Decisyon Products

AI-Powered
Lean MES

Decisyon's AI-powered Lean MES (DLMES) App is a state-of-the-art SaaS solution leveraging generative AI models to streamline production operations and enhance Overall Equipment Effectiveness (OEE). This innovative tool empowers manufacturers to optimize the efficiency and agility of their production lines through AI support, marking a significant advancement in operational efficiency and providing an intelligent, data-driven approach from the edge to the cloud.

Streamlined Bottle Operations - AdobeStock_849434128
Managers MNonitoring Success - AdobeStock_332232740

Key Features

Production Management

Scheduling: Fine-tunes scheduling of production activities, usually based on ERP planning.

Production Order Management: Tracks production orders from initiation to completion, ensuring efficient management of production processes.

Data Collection and Monitoring

Real-Time Monitoring: Provides real-time data on machine performance, production order status, and operational efficiency.

Traceability: Tracks every component of the production process, improving quality management and regulatory compliance.

Quality Management

Scrap and Rework Management: Monitors and manages production scrap and rework, helping to track quality losses

Resource Management

Machine and Equipment Management: Monitors usage and maintenance of machines and equipment to prevent breakdowns and improve operational efficiency.

Personnel Management: Manages human resources, including task assignments and monitoring employee performance.

Integration with Other Systems

ERP Integration: Synchronizes financial and operational data with ERP (Enterprise Resource Planning) systems.

SCADA Integration: Integrates with SCADA (Supervisory Control and Data Acquisition).

CMMS Integration: Triggers unplanned maintenance activities in customer CMMS systems.

Analytics and Reporting

Data Analysis: Utilizes generative AI models to monitor operational performance, identify inefficiencies, and optimize processes.

Reporting: Generates detailed reports on various aspects of production, including productivity, quality, and efficiency.

Overall Equipment Effectiveness (OEE) Management

OEE Monitoring: Measures OEE to evaluate overall equipment efficiency and identify areas for improvement.

Continuous Feedback

Establishes a continuous feedback loop, enabling the generative AI model to dynamically adapt to new insights and fostering a culture of continuous improvement.

Actionability

Provides actionable insights and facilitates prompt, informed decision-making. Ensures that data collected and analyzed by the MES leads to tangible actions that enhance production processes and overall efficiency through:

  • Real-time alerts
  • Integration with other systems
  • In-context task management
  • AI-driven chatbot

Overview Page

DLMES-Overview

Overview Line Board

DLMES Line Board
AI Processes - AdobeStock_726936726

Advanced MES AI Functionalities

SUPERVISORS

Process Optimization: Application of machine learning techniques to analyze historical data and identify opportunities for improvement in production processes, increasing efficiency and reducing waste.

Use Natural Language in Data Revision: In situations that require intervention, supervisors have the ability to use natural language commands. For example: 'Remove all orders assigned to line A for the current shift and redistribute them to available alternative lines' or 'Change the start time of this specific order 20 minutes in advance' . The DLMES app then automatically adjusts, with the supervisor retaining the ability to validate or refine the rescheduling as needed.

OPERATORS

Operator Assistant: A chat box interface assists operators using natural language by highlighting upcoming and completed activities, either automatically performed by the DLMES or requiring manual intervention.

MANAGERS

KPI / Queries Generation: Supports natural language commands for creating key performance indicators (KPIs) or generating data queries within the analytics section, simplifying the extraction of insights from data.

Solution Prescription: Recommends previously implemented solutions to address specific problems, Analyzes historical data and patterns to suggest proven solutions.

Manager Reviewing Data AdobeStock_825201872

Advanced Performance Analysis for MES

A strategic blend of advanced analytics and machine learning capabilities ensures comprehensive coverage of every aspect of your production line for enhanced decision-making and profitability. Examples include:

Overall Equipment Effectiveness (OEE)

Combines metrics of machine availability, performance, and product quality to assess production line efficiency. Advanced analysis identifies specific areas for improvement.

Process Variability Analysis

Uses statistical techniques like Analysis of Variance (ANOVA) to evaluate process variability, identifying sources of instability or deviations from standard processes to reduce defects and enhance consistency.

Root Cause Analysis of Inefficiencies

Employs Root Cause Analysis techniques to identify underlying causes of inefficiencies or production issues, allowing for targeted interventions.

Statistical Process Control (SPC)

Implements statistical control charts to monitor process variations over time, identifying patterns or anomalies in SPC data.

Employee Performance Analysis

Utilizes metrics of employee performance combined with production data to provide insights into skills and individual impact on production.

Predictive Maintenance Analysis

Uses machine learning algorithms to analyze maintenance data, predicting equipment intervention needs and reducing unplanned downtime.

Data Collection, Analysis, Visualization, Business Intelligence, and More
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