The Big Advantage of Computer-Integrated Manufacturing Software Is That It:
– This software streamlines operations and makes it easier to track inventory. Besides this, computer-integrated software can also offer a range of features, such as CAD/CAM and quality control, production forecasting, barcoding and other capabilities.
These tools can help companies cut down on the costs associated with traditional manufacturing processes such as labor and material procurement. the significant advantage of Computer-Integrated Manufacturing Software is that it can coordinate multiple machines to make complex products or parts more efficiently.
As companies shift from batch-oriented manufacturing to lean manufacturing, this software becomes more critical.
Every step of the manufacturing process in lean manufacturing systems is evaluated before any changes are made. Using computer-integrated manufacturing software during the evaluation phase can help ensure that your product is free from waste and defects.
These are 5 benefits of more intelligent, fully integrated operations :
1. Combine siloed data streams for business-wide improvements
Integration is precisely what its name implies: It connects what happens on the shop floor to what happens at the enterprise level. Manufacturing execution systems (MES), and dashboards that display trend data and connections between enterprise resource planning (ERP), are the most popular digital transformation routes.
There are some business-wide benefits to a connected system:
- Supply chain: Use real-time data to streamline your business’ supply chain. You can find out the amount of raw material used in production and where there is waste in order-taking and receiving parts. These tools can help you make better buying decisions, consolidate orders, hold less stock on-site, and use a Just in Time (JIT) approach to replenishment.
- Distribution: Integration distribution systems can communicate with production to inform them of any delays, outputs or defects. This allows for a more precise schedule which means that the business can achieve higher customer service KPIs and on-time delivery.
- All employees should have access to the same data source for business-wide decision-making. Integrated operations enable this by combining data sources like production rates, order fulfillment, output and process data or components and materials. Time-series data can also be used to analyze and identify patterns and trends in the business.
2. Improved quality
Quality standards are essential for manufacturing. Manufacturers can be proactive rather than preventive when it comes to quality control. This is especially true if quality reports are unavailable within 24 hours or product defects cannot be identified.
Quality can be improved through fully integrated operations:
- You can react faster to defects with live sensors. Sensors can perform quality checks at a much higher speed than line speed. These include temperature sensors and vision systems and check weighs that validate unit quantities.
- Accuracy: A quality check system can detect and remove defective products from the production line with greater accuracy. High-speed cameras can take pictures of jars in packaging centers to verify that they are within the specified dimensions and colors.
- Process improvements to reduce defects: Technologies that measure output and production data can help pinpoint faults in production and inform program changes.
3. Machine learning
More data can lead to greater integration, which can present a challenge. One concern is that we will be overwhelmed with data as IoT technology advances.
We are lucky that machine learning has made it possible to take the pressure off of humans. At the moment, standard data processing for integrated operations looks something like this:
Data collection from machines and production lines > data conditioning > data combination > data analysis > modelling and consolidation of disparate streams > data interpretation by humans >Slowly, more data analysis and corrective actions are being performed by slower machines. Data processing will be more like Data collection from machines and production lines > data conditioning > mixture of data streams > data analysis > modelling and consolidation > data interpretation > monitoring by humans >Artificial Intelligence and Machine Learning technologies can be:
- Analyze data at speed far beyond the human pace
- Analyze information not available to humans or data that they don’t consider important
- Connect seemingly unrelated events, such as correlating X, Y and a specific type of product defect, or failure
- Use predictive models and complex patterns to raise warnings or suggest corrective action.
4. Continuous improvement targets
Sometimes, even though it seems counterintuitive, business sense can be made – and data will show that.
The production line’s real-time data can be used to identify areas for improvement. Imagine that the machine speeds have increased from 5500 units per hour to 6000. The management is happy as they are producing more products in a shorter time. The system has a higher level of waste than it did when it was running at 5500. Although efficiency seems to improve, profit margins are higher when the machine is slower. These insights are only possible by correlating multiple data points at once.
Advanced data analytics has led to impressive results in continuous improvement programs. One top bio pharmaceutical company increased its yield by more than 50% without additional capital expenditure. How? How? Closely related production activities were grouped and placed in one database. Advanced analysis revealed which process parameters impacted yield and which were interdependent. This helped to identify areas where improvements should be made.
An integrated system can also help with preventative and predictive maintenance. For example, integrated systems can schedule maintenance checks automatically when sensors detect abnormal heat or friction.
The business can use the data to make an informed decision based on the interaction of factors, not individual factors.
5. Business-wide visibility
Organizations often get a better understanding of their entire operations when multiple data sets are combined. One case involved a large supermarket chain consolidating its scheduling, forecasting and traceability data across all its meat processing operations. A customized pull model was used to manage raw materials by storage needs. A comprehensive approach that utilized available data to track products from farm to consumer helped the supermarket chain save $50 per pallet and increase product shelf life by 1.5 Days.