Some Features Of Manufacturing Analytics That Make Everyone Love It
What are the benefits of manufacturing analytics and What is Manufacturing Analytics? | Knime? It can improve production processes, simplify supply chains, and create transparency. Advanced machine learning algorithms can identify opportunities and optimize processes, reducing failure rates and alerting staff of machine problems. Its use cases can also reduce scrap rates. The benefits of manufacturing analytics are numerous and diverse. But here are five of the best:
Supply chain analytics
Supply chain analytics starts with data. The solution you choose should connect to ERP, complimentary business systems, and other technology. It should also be collaborative. In addition to bringing data together, this solution should help you communicate with your suppliers and customers. Cloud-based solutions are ideal for this, as they are easier to manage and share. The features of supply chain analytics are endless and can benefit your company in many ways.
For example, one feature of supply chain analytics is demand management. This metric helps you analyze the relationship between demand and supply. It lets you predict how much inventory you need and eliminates wasteful production. You can better understand your customers’ behavior by tracking inventory and orders. You can also improve your customer service. For example, you can avoid the risk of acquiring a customer who won’t buy your products.
Predictive maintenance
If you are looking to implement predictive maintenance into your business, you need a solution that can help you optimize and schedule maintenance tasks. It requires defining the use case and obtaining a dataset that matches it. Once you have the data, you can explore it to identify patterns, such as degradation and failure. You can then use this information to build machine learning models. You can use several components to make predictive maintenance solutions, such as Google Cloud Platform.
Machine learning is one of the vital components of predictive maintenance. It can detect abnormalities in equipment behavior by studying its usage history. The quality of the data is critical. The maintenance aspect of predictive maintenance requires a computerized maintenance management system, which generates work orders for technicians when anomalies occur. Condition-monitoring devices can be installed on any asset and integrated with predictive maintenance technology. These devices can be mounted on various assets and used with other manufacturing analytics tools.
YET analysis
A steel producer found that the YET of the Yield Energy Throughput model revealed an increase in the uptime of individual assets. By applying predictive maintenance to personal assets, YET helped improve uptime, and even a slight improvement in operational efficiency enhanced earnings before interest and tax. Using this method, manufacturers can balance yield, throughput, and material costs to maximize profitability for each manufacturing process step. Its success is in the high level of customer satisfaction and profitability.
The manufacturing industry generates a lot of data – often in silos – spanning everything from suppliers and processes to equipment and sales. While predictive maintenance and YET analysis target individual machines, PPH maximization can optimize the interactions of thousands of devices.
Modularity
The idea of modularity is becoming more critical in the manufacturing industry. It allows manufacturers to reduce the time and costs of designing and building a product by disintegrating its constituent components and combining them later. It also provides for concurrent design tasks to be performed, which can decrease the overall time of the production process. In addition, modularity is used to create innovative products, such as those based on new concepts.
This design approach is called modularity and has a long history. Manufacturers have been using it for ages to make more complex products. For example, carmakers manufacture different parts at various locations and then combine them. This approach allows for the division of complex processes among multiple factories and even outsourcing certain features of them. To use modularity in manufacturing, you must understand your manufacturing processes
