Emerging technologies will lead the next wave of industrial automation
Published by : Industrial Automation
What is the kind of impact emerging technologies are having on industrial automation?
Large manufacturers have been using some automation and smart technology to streamline and optimise their processes and improve their operation and production efficiency. However, as manufacturers start moving towards the next industrial revolution and technologies available today that can analyse massive volume, variety, and velocity of data generated by various machines and sensors, there arises an opportunity to streamline this information to further improve the manufacturing process and most importantly start designing and developing connected products that can enhance customer satisfaction and services and open up avenues for new financial business models.
For instance, technologies like IoT allow us to collect any data from smart sensors; cloud allows to effectively store, manage and extract value from continuous streams of sensor data; AI allows to derive insights rapidly from massive streams of data and make sense of data; and block chain provides autonomous trust and compliance and improve the efficiency of business operations.
The combinatorial power of technologies like AI, IoT, Cloud and Blockchain will lead the next wave of industrial automation.
How is Artificial Intelligence helping the cause of fully autonomous manufacturing?
A manufacturing system can be fully autonomous if it can fully understand its data and its surrounding environment and execute decisions with less human intervention.
Data in the manufacturing context can be broadly classified into 4 types – structured data (a sequence of data or time series information from sensors); unstructured data like images from cameras use by robots (automated robotic systems) for navigation or automated product classification; a combination of structured and unstructured data, like in case of augmented reality where you superimpose images and text to derive an outcome; and fourth being behavioural and surrounding data where devices learn from interactions and integration, the cognitive aspects of the devices.
Using AI technologies helps us to derive intelligent insights for each of the above data scenarios, For instance, machine learning models can be built for analysing the time series information from sensors for anomaly detection, condition or predictive based maintenance and output can be fed to other systems to take corrective action, like ordering new parts.
Deep learning models like convolutional neural networks can be used for computer vision, which can be used by robots for automating any activity involving image analysis, like production categorisation, product placement, navigation, etc. Optimisation models can be employed at every step of process to identify bottlenecks and improve efficiency. I also feel, there will always be some tasks that cannot be fully automated and humans and robots will work together to achieve the highest level of automation.
Despite the many advantages, there are trust issues when it comes to Cloud Computing. Are the fears exaggerated?
Data is one of the most valuable assets for any company and with a fully autonomous manufacturing plant, safeguarding the data becomes extremely critical and important. I think the fear is more to do with someone getting access to the data in the cloud or someone getting access to a connected device/object inside a manufacturing plant (or an autonomous vehicle) and take control.
With the right security controls and audit and compliance in place, the fear would be negated. Also, technologies like blockchain would play a very key role in ensuring trust and compliance between connected systems/devices. Where there are regulatory and privacy challenges, edge computing provides an option where processing can happen at the edge, without the data leaving the manufacturing plant.
Can a technology like Blockchain reduce the inefficiencies in manufacturing operations, especially in supply chain?
Blockchain technology is a perfect use case for improving supply chain operations. Typically, there is lack of visibility around the entire operation process, and each party in the supply chain process maintains their own truth of information. There is no shared consensus and often arise lack of trust especially if they have not conducted business with each other before.
Blockchain provides transparency and accountability in the supply chain process, thereby improving visibility and quick decision making in supply chain process. Smart contracts can also be deployed on blockchain, which can change ledger states automatically based on the outcome (i.e., product shipped based on sensors, etc.), to speed up the supply chain process.
Is the pace of technology too overwhelming for most enterprises, especially MSMEs?
Yes, to some extent. While technology exists, it is not yet readily available for consumption. For instance, you don’t have readily available ML models for various autonomous manufacturing use cases. A considerable amount of time would be required for data preparation, model development, training and evaluation. IoT devices/sensor lifecycle, by default are not tracked by blockchain. Dealing with massive volume, variety, and velocity of sensor data needs cloud expertise on what storage and streaming technologies to apply.
The standards in blockchain are still evolving and picking up a blockchain provider also needs expertise based on your use case. Overall an integrated solution is not available and needs to be implemented.
What should be the roadmap companies should follow in adopting these technologies?
The approach is to take incremental steps and pick up a use case for realisation. The first phase of any use case is to understand what data is available and what needs to be captured and streamlined. Most of the cloud providers provide standard services for capturing, storing and analysing large volumes of data. Similarly, for realising IoT, a good starting point is to use IoT platform services from cloud providers and SDKs (on devices) to stream data from devices to cloud. Once the data is available for consumption, AI platform services can be used to build and deploy incremental models at scale.
I leave you with one of the approaches below on realising the roadmap by taking a real world example (see graphic).
For more details, refer to https://navveenbalani.dev/index.php/applications/internet-of- things-application-of-iot-in-manufacturing/
Navveen Balani leads and manages Technology at Bridgeweave. He has over 19 years of experience in building enterprise products using exponential technology, specialising in AI, Blockchain, IoT. Prior to his current role, he was the CTO and co-founder of a cognitive retail startup. He is a former IoT and Watson Lab Leader for IBM India Labs, where he was responsible for setting up the Watson Lab and worked with customers across the globe on evaluating and building cognitive and IoT solutions.
His view and expertise on building and realisng Internet of Things, is well documented through his bestseller book –“Enterprise IoT”, where he describes the architecture, use cases and reference for building IoT applications using combinatorial power of Data, AI, Cognitive and Blockchain. His book is acknowledged as one of the Top computing book for 2016 by computingreviews.com and is being used a reference by many IoT practitioners for building IoT architecture and solutions.
He is also an active blogger (navveenbalani.dev), author of several leading books (https://g.co/kgs/Ammbuf) and speaker at various leading conferences.