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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AI In Manufacturing, 2020 And Beyond

By Prateek Joshi

As we continue to dive deeper into Industry 4.0, the state of manufacturing is largely the same as it was several years ago. Recently, I came across an article on Forbes titled : Top 5 Digital Transformation Trends In Manufacturing For 2020 written by Daniel Newman. In this piece, he  talked about  a significant difference in the perception vs. reality regarding the state of manufacturing.

The manufacturing sector still has the same goals but the approaches within the industry have changed. The  number of firms using AI and IoT technologies  in their processes continues  to grow rapidly. Company goals are shifting in response to a new type of consumer that expects: (1) higher quality services and goods that can be produced, (2) delivered at a faster clip, and (3) manufactured with responsible practices. This puts pressure on companies to look for different approaches and solutions to fill customer needs. Additionally, companies are realizing that embracing AI and IoT technologies  can be a prudent financial decision as well. This is especially true because they can now reduce equipment downtime, cut down on waste, and improve their ability to predict maintenance breaks. It’s estimated that just 60 minutes of downtime can be equal to a $100,000 loss in a manufacturing environment. 

The article brings up many relevant points in the manufacturing sector, but three distinct pieces of information stood out to me. These trends will significantly impact  the industry in 2020 and beyond: 

  1. Manufacturing Is Fully Embracing The Automation Of Routine Tasks

90% of manufacturing companies in the United States have fewer than 500 employees. To some observers, this  might seem like a problem in the industry. But what you don’t see on the surface is that the reduction in workforce is a sign that companies leaving  behind inefficient old ways. It’s impossible to do everything manually, which opens the door for technology to resolve these issues and become more efficient.

  1. Consumers Are Dictating The Actions of Manufacturers

The speed at which manufacturing moves is driven by a group of consumers who  want higher quality goods, combined with responsible manufacturing practices . And this is not just coming from consumers! Corporate leaders are backing up this sentiment as well. Recently, the Business Roundtable issued a statement that companies should not just advance the interest of shareholders but “must also invest in their employees, protect the environment, and deal fairly and ethically with their suppliers.” Combining speed and responsibility requires not just a different philosophy, but intelligent action. Manual processes and limited operating information are being replaced by data intelligence platforms to keep up with this increasing demand. Companies need to be able to take immediate action on their collected data, which was echoed by 76% of manufacturing professional respondents that Plutoshift recently surveyed.

  1. These Innovations In Manufacturing Aren’t New

This massive shift in company practices could lead some to believe that this technology is a new phenomenon, but the number of companies who are using it has just increased. This means that more business leaders are seeing the benefits of taking manual tasks (especially ones that require a lot of labor hours) and automating these responsibilities.

It isn’t a secret that manufacturing is evolving at a rapid pace. Increased access  to data combined with  the widespread use of AI is eliminating the barriers that once held companies back from using IoT within their businesses .

With these barriers no longer an issue, companies are fully embracing these innovations and taking advantage of technologies that are already available. This shift provides a decrease in overall costs and an increase in efficiency, providing benefits to customers and companies at the same time.

Follow us on Twitter and LinkedIn for more company updates, and be on the lookout for our upcoming survey report about AI adoption in the enterprise world.

 

3 Ways AI Is Helping Manage Climate Change

By Prateek Joshi: 

Artificial Intelligence is an amazing tool available to researchers, industries, and scientists to help solve some of the world’s most complex challenges. While AI is not a panacea to the world’s most pressing issues, it is proving to be a critical tool in alleviating the strain on natural resources.

As AI applications become more sophisticated and widely available, people across industries and cultures are finding new ways to solve climate-related issues with smarter data usage. Below is a sample of some of the most promising and interesting AI applications in the field of climate change mitigation:  

  1. More Responsible Water Usage

Almost one-fifth of the world’s population, or 1.2 billion people, live in areas of physical water scarcity. Agriculture, industrial manufacturing, energy production, and mining command the lion’s share of water usage. Each one of these categories has significant room for improvement in how they use and treat the freshwater they consume every day. 

In the not-so-distant future, precipitation patterns will be less reliable and the demand on historical freshwater sources like aquifers will outpace natural replenishment rates. In such a situation, getting every ounce of value out of water resources will be critical to overall sustainability. Our company’s GROUNDED AI framework enables operators to automatically monitor critical water processes and have access to actionable information in real-time. One water treatment plant was able to reduce its overall water and energy usage/costs by 18% after implementing our GROUNDED AI solution. The U.S. manufacturing sector uses about 15.9 billion gallons of water per day, so every drop counts! 

  1. Better Climate/Weather Predictions

Agricultural activities worldwide rely heavily upon predictable rainfall and temperature patterns. This helps the farmers understand when to plant, harvest, and irrigate their crops for maximum efficiency. With historical climate patterns in flux, farmers will need better modeling technology to adapt to a less predictable climate. 

Microsoft’s AI for Earth initiative is combining cloud technology with AI-powered sensors to collect soil, tillage, and yield data for specific farms around the world. They are making this data available to other farmers via cloud-based applications. 

As other meteorology-focused technologies like sensors, satellites, and computer models continue to advance, AI will serve as a unifying service that will help make sense of the seemingly infinite data points they collect. 

  1. Better Emissions Tracking 

Every improvement AI brings to industrial processes will help us cut waste and conserve resources. But it’s the carbon emissions that ultimately need to be addressed to best mitigate the effects of climate change. A recent article in National Geographic outlines how Google is partnering with environmentally driven organizations to help better monitor carbon emissions from power plants with AI. 

“AI can automate the analysis of images of power plants to get regular updates on emissions. It also introduces new ways to measure a plant’s impact, by crunching numbers of nearby infrastructure and electricity use. That’s handy for gas-powered plants that don’t have the easy-to-measure plumes that coal-powered plants have,” the article explained. 

By better mapping and processing emissions data, governments and companies can better understand where problems are originating and how they can best address these problems going forward. 

GROUNDED AI For Business and Climate 

Just like digital transformation, targeted AI initiatives can improve top-line revenue as well as manage climate risks. The key aspect to note here is that AI initiatives require behavioral change across all lines of business. Addressing and managing climate change requires the same step-by-step commitment from companies that goes into any other business initiative — it’s the small steps that can add up to big returns. 

Follow us on Twitter and LinkedIn for more company updates, and be on the lookout for our upcoming survey report about AI adoption in the enterprise world. 

Plutoshift’s $8M Series A Funding — Fueling the growth of GROUNDED AI

By: Prateek Joshi, Founder of Plutoshift 

Earlier in September, we had the pleasure of announcing an enormous milestone for Plutoshift in the form of our $8 million Series A funding round. I’d like to take a moment to express our gratitude to all of our investors, customers, team members, and partners that have played a role in helping us bring GROUNDED AI to the industrial world. 

Here are just a few of the investors who have committed their support to our mission to help change the industrial world’s relationship with its most critical and underutilized asset — DATA.

  • Fall Line Capital
  • Unshackled Ventures
  • Dave Gilboa, co-founder of Warby Parker
  • Joey Zwillinger, co-founder of Allbirds
  • Nat Turner, co-founder of Flatiron Health

We’re already putting this funding to good use by hiring new members to our technology team, including several new full-time data scientists at our Palo Alto HQ and other new team members in our Denver office. 

What is Business Water? 

We are using this as an opportunity to initially focus our platform on manufacturers with a critical reliance on water in their processes. We have termed it “Business Water”.

Business Water is the use of critical resources to manage water in industrial operations, and it’s quickly becoming one of the biggest challenges in the manufacturing industry. By applying the power of AI, we are doing our part to help alleviate the impending water crisis facing industries worldwide. 

By unlocking insights from existing data sources in real-time and predicting what’s coming up next, the Plutoshift platform provides a holistic view of key performance metrics and resources that impact the bottom line of industrial businesses.

From food and beverage manufacturers to energy and chemical producers, the potential opportunities for cost savings through GROUNDED AI are limitless. It extends to just about any company that relies on industrial processes to produce its final output.

While we’re taking a moment to celebrate this achievement in our company’s history, we’re getting right back to work to help companies realize the value in their data. Schedule a time to view our demo, and we’d be happy to show you the Plutoshift advantage in action. 

 

Mastering AI Investments, One Drop At A Time

Eighty percent of businesses are investing in some form of AI today. With such an overwhelming number of firms seeing the potential in AI solutions in addition to Industrial IoT technology, one would imagine that a large percentage of CIOs and  managers would have a solid grasp of what it takes to both measure and communicate the ROI of their initiatives to their boards.

But as recent research has demonstrated, that’s not the case for the majority of business leaders. Many of them are still unclear on the tools, time commitments, and expertise required to successfully implement AI initiatives and reap the benefits of their investments.  

At Plutoshift, we’re tackling this deficit through Grounded AI solutions that are straightforward and hyper-focused on specific, practical, and data-intensive tasks. This framework helps operators have access to actionable insights about their processes from the data they already collect. 

Over the past few months, I have been sharing this vision behind Plutoshift and my outlook on AI with a number of customers and partners across manufacturing industries. Below is a synopsis of that vision that may serve to ease some of the anxieties around AI initiatives; a CEO’s playbook of sorts. 

 Data → Insight → Alerting → Action

For AI to have a material impact on business, solutions must begin with data and lead to actionable response to a problem or opportunity. My background in this area is in leveraging Deep Learning frameworks to understand the data that’s generated in the physical world, but I recognize that the AI models must serve up the action and not just the intel for the user to interpret.  

The measurements collected by a data collection system are only as valuable as the recommendations it can provide in real-time.  

Drive specific outcomes with Grounded AI 

Water is my passion, and Grounded AI is the engine we have designed at Plutoshift to ensure that AI actually works in the real world. There are many companies in this space that are pursuing an everything-and-anything model for AI. We don’t search for pie in the sky problems to solve, we go for practicality and that starts with real use cases tied to concrete ROI. 

In the case of Plutoshift, Grounded AI manifests itself in the form of Process Performance Monitoring. This is a specific application of AI that involves providing real-time access to performance metrics related to resource consumption for various processes. It’s a unified view that shows the current reality as well as future outcomes to the operators. To make it even more practical, the platform is currently oriented towards manufacturers with a critical reliance on water in their processes.

Built for scale

In my view of the world, data intelligence solutions must be scalable on several fronts: 

  1. Aggregation of data across multiple sources to have  a single unified source of truth
  2. Ability to drive ROI for business units
  3. Consistent management of information across the team 

While we believe that the ROI for an initial pilot must be attractive, the real payoff is recognized when the proven solution is executed across the organization and supported by the wrap-around customer experience that we have developed. 

Don’t Try To Boil The Ocean 

AI solutions have the potential to save a lot of time and money in the manufacturing industry, but only if businesses are able to execute in an efficient way that doesn’t drag down the entire organization. 

In my experience, AI has the ability to make a very positive impact if it’s deployed in a practical way. If you’re interested in learning more about driving business outcomes with Grounded AI, take a moment to schedule a demo of our solution here.