You lack real-time visibility into production processes, machine performance, and product quality.

Your existing MES platform is operating in data silos without integration with other, critical enterprise systems.

Your legacy MES cannot handle the flexibility and scalability needed to manage multi-site operations.

You lack predictive maintenance features resulting in frequent and expensive downtimes.

You struggle to comply with modern regulatory standards or support sustainability initiatives due to lack of real-time monitoring and reporting features.

Significant upfront capital is required for MES implementation and related software, hardware, and infrastructure upgrades.

Integrating new MES with existing legacy systems and custom-built software can be complex, time-consuming, and costly.

Implementing a next-gen MES requires significant changes to established processes, workflows, and employee roles.

Lack of specialized skills in data analytics, IT, and operations management.

Concerns about data security, regulatory compliance, and loss of control over sensitive production data when moving to a cloud-based/hybrid MES solution.

Info-Tech recognizes the role of CIOs in enabling smart factory and digitalization initiatives. This research aims to empower CIOs with the knowledge and tools necessary to evaluate and implement next-gen MES platforms to ensure their organizations remain competitive.

Info-Tech will provide:

• A comprehensive guide explaining the benefits of next-gen MES platforms and a buyers guide to help CIOs evaluate, compare, and eventually choose the best-fit MES solution.
• A deployment readiness tool to help CIOs assess their organizational readiness to implement a next-gen MES.

Traditional MES solutions are complex and inflexible, making them difficult to adapt to manufacturing processes or to scale with evolving operational needs.

Deployment involves substantial initial investments and ongoing maintenance expenses, which can be financially burdensome.

Legacy MES systems do not seamlessly integrate with modern technologies or other enterprise systems, resulting in data silos.

Traditional MES platforms feature complex user interfaces that are not intuitive, leading to user frustration and necessitating extensive training.

Legacy MES systems do not provide real-time data analytics, impeding manufacturers' ability to make timely decisions and respond swiftly to production issues.

1. Modular & Scalable

2. Seamless Integration & Data Access

3. Enhanced UX

4. Zero Downtime

5. Adaptability for Advanced Manufacturing

• Cloud-based and containerized solutions
• Flexible deployment options
• Minimal install
• Feature-based enablement (pay-per-use)
• Scalable for small-scale and large, complex operations

• Full horizontal and vertical integration
• Real-time data ingestion
• Simplified data access, supporting predictive analytics
• API-driven architecture to easily integrate with other systems (e.g. ERP, LIMS, QMS)

• Intuitive graphical user interfaces (GUI)
• Built-in tools for process optimization and exception management
• Support for advanced technologies like AR/VR and voice commands to reduce operator training needs

• Automated, zero-downtime maintenance and security patching
• Reduced validation time and faster upgrades
• Autonomous adjustments and ML-driven improvements to minimize manual interventions

• Support a variety of manufacturing models including discrete and process
• Standardized process libraries for efficiency and ease of reuse
• Scalable for both high-volume and highly specialized manufacturing

Automotive

$5.2 billion by 2027

Aerospace & Defence

$3.8 billion by 2027

Pharma & Life Sciences

$2.5 billion by 2025

Energy & Utilities

$1.8 billion by 2023

Packaging

$2.5 billion by 2025

• Mainframe-based solutions
• High cost and difficult to implement
• Locked manufacturers into a single vendor
• Limited flexibility, proprietary technology

• Shift to in-house servers and proprietary databases
• Lower cost than mainframe systems but still expensive
• Custom-coded solutions for specific needs, hard to scale
• Limited integration with other enterprise systems

• Adoption of IIoT and cloud technologies
• Real-time data visibility and predictive analytics
• Modular and scalable solutions adaptable to different manufacturing needs
• Integration with AI, big data, and enterprise systems (ERP, SCM)

• Seamless integration across the entire manufacturing ecosystem
• Full support for real-time decision-making
• Near zero downtime with continuous updates
• Advanced analytics, self-learning, and autonomous operations

Deployment

How the MES is installed and maintained (on-premises vs. cloud)

Important for understanding infrastructure needs, IT resources, and long-term management complexity.

Cost Structure

The upfront and ongoing costs associated with the MES

Vital for budgeting and understanding the long-term financial commitment of the system.

Technology Integration

Compatibility with modern technologies (e.g. IIoT, AI/ML)

Critical for future-proofing the MES and maximizing operational efficiency with advanced technologies.

Accessibility

Whether the system can be accessed remotely or only onsite

Key for enabling flexibility in managing operations from different locations or on-the-go.

Scalability

The ease of expanding system capacity as the business grows

Crucial for ensuring the MES can scale with business demands without excessive costs or delays.

Updates

How frequently the system is updated and how disruptive the process is

Ensures the system stays current with minimal downtime, which impacts production continuity.

Security

How the system is protected from cyber threats

Essential for safeguarding sensitive production and operational data, ensuring compliance and resilience.

Time to Value

The time it takes to implement the system and start seeing benefits

A critical factor in understanding how quickly the MES can deliver ROI and operational improvements.

Flexibility

Ability to tailor the system to specific operational needs

Key to ensuring the MES can adapt to unique processes and deliver the expected operational benefits.

Data Access

The ability to access real-time data and collaborate remotely

Important for operational visibility, especially for decision-makers overseeing multiple locations.

Disaster Recovery

The system’s ability to recover from failures and data loss

Important for maintaining business continuity and minimizing downtime during unexpected events.

Implementation Method

Implement a one-size-fits-all solution or prioritized capabilities first

Important for developing capabilities based on business value in a flexible manner.

On-premises deployment using local servers, requiring significant IT resources for setup and maintenance

High complexity & overhead

Requires hardware investments and long implementation times to scale

Limited scalability

Customizations are expensive and time-consuming

Low flexibility

High upfront capital investments in hardware and software with recurring maintenance and customization costs

High upfront costs

Infrequent, slow, manual updates that require downtime and significant IT resources

Costly updates

Data is stored locally, making it harder to access remotely or collaborate across locations

Limited real-time visibility

Difficult to integrate with modern technologies like IIoT, AI/machine learning (ML), and edge computing

Limited adaptability

Managed locally, requiring onsite security measures

Limited security

Disaster recovery and backups must be managed in-house, with slower recovery times in case of system failures

Slow recovery

Limited to on-premises access; difficult to manage remotely or collaborate with global teams

Limited accessibility

Implementation cycles often take months or years due to hardware setup and complex customization

Long TTV cycles

Hosted on the cloud, with no need for local infrastructure

Low complexity & overhead

Able to adjust resources quickly and scale operations easily

High scalability

Easily customizable with modular features and APIs for rapid adaptation

High flexibility & modularity

Subscription-based pricing model with lower upfront costs

Predictable operating costs

Automatic updates with little to no downtime, ensuring continuous delivery of new features and patches

Costly updates

Centralized data with real-time visibility from anywhere, enabling global collaboration and remote access

Near real-time visibility

Easily integrates with IIoT, AI/ML, and other emerging technologies, making it ideal for Industry 4.0 environments

High integrability

Managed by the cloud provider with advanced, up-to-date security protocols and compliance standards

Advanced security

Managed by the cloud provider, offering more robust disaster recovery capabilities

Fast recovery

Accessible from anywhere, allowing real-time monitoring and management from any device

Remote accessibility

Time-to-value often achieved within months due to cloud infrastructure

Short TTV cycles

Leading use cases for next-gen MES platforms

(Order does not reflect importance)

Ops. Efficiency

Business Growth

Customer Experience

Employee Experience

Risk & Resilience

ESG

Enables real-time production monitoring using IIoT sensors, resulting in optimized workflows and faster issue resolution

Enables predictive maintenance using AI/ML, resulting in reduced downtime and extended equipment life

Improves overall equipment effectiveness (OEE) using real-time analytics, resulting in enhanced productivity

Enables cycle time reduction using workflow automation, resulting in increased throughput and output

Enables energy usage optimization using energy monitoring tools, resulting in cost savings and sustainability gains

Enables optimized production planning using real-time data and AI, resulting in better resource allocation

Enables automated quality control using machine vision and AI, resulting in higher quality and fewer defects

Enables compliance tracking and automation using integrated regulatory databases, resulting in reduced compliance risk

Enables supply chain visibility using integrated data analytics, resulting in more accurate demand fulfillment

Optimizes inventory management using real-time tracking, resulting in reduced stock levels and improved turnover

Enables remote production monitoring using cloud connectivity, resulting in enhanced uptime and flexibility

Leading use cases for next-gen MES platforms

(Order does not reflect importance)

Ops. Efficiency

Business Growth

Customer Experience

Employee Experience

Risk & Resilience

ESG

Reduces downtime using predictive analytics, resulting in improved production efficiency and lower costs

Enables capacity planning using historical and real-time data, resulting in optimized production scalability

Enables custom order processing using workflow customization tools, resulting in enhanced customer satisfaction

Enables product traceability using blockchain technology, resulting in secure, compliant, and transparent production

Enables remote work enablement using mobile access, resulting in greater flexibility for employees

Enables sustainability reporting using environmental data tracking, resulting in improved compliance and ESG reporting

Enables real-time collaboration using shared data dashboards, resulting in improved cross-functional alignment

Enables customer order transparency using real-time status updates, resulting in improved customer trust

Enables automated data analytics using AI, resulting in continuous process improvement and decision-making support

Enables training and skill tracking using integrated HR tools, resulting in safer operations and better compliance

Enables predictive supply chain management using AI insights, resulting in proactive disruption handling

Production schedules, inventory levels, order requirements, cost data

Production progress, inventory updates, job completion statuses, labor tracking

Demand forecasts, supplier lead times, logistics schedules

Real-time production status, material usage, inventory needs for reorder

Product designs, bills of materials (BOMs), engineering changes

Production feedback, quality data, design compliance reports, process deviations

2D/3D models, assembly instructions, specifications, simulation results, material properties, machining parameters

Design issues, quality data, tolerances, material behavior, deviations, cycle times, scrap data

Quality standards, test requirements, compliance data

Quality control results, defect rates, noncompliance incidents, corrective actions

Equipment maintenance schedules, historical maintenance data

Real-time equipment status, production impact of maintenance activities, predictive maintenance alerts

Customer orders, delivery expectations, custom order specifications

Order status updates, production progress, estimated completion times, quality assurance confirmations

Inventory location, picking and packing instructions, shipping status

Finished goods inventory, production yields, component needs for restocking

Employee roles, certifications, training records, shift schedules

Labor hours, machine operation logs, skill utilization, training needs

Real-time machine performance, process control parameters

Process instructions, alerts for operator actions, production targets

Equipment life cycle data, asset status, maintenance schedules

Real-time asset usage, equipment efficiency data, maintenance triggers

Energy consumption targets, emissions standards

Energy consumption per unit, process energy efficiency, alerts on abnormal energy use

Production and operational data for reporting, historical analysis

Process KPIs, performance trends, efficiency metrics, resource utilization

Shipping schedules, transportation modes, delivery commitments

Order completion updates, shipment preparation status, real-time production updates affecting delivery

Aligns manufacturing operations with production planning, inventory, and financial tracking

Synchronizes production schedules, real-time inventory tracking, better cost control

Manages supply chain activities (demand forecasting, logistics, supplier management)

Optimized supply chain flows, reduced lead times, improved supplier collaboration

Handles design, development, and engineering, ensuring smooth transitions from design to manufacturing

Real-time updates, better quality control, faster time to market

Creates design-to-production workflow, designs and machining optimization

Minimum errors, faster prototyping, optimized production cycles, process adjustment

Automates quality checks and ensures compliance with industry standards

Automated quality assurance, reduced defects, compliance with standards (ISO, FDA, etc.)

Monitors machine health and schedules maintenance based on real-time data

Minimizes downtime, extends equipment life, improves production planning

Aligns production schedules with customer demand and order fulfillment

Real-time order status, improved customer satisfaction, demand-driven production planning

Manages storage, picking, packing, and shipping of goods

Faster inventory management, efficient order fulfillment, reduced waste and idle time

Manages employee data, work schedules, and certifications for operational safety

Ensures compliance, manages certifications, optimizes workforce scheduling

Provides real-time monitoring and control of shop floor operations

Real-time process control, integration of machine-level data, faster response to issues

Tracks and manages production assets and maintenance tasks

Maximizes asset utilization, prevents downtime, reduces total cost of ownership

Tracks and optimizes energy consumption linked to production processes

Optimizes energy use, achieves sustainability goals, identifies energy-intensive processes

Analyzes data to provide insights into production performance and efficiency

Data-driven decision-making, process improvement, enhanced reporting and analysis

Manages logistics, shipping, and transportation

Streamlines logistics, lead times, enables synchronization between production and shipping

IIoT

Next-gen MES gathers real-time data from machines via IIoT sensors, and devices.

Enables real-time monitoring, predictive maintenance, and process optimization

Big Data

Next-gen MES platforms procure, process, and analyze large production data sets.

Provides insights for process optimization, trend analysis, and demand forecasting

AI/ML

Next-gen MES use embedded AI/ML algorithms for predictive analytics.

Enables predictive maintenance, quality improvements, and process optimization

VR/AR

Next-gen MES supports integration with VR/AR for operator training and maintenance assistance.

Enhances visualization of complex processes, guides maintenance tasks, and reduces errors

Edge to Cloud

Next-gen MES leverages edge computing for local data processing while connecting machines to the cloud.

Improves real-time decision-making on the shop floor with cloud-based analytics scalability

Blockchain

Some next-gen MES systems can integrate with blockchains for secure, immutable production tracking.

Ensures transparency, compliance, and product authenticity in regulated industries

Additive Manufacturing

Next-gen MES platforms can incorporate 3D printing workflows with traditional production.

Enables customized, small-batch production, and rapid prototyping, and reduces lead times and waste.

Robotics

Next-gen MES platforms can coordinate with robots and automation systems to streamline workflows.

Improves precision, consistency, and efficiency by managing both robotic and human operators

Wireless Connectivity

Next-gen MES integrates with 5G/Wi-Fi 6 hardware for high-speed, low-latency factory floor connectivity.

Enhances real-time data exchange, enabling flexible, decentralized production setups and operational agility