The manufacturing industry has witnessed a massive transformation from mechanized engine powered production to digitized production. The digital sector has created innovative ways to optimize and automate production. The fourth industrial revolution and the impact of digitization has introduced embedded system technology in manufacturing. Real-time data transferred from sensors embedded in machines, components or stages of work processes needs to be constantly analyzed and processed to gain insights used to make necessary adjustments.
Data is not only limited to sensors. It resides in various sources like product or machine design data, product and process quality data, manual operation records, etc. The main challenge is not with gathering this large amount of data, but with effectively extracting valuable insights from it. Conventional IT systems lack the ability to leverage data and extract insights from it, and as a result data just sits wasted in isolated systems. Advanced data analytics is key to unlocking critical insights and diagnosing and correcting manufacturing processes.
Summarizing data analytics scenario in manufacturing
According to Wipro, about 86 percent of manufacturers showed an increase in data gathering. About 90 percent of manufacturers claim to have mature data analysis capabilities. Yet, only 22 percent of manufacturers have predictive analytics to increase production. This suggests manufacturers are not making the most of their data.
The Illustration below summarizes different problems faced in managing manufacturing data by the corresponding percentage of users.
How BIRD supports manufacturing analytics
BIRD provides the much needed full-stack data management and augmented business intelligence platform to extract meaningful insights from vast amounts of batch and IoT data, empowering users across organizations to make strategic and astute business decisions. Get real-time unparalleled insights into your manufacturing performance using our data analytics platform.
- Garner data from direct sources like embedded sensors or indirect sources like operators.
- Track equipment failure and shortage to measure direct cause and effect of manufacturing activities.
- Track and improve performance and quality by detecting and analyzing slow cycles, idle times, process defects, etc.
- Use real time data and advanced machine learning models to identify patterns indicating potential failures.
- Proactively reduce non-production time and prevent loss to down-time.
- Track usage data patterns to alert consumers on replacement times.
Optimize supply chain management
- Use advanced statistical and machine learning models to improve forecasting accuracy and inventory planning.
- Monitor supplier performance (delivery, service, price, etc.), and devise strategies to improve supply quality.
- Effectively analyze and plan inventory turns, handle out-of-stock situations, and other resources.
Warranty cost reduction
- Detect increases in failure or claim rates (assets, dealers, etc.) and raise subsequent alerts.
- Track frauds or possible outliers on claim submissions.
- Automate claim processing, thus reducing expenses from manual resources.
- Analyze customer feedback and complaints on services and products and address them accordingly.
Key benefits with BIRD
Eliminate data silos
Use our connectors to integrate your data one place
Use multiple ML models for forecasting, prediction and text analytics
Avail real time analytics with advanced visualizations
Big Data architecture
Event driven architecture to ingest and process real-time data
Use high performance and extensive data preparation features
Universal data model
Create single data models with multiple fact tables
Augmented analytics through BIRD
While artificial intelligence can be considered a hot topic in the field of data analytics, augmented analytics technology is the future of analytics. Utilize BIRD’s augmented analytics feature to carry out the following functionalities:
- Understand factors affecting key performance indicators and comprehend subsequent behavior using simple language.
- Improve asset repairing through a natural language generation feature using a semantic approach.
- Visualize possible defects in processes or products by using imaging or video technologies.
- Get machine efficiency data for various factors like weather, product specifications, geographical location, etc.
BIRD comes along with different models like linear regression, clustering, classification, forecasting, random forest, text analytics, density-based clustering, and logistic regression.
Take Action from Insights
Leverage BIRD’s automated Smart Insights. BIRD uses artificial intelligence and machine learning techniques to automatically transfer raw data into recommended actions. It delivers personalized in-context information and helps save time from analysis to action.
The powerful and collaborative on-the-go storyboards display real-time insights seamlessly, regardless of user location, ensuring timely business decisions.