DEEP DATA. FEATHER-LIGHT IOT.
Software Analytics
Connect your Turck Banner device using Auk Dashboard
Manufacturers who are manufacturing in the ‘old school’ way have found themselves in the throes of Industry 4.0 which has proven itself not just effective in increasing production efficiencies within the factory but also improving customer satisfaction and the overall business.
Industry 4.0 is more than just automation. Automation is a practice that has been around for decades since the first industrial revolution. Rather, as Roblek et al. (2016) rightly pointed out, Industry 4.0 is about the use of Internet of Things (IoT) to improve knowledge processes—how billions of data points can be located, acquired in real-time from machines and customers, shared, disseminated and collectively used by the different functions in the business to respond agilely to both internal and external gaps and opportunities.
In an environment of increasing volatility and rapid changes, it is even more important now than before for manufacturers to tap on Industry 4.0 technologies to gather data-driven insights and establish formidable advantages over competitors in the areas of performance management, maintenance, quality control, and energy management. The marathon in manufacturing post-COVID is still young and the time to catch-up is now.
1) Driving Productivity Gain with Reliable Real-time Data
One key productivity performance metric used for decades to identify hidden capacities and improve the productivity of operations would be the Overall Equipment Effectiveness (OEE). By breaking down and analysing OEE, manufacturers can easily pinpoint the different areas of losses (e.g. equipment breakdowns, material shortage, scrap parts and more). In practice, however, such critical information is often obscured by the manual way of data collection and ‘guesstimation’ of downtime figures and reasons. We can connect all device especially with Turck Banner product.
In order to improve the accuracy and reliability of OEE as well as to obtain more insights for productivity improvements, manufacturers looked towards the use of Industry 4.0 technologies. Through the use of sensors and/or IoT devices, manufacturers can now automatically collect quasi-real-time data points at high sample rates such as output per minute and speed losses from machines, production lines and even the entire production floor. Such information cannot be tracked by manual data collection as they frequently occur throughout the day, and at short durations.
2) Pioneering More Effective Maintenance
An increasing number of maintenance engineers are moving away from breakdown and time-based maintenance to usage-based and/or condition-based maintenance. Compared to the former, the latter allows them to call for maintenance and repair the machine ahead of a breakdown, thereby significantly reducing downtime.
The fastest and easiest way to implement usage-based maintenance is to use Current transformers (CT) sensors to track machine run-hour. CT sensors are non-invasive sensors which are affordable, readily available and can be deployed within minutes to track the motor or equipment run-hour. Notifications can be triggered and sent when the run-hour exceeds the recommended duration.
3) Reimagining Quality Management
To establish long-term competitiveness and sustainability, maintaining processes quality and ensuring quality management is vital. With the advent of Industry 4.0 technologies, manufacturers are able to gather more measurement data than before and detect defects that would otherwise go unnoticed by manual quality inspections.
There are three popular ways of ensuring quality. The first is the use of process capability to ensure product quality. By integrating real-time data with pre-built Statistical Process Control (SPC) modules within real-time monitoring systems, the Quality Control and Production Engineers can easily monitor the Process Capability Index (Cpk) from the dashboard, or toggle between the control charts and yield trends to manually spot any special cause variation. Whenever a performance deviation is detected, the relevant stakeholders would be alerted and implement corrective actions to address such nonconformities issues.
4) Realising Energy Management and Consumption Savings
One of the ways to maintain efficient and effective consumption of energy would be to take action based on the insights derived from Industry 4.0 energy management systems (EMS). For a start, the EMS must be automatically fed with the IoT-collected machine data. This can be done via the cloud for a hassle-free experience. After which, the EMS can help to identify and quantify energy losses incurred across different batch processes. This is achieved by analysing OEE with other data such as process cycle time, and energy consumption. The EMS is also capable of constantly monitoring factors like applied power and harmonics which often cause power interruption, unnecessary downtimes and safety issues. Given these insights, manufacturers can then prioritize and decide on the most suitable energy management strategies to seek improvements.
Our Features
System-Level Modelling
Production line, factory performance can be analysed as a system to gain greater visibility on overall performance.
Identify potential bottlenecks automatically, essentially focusing your attention to solve the most critical points.
Deep Dive Machine View
Deep dive to individual assets to investigate issues using real-time, high-resolution data, where OEE is automatically color-coded into 11 different categories.
Pareto Analysis
Deep dive into the top 20% reasons that cause 80% of operational losses and compare it across different shifts, production lines and factory-wide.
Trend Analysis
Identify correlation of critical parameters to detect the anomaly and actuate an action.
With this, important personnel can be notified immediately and have the issues acted upon in the shortest time possible.
Notification
Identify critical parameter thresholds and set SMS or e-mail notifications for important personnel to be notified and issues acted upon in the shortest time.
Dynamic Standard-Time Management
A machine producing different SKUs may have a variety of CTs.
Auk analytics engine takes this into account to calculate the SKU-specific OEE and to ensure a highly refined and accurate OEE analysis.
Root-Cause Analysis
A quick helicopter view of multiple plants for the single entity, allowing you to focus your resources on the plants that require more attention.
With this feature, you can also easily conduct cross plant performance comparison.
Other Features
Including regression analysis, utilization heatmap, multi-level entity navigator, etc.
