The “Waze/GPS” of Manufacturing

I don’t want to run before I can walk BUT while there’s still many manufacturers out there needing to take their first few steps with Digital Transformation, there’s an ever increasing number needing to dramatically improve their agility.

New customer expectations, supply chain instability etc etc are all driving a far more flexible, adaptable  way to manufacture across existing plants. 

In the everyday, we plan out and take our journeys to the office, to the grocery store or to get away and each time, we plug into our Google/Apple Maps, Waze or inbuilt vehicle GPS. We set-off, and the satellites see a slow-down ahead and re-routes us.

We need the same in Manufacturing!

We need an AI/ML based advanced Factory Positioning Scheduler or something similar. Let’s take a look…

The advantages of an AI based planning, scheduling & routing system for manufacturing

Such a system can offer a number of advantages, similar to the benefits provided by your in-car GPS for navigation and traffic management:

Improved Production/Delivery Performance:

At the heart of it all sits an optimized delivery performance. You could dynamically adjust your production schedules and routing based on demand fluctuations, ensuring on-time delivery to customers and reducing lead times:

• Real-time Visibility: The system could provides real-time visibility into your entire manufacturing process, from order creation to product shipment. This visibility allows you to track progress, identify bottlenecks, and proactively address issues that could impact delivery times.
• Dynamic Scheduling: Production schedules could be always  in real-time based on current conditions, including machine availability, material availability, and workforce capacity. This ensures that your production aligns with customer demand, reducing the risk of delays.
• Efficient Resource Allocation: AI algorithms allocate resources efficiently, ensuring that the right machines and manpower are available at the right time. This minimizes any idle time and maximizes production throughput, which can lead to faster order fulfillment.
• Inventory Management: AI is able to optimize inventory levels, ensuring that raw materials are available when needed and that finished goods are produced according to demand. This prevents overstocking or understocking issues that can affect delivery times.
• Optimized Routing: The routing optimization component will ensure that materials and products are efficiently routed through the manufacturing facility. This reduces travel time, minimizes handling, and speeds up your production process.
• Demand Forecasting: The system will use AI models to forecast customer demand accurately. With better demand forecasting, you can adjust production schedules and inventory levels to match anticipated orders, reducing the risk of overproduction or stock outages.
• Shorter Lead Times: AI-based optimization can lead to shorter production lead times, allowing you to respond more quickly to customer orders and changes in demand.
• Order Prioritization: The system can automatically prioritize urgent or high-value orders, ensuring that they are produced and shipped promptly, even during high-demand periods.
• Exception Handling: AI algorithms can identify and handle exceptions, such as rush orders or unexpected changes in production requirements, in a way that minimizes disruptions to the overall production schedule.

Optimized Resource Utilization:

AI can analyze real-time data on machine availability, raw material inventory, and workforce schedules to optimize the allocation of your resources, reducing idle time and maximizing efficiency.

Reduced Downtime:

Similar to your Tesla predicting range and recommending a  re-charging station, predicting maintenance needs and scheduling downtime during low-demand periods, AI can help you minimize unplanned production stoppages, thereby increasing overall equipment effectiveness (OEE).

Cost Reduction:

AI can identify cost-saving opportunities such as optimizing production sequences, reducing energy consumption, and minimizing material waste, leading to lower manufacturing costs.

Enhanced Quality Control:

AI can monitor production processes in real-time, detecting deviations and anomalies that may lead to defects or quality issues, allowing for immediate corrective actions.

Reduced Rework:

Improved quality control mechanisms reduce the likelihood of defects and rework, which can cause delays in product delivery.

Dynamic Routing:

Similar to a GPS real-time traffic updates, AI can adapt manufacturing routes based on changing conditions, such as machine breakdowns or unexpected material shortages, to ensure production continuity.

Increased Agility:

You can quickly respond to changing market demands or supply chain disruptions by leveraging AI to reconfigure schedules and resources on the fly.

Scalability:

As manufacturing operations grow or change, AI systems can adapt to accommodate new product lines, production facilities, or process improvements.

Environmental Impact Reduction:

AI can help minimize waste, energy consumption, and carbon emissions by optimizing manufacturing processes and logistics.

Predictive Maintenance:

Similar to your GPS alerting you about upcoming road hazards, AI can predict equipment maintenance needs and help prevent unexpected machine breakdowns that can disrupt production schedules. This proactive maintenance approach reduces downtime and keeps production on track.

Increased Worker Safety:

By optimizing scheduling and routing, AI can reduce congestion and bottlenecks in the manufacturing facility, potentially reducing the risk of accidents.

Real-time Tracking and Reporting:

Manufacturers can gain real-time visibility into production progress, allowing for timely adjustments and better reporting to stakeholders.

Competitive Advantage:

Adopting an AI-based system can differentiate you from competitors by delivering higher efficiency, better quality, and improved customer service.

Customization:

The AI system can be tailored to specific manufacturing requirements, whether for discrete manufacturing, process manufacturing, or a combination of both.

Designing the system?

Designing an AI-based planning, scheduling, and routing system for manufacturing involves several key components that work together to provide a comprehensive solution. These components should be carefully designed and integrated to ensure the system’s effectiveness and flexibility. Here are what I’d consider to be the main components:

Data Integration and Collection:

• Data Sources: Gather data from various sources, such as ERP, SCM and PLM systems, production machines, sensors, inventory databases, and external suppliers.
• Data Preprocessing: Clean, validate, and transform raw data into usable formats for analysis.

Machine Learning and AI Algorithms:

• Predictive Analytics: Use machine learning models for demand forecasting, equipment failure prediction, and other predictive tasks.
• Optimization Algorithms: Implement algorithms for resource allocation, production scheduling, and routing optimization.
• Reinforcement Learning: Utilize reinforcement learning to fine-tune decision-making policies in dynamic environments.

Real-time Monitoring and Control:

• Real-time Data Streams: Step-up IIoT and continuously monitor and analyze your real-time data streams to make immediate decisions.
• Control Systems: Implement controllers to adjust production parameters and schedules in real-time based on changing conditions.

User Interface and Visualization:

• Dashboard: Through your MES, provide a user-friendly interface for managers and operators to access information, monitor operations, and make decisions.
• Visualization Tools: Display key performance indicators (KPIs), production schedules, and resource allocations in a visually intuitive manner.

Integration with Manufacturing Equipment:

• SCADA Systems: Connect to Supervisory Control and Data Acquisition systems to control and monitor production machines.
• IoT Devices: Integrate sensors and IoT devices to collect the aforementioned data from machines and equipment.

Inventory Management:

• Inventory Optimization: Implement algorithms to optimize raw material and finished goods inventory levels.
• Inventory Tracking: Monitor inventory in real-time and trigger alerts for reordering or shortages.

Scheduling and Routing Engine:

• Production Scheduler: Develop a scheduling engine that optimizes the sequencing of production orders, taking into account machine capacities and their constraints.
• Routing Optimization: Implement algorithms for optimizing the routing of materials and products within the manufacturing facility.

Communication and Alerts:

• Notification System: Set up alerts and notifications for critical events, such as equipment failures or deviations from production schedules.
• Communication Protocols: Establish communication protocols for seamless data exchange between different system components.

Demand Forecasting:

• Demand Models: Develop models to forecast customer demand accurately.
• Demand Sensing: Continuously update demand forecasts based on real-time market data.

 Analytics and Reporting:

• Advanced Analytics: Provide advanced data analytics capabilities for in-depth insights.
• Reporting Tools: Generate customizable reports for performance analysis and decision support.

 Feedback Loop and Continuous Improvement:

• Incorporate feedback mechanisms to collect data on system performance and user interactions, allowing for continuous improvement and model refinement.

Predictive Maintenance Module:

• Equipment Health Monitoring: Continuously monitor the condition of manufacturing equipment to predict maintenance needs.
• Maintenance Scheduling: Schedule maintenance activities proactively to minimize downtime.

I’d like to think after the successful design and integration of these components you’ll have something close to a robust AI-based planning, scheduling, and routing system for manufacturing that can deliver the advantages mentioned earlier, including increased efficiency, reduced costs, and improved production quality.

Is it an extension of MES/MOM or an IIoT Solution?

The decision of whether an AI-based planning, scheduling, and routing system for manufacturing should be integrated into a Manufacturing Execution System (MES)/Manufacturing Operations Management (MOM) solution or an Industrial Internet of Things (IIoT) platform depends on several factors, including the specific requirements of the manufacturing operation, the existing technology infrastructure, and the goals of the implementation.

Here’s a few thoughts on each:

Integration into MES/MOM:

Comprehensive Control: MES/MOM systems are designed to provide comprehensive control and visibility into manufacturing operations. It’s your digital plant leader!Integrating the AI-based system into an MES/MOM solution can offer end-to-end management of production processes, quality control, and data tracking.

Process Orchestration: MES/MOM solutions typically include features for process orchestration, recipe management, and work order management. This integration can facilitate seamless coordination between scheduling, routing, and production execution.

Quality Management: MES/MOM systems  include quality management modules that can work in conjunction with the AI system to ensure product quality by monitoring and controlling production processes.

Regulatory Compliance: If regulatory compliance is critical in your industry, an MES/MOM system can help manage compliance requirements effectively.

Integration into IIoT Platform:

Real-time Data Processing: IIoT platforms excel at collecting, processing, and analyzing real-time data from sensors and devices. If your primary focus is on real-time monitoring and control of your equipment only, an IIoT platform might be the better choice.

Line Scalability: IIoT platforms are often designed to scale easily to accommodate a large number of devices and data sources, making them suitable for complex manufacturing environments with a high degree of automation.

Data Integration: IIoT platforms can seamlessly integrate data from various sources, including sensors, machines, and external systems, which is beneficial for AI-based systems that rely on diverse data inputs.

Edge Computing: IIoT platforms often support edge computing, allowing for data processing and decision-making at the edge of the network, which can be advantageous for real-time control and optimization.

Customization: IIoT platforms can be customized and extended with a wide range of IoT applications, making them adaptable to specific manufacturing needs.

In many cases, the best approach may be a combination of both MES/MOM and IIoT technologies, where the AI-based system is integrated into the broader manufacturing technology ecosystem. This can provide the advantages of both real-time data processing and comprehensive control and visibility.

Ultimately, the choice comes down to why do you want an AI based Planning, Scheduling & Routing System? For me it feels like you want greater leadership, control and agility in your plant, and for this I’d always integrate as part of Manufacturing Operations Management.