FactoryWorx Production Reporting Management
Production Reporting System and its Management
Over the past few years, Internet of things has aimed each consumer in almost every industry. This has been empowering businesses to embrace industry 4.0. for providing smarter services with smart factories. Industries have now realized that the fast increasing data has increased productivity by detecting bottlenecks in the system resulting in better decisions in production reporting system and management and thereby driving better business management.
In all industries, production reporting and management has been always a challenge. This was due to the unavailability of the components that were needed to be delivered in production managing. Automation controllers in manufacturing plants collect the needed data and have made production management a possibility.
Reduce manual data collection Labor Costs: With the introduction of automation in production reporting, manual data collection processes are now avoided, resulting in reduced labor cost and improved data accuracy.
Develop Inventory Accuracy : When you update the inventory system in your factory electronically with respect to real time production monitoring system, you can track down order status and production flow and can update the Enterprise Resource Planning (ERP) system automatically.
Regulate accurate Job Elements: When reporting was done manually, determining the standards in each job stage were difficult. But with automated FactoryWorx production monitoring, during data collection, it is simple to calculate production life cycle time, run time, down time, setup time and labor hour standards to develop accurate Job costs.
Practice KPI Dashboards: Real-Time KPI (Key Performance Indicator) screens display huge bulk of data. FactoryWorx cloud displays OEE (Overall Equipment Effectiveness) status, current downtime reason, average cycle time or production rate, etc.
OEE reporting: The AI enabled controller features adaptive intelligence that identifies and spots normal and abnormal features in each of the equipment.
MTBF (Mean Time Between Failures) is a measure of how often something will fail.
MTTR (Mean Time To Repair) is a measure of how long it will take to fix a problem.
Together MTBF and MTTR help increase the risk.
Automated inventory monitoring: Use of AI removes manual inventory control. AI helps to automatically to keep track of inventory purchase and optimization processes. Also if any inventory tools need repair or a repurchase, AI will send an automatic notification about the needed repurchase. With AI’s pin pointed and precise results tracking becomes easier as it eliminates the errors that occur in manual tracking.
Achieving industrial efficiency: A number of AI enabled machines are improving production monitoring efficiency. AI soft wares and applications are aiding manufacturers to monitor quantities of raw materials, production time, temperatures needed in each step of production, errors, downtime, repair time, automatically.
Meeting customer demands: Due to manual tracking, manufacturers miss out important items to count. AI helps in automated real time monitoring and regulation of the inventory assets. AI automated machineries help manufacturers in improved supply to customers and plan production as per the customer needs. AI helps customers in letting them know the correct information about any product. The inventory management solution of AI bends the inventory levels as per the customer expectations change. This helps in cost reduction, improving repair control and resulting in an efficient inventory and production monitoring.
Reduce Quality Issues: A time consuming task in manufacturing is the testing of produced goods and confirms that meet the quality levels. In manual testing by humans, at least a small degree of error is certain. With AI-enabled test abilities, quality issues can be acknowledged and addressed sooner, permitting for more yielding manufactured goods that’s fine and set to ship the instant it is out from the production line. With AI enabled machinery quality control has become, faster that can detect errors and loopholes easily. And since AI is also able to spot and detect problems in real time, this information can be fit into the inspection stream and accomplish progressive analytics to avoid the issues from occurring again.
SPC (Statistical Process Control): SPC is possibly the most imperative and most frequent use of Artificial Intelligence. Statistical Process Control (SPC) is the use of statistical tools and considers monitoring, regulating, handling, and refining procedures and performance of the progression.
Predictive Maintenance results in Less Downtime: One of the most promising use cases of AI in manufacturing is its ability to effectively remove both intentional and unintentional downtime triggered by damaged equipment.
For ages, manufacturers have used sensors and preventive maintenance to manage and evaluate the performance of their tools. But outdated preventive maintenance needs a reasonable bulk of manual investigation of huge bulks of data to harvest any actionable decisions. Similarity, because it is combined with AI expertise, predictive maintenance can influence cutting-edge tools and analytics to forecast errors before they happen. This allows organizations to avoid downtime overall, ensuing in augmented performance and avoiding the run-to-failure situation.