Data Analytics and Decision Support

The Data Analytics Module supports operational excellence by transforming large data sets collected during production processes into meaningful insights. Equipped with machine learning and artificial intelligence algorithms, this module analyzes historical data, identifies trends, and allows you to predict potential future problems.

Thanks to its advanced features such as real-time analysis, anomaly detection and predictive maintenance, it prevents production errors before they occur, increases efficiency and optimizes resource usage. In this way, managers make more informed decisions and businesses gain a competitive advantage.

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Data Analytics and Decision Support

Data analytics module; integrates data from production, analyzes the past, predicts the future. Measures performance, presents it with graphics, produces improvement suggestions. Provides a strong decision support infrastructure for all processes.

Data Collection

Diagnostic Analysis

Predictive Analysis

Performance Analysis

KPI Management

Data Collection and Integration

It enables data from all sources in the production line to be integrated, transformed for analysis and made available in real time. Thanks to the integrated data structure, production processes are managed more accurately, traceably and data-based.

Features

Identifying Data Sources
Data Transformation and Cleansing
Data Labeling and Categorization
Real Time Data Stream

Defining Data Sources
All data providers related to production such as PLC, SCADA, IoT sensors and ERP systems are defined in the system and data connection is established. Thus, data from all processes are collected in a central structure.

Data Conversion and Cleaning
Data from different sources are converted into a suitable format for analysis. Missing, inconsistent or erroneous data are filtered and cleaned by the system, and a reliable data set is created.

Data Labeling and Categorization
Collected data is categorized and labeled as production performance, quality control, material consumption, etc. This classification facilitates filtering and comparison in advanced analyses.

Real-Time Data Stream
Data is instantly transferred to the system and monitored live. Changes in production, performance fluctuations or quality deviations can be analyzed simultaneously.

Diagnostic Analysis

It detects the origin of errors using past production data, analyzes the causes of performance declines and provides clear data for improvement. It aims to prevent recurring problems by identifying weak points in processes.

Features

Root Cause Analysis
Production Stoppage and Resource Depletion Analysis
Productivity Deviation Analysis
Risk Assessment

Root Cause Analysis:
Production errors, quality problems or machine failures are analyzed in detail to reveal the root causes of these events. In this way, permanent improvements are provided instead of temporary solutions.

Production Stoppage and Resource Depletion Analysis
Unexpected production stoppages, energy or material shortages, etc. are analyzed and process-based suggestions are developed to prevent recurrence. Stoppage data is systematically reported.

Productivity Deviation Analysis
Differences between planned production targets and actual performance are analyzed. Deviation points are determined and information is provided to management for efficiency increasing measures.

Risk Assessment
Potential risk areas are determined by analyzing past deviations in critical production parameters. Thanks to these assessments, preventive actions can be planned in a timely manner.

Predictive Analysis

It predicts future risks, failures and resource needs that may occur in production processes with artificial intelligence and machine learning algorithms that learn from past data. Thus, proactive decisions are made and continuity and efficiency in production are ensured.

Features

Trend and Forecast Modeling
Predictive Maintenance
Demand Forecast
Stock and Logistics Forecast

Trend and Forecast Modeling
Historical production data is analyzed to identify performance fluctuations and potential deviation trends. In line with these trends, the system provides early warning for future risks.

Predictive Maintenance:
Data such as machine operating hours, temperature, vibration and energy consumption are analyzed to identify equipment at risk of failure in advance. This prevents unplanned downtime and makes maintenance processes more efficient.

Demand Forecast
Demand forecasts are made by analyzing product-based sales trends and production data. Thanks to these forecasts, production planning is made more accurately and raw material supply processes are optimized.

Stock and Logistics Forecast
Critical stock levels are estimated in line with material consumption rates and production trends. This data enables more effective and uninterrupted management of supply chain processes.

Performance and Productivity Analysis

It identifies bottlenecks and inefficiencies by analyzing how effectively machinery, labor, and resources are used in production processes. Thanks to the suggestions developed, production processes are made faster, more economical, and more sustainable.

Features

OEE (Overall Equipment Effectiveness) Analysis
Workforce Productivity Analysis
Energy Consumption and Resource Usage Analysis
Suggestions for Improvement

OEE (Overall Equipment Effectiveness) Analysis
The availability, performance and quality rates of the machines are analyzed to calculate the overall equipment efficiency. Thanks to these rates, the real capacity and losses of the production line are clearly revealed.

Work Force Efficiency Analysis
Personnel efficiency is measured by evaluating operators' task completion times, workforce distribution and contributions to production targets. Training, task assignment or shift planning can be improved in line with these analyses.

Energy Consumption and Resource Usage Analysis
The amount of resources such as electricity, water, and compressed air consumed during production is analyzed to determine unnecessary consumption areas. In this way, sustainable production strategies can be developed.

Improvement Suggestions
In line with the collected data and analyses performed; applicable suggestions are presented to managers by the system in order to increase operational efficiency, provide energy savings and optimize processes.

Performance and Productivity Analysis

Features

OEE (Overall Equipment Effectiveness) Analysis
Work Force Productivity Analysis
Energy Consumption and Resource Usage Analysis
Suggestions for Improvement