A step-by-step guide for implementing digital operational excellence using Factbird. This guide will take you through our 6 steps to help you reach the full potential of your equipment and drive your operational excellence.

Level 1 - Track Flow

Connect one sensor to the bottleneck of the manufacturing line to count and time-stamp the flow of products. After connecting the sensor we can see the live data in Factbird and it looks like this:

From that single data point we can extract a lot of information.

• When did we start and finish
• How much have we produced
• How much value adding time (The theoretical time the line would have run to produce the good products in the selected time period)
• How many stops and how much down time
• Average stop duration
• Longest non-stop duration
• Product cycle time (speed)

Many of these KPIs can be viewed directly under the live graph

Next is to start analyzing how these KPIs develop over time. Click on any of the KPIs to navigate to the trend analysis and start understanding in which direction the KPI is trending.

Level 2 - Track Stop Reasons

This level is about understanding the reasons why the equipment is not performing as expected. When the line stop, we will track the stop time and ask the operator the reason for the stop - or if possible, get the stop cause directly from the machine PLC. Stop causes are categorized into the four main OEE categories.

The data you can get from tracking reasons for stops are

• Reason and duration for every stop on the line 24/7/365
• Split of time for technical stops, change-overs, breaks, repair, etc.
• Pareto chart(See figure above) showing what to improve in prioritized order
• Operator feedback to stops reasons (Stop comments)
• Visualisation of individual stop causes over time (see scatter plot on figure below)

The analytics page allows users to deep dive into what are the largest contributors to downtime per shift/day/week/month etc. We recommend that you keep focusing on 3-5 top stop causes during the continuous improvement meeting with the operators and technicians. Use the scatter plot in analytics to understand if the actions taken are having the desired effect.

Level 3- Set Shift & Batch Targets - Performance Tracking

Drive performance tracking in real-time by setting targets to the line performance. Targets need to be realistic and based on the best performance the line has been observed with. Start by setting the validated and expected speed for the line in sensor settings. This will allow you to see if the line is running at full speed or as in the case below here under performing because of speed loss. On the left side of the graph the actual speed (blue line) is below the validated speed (green line). On the right side of the graph the actual speed and the validated speed aligns showing the line running at it’s full potential.

If we are running multiple products on the same line which have different targets we can use the batch feature to set product specific targets. From batch tracking we can track following KPIs:

• Cycle time per product
• Duration time for different batch sizes per product
• Time loss data for technical stops, speed loss, change-over activities and non-production related activities per product

Targets can also be set for each OEE category to create high-level and summarized KPIs. These are often used to compare different line performance or consolidating a manufacturing areas performance.

We highly encourage to engage operators, technicians etc. to engage with performance and targets. Giving insight to real-time performance can drive behavioral change and ownership on the shopfloor, which is why we created the Batch Dashboard. The Batch Dashboard is a visualization of the line performance which is designed to be displayed on large screens around the line, allowing everyone to instant see if the line is ahead or behind.

PCS here is pieces, but the unit can be customized in sensor settings to reflect what is being counted.

Level 4 - Monitor process performance, rejects and defects

Process performance is not only defined by the output but also on rejects and defects. Rejects and defects are often an indication of weather the process is in control or not. In Factbird you can add sensors for counting rejects on the line and defects can be registered on the batch.

The line output is shown by the blue graph below whereas the individual reject stations are shown as the green/red/turquois lines.

At this level you can track process performance by

• Defects and rejects per product/batch

Level 5 - Automate predictive and preventive maintenance

Sensors are built on the critical equipment to log parameters and detect unwanted variation that could lead to failure. Sensors placed on critical equipment are tracking if the equipment is starting to develop a problem. An example could be a vibration alarm on a CIP pump. At Factbird we monitor the normal operation and allow users to configure alarm thresholds to indicate when an unwanted variation is present. The data can be correlated to the manufacturing output to create a baseline. Below figure shows the vibration as the green line and the threshold as the blue line.

We often see current sensors on electrical motors, vibration sensors for pumps and conveyor belts. Because equipment wear and tear is easy to monitor.

Level 6 - Build intelligence in with control charts and forecasting

All processes are influenced by process parameters such as temperature, humidity etc. Process performance is often highly dependent on the process parameters within the control limits. With Factbird you can experiment with collecting process parameters and correlating them with the line performance or quality performance to find the optimal setpoints and control limits. Factbird can give early warnings when setting up a control limit to warn and notify when a parameter is starting to slip outside the limits.