By leveraging the power of the Internet of Things, smart devices can revolutionize the way we interact with everyday household items. Moreover, the integration of artificial intelligence opens up a realm of new possibilities, providing valuable insights for users and manufacturers alike. The best and most efficient way for such cases is to connect them to a platform that acts as a one-stop shop for all stakeholders involved. In this perspective, we will delve into the digital innovation possibilities brought by connected devices, how they can create added value for all stakeholders involved, and how you can go the extra mile to differentiate from other brands and create loyal customers. Ready to take your rightful place of being the #1 provider in your product market?

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The post The untapped innovation potential of connected appliances & AI appeared first on Verhaert Masters in Innovation.

One of the best ways to do this is by creating digital relations with stakeholders in your supply chain. By using AI as a lever, you can gather insights that will help you make informed decisions about your business and products. Continue reading and you will discover several digital innovation tips you can apply to your own business to achieve just that.

Curious? Great, let’s discover them!

Create a digital relationship with stakeholders in the supply chain

Shedding light on the supply chain

One of the main challenges facing the construction industry is the complexity of the supply chain. Manufacturers of building materials and equipment typically sell through wholesalers. They therefore have no idea who buys their products and why (or why not), which of their products are most popular and how (or in what areas) they could sell more of them. They also have no idea whether the intended use coincides with the customer’s actual use or how often they are used. In short, they lack overall user insight.

Creating a digital relationship with stakeholders can simplify and optimize the overall process, reduce human error, and improve brand loyalty through installers and builders. This brings me to my next point: one of the most important stakeholders are installers because they are closest to the end user. They select which materials will be used and can easily get feedback from consumers.

Fostering brand loyalty

Brand loyalty is essential in a highly competitive industry and cannot be taken for granted. The best way to foster it is by creating an added value for stakeholders. The biggest added value you can offer them is solving their major problems and saving them time in their processes. This can be achieved with digital solutions using a cloud-based platform that facilitate the flow of information.

An example could be a mobile application that allows construction managers, installers or do-it-yourselfers to calculate in real time how much material they need for a particular project. This could be done by simply taking a picture of the construction site and adding some measurements or even integrating satellite imagery. The application can then provide them with real-time inventory options, offer pickup or delivery services and keep them updated on new materials and product releases used for their typical projects.

Creating overview and accountability

The key value for the manufacturer here is that these digital relations allow real-time tracking of materials from the production site to the construction site. These platforms can be used to communicate with suppliers and track orders, creating greater transparency and accountability throughout the supply chain. This not only helps ensure materials arrive on time, but also helps avoid delays and cost overruns. And those aren’t the only insights you can get from your data.

Gain insights to become more agile

Data is gold and those who know how to mine it will become the new royals in the construction industry. By leveraging the power of AI you can improve safety, efficiency, cost savings, and project management. It can also help speed up your decision-making by analyzing your data, providing real-time and even predictive analyses to gain insights into the performance of your company, products and services.

Faster and more accurate

AI can analyze data from various sources, such as project timelines, material usage and worker productivity. As such, it provides real-time insights that help construction companies make faster and more accurate decisions making them more agile.

Proactive action

For example, AI can be used to predict material shortages or identify parts of the project that are in danger of falling behind. This allows companies to take corrective action before problems arise, minimizing delays, cost overruns and inventory depletion.

Optimizing workforce

In addition, AI can help construction companies optimize their workforce by identifying areas where workers could be more productive, which can lead to greater efficiency and lower costs.

Smart equipment for a smart business

Typical project delays

Another problem that delays construction projects is faulty equipment. Sometimes machines break down, and with no spare parts in sight, work sometimes needs to be delayed for days, even weeks. This slows down projects and can also lead to extremely dangerous situations for the workers operating the equipment.

Predictive maintenance to the rescue

One solution to prevent these dangerous situations is using predictive maintenance. By monitoring your equipment with sensors and connecting it to a platform, you can analyze the data. AI can predict when maintenance is needed or when something is out of order (anomaly detection). It can remind you when it’s time to stock up on spare parts, reducing downtime and minimizing the risk of equipment failure.

Equipment tracking on site

Another major problem that connected devices solve is equipment tracking. Finding and forgetting equipment can cause significant delays and affect productivity on a construction site. This is especially true on larger construction sites where there is an even bigger need for coordination and organization of equipment. I often wonder how many hours are lost searching for equipment or having to drive back to the office because someone forgot to pack equipment.

Optimizing products and service

The big advantage for equipment manufacturers is that they too have access to this data, can analyze it and take action accordingly. Perhaps a particular product always breaks down the same way, an overpriced feature is never used or not as user-friendly as initially thought, making people reluctant to remain loyal to the brand. By optimizing these products based on facts and data, customers will remain loyal because of the excellent additional services you provide in addition to your already excellent product.

The benefits in a nutshell

There are three essential digital strategies that help construction companies stay competitive in today’s fast-paced environment. First, building digital relations with stakeholders in your supply chain. Second, using AI to generate insights to be a more agile business. Third, using the collected data to take your equipment to the next level. By embracing these technologies, construction companies improve customer loyalty & transparency, reduce delays and cost overruns, and make more informed decisions faster.

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Let’s start with an example of the training process of a large language model. Don’t fall off your chair reading the rest of this sentence, but that process can produce up to five times the amount of carbon emissions of an average car over its entire lifetime. Yes, you read that right: up to 5 times! Take a moment to let that sink in because this issue will only grow over time as more and more data is being collected annually and more and larger (smarter) models are being trained on this data. So what can we do to deal with this growing problem?

The premise above might sound familiar if you’ve already read the blog by Jente or seen the talk by Bart De Vos and Niels on this topic. If not, we definitely recommend doing so first to get a general scope. In this perspective we dive deeper into this issue, shifting the scope to techniques focused on reducing the energy consumption of deep learning algorithms.

Decision-making AI

Let’s start by asking a very obvious question: “Do we really need a prediction that frequently?”. A prediction is not always needed, sometimes it’s perfectly fine to wait a bit or to even skip it completely. Reducing the number of inference tasks means a reduction of computations and subsequently also the energy consumption.
For connected devices, a second question comes up: ”Where should the inference be performed?”. Both cloud and edge have their advantages and disadvantages that need to be balanced against each other to determine the best choice.

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More data means more (and sometimes even bigger) models, which in turn increases energy consumption. We’re already aware of this rising problem, so we need to act NOW before it is too late. How do we do this?

1. We can start by optimizing the energy efficiency:

Connect the device to the cloud

In the cloud, hardware gets shared, meaning the power consumption gets centralized in one location. This gives us the opportunity to optimally use the hardware, optimize the power consumption and get large gains in return. Additionally, the necessary cooling for these large data centers can be optimized and the produced heat can even be recycled!

However, this poses scaling problems when the fleet of connected devices grows. To circumvent this, we can move the intelligence to the edge of the devices.

Reduce the energy consumption on the edge

For embedded or edge devices, the hardware doesn’t get shared. In this case, you need to maximize the ‘idle task’. Within the idle task, put the system in low power by using the low power level features of the hardware. Other possibilities are to reduce the complexity of the code or the number of computations.

2. Take into account the network quality:

Network-wise, not only the availability but also the quality (especially the latency) of the network should be considered with regard to power consumption. When the network connection is bad it takes the hardware longer to send the same amount of data, thus consuming more power. Research has even shown this relation is exponential!

So how can you tackle this latency problem? One possibility is to combine the data in batches before sending it to the cloud. An even better approach is to wait until the network connection is better, to reduce the required energy, to send these batches. You can also do parallel computing which is more efficient. But this ‘caching of data’ also brings problems along with it, i.e. you have to wait longer to get an answer back, so the Quality of Service (QoS) might be lower.

Both approaches discussed above come with advantages but also disadvantages. Each approach has its possibilities to help minimize energy usage, influencing the actual energy consumption.

3. If you can measure it, you can manage it!

So how do you decide which task to perform on either the edge or in the cloud? It might seem contradictory, but you can train an AI system with reinforcement learning to maximize the energy efficiency of each device (individually) throughout its lifetime. But in order to optimize something, you first need qualitative data and to get that data you need to measure whatever you want to optimize.

To make this concrete, let’s again look at optimizing the energy efficiency of a connected device. After deciding where to perform a task (edge or cloud), you can use the measurements of the power consumption to teach the model how good the decision was. This, however, also requires energy and time since you need to collect data and train the network. In the end, it’s a balancing exercise worth the effort, not only taking into account the technological but also the business side!

Key takeaways

1. The energy efficiency can be optimized, either in the cloud or on the edge.
2. The network quality, especially the latency, is also a key factor for energy consumption.
3. With the use of AI, the optimal load balancing can be determined for each connected device (individually) to optimally use its battery.

Any questions or want to know more about this topic? Watch the complete presentation during our InnoDays webinar or get in touch!

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