment exists. As AI continues to develop, it identifies and delivers business value.

Just think of the potential in a manufacturing company, running 365x24x7, producing 1,000 units per hour across two shifts per day with multiple production lines and plants. Optimizing productivity, minimizing downtime, and maintaining quality and scrap standards can make or break revenue and cost targets. A 1% scrap reduction or 1% productivity increase will quickly achieve revenue and cost-saving objectives, but only if identified in real time with actionable data communicated to key stakeholders to impact the process.

Embracing implementation best practices and challenges

When seeking to implement an MES/MOM solution with AI, organizations should consider several best practices to ensure success. Whether your organizational goals involve increasing throughput, rapid system deployment, shop-floor visibility, quality improvements, reducing waste, etc., the identification of desired outcomes is crucial to developing a tailored solution that best fits those needs.

• Conduct a needs assessment: Identify key objectives such as increasing throughput, improving shop-floor visibility, enhancing quality control, reducing waste, or enabling rapid system deployment.
• Standardize and clean data: AI models rely on high-quality data. Conduct audits to ensure completeness, consistency, and integrity to prevent errors in AI-driven insights. • Integrate with legacy systems: Assess existing hardware, software, and communication protocols to mitigate risks and avoid compatibility issues.
• Select strategic implementation partners: A trusted service vendor can provide expert support, expedite time to value, and ensure seamless integration without disrupting operations.

Despite the benefits, manufacturers face several challenges in MES/MOM adoption:

• High implementation costs: The upfront investment can be significant, particularly for small and medium-sized manufacturers.
• Complex integration with legacy systems: Older infrastructure may require modifications or upgrades to work with modern MES/MOM solutions.
• Workforce skill gaps: Training and development are necessary to ensure personnel can effectively manage and maintain AI-driven MES/MOM solutions.

Manufacturers that fail to invest in MES/MOM and AI risk falling behind. Without the right technology in place, inefficiencies persist, downtime increases, and production optimization remains out of reach. AI-driven MES/MOM solutions not only improve operations but also serve as a competitive advantage, accelerating time to value and positioning companies for long-term success.

What’s next for manufacturers? Coupling AI with MES/MOM solutions

Manufacturing Execution Systems and Manufacturing Operations Management Solutions are the backbone of modern manufacturing. Coupled with AI, these systems go beyond traditional efficiency, quality, and traceability goals, enabling manufacturers to predict disruptions, optimize processes dynamically, and maintain a high-quality production environment.

However, technology alone is not enough — successful implementation requires strategic execution, problem-solving, and a deep understanding of people and processes. At NTT DATA, we specialize in delivering tailored MES/MOM solutions that align with business needs. We don’t just implement technology; we work closely with teams to ensure seamless integration, minimize disruption, and maximize ROI. With extensive expertise across multiple technologies and industries, we streamline business processes, eliminate bottlenecks, and empower manufacturers to make data-driven decisions with confidence.

The future of manufacturing is intelligent, agile, and AI-powered—NTT DATA is here to help manufacturers embrace it.

Ready to transform your manufacturing operations with AI-driven MES/MOM systems? Discover how NTT DATA can help you achieve seamless integration, real-time insights, and maximum ROI. Explore our Manufacturing solutions today!