Predictions Series: Interview with Sastry Malladi, CTO at FogHorn
Sastry Malladi, CTO at FogHorn explains the role of AI ML, video analytics, and Edge Computing in the modern manufacturing industry.
Tell us what you think about the role of AI ML in the decarbonization of certain industries?
Machine learning and AI will expedite decarbonization in carbon-heavy industries.
Industries like manufacturing and oil and gas have been slow to implement decarbonization efforts as they struggle to maintain productivity and profitability while doing so. However, climate change, as well as regulatory pressure and market volatility, are pushing these industries to adjust.
To avoid the worst climate impacts, global greenhouse gas (GHG) emissions will not only need to drop by half by 2030, but then reach net-zero around mid-century and oil and gas and industrial manufacturing organizations are already feeling the pinch of regulators, that want them to significantly reduce CO2 emissions within the next few years.
Technology-enabled initiatives were vital to boosting decarbonizing efforts in sectors like transportation and buildings – and heavy industries will follow a similar approach.
As a result of increasing digital transformation, carbon-heavy sectors will be able to utilize advanced technologies, like AI and machine learning, using real-time, high-fidelity data from billions of connected devices to efficiently and proactively reduce harmful emissions and decrease carbon footprints.
Do you think automation can safeguard the worker’s life, especially during the pandemic crisis?
Automated safety monitoring will save businesses millions in workers’ compensation costs.
Workplace safety has always been a priority for manufacturers, but it takes on new significance in light of the pandemic.
Businesses paid almost $1 billion per week in direct workers’ compensation costs (pre-COVID), enabled in part by ineffective monitoring systems. Indeed, the manual nature of traditional health and safety audits means the potential for error is significant and time-consuming.
As businesses worldwide consider back-to-work strategies for early-to-mid 2021, many will upgrade and future-proof existing worker safety systems and processes.
Real-time, streaming data processing will reform legacy best practices, make up for the error-prone shortcomings of resource-intensive manual audits, and provide real-time insights and centralized visibility into workplace health and safety.
What about real-time data and IoT sensors?
Through real-time data processing from strategically located IoT sensors and cameras, companies will collect and process employee health data – from temperatures detected by thermal cameras, to coughs heard by audio sensors, to video analytics of employee social distancing – to get ahead of critical issues.
Rapid identification of potential health and safety hazards will enable enterprises to respond to risky situations in seconds rather than reviewing data later or waiting to individually scan each employee. These capabilities will be calibrated to monitor an organization’s specific health and safety needs, even beyond COVID-19. For industrial organizations, improving safety will also include ensuring the on-going use of protective gear, such as safety goggles, reflective vests, hard hats, and more.
Also, these modernized technologies will be customized to detect potential environmental safety hazards, such as falling objects and trip hazards.
How does Edge Computing enable Warehouse Managers to gather intelligence into online inventory management?
Warehouse managers implement mobile edge computing to keep up with 50% more orders as a result of current and post-COVID online shopping trends.
Today, many warehouse and logistics operations are under pressure to significantly reduce order-to-delivery timelines, driven by increasing consumer demand and expectations. To help organizations meet these vastly accelerated timelines and improve operational visibility, industrial mobile devices, equipped with specialized applications, will make it possible to track and manage warehouse logistics in real-time, at any location. These capabilities could not come at a better time.
A total of 165 billion packages were shipped in the United States in 2019. Not surprisingly, e-commerce order growth is up 54% compared to this time last year, heavily stimulated by consumer buying shifts driven by stay-at-home orders.
Are you referring to the hand-held devices?
Powered by enterprise-wide Industry 4.0 initiatives, the adoption of industrial handheld devices has been growing steadily over the last few years. COVID-19 has further accelerated the adoption of mobile technologies based on the flexibility and portability these types of devices enable, compared to hardwired computer and control stations that are more static and make it harder for its user to socially distance. Indeed, according to GSMA Intelligence, IIoT connections will overtake consumer IoT connections in 2023, driven in part by the opportunities battery-powered, low-cost mobile devices will deliver.
In 2021, warehouses will pair the low-latency processing power of the edge with the mobility of handheld devices to enable real-time operational insights on mobile devices unrestricted from fixed locations or even cloud connectivity.
This flexibility ensures warehouse workers are kept in the loop of all internal operations and changes at all times and without having to alter their current daily routines. In turn, mobile edge solutions can enable workers to more instantaneously share information and insights across the warehouse, ensuring that every worker is on the same page at all times. Mobile edge AI enables a new class of industrial edge computing applications that empowers industrial workers to quickly identify production or environmental irregularities and correct them. This not only prevents costly machine downtime and product quality issues but also improves employee safety conditions.
Could you further explain about the role of Video Analytics and how it could improve the performance of various factory operations?
Increasing the use of video and other high-resolution, high bandwidth sensors increase the demand for edge AI.
Digital transformation is sweeping through every industry, prompting organizations to install audio, video, and vibration sensors across their operations.
These video and other high-resolution, high-bandwidth devices are critical in enhancing the quality of data insights to help organizations in a wide variety of industries identify issues, challenges, and opportunities. However, being able to analyze high-fidelity, high-resolution, raw machine data in the cloud is often expensive and does not happen in real-time due to transport and ecosystem considerations.
Organizations often depend on down-sampled or time deferred data to avoid significant cost constraints, and as a result, organizations miss critical insights as they’re looking at incomplete datasets.
Tell us more about Edge for the industrial revolution and what it looks like for 2021?
In 2021, artificial intelligence capabilities at the edge will help organizations transform video data from IoT connected sensors into actionable insights in real-time. Edge AI will play an essential role in evaluating and delivering heightened data quality and effectiveness, as edge-enabled solutions will perform real-time analysis of voluminous data streams and identify only the most valuable insights for further processing.
We will see increasing adoption of edge AI technology as early adopters reap the benefits of real-time streaming analytics.
For example, by utilizing edge AI-powered analytics, industrial organizations can create an autonomous defect detection system within an existing manufacturing process – or automotive manufacturers can fast-track the road to autonomous by improving road safety monitoring.
Thank you, Sastry! That was fun and we hope to see you back on AiThority.com soon.
As CTO of FogHorn, Sastry is responsible for and oversees all technology and product development. Sastry is a results driven technology executive with deep technology and management experience of over two and half decades. His areas of expertise include developing, leading and architecting various highly scalable and distributed systems, in the areas of Big Data, SOA, Micro Services Architecture, Application Servers, Java/J2EE/Web Services middleware, and Cloud Computing to name a few.
FogHorn is a leading developer of edge AI software for industrial and commercial IoT application solutions. FogHorn’s software platform brings the power of advanced analytics and machine learning to the on-premises edge environment enabling a new class of applications for advanced monitoring and diagnostics, machine performance optimization, proactive maintenance and operational intelligence use cases. FogHorn’s technology is ideally suited for OEMs, systems integrators and end customers in manufacturing, power and water, oil and gas, renewable energy, mining, transportation, healthcare, retail, as well as smart grid, smart city, smart building and connected vehicle applications.