Manufacture | Lucid Sphere AI

Key Features of Manufacturing Data Analytics

Automated ingestion of structured and unstructured data from all the integrated sources (IIoT and production event data, inventory stock level reports, etc.).
Cost-effective storing of all data types in the optimal storage formats.
Batch and real-time manufacturing data processing.
Automated data cleansing and unification to get accurate, de-duplicated data and avoid erroneous analytics results, e.g., false stock-out alerts.
Aggregating data into a reliable data source ready for analytics querying across all departments and user roles.

Online Analytical Processing (OLAP) for multidimensional slicing and dicing of manufacturing data (e.g., defective products by shift, production line).
Calculating manufacturing KPIs and metrics: e.g., production volume, downtime, OOE & OEE, throughput.
Diagnostic analytics based on historical data and ML-based root cause analysis across multiple variables to establish complex dependencies (e.g., between maintenance intervals and low OEE).
AI-powered predictive and prescriptive analytics for predictive maintenance and smart optimization recommendations.
Customizable dashboards with self-reporting and drag-and-drop functionality for easy data representation.
Scheduled and on-demand reporting.

Automated identification of the cost calculation format based on the product type.
Automated calculation of the product manufacturing cost based on the analysis of direct materials and labor costs and manufacturing overhead (MOH).
Calculation of the optimal product price based on overall production costs.
AI-based identification of cost-saving opportunities, e.g., intelligent suggestions on the optimal power consumption patterns.
Automated product cost update in case of the material/labor/MOH cost change.

Automated calculation of asset KPIs: throughput, machine downtime, capacity utilization rates, etc.
Real-time monitoring of machine data (e.g., availability, condition, resource utilization) that is acquired through PLCs, IoT sensors, etc.
Real-time equipment monitoring (e.g., equipment condition and environment monitoring).
AI-based machinery and equipment analysis to identify abnormal patterns.
Physics-based modeling with multiple process conditions variables to identify optimal OEE and machine operating patterns.
Real-time IoT-based analytics to enable predictive equipment maintenance by forecasting potential hazards and failures and sending the corresponding alerts.

Shaping optimal production schedules based on the analysis of resource utilization, production constraints, and more.
Identifying production bottlenecks.
Production quality control.
Analyzing employee workload/productivity based on production data, work order times, etc., to optimize employee shifts, and jobs assigned.
Identifying production hazards related to employee safety and environmental regulations.
Running ML-powered what-if scenarios for multiple production conditions (e.g., machine load/idle time, the number of operators) to identify optimal conditions.

Identifying the most profitable and reliable suppliers based on their KPIs analysis (e.g., lead time, defect rates).
ML-powered demand forecasting based on the analysis of historical data, current market trends, and competitor activity.
Spend forecasting and procurement optimization.
Inventory & safety stock optimization.
Order fulfillment prediction and fulfillment optimization.
Running ML-powered what-if scenarios with changing variables (weather conditions, shipment routes, employee availability, etc.) to optimize logistics.

Automated calculation of sales KPIs: sales growth, sales per rep, etc.
Automated setting and monitoring of sales goals, e.g., revenue target per product line.
AI-powered product demand and sales forecasting.
Providing AI-based recommendations on upselling and cross-selling opportunities, e.g., offering after-purchase product installation services.

Automated B2B customer segmentation per business sector, cooperation duration, etc.
Automated B2C customer segmentation based on geographical, demographic, behavioral, and other parameters.
Multi-vector customer analytics to identify the most profitable segments and shape relevant loyalty strategies, enable efficient targeting, discount management, and more.
Analyzing customer warranty requests in order to identify product flaws and optimize future product lines.