Manufacturing Data Analytics
A Full Overview
ScienceSoft applies 34 years of experience in data analytics and manufacturing IT to help businesses build scalable analytics solutions that drive process improvements, optimize equipment utilization, and increase profitability.
Manufacturing Data Analytics: The Essence
Manufacturing data analytics is needed to consolidate and analyze data from all manufacturing IT systems: equipment management, production scheduling, manufacturing execution, etc. Such solutions turn disparate data into comprehensive insights to identify production bottlenecks, optimize resources utilization, increase OEE, and drive significant cost savings.
With AI/ML-powered predictive capabilities, manufacturing data analytics software can also enable preventive asset maintenance, intelligent production scheduling optimization, smart supply chain management, and more.
- Integrations: ERP, accounting software, manufacturing CRM, MES, OEE software, asset management software, SCM software, HR management system, and more.
- Implementation costs: $70K–$1M, depending on the number of integrated sources, the availability of advanced AI/ML capabilities and real-rime analytics, and more.
- ROI: up to 315% over 3 years with payback in <6 months.
Manufacturing data storage & processing
- 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.
Manufacturing data analysis & reporting
- 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.
To provide a holistic view of the manufacturing business performance across all facets: production, procurement, sales, etc.
- To enable in-depth customer analytics, including customer segmentation and customer sentiment towards a specific product/service.
- To enable sales performance analytics.
- To analyze factors that influence customer satisfaction.
- To predict demand and discover new sales opportunities.
To get insights on business revenue, expenses, fixed assets, liabilities, taxes, payroll, etc. in order to optimize accounting and financial planning.
- To collect and utilize production and equipment condition data for historical and real-time analytics.
- To detect potential issues and send commands for immediate corrective actions (with real-time analytics).
- To provide smart recommendations on resource utilization, production planning, etc.
To assess equipment productivity on different levels of granularity and suggest optimal loss prevention and OEE improvement strategies.
To optimize asset utilization and productivity, enable predictive and preventive maintenance, and reduce operational costs.
To optimize SCM across all of its facets: procurement, inventory, supplier, order, and logistics management.
To get insights on trends and optimization opportunities in employee management.
Note: We can also integrate your manufacturing data analytics software with other business-specific systems: e.g., CMMS, automated visual inspection software, warehouse management software, and more.
To enable enterprise-wide data transparency with tiered data access management, allowing all manufacturing stakeholders to make timely decisions with the help of analytics insights restricted to their specific field of responsibility.
To ensure that the manufacturing data under analysis is complete, accurate, up-to-date, and consistent, which is essential to avoid misinformed business decisions that can lead to financial, performance, and reputational losses.
To create a highly adaptable manufacturing data analytics solution that will be easy to implement across new use cases, machines, and production sites for smooth and cost-efficient evolution.
To enable secure transmission of manufacturing data throughout the network of interconnected systems, devices, and sensors, making sure the data is protected against cyberattacks and unauthorized access at every touchpoint.
How It Works in Practice: Success Stories by ScienceSoft
Sales Analysis and Forecasting for a Dairy Manufacturer Exporting to 20+ Countries
- Cleansing the Customer’s historical sales data to enable accurate predictions.
- Automated choice of the most relevant statistical model depending on data recency.
- Granular comparison of plans vs. actual sales per product category, brand, store, and region.
- Identification of a potential 15% sales increase thanks to advanced analytics.
Development of the World’s Largest PLM with Analytics and Reporting Module
- Creation of one of the world’s largest supply chain networks to streamline product lifecycle management for 20K+ retailers, manufacturers, and suppliers.
- Implementation of a multi-level data warehouse.
- Raw data aggregation from 20 globally distributed databases.
- Full analytics and reporting capabilities available via an intuitive multilingual user interface.
Development of a BI Solution for a Food Manufacturer and Distributor
- Smooth integration with multiple on-premises and cloud data sources, including legacy systems.
- Easy data management with automated and semi-automated data collection and scheduled refreshment.
- A highly structured data warehouse ready for analytics queries.
- Regular and ad hoc reporting supported by three OLAP cubes.
- Power BI reports with customization capabilities for highly adaptable analytics.
A basic solution that:
- Enables batch data analytics.
- Enables the analysis of key production KPIs.
- Integrates with key data sources like production systems.
A solution of medium complexity that:
- Enables batch and real-time data analytics.
- Enables the analysis of key KPIs across multiple business facets: production, supply chain, sales, inventory, etc.
- Provides rule-based and ML-powered analytics.
- Integrates with key corporate software (e.g., ERP, accounting software, HR management system).
An advanced solution that:
- Enables batch and stream data analytics, including real-time IoT and big data analytics.
- Enables AI-powered analysis and forecasting of all required business KPIs.
- Provides advanced prescriptive and predictive analytics for the optimization of production, procurement, OEE, etc.
- Integrates with multiple back-office systems.
Want a more precise figure?
ScienceSoft’s team is ready to estimate the cost of your specific data analytics solution.
*Software license fees are not included.
Implementation of data analytics in manufacturing brings:
Up to 315% ROI
over 3 years due to implementation of real-time data analytics
Up to 15% increase
in productivity due to AI-powered SCM optimization
Up to 15% increase
in annual profit due to IIoT-based analytics
Popular Software for Industrial IoT Analytics
Sensor-equipped objects are one of the major data sources for manufacturing data analytics software. Below, ScienceSoft describes the most popular platforms we use to ensure advanced analytics of IoT data.
You can see how such solutions work by exploring our smart factory demo.
- AWS offers 4 different sensor analytics services for solutions of different complexity: e.g., event-based responses with AWS IoT Events vs. comprehensive analytics with AWS IoT Analytics.
- 7 specialized products and services for sensor device management and connectivity.
- Offers a dedicated service for manufacturing: AWS IoT SiteWise for industrial data analytics.
- Easy integration with other AWS tools and services: e.g., Amazon QuickSite for visualization, SageMaker for ML, Amazon Kinesis for stream processing.
- Broad hardware compatibility thanks to Amazon’s multiple partnerships with device manufacturers.
Although AWS offers a dedicated analytics service for manufacturing, its capabilities are limited in terms of industry-specific features and capabilities. Moreover, the vendor hasn’t yet developed a sufficient network of partners and developers in the domain.
Lower pricing ranges are for a larger volume of messages.
Device connectivity: $0.08 per 1M minutes of connection per device.
Messaging: $0.7–$1 per 1M messages.
Free tier usage: available for the first 12 months within the defined processing, storage, and scanning limits.
Microsoft Azure IoT
- Microsoft offers 8 different services and products for building solutions of different complexity levels, e.g., Azure IoT Hub for device management and Azure IoT Central for analytics functionality.
- Smooth integration with Microsoft’s analytics services, including Azure Machine Learning and Azure Databricks.
- Robust security-focused services (e.g., Azure RTOS, Azure Sphere).
- Efficient tools and services for edge deployment.
- A good choice of integrators and technology partners thanks to Microsoft’s extended partnership ecosystem.
As the vendor doesn’t offer manufacturing-specific services or tools, connecting services into a tailored solution and optimizing costs requires special skills and can be very time-consuming.
The tiers depend on the number of messages sent daily.
Device connectivity: $0.08–$0.70 per month per device.
Messaging: $0.7–$0.015 per 1K messages.
Free tier usage: available for the first 12 months and covers the most popular Azure services + 25 always-free services and a $200 credit to explore Azure for 30 days.
ScienceSoft is an IT consulting and software development company headquartered in McKinney, Texas. Since 1989, we help manufacturing companies leverage advanced data analytics to drive their business growth. We have developed mature quality and security management systems supported by ISO 9001 and ISO 27001 certifications to provide our clients with world-class software and guarantee full safety of their data.
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