Plant Logic integrates data systems that do not natively talk to one another. Data is absorbed from these multiple data sources, and decisions, tasks, and actions are then triggered. Additionally, RG provides this powerful MES at a fraction of the cost of its competitors. Get the data you want, in real time, all the time.
"The tree represents how Plant Logic MES absorbs data and filters it through its data structure - much like a tree absorbs nutrients and distributes it throughout."
The Leaves trigger an event or action. These actions can be anything from simple data collection and as complex as triggering equipment state changes.
Branches represent a decision to be made with the data absorbed by the roots. When a Branch’s decision criteria are met, the data is passed up to that Branch’s leaves.
As data outputs from your multiple systems, the roots absorb that data. This data is then passed up to the branches.
Objective: Automate inventory monitoring and restocking in a retail environment using Plant Logic MES’s tree-basedarchitecture.
Roots:The Input Layer. Plant Logic MES receives data from various sources, such as RFID Scanners (Track productmovement in and out of shelves), Shelf Weight Sensors (Detect when items are removed or shelves are emptying), POSSystem Data (Monitors sales trends and checkout activity), Inventory Database (Provides current stock levels andreorder thresholds), Customer Foot Traffic Sensors (Predicts demand based on store activity).
Branches:The Decision Layer. Plant Logic MES evaluates the incoming data to make decisions, such as: Stock LevelCheck (Is the current stock below the reorder threshold?), Sales Velocity Analysis (Are items selling faster than usual?),Shelf Depletion Pattern (Are shelves being emptied faster than expected?) Demand Forecasting (Is increased foot trafficlikely to cause a stockout?), and Supplier Lead Time Check (Is there enough time to restock, before running out?). If anyof these conditions are met, the system flags the item for restocking.
Leafs:The Action Layer. Based on the decisions made, Plant Logic MES then executes actions, such as: Auto-Reorder(Sends a purchase order to the supplier), Notifying Staff (Alerts store employees to restock shelves from the backroom),Dynamic Pricing (Adjusts prices based on demand and stock levels), Report Generation (Logs the event and updates theinventory system), and Customer Notification (Sends alerts to customers if a popular item is back in stock)
Integrate with e-commerce platforms to sync online and in-store inventory.
Use AI Vision Systems to detect misplaced or misaligned products.
Implement seasonal logic to adjust thresholds during holidays or sales.
Reduction of out-of-stock incidents.
Optimization of inventory turnover.
Enhancement in customer satisfaction
Minimization of manual inventory checks.
Objective: Utilize Plant Logic MES to monitor production line conditions and automatically assign material transporttasks to AGVs/AGFs when specific manufacturing needs arise.
Roots:The Input Layer. Plant Logic MES receives data from various sources, such as Production Line Sensors (Detectsmaterial shortages or completed batches), MES (Provides job schedules and work orders), Inventory ManagementSystem (Tracks raw material and WIP (Work-In-Progress) levels), and AGV/AMR Status Feeds (Reports availability,location, and battery levels of multiple robots).
Branches:The Decision Layer. Plant Logic MES evaluates the following: Materials Demand Check (Is a workstationrunning low on raw materials?), Job Queue Status (Are there pending jobs requiring transport?), AGV/AMR Availability(Is there a robot available and nearby?), Priority Rules (Does this job have a higher urgency than others?), and RouteOptimization (Is the path to the destination clear and efficient?). If the conditions are met, a transport job is createdand queued.
Leafs:The Action Layer. Plant Logic MES executes and Create a Job Ticket (Generates a job with pickup/drop-offlocations and priority), Assigns to an AGV/AMR (Sends the job to the most suitable robot), Updates Systems (Logs thejob in the MES and inventory systems), and Notifies OperatorsSends status updates to the floor supervisors).
Integrate with digital twin systems to simulate and optimize job assignments
Use machine learning to predict future transport needs based on production trends.
Enable multi-robot coordination for complex tasks like batch deliveries.
Reduction of downtime due to material shortages.
Optimization of robot utilization and traffic flow.
Enhancement of the responsiveness to real-time production changes.
Minimization of manual intervention and improvement in traceability.
Objective:Plant Logic MES is deployed in a manufacturing facility to monitor the quality of products on a conveyorbelt.
Roots:The Input Layer. Sensors continuously feed data into Plant Logic MES, including:
Camera capturing images of each product.
Weight sensors checking if the product is within an acceptable mass range. Temperature sensors ensuring the product was processed correctly.
Branches:The Decision Layer. Plant Logic MES then evaluates:
Is the product weight within the acceptable 95-105g range?
Does the image analysis detect any visual defects (e.g., cracks,discolorations, etc.)?
If ANY condition fails, the Branch node flags the product as DEFECTIVE.
Leafs:The Action Layer. The corresponding Leaf node sends a signal to the robot arm. The robot arm then removesthe defective product from the conveyor belt and places it in a rejection bin. A log entry is created for traceability.
If multiple defects are detected in a short time, Plant Logic MES escalates the issue to a human operator, toanalyze the situation and address it accordingly
The system can adapt thresholds dynamically based on historical data (e.g., seasonal variations in productquality).
Reduction in human error in quality control, increasing baseline profits.
Increased efficiency and consistency.
Providing traceable logs for ongoing compliance and analysis.
Take control of your processing costs and deliver better rewards to your best customers.
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