iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms
Beaconzone founder, Simon Judge, has posted a new article on Linked on Improving Factory Productivity in the Age of Covid-19. It takes a look at how factories are needing to scale up (or down) while, at the same time, maintaining social distancing.
Digitising manual production lines provides visibility through the use of contact-less measurement that can be used to improve productivity. The article explains how to get started.
A common problem in factories is manual searching for stock for input to manufacturing. Stock is usually stored in boxes or pallets and can be in one of many rooms, warehouses or might already be somewhere on the factory floor. A large amount of stock arrives and leaves every day leading to logistical challenges keeping up with the whereabouts of goods. Timely delivery of components or sub-assemblies is critical to ensure smooth flowing of production and making best use of factory resources.
Manual paper-based processes are extremely inefficient and prone to human error. Old fashioned RFID or barcodes are also susceptible to error because data is only as up to date as the last scan and a recent scan might not have occurred.
Bluetooth is an ideal technology for solving this problem because it provides real-time location. We previously wrote about the advantages of using beacons in industry and how Bluetooth is suitable for use on the electrically noisy factory floor.
We offer multiple solutions for tracking stock and can adapt them to your exact needs, for example integrating with your existing systems. Once you have a tracking system in place you can use it for extra purposes such as locating jobs/work orders, monitoring machine/people capacity and providing for location based instruction/tasks. Sensing open/closed, on/off and quantities such as temperature and vibration enables diagnostics, monitoring and prognostics.
Read about Asset and Pallet Tracking for Manufacturers
Ignition is a HMI/SCADA system used for factory machine control and monitoring. It uses web-based technology running on an on-site server. It’s configured using a drag and drop user interface to provide HMI/SCADA controls, dashboards, historical trending, database access, reporting, alarming, security, sequential function charts, redundancy and failover control.
Ignition 8.0 has support for Bluetooth in the Perspective App running on mobile devices. It reads iBeacon and Eddystone formats. This allows for functionality based on location.
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In a previous post we asked ‘What is Productivity?’ and shared how the first wave of IT productivity related to cloud computing, customer relationship management (CRM) systems and enterprise resource planning (ERP) was only taken up by the top 5% frontier companies.
We explained how IoT, 4IR and AI machine learning will improve productivity but again, likely only for frontier companies. The difference this time is that the newer technologies will have more far reaching consequences. The frontier companies will further extend their reach over the laggards. The majority of the 5% are large companies with large budgets who are able to engage consultances such as IBM, Deloitte, Atos, PwC, WiPro, Accenture and KPMG. But what of the small to medium enterprises (SMEs)? Can they compete?
In most countries, a large proportion of companies are small to medium size. For example, in the UK, the Office for National Statistics says 98.6% of manufacturers are (SMEs). These organisations are more price sensitive and usually don’t have the luxury of significant financial resources for engaging the top consultancies and implementing their expensive solutions. Small and medium sized organisations have previously found it difficult to digitise due to the lack of availability of reasonably priced solutions.
However, solutions doesn’t have to be expensive. Low cost sensors such as Bluetoooth beacons, motion cameras, consumer AR can be combined with affordable cloud services to create solutions on a ‘shoestring’ budget. This is the aim of the University of Cambridge and University of Nottingham’s ‘Digital Manufacturing on a Shoestring’ initiative. The Institute for Manufacturing (IfM) is helping manufacturers benefit from digitalisation without excessive cost and risk. View the project’s latest news and communicate with them via Twitter.
Read about Beacons in Industry and the 4th Industrial Revolution (4IR)
There’s an informative video presentation on the Bluetooth SIG web site on Simplifying Multi-Vendor Mesh and Sensor Networks. It provides an introduction to Bluetooth mesh and explains the ways in which it can provide for Industrial IoT (IIoT).
To add to this, Bluetooth Mesh is suitable for use on the factory floor where the environment can be electrically noisy. Standard Bluetooth Mesh uses advertising on several channels rather than (GATT) connections so as to provide for more reliable communication in environments with wireless interference.
Read about Beacons in Industry and the 4th Industrial Revolution (4IR)
Read about Beacons and the Bluetooth Mesh
U-Blox has a useful article on Seven reasons why Bluetooth is perfect for the Industrial IoT, echoing some of our observations on Bluetooth LE on the Factory Floor. The article explains why Industrial Internet of Things (IIoT) systems and networks should consider Bluetooth as the communications infrastructure.
As U-Blox mentions, there’s predicted to be 5.2 billion Bluetooth device shipments by 2022. This data is from the latest Bluetooth SIG/ABI research:
Read about Beacons in Industry and the 4th Industrial Revolution (4IR)
There’s lots said about the advantages of Industry 4.0 or Digital Transformation and the associated new technologies but it’s a lot harder to apply this to the context of a business that has legacy equipment and no real way of knowing where to start.
Our previous article on productivity explained how, historically, digital transformation has been only been implemented in the top 5% ‘frontier’ companies. These have tended to be very large companies with large R&D budgets that have enabled customised digital solutions. More recently, the availability of less expensive sensors and software components have extended opportunities to the SME companies. These companies are already realising gains in profitability, customer experience and operational efficiency. Unlike previous technologies, such as CRM, the newer technologies such as IoT and AI are more transformative. Companies that don’t update their processes risk being outranked by their competition with a greater possibility of going out of business. But where do you start?
The place to start is not technology but instead something you and your colleagues fortunately have lots of experience of : Your company. Take an honest look at your processes and work out the key problems that, if solved, would achieve the greatest gains. You might have ignored problems or inefficiencies for years or decades because they were thought to be insolvable. Technology might now be able to solve some of these problems. So what kind of problems? Think in terms of bottlenecks, costly workrounds, human effort-limited tasks, stoppages, downtimes, process delays, under-used equipment and even under-used people. Can you measure these things and react? Can you predict they are about to happen? This is where sensing comes in.
The next stage is connectivity. You will almost certainly need to upgrade or expand your WiFi and/or Ethernet network. It can be impractical to put sensors on everything and everyone and connect everything by WiFi/Ethernet. Instead, consider Bluetooth LE and sensor beacons to provide a low cost, low power solution for the last 50 to 100m. Bluetooth mesh can provide site-wide connectivity.
Initially implement a few key improvements that offer good payback for the effort (ROI). The improvements in efficiency, productivity, reduced costs and even customer experience should be enough to convince stakeholders to expand and better plan the digital transformation. This involves replacement of inefficient equipment and inefficient processes using, for example, robotics and 3D printing. It also involves analysing higher order information combined from multiple sources and using more advanced techniques such as AI machine learning to recognise and detect patterns to detect, classify and predict. This solves problem complexity beyond that able to be solved by the human mind or algorithmic program created by a programmer.
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Read about Beacons in Industry and the 4th Industrial Revolution (4IR)
Tracking loaded pallets saves a significant amount of time, effort and hence cost in logistics and manufacturing. Pallets might contain stock, components or sub-assemblies. The top usecase is finding a particular pallet quickly. However, sensing need not be limited to pallets. Extending tracking to forklifts, racking and even people provides additional business benefits. For example, we have previously been asked to provide for the following scenarios:
“I need to ensure best use of my forklifts. Tell me when one hasn’t moved for 2 minutes.”
“I need to know if a forklift has crashed into racking.”
“I run a warehouse containing hazardous materials. If evacuation is needed, I need to know everyone has evacuated the building even if someone has temporarily gone off site”
These situations, and more, are solvable using Bluetooth beacons (In terms of asset tracking, beacons are sometimes called tags).
RFID and barcodes can also be used for tracking. However, the information is only as good as the last scan that might have been a long time ago after which the asset might have moved without being scanned. The advantage of beacons is that they repeatedly advertise so the information is always up to date.
Further advantages of Bluetooth tags are:
Software such as our BeaconServer™ and BeaconRTLS™ can be used to track what’s where over time, create notifications and integrate with your existing systems.
Once you have some current and historical data you can also start to do clever things such as automatically detecting and predicting over or under utilisation, finding pinch points or detecting valuable equipment about to be stolen.
Read about Asset Tracking for Manufacturers
The traditional IoT strategy of sending all data up to the cloud for analysis doesn’t work well for some sensing scenarios. The combination of lots of sensors and/or frequent updates leads to lots of data being sent to the server, sometimes needlessly. The server and onward systems usually only need to now about abnormal situations. The data burden manifests itself as lots of traffic, lots of stored data, lots of complex processing and significant, unnecessary costs.
The processing of data and creating of ongoing alerts by a server can also imply longer delays that can be too long or unreliable for some time-critical scenarios. The opposite, doing all or the majority of processing near the sensing is called ‘Edge’ computing. Some people think that edge computing might one day become more normal as it’s realised that the cloud paradigm doesn’t scale technically or financially. We have been working with edge devices for a while now and can now formally announce a new edge device with some unique features.
Another problem with IoT is every scenario is different, with different inputs and outputs. Most organisations start by looking for a packaged, ready-made solution to their IoT problem that usually doesn’t exist. They tend to end up creating a custom coded solution. Instead, with SensorCognition™ we use pre-created modules that we ‘wire’ together, using data, to create your solution. We configure rather than code. This speeds up solution creation, providing greater adaptability to requirements changes and ultimately allows us to spend more time on your solution and less time solving programming problems.
However, the main reason for creating SensorCognition™ has been to provide for easier machine learning of sensor data. Machine learning is a two stage process. First data is collected, cleaned and fed into the ‘learning’ stage to create models. Crudely speaking, these models represent patterns that have been detected in the data to DETECT, CLASSIFY, PREDICT. During the production or ‘inference’ stage, new data is fed through the models to gain real-time insights. It’s important to clean the new data in exactly the same way as was done with the learning stage otherwise the models don’t work. The traditional method of data scientists manually cleaning data prior to creating models isn’t easily transferable to using those same models in production. SensorCognition™ provides a way of collecting sensor data for learning and inference with a common way of cleaning it, all without using a cloud server.
Sensor data and machine learning isn’t much use unless your solution can communicate with the outside world. SensorCognition™ modules allow us to combine inputs such as MQTT, HTTP, WebSocket, TCP, UDP, Twitter, email, files and RSS. SensorCognition™ can also have a web user interface, accessible on the same local network, with buttons, charts, colour pickers, date pickers, dropdowns, forms, gauges, notifications, sliders, switches, labels (text), play audio or text to speech and use arbitrary HTML/Javascript to view data from other places. SensorCognition™ processes the above inputs and provides output to files, MQTT, HTTP(S), Websocket, TCP, UDP, Email, Twitter, FTP, Slack, Kafka. It can also run external processes and Javascript if needed.
With SensorCognition™ we have created a general purpose device that can process sensor data using machine learning to provide for business-changing Internet of Things (IoT) and ‘Industry 4.0’ machine learning applications. This technology is available as a component of BeaconZone Solutions.
The February 2019 edition of ComputingEdge magazine from IEEE has an article on From Raw Data to Smart Manufacturing (pdf – current and back issues freely available).
The article describes what they call a ‘Semantic Web of Things for Industry 4.0 (SWeTI) platform’. Although it’s very useful, it’s less of a platform in the software sense and more of an ecosystem or model.
The platform describes usecases, tools and techniques for smart applications. Using this model, BeaconZone operates in the Device, Edge and Data Analytic layers. We provide for smart devices and tools, gateways, storage, machine learning (ML) and analytics.
Read about Benefits of Beacons
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