AI Automation in Manufacturing: 5 Key Business Functions Being Trusted to AI

By Prateek Joshi

Business Insider predicts that by next year, manufacturers will spend approximately $267 billion on IoT technology. One of AI’s key roles in that massive investment is its ability to automate and manage the millions of micro-decisions that manufacturers have to make. This enables them to deliver high-quality goods that customers have come to expect. While AI in manufacturing is not a catch-all for every business challenge, there are many specific business functions manufacturers can easily automate for greater cost-efficiency, safety, and accuracy across the manufacturing process.

Here are five examples that show how AI-based decision support systems are automating tasks and helping alleviate the labor shortage in the manufacturing industry:

1. Equipment Maintenance

For factories and industrial operations that are expected to run 24/7, equipment downtime can be a major operational challenge. Whether it’s planned maintenance on a membrane in an industrial beverage plant or an emergency repair on a pipe in an oil well, operators need to be able to manage and react to equipment issues as fast as possible.

IoT sensors can monitor factors that affect equipment conditions across industries such as oil temperature, salinity levels, and vibration levels. IoT sensors can give operators critical insights into wear-and-tear as well as emergency issues, allowing them to shut down equipment to prevent catastrophic failure or take other appropriate actions. The speed of response is an important factor in minimizing the potential losses: unplanned downtime costs manufacturers an estimated $50 billion annually.

2. Quality Control

In an increasingly competitive market, manufacturers cannot afford to waste resources on subpar products. AI algorithms can proactively identify mistakes and abnormalities that can occur at any time along the production process.

There are business tasks that human workers will always be better suited for, but machines can be more appropriate to perform quality control tasks than manual inspectors are.

According to recent Mckinsey statistics, deep-learning-based systems can provide defect detection improvements up to 90% compared to a human inspector.

3. Supply Chain Optimization

AI’s contributions to the manufacturing sector don’t stop at the production line. Algorithms can help companies improve how they deliver their products to their consumers via predictive analytics. Better informed firms are able to shift from a reactionary model to a more profitable and predictive one.

IoT sensors can collect a myriad of data along the industrial supply chain, from transportation and energy consumption to raw material cost fluctuations to weather patterns and other market conditions that can have an impact on a company’s bottom line.

4. Time-Consuming Parts of the Design Process

While automation’s benefits can be clearly observed on the factory floor, AI also is helping streamline and optimize the design process in manufacturing. For small and incremental improvements to a product’s design, AI algorithms are able to explore millions of different tweaks and adjustments to a design to optimize its performance. Factors such as material usage and efficiency, structural strength, and weight can all be assessed and improved upon with AI algorithms.

5. Dangerous Manual Tasks

Safety is an inherent problem when workers and traditional machines are sharing the same space. While rare, accidents are always a possibility when workers are operating near powerful machines with no cognitive awareness.

Cobots are AI-powered collaborative machines designed to safely work alongside skilled laborers in industrial environments. Cobots can assume physical tasks such as heavy lifting and repetitive tasks that require a degree of fine-motor control. At an estimated cost of about $24,000 each, the machines can prove to be a useful and affordable supplement to skilled laborers on a factory floor.

While there is enormous potential in automation for the manufacturing industry, it’s not always an easy task to implement an AI project. Be on the lookout for our upcoming survey report that takes a closer look at the challenges faced by manufacturers and other business leaders when implementing AI initiatives in their businesses.

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3 Ways Big Data and A.I. Will Positively Impact Agriculture

The Agricultural Revolution was one of the most defining moments in human history, providing opportunities for billions of people to live richer, healthier and more fulfilling lives. We owe almost everything we have today to a few innovative pioneers from tens of thousands of years ago who recognized the value in farming. 

But we shouldn’t view the agricultural revolution as a singular, stagnant event. With global food demand expected to rise by 59–98% between 2005 and 2050, and the realities of a harsher, less predictable climate becoming more apparent every day, we owe it to our ourselves as well as our Neolithic ancestors to continue to find ways to innovate the way we create and use resources to keep the world fed. 

At Plutoshift, we are particularly interested in the vital role artificial intelligence and big data will play in keeping farmers efficient, sustainable and profitable.

Here are three ways modern-day pioneers are unlocking their data and leveraging ‘grounded’ A.I. to deliver meaningful outcomes.

A.I. will provide actionable insights faster than ever

One of the biggest challenges modern farmers face is getting the biggest yield from their crops in a sustainable way in the face of  an unpredictable environment. Farmers in Africa, for example, lose an estimated 49% of their expected total crop yield per year to factors like pests, disease and water measurement issues. 

AI-powered applications can both measure and act on factors that affect yields, such as soil acidity, moisture levels, fertilizer application, and a farm’s risk of pest infestation. Farmers in Texas have used the power of AI to compare historical satellite images of farmland to accurately predict when a swarm of potentially devastating grasshoppers was making its way to crops, providing warning much earlier than ever possible before. 

A.I. and smart data use can aid sustainability 

Research shows that agriculture is responsible for up to a quarter of all human-caused greenhouse gas emissions. Additionally, irrigation for agriculture is responsible for 70-90% of the world’s freshwater usage. When combined with land and fertilizer usage, these factors take a significant toll on the world’s resources and environment. 

How then can we use technology to mitigate the cost to the environment, while still feeding a hungry world? 

In the case of irrigation, advances in A.I. applications are helping both large and small-scale farms use water more responsibly and efficiently. One organization, DHI GRAS, developed a solution to help farmers reduce water consumption in their fields via A.I. algorithms that use thermal and optical satellite data, combined with continually updated meteorological data.

With data-driven insights provided, farmers can use exactly the right amount of water for maximum yield while limiting waste. 

A.I. can refresh a struggling labor market

While agriculture was once the most popular industry worldwide, employment numbers have dwindled during the past few centuries. In fact, in 1870, almost 50 percent of the U.S. population was employed in agriculture. As of 2008, that number has dwindled to less than 2 percent. Thanks to automation and other sources of innovation that have increased yields per acre, it simply takes fewer workers to successfully run a farm than in years past. 

Data-driven farming is set to provide massive economic opportunities. It has been estimated that smarter farming practices could generate $2.3 trillion overall worldwide annually, with $250 billion coming from AI and data analytics alone.

With a significantly increased demand for food and richer dietary expectations awaiting the industry in the next few decades, data-driven solutions (and the skills to provide them) will be in high demand.  

Interested in learning more about how grounded A.I. can lead to relevant and actionable data insights? Sign up for a demo of our easy-to-use platform here. Also, be sure to stay connected by following us on LinkedIn and Twitter.

Infographic – The Challenge of Turning Data Into Action Report

Manufacturing professionals are overwhelmed with data from remote sensors, connected devices and software from a myriad of systems.  The difficulty comes in analyzing the data efficiently and gaining immediately actionable insights to improve processes and drive costs down.

We surveyed 500 mid-level manufacturing professionals to better understand this issue and how manufacturers can better utilize this data to drive real ROI.  This infographic summarizes the results and offers our solution.

About Plutoshift: PlutoShift offers a process performance monitoring solution for a variety of process industries, including food, beverage, water and chemicals. We bring together data on one easy-to-use platform, contextualizing the information and measuring the bottom-line financial impact.

Download the report infographic here.

Download the full survey report here to read more about the challenges facing the manufacturing industry and how PlutoShift services can streamline data collection.

What we learned from hosting our first customer event

There comes a point in every B2B SaaS startup’s life when you feel the irresistible urge to host a customer event. There are many good reasons to do it. In our case, we did it because we love spending time with our potential customers and exchanging knowledge with them. We thought Austin would be a great place to host it. Tuesday, August 21st, was a hot day down there. Just perfect for a few cool drinks at the Roosevelt Room in downtown Austin and some good conversation about cowboy boots, BBQ, and Artificial Intelligence.

Plutoshift hosted this event for the Industrial team at Carollo Engineers. Their group came from all over the United States and Plutoshift had plenty to talk about. However, the topic of water was never too far away. Plutoshift’s Northern California location led to discussing wine, but eventually found it’s way to novel water reuse solutions at California vineyards. The topic of fishing somehow led to desalination plants, and skiing led to … wait for it … après-ski drinks, which led to reverse osmosis membranes in ethanol plants. Yes, the experts at Carollo care about their work.

The event, apart from giving us a chance to get to know each other, was an opportunity for the Carollo team to learn the latest in implementing machine learning and asset performance management from Plutoshift. We shared our latest work with Carollo and discussed how to take this into future projects. We touched on the advantages of a revenue-centric APM approach and also some of the challenges industrial water and wastewater companies have with implementing machine learning solutions.

Among the challenges we discussed was the lack of open source data. One thing that has put this industry behind others is the anonymous sharing of data from processes. This collaborative sharing is the key to accelerating the adoption of machine learning. Other industries, including energy, have formal programs to facilitate this type of data sharing to the betterment of the industry as a whole.

To wrap up the night, we had a frank conversation about how data sharing might be initiated. There were some good ideas that were exchanged and better still, there was enthusiasm to pursue those ideas. Perhaps the Roosevelt Room will be remembered as the launchpad for this very important component to bring revenue-centric APM approach to industrial water and wastewater plants in the future.