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Is AI in Manufacturing Delivering the Dreams of Smart Factories and Industry 4.0?

Back in 1998, Steve Jobs had just returned to Apple and was preparing to release the first iMac desktop computer in August of that year. The only problem? Many people hadn’t started using ... - Machine learning predictive analytics with no-code
7 March, 2023
Est. Reading: 8 minutes

Back in 1998, Steve Jobs had just returned to Apple and was preparing to release the first iMac desktop computer in August of that year. The only problem? Many people hadn’t started using the internet yet. Jobs personally recruited Hollywood legend Jeff Goldbloom for TV ads to not only sell the iMac—but the internet itself—including the power of email and photo and video sharing.

It’s the same today with manufacturing—the ghosts of old technology and traditional quality improvement (QI) methods are holding back the true potential of smart factories. 

That potential is huge—but estimates vary. A recent report from Statista projects that the smart manufacturing market will skyrocket from $263 billion in 2021 to $1.1 trillion by 2028. 

McKinsey’s recent research, meanwhile, projects the value creation potential of manufacturers implementing Industry 4.0 to be a whopping $37 trillion by 2025. 

Yet, McKinsey  also reports that today only 30 percent of companies are gaining value from scaled-up Industry 4.0 solutions in manufacturing. If Industry 4.0 has so much potential—what is keeping so many manufacturers in this so-called “pilot purgatory?”

In this post you’ll see:

  • The main components of Industry 4.0—and the obstacles manufacturers are facing to be successful in the Fourth Industrial Revolution
  • The top Industry 4.0 features and benefits 
  • Some interesting 4.0 use cases

What is Industry 4.0?

The digital revolution—or Third Industrial Revolution—rose out of the ashes of the Second World War and brought us:

  • The transition from mechanical and analogue electronic technology to digital systems
  • Computers, other kinds of electronics, the Internet, and huge advances in computing power 
  • Innovative ways of generating, processing, and sharing information
  • A truly global revolution that made the entire world 10 times wealthier—not just the USA and Western Europe. 

Whereas the Third Industrial Revolution started to automate data, the Fourth Industrial Revolution—or Industry 4.0—is completely reliant on data. Industry 4.0 is bringing about the following innovations for the manufacturing sector:

  • Augmented Intelligence: Advanced analytics, machine learning, and artificial intelligence
  • Data Connectivity: New ways to store and collect data, including  cloud technology, edge computing, blockchain, and sensors
  • Next Generation Innovation: Additive manufacturing (such as, 3-D printing), renewables, nano, bio, and robotics
  • Shifting Reality: Virtual reality (VR) and augmented reality (AR), autonomous guided vehicles, robotics, and automation

In addition to breaking free from the 3.0 mindset—smart manufacturers need to ensure that they are focusing on real business value and that their employees are constantly upskilling.

What Can Industry 4.0 Still Learn from the 2nd Industrial Revolution?

Henry Ford is one of the biggest names from the tail end of the Second Industrial Revolution. Ford believed that the most beautiful things in the world are those from which all excess weight has been eliminated. This helped him develop durable and light-weight cars. 

Ford also believed that you should “be ready to revise any system, scrap any method, abandon any theory, if the success of the job requires it.” This empowered Ford to increase wages and find and train the best talent. He also adapted the assembly line production of a Chicago slaughterhouse to his new car plant in Detroit. 

MIT Sloan senior lecturer John Carrier believes that there are three main barriers holding potential manufacturers back from innovating like Ford in the age of 4.0: 

  • Failure to standardize: Ford streamlined production—today there are still too many outdated ways of working. These variations make it hard to discover the business processes and workflows that reduce business risk, improve efficiency, and enable companies to scale.
  • Lack of preparation: Many enterprises are ready to invest in the latest sensors and advanced software—but they’re not always prepared to train and retrain the staff required to actually use those tools to provide maintenance and augment diagnostics. Technology has advanced—but Ford’s insistence on upskilling staff still rings true.
  • Hanging on to Old Technology: In life, people love what’s familiar—unfortunately, this presents a challenge for manufacturing as some employees don’t have the confidence to use new technology. This is especially true when employees are put under pressure. Phasing out old ways of working is one of the best ways to derive value.

Besides employees hanging on to old technology, managers, foremen, and executives also need to shed many tenets of their outdated QI (Quality Improvement) measures, such as:  

  • Lean Manufacturing (LM)
  • Six Sigma (SS) and Lean Six Sigma (L6S)
  • Theory of Constraints (TOC)
  • Total Quality Management (TQM)

While traditional QI methods may improve efficiency, reduce waste, and increase productivity—they still try to improve product quality without truly learning from defects. Instead, they simply trace and remove them. Traditional QI methodologies also don’t fully take advantage of most of the cutting-edge, data-driven technologies—nor do they consider predictions or their consequences. 

Newer approaches such as Zero Defect Manufacturing (ZDM)—which actually use Industry 4.0 technologies—are working to finally bring forth a successful digital and green transition.

And what about the Ford Motor Company? Today Ford can detect trends in manufacturing data—such as cycle time, throughput, warnings, faults, maintenance, shift notes, and product quality—from Ford production plants located all over the world. Ford leverages this data to predict machine failure, identify the root causes of the problems, and make timely repairs. 

Ford is also staying true to its original founder’s idea to upskill workers. In Ford Germany, for example, a ramp-up plan was started to address closing the skills gap with a staggered approach that fully supports the business cycle plan delivery. 

New re- and upskilling concepts were developed in cooperation with external intellectual partners (universities and Ford’s Research & Advanced Center) which has produced the conceptualization of numerous Ford-specific compact courses, e.g., on Battery-Electrified Vehicles, Connectivity and Communication, and Cyber Security.

Zero Defect Manufacturing Demands Conformance to Requirements

Defects are not free. If a substantial proportion of the workforce has to correct defects, then the company is not not only paying to make the defects—but correct them as well. Lower quality equates to higher costs. Zero defects is a way of thinking and doing that reinforces the notion that defects are not acceptable, and that everyone should “do things right the first time.”

Zero Defect Manufacturing demands four absolutes:

  • The definition of quality is conformance to requirements.
  • The system of quality is prevention.
  • The performance standard is zero defects.
  • The measurement of quality is the price of non-conformance.

By incorporating quality control principles and preventative measures into the manufacturing process—quality will be guaranteed, costs will be saved, and profits will be increased. Hit the highest quality marks the first time and avoid the failures and imperfections later that cost money and your reputation.

Quality Management is Ballet—Deliberately Designed, Planned, and Programmed

Quality management is ballet—a ballet is deliberately designed, discussed, planned, examined, and programmed in detail before it is performed. This is from Philip Crosby’s Quality is Free: The Art of Making Quality Certain, a book showing how companies can save money using  his quality concepts. 

Today, the smart manufacturing ballet starts—as you may have guessed—with data. Bosch, for example, uses its AI-powered Manufacturing Execution System (MES) to automatically collect, process, and analyze data from a variety of sources in near real time to determine fluctuations in a wide range of manufacturing processes.

Next, Industry 4.0 software Nexeed interprets and visualizes the data and codes, the AI system recommends a course of action, and an associate makes the final call. If, for example, a Bosch drill hole is off from the specific placement, the AI system independently initiates the necessary steps to set it straight.

At times, cameras support the AI system by recording the manufacturing process. Based on patterns it has learned, the system identifies deviations, and action can be taken immediately. Field and customer data is also linked to the platform in individual cases.

Germans originally came up with the phrase “Industrie 4.0” (Industry 4.0) back in 2011. They envisioned smart factories with machines that are connected, communicate with each other, and organize themselves—with people at the center of it all, orchestrating more efficient, more flexible, and more customized production. 

It might not be Swan Lake, but reducing the number of manufacturing defects with AI is an art form beloved by quality control managers and manufacturers across the globe.

Watching 4.0 Dreams Come True

Since the German government coined the term Industry 4.0 in 2011, there have been quite a few incredible manufacturing innovations: 

Supplementing Supply Chains: For years, DHL has been hampered by a shortage of supply chain talent. The American-founded German logistics giant has countered by committing to a multi-year, $15 million investment deal with Boston Dynamics Stretch Robot for its North American warehouses.

By automating warehouse operations, DHL has successfully accelerated its commitment to Digitalization—and added another 4.0 deployment across their supply chain. Other recent supply chain upgrades include: 

  • Autonomous forklifts
  • Autonomous trailer offloading
  • Autonomous robot replenishment and putaway 
  • Autonomous robot pick carts and more

The result? DHL’s digitization deployments have produced:

  • Near-perfect employee satisfaction surveys with the technologies
  • 50–100 percent increase in warehouse labor productivity
  • 30 percent increase in facility throughput

Taking Off With Additive Manufacturing: In the 1980s, Chuck Hull, “the father of 3D printing,” was making tough coatings for tables using ultraviolet lamps. By suggesting a new way to use the UV technology—he created and commercialized Stereolithography or “SLA” printing—an early and widely-used 3D printing technology. 

Decades later, the impact that 3D printing—or additive manufacturing—has has on Industry 4.0 is undeniable: 

  • General Electric: The manufacturing giant installed their first additive manufactured parts on GEnx commercial airline engines. Compared to conventional manufacturing methods, this has resulted in a 10% weight reduction and 90% waste reduction. 
  • Boeing: The global aerospace manufacturer has managed to reduce production costs for each of its 787 Dreamliners by as much as $3 million with the help of 3D printing. 
  • Haute Couture: Some people focus on the big names at the Met Gala or the Kardashians—others look at the additive manufacturing tech that goes into making the latest red carpet smoke from Balenciaga. 

Whether it’s taking off in a plane—or strutting your stuff at the latest premiere—additive manufacturing is projected to hit around $76.20 billion by 2030 with a CAGR of 20.9 percent during the forecast period 2022 to 2030.

Erasing a Carbon Footprint: They might not be wearing a designer pair of Balenciagas, but Bosch engineers are no longer leaving a carbon footprint. Just about a decade after helping launch Germany’s Industry 4.0 initiative, Bosch has gone carbon neutral.

Intelligent algorithms help Bosch make energy consumption predictions, avoid peak loads, and fix deviations in energy consumption. In addition to going green and moving towards zero-defect production with AI, the World Economic Forum has honored a Bosch plant as an Industry 4.0 lighthouse project.

Delivering AI-Powered Predictive Maintenance: Safe and efficient infrastructure benefits everyone. Infrabel, which builds Belgium’s railways network and infrastructure, has worked to digitize their data and implement digital rulers and a Switch Video System (SVS) that films the switches and crossings (S&C) in high resolution to help measure trains. 

Infrabel is also monitoring safety with IoT sensors that transmit data to a central platform. They then use a machine learning engine to augment their predictive maintenance efforts. The reported result? 

  • 7,000 km of rail lines checked automatically
  • Increases staff safety

It’s nearly impossible to monitor tens or hundreds of sensors and identify failures before happening manually—so implementing ML helps identify patterns that aren’t visible to the naked eye. By utilizing machine learning, organizations can maintain machines and equipment better while saving money and extending the lifetime of their valuable assets.

In fact, a recent study from the Deloitte Analytics Institute states that—on average—predictive maintenance increases productivity by 25 percent, reduces breakdowns by 70 percent, and lowers maintenance costs by 25 percent. Other industrial applications of machine learning include:

  • Predicting energy consumption for plants and factories
  • Forecasting Co2 emission levels
  • Realtime testing on the production line
  • Assessing the results of functional tests

In the same way, ML Studio makes AI accessible for organizations that need to solve real-world business problems by providing clients with a robust and ready-to-use End-to-End AI platform that doesn’t go off budget.

Contact us for a personal demo.

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