Improving milk bottling production and increasing machine utilization through AI + IoT.
Pasteurized whole milk has a shelf life of 12 to 14 days, whereas the shelf life of skimmed and flavoured milks is even shorter. Since dairy production operates on slim margins and there are few avenues for rework in a bottling plant, factories must process product quickly and with as little waste as possible.
Whilst many milk bottling facilities are technically advanced, due to having to meet the highest food handling standards, they do not necessarily have the money to invest in production and OEE monitoring. With that in mind, how can a bottler achieve real-time continuous monitoring so that they can improve overall profitability, all with minimal investment?
Our Real-time OEE & Machine Utilization Monitoring AI + IoT solution.
The Operations Management team at this production facility believed that additional operational improvements could be found on the bottling floor, but in order to achieve them, they needed access to real-time data, including information from different machines and automated downtime tracking. We focused on delivering a solution to answer these questions quickly, but that could also provide a long-term path for performance measurement: at Rayven, our philosophy is always to start small, show a clear ROI fast, and grow over time.
Defining what data to collect is critical
We started by identifying the key metrics in order to achieve the desired business outcomes. In this solution these included:
- Production targets
- Machine status
- Downtime duration
- Downtime reasons
- Unit weights
- Target weights
- Production rate
- Production schedule
Initial goals of the solution
The first goal of the project was to connect to the factory’s SCADA to the AI + IoT solution to ensure that the right data was collected in a consistent and reliable manner, and to ensure the data’s quality and integrity. In order to achieve this, we included these features in our IoT solution:
- Monitoring of critical operational data via a web-based and mobile system
- Equipment monitoring in the AI + IoT solution via the Citect SCADA
- Development of a secure, read-only connection for SCADA integration
- Creation of business logic for the application in-order to meet the Operation Management team’s objectives
- Notifications via email or SMS when there was downtime, or when throughput dropped below the hourly target
- Collection of historical data so that the business could start testing different logic to automate reasons for downtime
- Connection of the ERP system, to collect daily target information, as well as package weight and sizes
- Testing of the application, making sure all of the above goals are met based on the below solution architecture.
Before setting the solution live, we tested four critical aspects of the solution:
The Rayven AI + IoT platform is built with security as a top priority.
Rayven’s proprietary security architecture ensures data is secure at all points of the environment. The solution includes data encryption in transit and is encrypted from device to cloud (device-dependent). Devices are authenticated using device keys (device-dependent) and 256-bit SSL encryption is used between end-user devices (PCs, tablets, mobile phones) and the cloud, which protects confidentiality, data integrity and availability. In transit from device to cloud, we have SHA-256 with RSA Encryption, automated at-rest encryption using 256-bit AES encryption (optional), and during use (from cloud to screen) SHA-256 with RSA Encryption. The solution additionally conducts device security checks via automated polling and/or pull request as well as having security (Bearer) tokens that authenticate devices and services, meaning that keys don’t need to be sent on the network.
The production line’s supervisory equipment can directly impact product output and quality, so the connection between the AI + IoT solution and the machines needs be secure and omni-directional. To achieve this, we created a secure bridge from the supervisory equipment to the rest of the enterprise in order to maintain network isolation. By combining a secure and encrypted transmission path, together with a dedicated and direct connection (that eliminates connecting into the factory’s network), we were able to establish a fast and secure connection.
Establishing a continuous, reliable connection ensures that the data held within the IoT solution is always up-to-date and that you can rely on it to make critical business decision in real-time. Because of this, it was critical for us to test that the data we were seeing on the factory floor 100% matched what we were seeing in the dashboards of the solution and that we created a back-fill capability so that data would still be received in sequence in the event that communication ever went down.
Industrial Data Science
The objective of exploratory data analysis was to observe trends in the data and compare them with what was happening on the factory floor, which included:
- Real-time calculation of ‘saleable volume’
- Real-time forecasting using a pitch chart of hourly bottles of milk based on real-time production throughput
- Output in litres and in bottles per minute, hour, day, and week
- Automated identification of downtime and downtime reason entry screens
- Analytics of downtime reasons and duration
- Production rate efficiency
- Monitoring of pasteurisation process and process tank volumes
The outcomes derived from the solution include increased run time and throughput, with an additional gain expected as the team use the new metrics to track and improve changeover between products.
Data sources used to deliver the Real-time OEE & Machine Utilization Monitoring AI + IoT solution.
New and existing data was used to create the custom applications and business insights that come from the Real-time OEE & Machine Utilization Monitoring AI + IoT solution.
Key features of our Dairy Facility AI + IoT solution.
All of these features were customized to fit the customers specific business objectives.
See the throughput of all your machines, continuously and in real-time.
Automated and real-time pitch charts show your exact performance and how far you have deviated from the target, helping you to forecast your end-of-shift output and take immediate action if production is lagging.
Alerts and notifications
Automate alerts and notifications with andons, emails and SMSs when critical parameters are breached.
Develop and optimize Ensure your processes so that you can have the right people respond based on agreed protocols.
We deliver real-time insights to your screen, but many times you want to review a scheduled report. These can be automated and delivered to your schedule to all or selected users.
Automated identification of OEE
Monitor rate losses, quality losses and production downtime with automated data collection, augmented by human-entered reason codes.
The Real-time OEE & Machine Utilization Monitoring AI + IoT solution is delivering real business outcomes.
A better understanding of downtime and rate losses give greater predictability and confidence that production targets can be met (and increased).
Understanding what causes rejects and how often they are occurring, as well as when, enables you to solve the root causes and increase yield.
Assets need to sweat, you can ensure your production lines are up when they need to be by understanding, and acting on the root causes of equipment downtime.
Optimized output rates, controlled unit weights, reduced downtime and less scrap all equal increased throughput.
By maximizing production uptime and material conversion, as well as minimizing waste, means more profit for your business.
Better customer service
Retail chains place high-demands on food and beverage suppliers. Ensure on-time order completion by increasing line OEE.
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