Other InetSoft Features
In addition to data mashups, users can also build their own analytical dashboards through a simple drag-and-drop tool, constructing automatically generated KPI’s and visually organizing dashboard elements according to their preference. Performing even complex analyses without having to code SQL or any other language makes the InetSoft tool easy to work with for data explorers of any skill level. Another convenient aspect of the software is that InetSoft's BI can run on any operating system including Windows, Linux, Solaris, HP Unix, and Mac OS.
Data exploration is further enhanced with features such as brushing. The brushing tool highlights a specific subset of data across all charts on a dashboard, providing a different angle to view, seeing how the selected data breaks down among different categories.
With data exploration, the tool you’re using makes all the difference. With InetSoft's software for data analysis, powerful data mashups, reports, and interactive dashboards are easy to establish and analyze, for faster answers, and better decisions.
What Kind of Data Analyses Do Chemical Manufacturing Companies Do?
Chemical manufacturing companies engage in a wide range of data analyses to optimize their processes, ensure product quality, manage resources efficiently, and make informed business decisions. Here are several types of data analyses commonly performed in the chemical manufacturing industry:
- Statistical Process Control (SPC): Monitors and controls manufacturing processes by analyzing data in real-time, ensuring that variations stay within acceptable limits.
- Six Sigma Analysis: Aims to minimize defects and variations in production processes to improve efficiency and quality.
- Quality Control:
- Chemical Analysis: Involves the use of analytical techniques to verify the composition and purity of raw materials, intermediates, and final products.
- Quality Assurance (QA): Analyzing data to ensure that products meet specified quality standards and regulatory requirements.
- Supply Chain Management:
- Demand Forecasting: Uses historical data and predictive analytics to estimate future product demand, helping companies optimize inventory levels.
- Logistics Optimization: Analyzing transportation and distribution data to enhance efficiency in the supply chain.
- Energy Management:
- Energy Consumption Analysis: Identifying areas of high energy consumption and implementing measures to reduce energy usage, which can lead to cost savings and environmental benefits.
- Environmental Monitoring:
- Emissions Tracking: Analyzing data related to emissions and waste to ensure compliance with environmental regulations and to implement sustainable practices.
- Safety Analysis:
- Incident Data Analysis: Examining data related to safety incidents to identify root causes and implement preventive measures.
- Risk Assessment: Evaluating potential risks associated with various processes and materials.
- Research and Development:
- Experimental Data Analysis: Analyzing experimental results to optimize formulations, improve product performance, and accelerate research and development processes.
- Cost Analysis:
- Cost-Benefit Analysis: Evaluating the costs associated with various production processes and determining the most cost-effective strategies.
- Customer Feedback Analysis:
- Market Research Data: Analyzing customer feedback, market trends, and consumer preferences to adapt products and strategies accordingly.
- Regulatory Compliance:
- Compliance Monitoring: Analyzing data to ensure adherence to various regulatory standards and requirements.
“Flexible product with great training and support. The product has been very useful for quickly creating dashboards and data views. Support and training has always been available to us and quick to respond.
- George R, Information Technology Specialist at Sonepar USA
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