Manufacturers are leveraging on IIoT that plays a vital role across all manufacturing data to improve their operational efficiency, quality improvements, and equipment effectiveness. Cantier SPC, WIP, CMMS, RMS & OEE modules collect huge amount of data during the production processes like machines used, operators, materials, defects, in-process quality data, recipe parameters, equipment availability, equipment performance, and others. In order to fully understand their shop floor, Cantier Manufacturing Intelligence (MI) analyzes the data with context. Cantier MI provides just the right amount of the right information to the right role at the right time to allow them to make the right decision and take immediate action. Cantier MI can help manufacturers make timely decisions with KPI-based dashboards and reports. MI communicates across systems and perform autonomous actions. It predict issues by applying artificial intelligence and machine learning technology.

SMART Control Room

Manufacturers are leveraging on IIoT that plays a vital role across all manufacturing data to improve their operational efficiency, quality improvements, and equipment effectiveness. Cantier SPC, WIP, CMMS, RMS & OEE modules collect huge amount of data during the production processes like machines used, operators, materials, defects, in-process quality data, recipe parameters, equipment availability, equipment performance, and others.

Machine Learning (ML)

Machine learning presents its opportunities by looking at any situation to derive new knowledge from the past experience, using their petabytes of historical data. By using various statistical methods and deep learning, the technology identifies recurring patterns and apply the results to the new transactional data streams. Cantier Manufacturing Intelligence detects trends in real time and predicts upcoming failures. Although Machine Learning can be applied in various data that are collected from the IIOT devices and Sensors, Cantier MI is focused on the following key areas where manufactures save cost tremendously with shorter period of ROI.

Equipment Failures and Predictive Maintenance

Usually, maintenance is conducted either by time based (Preventive) or when problem occurs (Corrective). In preventive maintenance, machines still have chances of failure before the actual schedule as the resources may have used more than the regular usage. In corrective maintenance, no choice but to schedule for a repair work with the loss of production hours. With the historical equipment failure and downtime data collected, Machine learning identifies the patterns of equipment or equipment’s part failure and determines when the actual maintenance should occur to avoid breakdowns. Cantier MI provides necessary alerts in advance and creates work orders for such maintenance activities.

Machine Learning on Quality Control

Though there are still manual inspections to identify quality issues, mostly measurement equipment is used to inspect parts and products manufactured in the plants. Machine Learning is applied to the historical data of both input parameters (recipes) and output parameters and establish trends and patterns. This patterns helps to predict the quality issues during the production and eliminates wastes. Cantier Manufacturing Intelligence alerts the operators and supervisor when there will be potential quality issues. On the other hand, Machine Learning can also be used to visually inspect products and identify flaws much faster than how it is manually done by human. Examples: Cracks, Misaligned Labels, Misalignments, etc.

Machine Learning on OEE

ML plays a key role in improving OEE by looking at the historical data. This metric measures the availability, performance and quality of manufacturing equipment, which are all improved with the integration of deep-learning neural networks that quickly learn the weaknesses of these machines.

Key Benefits

Alerts and Notifications

The dynamic alert capabilities ensures that users are instantly notified when an out-of-control incident happens.

Proactive Decision Making

An in-depth analysis of all production and QA processes so as vital patterns and trends can be uncovered and issues can be corrected as quickly as possible

Dashboards

Make timely decisions with KPI based dashboards. Help summarize the voluminous data with context to take immediate actions