Why Businesses Use IoT to Achieve Sustainability Targets

Why Businesses Use IoT to Achieve Sustainability Targets

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The Internet of Things has become the lifeblood of the drive to net-zero emissions. If you’ve been paying attention, you’ll know that the combination of green technology and renewable energy is a force to be reckoned with when it comes to sustainable transformation, and this is increasingly being recognized by global business leaders.

Huge business properties such as factories, massive warehouses, nationwide stores tend to consume energy in pretty unsustainable ways. Assets staying on when they don’t need to be, unnecessary over-performance, lights turned on in naturally bright rooms, etc. However, some businesses have copped on to the new Industrial Revolution 4.0. Or at least historically, they have done.

That basically translates to the age of IoT. By connecting all of their ‘things,’ businesses are essentially turning their facilities into smart facilities, meaning that they can monitor absolutely all of the data from their assets and, as a result, manage their energy usage and efficiency like never before. I’m no mathematician, but the formula is more efficient + less consumption = more carbon neutrality.

According to a report by Ericsson, IoT could help reduce greenhouse gas emissions by up to 63.5 gigatons, or 15 percent, across all industrial sectors by 2030.

Using IoT to Achieve CSR Goals

Corporate social responsibility goals have become a much bigger obligation in recent years, now that we’ve taken the health of the entire planet more seriously (looking back on it, it does seem a bit daft that we didn’t before). So sustainable operations have been, quite rightly, bumped up the business priority list.

A few elements at play here: net-zero 2050, sustainable development goals, and the CSR goals of individual businesses are all helping perpetuate environmentally friendly business activity. One of the key pillars holding up these three hugely influential decision-making factors is energy consumption and, more importantly, its reduction.

How Does IoT Affect Efficiency and Sustainability?

While working at Hark, I’ve been in the atmosphere of some really transformative IoT projects and had the pleasure of seeing, in real-time, how sensors, gateways, and intuitive software can really positively impact sustainability.

Allow me to break down some of the common solutions:

Energy Consumption Monitoring

Having real-time visibility of power-consuming assets in a single system is a total game-changer. It provides insight into misbehaving assets (i.e., ones that are over-consuming), areas that require maintenance, comparisons in how different facilities are performing, and much more. The visual aspect of using custom dashboards means that reporting and making effective changes is miles easier when using a toolkit like The Hark Platform (whoops, shameless plug).

Remote Asset Performance Monitoring

Fairly similar to the above, this solution is a tool to help spot anomalies in cost and asset operation. Remote asset performance monitoring works best with alerts and notifications to help managers prioritize their asset maintenance.

There are a few really magical things about this, the first being that almost any variable can be monitored. Temperature? Sure. Location? Yep. Energy use? Duh. Internal/external lighting? Yes, even that. So many things can be monitored, therefore meaning you can completely optimize how your estate runs.

Your factory in Surrey looks to be consuming way more energy than your other UK factories, and you’re wondering, ‘what’s the craic?’. Upon checking, you see that your Surrey lighting rigs have been left on for three years straight instead of being turned off at night! It’s cost you an absolute bomb and emitted lots of wasted carbon, but now that you’ve found the source of the issue, you can nip it in the bud and reduce that unnecessary usage. By monitoring indoor and outdoor light, you can automatically turn lighting rigs on and off at the necessary times for maximum efficiency.

Predictive Maintenance

Avoiding downtime while increasing overall equipment effectiveness by monitoring assets, spotting anomalies, and pre-emptively taking action. This pretty much comes off of the back of remote asset performance monitoring. It’s simple, really: by monitoring how an asset currently is performing, we can forecast how it will run in the future.

These forecasts are done on what we call digital twins – a digital representation of a real asset, which allows you to estimate when maintenance might need doing, when failures are likely to occur, when energy might be over-consumed, and so on.

These solutions and more are the screws and nails of smart facilities and ultimately will be a must-have in the future. Already the benefits of reducing wasted carbon within corporate facilities are being felt, not only by the environment but by businesses who are savings heaps of money on energy and asset bills.

Energy Harvesting and IIoT: Sustainability for the Industrial Internet of Things

The world is facing gigantic ecological and economic challenges. Future-proof technologies are to shape the Internet of Things (IoT). The energy supply for millions of communicating devices is a key challenge. On a large scale, renewable energies have long been an integral part of energy generation. Fields with solar cells that generate energy from sunlight or wind turbines now dominate the landscape. This form of energy generation also exists on a smaller scale. This is called “energy harvesting.” Small energy converters “harvest” energy from movement, light, or temperature differences. These amounts of energy are sufficient to power a wireless sensor and transmit data via radio.

'Thanks to this energy harvesting technology, radio sensors are becoming sustainable because they don’t even need cabling or battery power, which is not only environmentally friendly but also saves costs.' -EnOceanClick To Tweet

Energy harvesting for radio-based products that are already in mass production include four different sources:

1. Motion – the press on a switch, moving machine parts, the rotary motion of a handle

2. Light – the indoor or incoming sunlight in a room

3. Temperature differences – between a heat source such as a radiator, pipes, or boiler and the environment, and day and night variations

4. Electromagnetic field – a contactless coil in a cage clamp clipped around a cable powers the meter and measures the line current

For each of these sources, there are different energy converters with different power parameters. The type of energy generation, together with the corresponding power yield, decisively determines the possible sensor applications.?

Improved Sustainability

Thanks to this energy harvesting technology, radio sensors are becoming sustainable because they don’t even need cabling or battery power, which is not only environmentally friendly but also saves costs. Replacing a single battery in an industrial environment typically costs $300 US dollars. Although battery replacement in itself is a relatively fast process, traveling to the site, locating the sensor, testing the device, and documenting the process all dramatically increase the labor costs. Very often, batteries are said to have a service life of several years, but in practice maintenance companies are often replacing them every one or two years, at the latest, in order to avoid early failures.

Resource-saving and environmental protection are also becoming increasingly important; the prices for copper are steadily going up and the harmful components, as well as safety aspects, of batteries are a serious problem. Wireless energy harvesting sensors are a sustainable solution that take both the financial aspect and the effects on the environment into account.

In Operation for The Industry

Sensors have a key function in industrial production. They can be used, for example, for quality and process monitoring or condition-based maintenance. The range of applications is wide and is developing in the direction of an industrial Internet of Things (IIoT) due to the increasing use of wireless sensors. By combining energy-saving radio with local energy converters, battery-free and thus maintenance-free sensors can also be mounted directly on moving parts or in hermetically-sealed environments, for example, for measuring the position of mechanical parts, the current consumption, or the temperature of mechanical parts, liquids, or gases.

Sensors in Quality Control

Quality monitoring is used to control the entire production process and to ensure the desired properties of the end product based on various parameters. For this purpose, environmental factors such as temperature, humidity, and air quality, or process factors such as position or temperature must be monitored.

Automated monitoring systems need data generated by sensors. To do this, however, these sensors must fit seamlessly into existing production processes and must not require special training or generate follow-up costs in the ongoing operation. This is where the integration of self-powered and thus maintenance-free sensors offers decisive advantages.

Condition-based Maintenance with Battery-free Sensors

In addition to the product, machines must also be monitored to ensure a smooth production process. These are often subject to high wear, so early identification of problems and appropriate countermeasures are important prerequisites for continuous quality assurance and protection against production downtime.

A fundamental problem of maintenance planning is the calculation of the intervals between each maintenance cycle. On the one hand, the time between maintenance dates must be as short as possible in order to detect any deviations before a major problem occurs. On the other hand, each maintenance involves high costs for personnel and idle machines.

In many cases, it is possible to gain valuable information by monitoring a few simple parameters. For example, a rise in temperature can indicate higher friction and thus wear. Wireless temperature sensors can be used for the measurement processes. Humidity sensors monitor whether water is leaking to prevent water damage. Temperature and humidity sensors also provide information on air conditions to always ensure consistent air quality.

That is why wireless energy harvesting sensors are ideal for a wide range of industrial applications. They are maintenance-free, flexible, and inexpensive to install – ideal features for ensuring not only high-quality standards but also greater sustainability in the Industry 4.0 environment.

IoT in the Factory Building

In manufacturing, IoT enables significantly more efficient, flexible, and individualized production. With the help of sensors networked with an intelligent IoT platform, it is even possible to create a digital twin, i.e. an exact virtual image of a machine throughout its entire life cycle. Digitization is also advancing rapidly in buildings. This leads to automated service processes in facility management, higher energy savings, and greater individual well-being for users. One thing is essential for both industrial processes and factory buildings: battery-free wireless sensors.?

Key Features of a Comprehensive MLOps Platform

Artificial Intelligence (AI) and Machine Learning (ML) present a boon for businesses as technologies with the potential to help organizations make better predictions, create innovative services for customers, and deliver faster business outcomes. Finance teams, operations, customer success, and marketing departments stand to benefit. That said, the overarching reason that organizations are facing challenges and delays in bringing ML models into production stems from the fact that models are different from traditional software, and most organizations don’t yet have frameworks and processes for dealing with these differences. Let’s take a look at how a MLOps platform can help with efficiency and collaboration.

'Any MLOps platform should take a human-centered approach - meaning it is designed to provide users with the critical information they need' -Navin?

What is MLOps?

MLOps is a practice for a subset of the ML model life cycle to help teams deploy, manage, and maintain machine learning models and drive consistency and efficiency across an organization. Similar to DevOps—a set of practices that integrate software development with IT operations—MLOps adds automation to streamline the orchestration of steps in the workflow that begins once a model is ready to go into production.

A machine learning model’s life cycle spans many steps, and typically they are all managed by different people across discrete systems that need to be connected. These systems are used for data collection, data processing, feature engineering, data labeling, model building, training, optimizing, deploying, risk monitoring, and retraining. And in each organization, different people and teams may own one or more steps.

In an ideal environment, ML models are solving company problems and driving better decision analyses. However, only a fraction of these models enters production. Even then, it typically takes months for a successful model to become active, according to Gartner. That’s because the process of deploying a machine learning model into production is often disjointed. Siloed teams of data engineers, data scientists, IT ops professionals, auditors, business domain experts, and ML engineering teams operate in a patchwork arrangement that bogs down the process.

Downfalls of MLOps

Part of the problem is that MLOps is still an emerging discipline, and different people perform the tasks that span MLOps in each organization. In some organizations, data scientists are involved in nearly every step of a model’s life cycle; in others, there may be discrete teams for each phase or teams that own one or more areas. For organizations to realize the full value of ML, models need to be put into production quickly and at scale. There needs to be a guide for how to handle MLOps that makes sense for an enterprise’s goals and the structure of its team. Because of this, MLOps platforms are taking on an increasingly critical function in expediting the ML efforts of organizations.

Platforms have the potential to deliver a blueprinted strategy to create repeatable and streamlined processes, regardless of whether the industry is manufacturing or financial services—or any other industry. End-to-end platforms can save an innumerable amount of time because of how many models can be deployed and monitored simultaneously while operating at the speed businesses need. The best MLOps platforms provide solutions for all ML stakeholders so they can not only deploy and manage models at scale but also foster efficiency through collaboration and communication across the different people using the platform at different stages. Let’s take a look at the four main aspects of a successful MLOps platform.

A Successful MLOps Platform

#1: A Collaborative Experience for all Stakeholders

Given that the key stakeholders—from data teams to engineers to risk auditors—tend to function in silos in many organizations, simplifying the process to enable any user to perform a specific role leads to better outcomes for efficiently performing tasks. Platforms that enable collaboration across an organization provide teams the ability to quickly operationalize models, regardless of the tools data scientists used to create those models. There is no longer a need to restrict any other user, such as machine learning engineers or IT teams. Each platform user should be able to use the tools they already have and leverage their expertise with those tools. Having a single, collaborative interface that can intuitively guide a user through the steps that abstract the complexity of the process is a beneficial component of MLOps.

#2: A User-First, Modular Architecture

Given that many organizations may be handling MLOps differently, platforms that meet them where they are offer immediate value. A platform with a modular architecture provides organizations the necessary flexibility to get up and run quickly by enabling each person to use the platform functionality that they need when they need it rather than forcing them to operate in a linear fashion.

For example, an organization may have data scientists with a preferred set of tools but who lack the ability to easily deploy or monitor models in production. An MLOps platform designed with openness and users in mind will offer easy plug-and-play components so each user can make decisions on the best cloud, database, repositories, and other components to use without having to make sweeping changes. Every company will implement the process of operationalizing models a bit differently, and modular architecture enables MLOps teams to leverage their entire suite of tools and seamlessly bring specific components of the platform into their ML workflows.

#3: An Emphasis on Optimization

As models become larger and increasingly more complex, one of the challenges organizations often run into is a dramatic increase in hardware or computing needs. Machine learning is, by definition, data-intensive and will cost organizations a lot of money without careful consideration of the infrastructure in place. Models that take a long time to head to production coupled with rising costs for hardware are a recipe for tension with executives and leadership weighing ROI in an organization.

MLOps platforms that can optimize models and present model performance and cost-saving data in a format that helps users make decisions based on the factors most important to them can alleviate some of the challenges organizations face as they ramp up ML modeling and production. As more companies deploy more ML models to more devices that can be on any cloud, edge devices, or on-prem, the ability to optimize models will become increasingly important.

#4: An Ability to Continuously Monitor Models in Production

It’s important for an MLOps platform to accelerate the process of bringing models to production. But once there, the real work begins, and platforms need to enable teams to continuously monitor risks, such as model performance and unstructured data, and quickly take action to mitigate operational and reputational risk.

ML models are not static. They are trained and tested in environments that are controlled, but when models are deployed into production they are making predictions based on real-world data which can be quite different for a variety of reasons. For example, a model’s performance or accuracy in predictions can change. Models also experience different types of drift, such as data drift, for when there is a significant change in buying patterns. This happened during COVID, for example, and led to former distribution patterns no longer being accurate.

Simplifying the Process

To help teams continuously monitor models in production, MLOps platforms should simplify the ability to:

1. Set alerts based on custom thresholds.

2. Provide quick at-a-glance access to key data points showing which models are failing.

3. Rapidly identify the root cause and take action.

Leveraging an integrated platform allows for the creation of a customized risk monitoring plan before and after deployment. A comprehensive approach to mitigating risk includes evaluating uncertainties within the data to guide AI/ML teams along the right path.

Platforms Must Put Humans First

We are still in the early stages of figuring out how to best use ML in enterprises. Any MLOps platform should take a human-centered approach—meaning it is designed to provide users with the critical information they need, an intuitive way to complete the tasks they need to complete, and the ability to collaborate and communicate with other stakeholders and colleagues. Platforms that put human workers first help build trust between people and ML. This garnered level of trust between person and machine helps ease the mind of the worker and allows the technology to perform many of the statistical tasks to aid these workers. The intentional design of such platforms will continue to focus on augmenting and amplifying human intelligence and delivering new opportunities to promote collaboration with advancing AI and ML initiatives.