Manufacturing Data Analytics

Swift Web Design applies 5 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.

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Manufacturing Analytics: The Essence

Manufacturing 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 resource 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-time analytics, and more.
ROI: up to 315% over 3 years with payback in <6 months.

Key Features of Manufacturing Data Analytics

At Swift Web Design, we develop custom manufacturing analytics solutions that match the unique needs of each of our customers. Below, our consultants list the features that our clients in manufacturing require most frequently.

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.

Production analytics

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.

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.

Cost analytics

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 labour 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/labour/MOH cost change.

Supply chain analytics

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 fulfilment prediction and fulfilment optimization.
Running ML-powered what-if scenarios with changing variables (weather conditions, shipment routes, employee availability, etc.) to optimize logistics.

Customer analytics

Automated B2B customer segmentation per business sector, cooperation duration, etc.
Automated B2C customer segmentation based on geographical, demographic, behavioural, 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.

Asset analytics

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 modelling with multiple process conditions variables to identify optimal OEE and machine operating patterns.
Real-time IoT-based analytics enable predictive equipment maintenance by forecasting potential hazards and failures and sending the corresponding alerts.

Sales analytics

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.

Essential Integrations for a Manufacturing Data Analytics Solution

Swift Web Design recommends integrating manufacturing data analytics software with the following systems:

HR management system

To get insights on trends and optimization opportunities in employee management.

Asset management software or EMS

To optimize asset utilization and productivity, enable predictive and preventive maintenance, and reduce operational costs.

Enterprise resource planning (ERP)

To provide a holistic view of the manufacturing business performance across all facets: production, procurement, sales, etc.

Accounting software

To get insights on business revenue, expenses, fixed assets, liabilities, taxes, payroll, etc. in order to optimize accounting and financial planning.

Overall Equipment Effectiveness Software (OEE)

To assess equipment productivity on different levels of granularity and suggest optimal loss prevention and OEE improvement strategies.

Supply chain management software (SCM)

To optimize SCM across all of its facets: procurement, inventory, supplier, order, and logistics management.

Production systems (HMI, PLC, SCADA, MES)

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.

Manufacturing CRM

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.

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.

Factors That Drive High ROI of Analytics in Manufacturing

With 5 years of experience in data analytics and implementing manufacturing IT solutions, Swift Web Design has defined the key factors determining the success of manufacturing analytics software.

Data democratization

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.

Data quality

To ensure that the manufacturing data under analysis is complete, accurate, up-to-date, and consistent, it is essential to avoid misinformed business decisions that can lead to financial, performance, and reputational losses.

Scalability

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.

Security focus

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.

Cost and Benefit of Manufacturing Data Analytics Software

The cost of manufacturing analytics may vary from $10,000 up to $1,000,000*, depending on the solution complexity, the diversity of integrations, data types, the scope of advanced capabilities, and more.

$70K–$170K

A basic solution that:

Enables batch data analytics.
Enables the analysis of key production KPIs.
Integrates with key data sources like production systems.

$200K–$400K

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).

$400K–$1M

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

*Software license fees are not included.

Implementation of data analytics in manufacturing brings:

Up to 315% ROI

over 3 years due to the 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

We're Here to Help with Your Manufacturing Data Analytics

Consulting on manufacturing data analytics

We can analyze your case and offer the optimal architecture, tool stack, features, and integrations. You also get a comprehensive project roadmap, implementation cost and time estimates, and a risk mitigation plan.

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Implementation of manufacturing data analytics

We can build a robust and scalable data analytics solution that is fully tailored to your needs. Our specialists are ready to tackle every aspect of the project: from software design, development, and integration to QA, user training, and support.

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