The Role of Analytic Software in the Manufacturing Industry
Data analytics in the manufacturing industry is intended to improve production processes by analyzing equipment and operational data. Data analytics can help manufacturing companies optimize essential operational functions, including predictive maintenance, production planning, and product quality.
While data analytics is not a new concept to the manufacturing sector, the methods of gathering and analyzing data have evolved significantly. Manufacturing analytics software has removed the need for employees to manually collect and analyze data, removing the risk of human error. In addition to producing accurate analytics, using a software solution for this also saves the company time by removing the need for employees to collect data.
Understanding the Importance of Analytics in Manufacturing
Manufacturing analytics software is designed to process big data and transform it into a comprehensive format with actionable insights. Having data analytics to make data-driven decisions in a manufacturing business is essential to prove it’s worth the operational impact.
A manufacturing analytics solution can identify ways to optimize processes, reduce costs and energy consumption, and improve supply chain management. With access to real-time manufacturing data, companies can make timely changes backed by advanced analytics.
Benefits of Implementing Analytic Software in Manufacturing
Having reviewed why a data analytics solution can have a significant impact on a manufacturing company, let’s take a look at some of the key benefits of using analytics software.
Improved Customer Satisfaction
Customer satisfaction is a priority for most manufacturing companies, as consumers drive product demand. As more customers seek out customizable products, data analytics can give companies a competitive edge. Using the actionable insights gathered from data analysis, companies can move products through the manufacturing process at a quicker rate, improving customer satisfaction with the final product.
Reduced Costs
The insights gathered from a data analytics solution can reduce operational costs in multiple ways, including preventive maintenance, operational equipment effectiveness, and supply chain optimization. Identifying pain points and bottlenecks in these processes can allow manufacturers to make the necessary changes to reduce expenses and maintenance costs.
Increased Revenue
Since data analytics can provide companies with real-time data on the production line, demand signals, and current inventory, companies can effectively adapt to changes in the market. Companies can use this information to order raw materials, optimize employee schedules, and restructure manufacturing processes.
Enhanced Operational Efficiency
The simplest way to improve operational efficiency is to monitor equipment and machine health to ensure they are running correctly. By using the Industrial Internet of Things (IIoT) technology to monitor equipment, companies can be notified whenever an issue is detected. Companies can also use predictive analytics to schedule preventive maintenance to keep equipment in good health and avoid downtime.
Key Factors to Consider When Choosing Analytic Software
When deciding on an analytics software solution, consider the following factors to ensure a quality experience.
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Scalability: Having an analytics software solution that is scalable is a factor every company must look out for. Having a software solution that can grow with the company will ensure it can be used for years to come.
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Extensive Data Modeling: Finding a solution that allows companies to make use of data modeling functions will ensure the analytics will provide accurate and valuable insights. Having the ability to model the data gives companies a chance to structure their data in an impactful way.
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User-Friendly Experience: Choosing a user-friendly software solution is essential to gaining the most value. Having a user interface design that is intuitive for users to navigate will ensure they use the solution to its greatest capabilities.
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Security: Security is a top priority for most software solutions, especially when it comes to data analytics software. Having robust security measures will ensure all stored data is protected, including access controls, multi-factor authentication, and data encryption protocols.
Future Trends in Manufacturing Analytics
Technology is constantly evolving, and the manufacturing industry will continue to adapt. Let’s take a look at some of the future trends in manufacturing analytics we can expect to see.
Predictive Analytics: Next Big Thing in Manufacturing Analytics
Predictive analytics uses collected data to analyze trends and predict future trends. A common use of predictive analytics in the manufacturing sector is to perform predictive maintenance on machines and equipment.
Using data to perform predictive analysis can help manufacturers predict when maintenance will be needed based on historical data. The longer manufacturers use analytic software and collect data, the more accurate the predictive analysis will be.
The Rise of AI and Machine Learning in Manufacturing Analytics
Artificial intelligence (AI) and machine learning (ML) technology are rapidly becoming more common, and the manufacturing industry is finding ways to benefit from it. AI technology can be integrated into manufacturing software to automate repetitive tasks, such as payroll or order processing, allowing employees to spend more time completing valuable work.
One way manufacturers are using ML technology is to improve quality control. Historically, quality control checks were performed by employees, taking up time and leaving room for human error. ML algorithms can use sensors and cameras to identify products that don’t meet the standards.
Role of IIoT in Shaping the Future of Manufacturing Analytics
IIoT technology significantly impacts data analytics capabilities in the manufacturing industry. With IIoT technology, manufacturing software can monitor equipment health, environmental conditions, and asset location. This information can also be transformed into manufacturing data for companies to analyze.
Preventive maintenance is becoming increasingly important to manufacturing companies as a way to keep equipment in good health and reduce downtime due to malfunctions. IIoT sensors can be used to monitor equipment and gather data to determine when maintenance should be done to avoid any breakdowns.
One example of an industry using IIoT technology is automotive manufacturers. Some uses include vehicle navigation software, fleet management software, and automotive management software. Being able to track equipment and vehicles throughout the manufacturing process gives companies data to determine ways to streamline processes.
Work With Geneca Today
Are you ready to advance your manufacturing company using insights gathered with data analytics? Reach out to Geneca today! We’re ready to determine the best solution for your company so you can improve your business intelligence capabilities.