Why Manufacturing Companies Continue to Struggle with AI Implementation Projects

By Prateek Joshi 

Manufacturing companies know that Artificial Intelligence (AI) can have multiple business advantages for the frontline team and on the overall bottom line. But despite companies’ best intentions and AI’s clear potential, many companies struggle to fully utilize AI. This is leading them to reevaluate their strategy on how to leverage AI for their business, according to our new report

The usage of AI  at the enterprise level is continuing to grow, but the  projects are often loosely defined and can take longer than anticipated to show returns. This has the potential to limit the progress that AI can provide. To further understand the hurdles standing in the way and opportunities AI can bring to manufacturing, we surveyed 250 manufacturing professionals who have visibility into their company’s AI strategy.

Continuing The March Toward AI Implementation

Companies have good intentions when they begin the AI implementation process, but the complexity of AI can pose problems and cause companies to reevaluate their strategy. 

  • 61% said their company has good intentions but needs to reevaluate the way it implements AI projects
  • Only 17% of respondents said their company was in full implementation stage of their AI projects
  • 34% said their company has struggled to keep its AI project(s) in scope because there was a lack of expert guidance at the planning phase of the project

 

What is Really Holding Companies Back

Having a mature data collection and storage system is essential for AI implementation projects. Without the ability to collect and store  data in a timely manner, manufacturing companies can’t get far with AI implementation. Manufacturers are realizing it takes more time than anticipated to get their data systems up and running. 72% of manufacturing companies said it took more time than anticipated for their company to implement the technical/data collection infrastructure needed to take advantage of the benefits of AI.

Internal buy-in is important for any company project, but it’s especially important for something as complex as AI. Just like marketing, business development, or any other business function, commitment from key stakeholders is essential for success.

Internal buy-in from employees needs to be taken into account when implementing AI projects. 62% said their company took more time than anticipated to acquire internal buy-in and commitment in implementing AI. This lack of internal buy-in has also caused 34% of employees to say that there is a lack of engagement toward these projects.

Important Factors For Successful AI Projects

We found that issues with internal buy-in, decisions regarding who should use the data and lack of budget consensus related to AI projects can slow down implementation in the manufacturing industry.

It’s important to have a mature data collection and analysis infrastructure and to agree on specific business outcomes before implementing and incorporating AI into an industrial workflow. Multiple reasons are given when companies make the decision to begin implementing AI. 54% said that cost savings were the top business problem that they were trying to solve, followed by 49% that said automating tasks was the top reason.

These kinds of projects can lose focus within the company and encounter multiple problems. Less than half (47%) said their company has kept AI projects in scope and focused on deliverables. Moving to a focused approach that can manage the complex process of AI can help to eliminate these issues and help companies stay focused on the long-term goal.

The use of AI technology in the manufacturing industry has the potential and opportunity for companies to empower the frontline team with automated performance monitoring for any industrial workflow. AI can help businesses drive ROI by reducing resource consumption, operating costs, and reliably predict the current state of their business predictions, whenever they need it. 

How do your company’s experiences with AI match up to the respondents? Read more about the report and the methodology here. 

 

Industrial Automation: 4 Industrial AI Predictions for 2020

By Prateek Joshi:

2019 was a banner year for AI! Almost every business magazine and tech publication published a deluge of articles about AI and its impact on the industrial world and industrial automation. While some of the speculations about AI were overblown, the majority of the attention dedicated to the state of AI by analysts and the media is well warranted.

AI’s impact on industrial automation has already dramatically changed the way companies think about their operations and how they collect and use their data. 2020 will undoubtedly see some of the recent trends in AI continue to intensify, but what else should industrial operators and C-level executives expect for the start of the new decade? Here are four predictions for the world of industrial AI in 2020:

  1. Business Intelligence will Become More Accessible

Access to data is critical for any industrial operator, but many companies are hindered by siloed data sources that cannot be easily accessed by the entire company. Business functions such as budgeting, sales, and performance monitoring all rely on data that’s often needed in real-time.

Data analysts in large organizations are under increased pressure to make actionable recommendations out of unstructured and siloed data. There are now vertical-specific products available that can centralize data and allow everyone to access it across the organization. 2020 will be the year that companies commit to making their data more accessible to their workers as a business imperative.

  1. AI Will Continue to Spread to New Industries

Manufacturers are continuing to invest in AI solutions that address issues like preventative maintenance and the automation of certain tasks. Based on this progress, the less obvious industries will invest in this technology as well.

Verticals that are closely related to the manufacturing industry such as supply chain management and logistics are benefiting from AI applications, with logistics managers using AI applications to help forecast demand trends in key markets that can optimize their supply chains.

Companies like Amazon are also using AI-powered machines to help automate manual tasks in their warehouses. While these machines can’t complete complicated tasks, they are proving to be useful aids to workers who can pass off some repetitive and labor-intensive jobs.

On a broader scale, CISOs and security professionals are facing more complicated threats every year. With talent shortages and an overall increase in the number of IoT devices connected to any given server, companies can no longer rely on legacy or manual processes to respond to security events.

AI applications are helping to automate threat detection and can provide around-the-clock monitoring services for enterprises that are always at risk.

  1. AI Will Change The Way Work Gets Done

Much like the combustion engine or the wide-spread adoption of electricity, AI will have an impact on the way people do their jobs. More specifically, it will impact the way work gets done. AI applications will continue to enhance workers’ roles through automation and problem solving, helping to manage the burden of the millions of micro-decisions workers make in their jobs.

While the best industrial AI applications are easy and intuitive to use, the rapid adoption of AI will create specialized job opportunities. Recent reports have estimated that the adoption of AI will create 58 million more jobs than it displaces in the next few years.

There are instances where workers need to adapt to new technology, but lack the proper access to getting trained. This is where education leaders, labor organizations, local governments, and tech companies have stepped in. They are committed to the reskilling initiatives that will address the needs of workers.

  1. Companies Will Demand Accountable AI Projects

As the field of AI moves from the theoretical to the applicable, companies will demand projects that can deliver targeted results and stay on budget. According to our new report, 33% of manufacturing professionals say their company struggles with their budget scope with either being over budget or finding the resources to implement AI.

While the benefits of using AI applications at scale are clear, reaching that ROI can be a challenge for some companies. Read more about this and some of the other challenges companies are facing when implementing AI in our new report.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

AI for Coffee Manufacturing: 3 Ways AI is Energizing The Coffee Industry

By Prateek Joshi:

From bean to barista, the global coffee industry is valued at over $100 billion. For a producer, distributor or manufacturer in this massive industry, the use of AI for manufacturing can play a vital role in optimizing critical processes. 

Specifically, in retail, agricultural and manufacturing operations, the coffee industry is discovering ways AI applications can benefit everyone from small-scale farmers to large industrial plants. Below are three examples where AI is benefiting the coffee industry.

  1. AI is helping farmers protect their crops.

The increased use of AI applications in agriculture has the potential to help farmers across the world protect their beans from disease and optimize growing conditions by monitoring factors like soil and moisture levels. AI has proven to be especially beneficial to farmers in developing nations in Latin America and Africa, where they can utilize advanced warnings about pests that threaten their crops, as well as receive data-driven insights that can help them adapt to the effects of climate change.

With the price of coffee beans at some of the lowest in a decade, AI and machine learning can provide actionable intelligence and decrease the negative impacts of the massive, yet volatile market. 

  1. AI is helping coffee at the industrial level.

The benefits of IoT-enabled AI are not restricted to the farm. Industrial operations perhaps have the most to benefit from the technology. And with some of the largest coffee companies in the world like Starbucks seriously investing in AI solutions for their industrial processes, the rest of the industry is undoubtedly paying close attention. For example, the word ‘digital’ was used over 40 times on a Starbucks investor call in Q4, 2019, which can only suggest that finding and investing in technology solutions is top-of-mind in the boardroom. 

These digital initiatives are improving industrial processes at coffee manufacturing plants in a number of ways. From decreasing equipment downtime at bottling facilities to monitoring the performance of key assets such as water, chemicals and labor at large plants.  

  1. AI is providing advanced insights into transportation and logistics.

While IoT and AI applications are still in their relatively early days of use, the technologies are quickly gaining steam and don’t appear to be a passing trend. Leading authorities have predicted that half of all manufacturing supply chains will be using some form of AI by 2021.

The use of AI by large coffee producers like Starbucks shows that AI is continuing to deliver numerous benefits to coffee growers and producers. AI is providing benefits to the supply chain such as identifying what time of the year is most advantageous to carry specific varieties in stores. 

Further up the supply chain, AI applications can predict order patterns to reduce or eliminate penalties caused by missed OTIF (On Time in Full) deliveries, providing benefits to every stakeholder in the process.

As outlined above, there is no place in the coffee industry that the use of AI wouldn’t benefit. On the production and retail side, companies in the coffee industry can automate inventory orders and predict equipment maintenance and staffing needs. For farmers, increased AI usage can lead to an improvement in water quality, increased efficiency in coffee processing, packaging, as well as provide a positive impact on the overall bottom-line. On the manufacturing side, issues like unplanned equipment downtime and asset quality can be mitigated with the right AI system. 

Curious about how to get started with AI in your company? Be on the lookout for our new report in January of 2020 that explores some of the challenges manufacturing companies face in getting their AI projects off the ground, as well as how to overcome them. 

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.