Data, Cognitive Technology, and Industry 4.0

Stephen DeAngelis

August 20, 2019

There is continued buzz about Industry 4.0 — the nickname adopted for the ongoing fourth industrial revolution. Anant Kale (@anantkale), Co-Founder and CEO of AppZen, explains, “The first industrial revolution mechanized production using steam power, the second gave us the assembly line for mass production, and the third industrial revolution gave us advanced automation using IT and electronics. We are at the cusp of a fourth industrial revolution driven by artificial intelligence, robotics, and web services. This latest revolution is proving to be the most disruptive yet, taking automation to levels most of us can’t imagine.”[1] One element of Industry 4.0 left out by Kale, but one upon which the entire revolution rests, is data. Patrick Murphy (@PMurphy_Work), partner and practice leader in cognitive manufacturing, at IBM, asserts, “The average factory produces more than a terabyte’s worth of information every day.”[2] He adds, “99 percent of this data isn’t being analyzed.”

 

Fans of Indiana Jones’ movies, will remember the scene at the end of the first movie where the crate containing the Ark of the Covenant is placed in a huge warehouse containing thousands of similar crates. The implication is pretty clear — the likelihood of the Ark being discovered in the warehouse is slim to none. That warehouse is a good analogy for how Murphy sees data collection in manufacturing. He asserts, “[Most collected data are placed in] a manufacturing box that contains information but can’t effectively leverage it to streamline production.” In other words, most data is getting lost in the dataset warehouse. The key to success in the Industry 4.0 era is to find the box, open it, and leverage the data it contains. The best way to do that is by utilizing cognitive technologies (i.e., artificial intelligence (AI) and advanced analytics).

 

Big data and manufacturing

 

Megan Ray Nichols (@nicholsrmegan) notes, “In manufacturing, even without modern data systems, you need to be aware of vast quantities of information. The reports include data about your personnel, the machines and equipment at your disposal, their status or condition, upcoming projects and deadlines — and even quality assurance concerns for the products or goods you are developing.”[3] Maintaining awareness and gaining insights from generated data is complex. Fortunately, cognitive technologies can handle complexity. Murphy argues, “As the shift to Industry 4.0 picks up speed, enterprises are looking for ways to empower process visualization and lay the foundation for cognitive manufacturing.” Nichols suggests five ways cognitive technologies can use big data to improve manufacturing. They are:

 

1. Improved Operational Efficiency. Most manufacturers see Industry 4.0 as a way of improving operations. According to Nichols, leveraging cognitive technologies to mine big data can result in highlighting “bottlenecks, inconsistencies and even problems you may not have known about otherwise.” She adds, “Even when looking at something like product quality, [big data analytics] provides more information you can then use to improve existing strategies or develop new ones. … It fosters a cyclical stream of improvement to not only produce better results, but also enhance existing operations.”

 

2. Greater Visibility. You can’t solve a problem you don’t know about. Nichols explains, “Using big data and advanced analytics — especially in the supply chain — it becomes possible to measure and quantify just about every aspect of manufacturing. From product development and quality assurance to delivery and distribution, you suddenly have a multitude of details about performance, progress, inefficiencies, and problems. You also have a more comprehensive oversight, which leads to improvements in efficiency and management. It is vital for streamlining workflows and processes, as well as building proper communication between you and your partners and vendors.” End-to-end supply chain visibility is a long-term goal of most enterprises; however, it can’t be achieved without the cooperation of all stakeholders.

 

3. Lower Warranty and Support Costs and Improved Customer Service. Nichols explains, “On some level, you will always need to honor product and service warranties, especially as goods reach the end of their lifecycle. But poor manufacturing and design can lead to inordinately high levels of support and product recalls. Therefore, it’s vital to fine-tune manufacturing and mitigate the associated costs. … Big data and advanced analytics are useful for implementing a comprehensive and reliable quality assurance process. Using incoming data and performance metrics, you can determine whether you are meeting customer sentiment and quality perceptions. You can also learn things like the average lifecycle of a product, how goods hold up in varying conditions or environments and even identify design flaws to remedy in future iterations.”

 

4. Mitigate External Risk. Nichols admits, “Some risks of doing business are out of your control. Supply chain dependencies, for instance, come with a degree of risk. If your materials provider suddenly hits a snag in production or the quality of the materials they are providing decreases, it is going to have a significant impact on your processes and output.” Nevertheless, she explains, cognitive technologies and real-time data can help mitigate emerging disruptions. She explains, “Yes, they can and do happen, and there’s no stopping them at times, but you can better adjust if you are up to date. For example, maybe a severe weather pattern or materials shortage has affected the local markets. You can identify this early with analytics tools, and either adjust your processes to meet the changes or even source materials from elsewhere to make up for the losses.”

 

5. Mass Personalization. Nichols writes, “Consumers today demand a more personalized, relevant experience, which can be difficult to achieve with conventional manufacturing processes. Typically, you follow a one-size-fits-all approach and make minor edits or revisions over time to accommodate customer sentiment. Big data turns all that on its head. You now have access to real-time insights on customer behavior, needs and demands, as well as their reactions or evolving preferences. In short, you can design and create goods to match precisely who your customers are and what they want.”

 

According to Murphy, these benefits remain aspirational for many companies. “While companies recognize the critical role of cognitive-driven manufacturing,” he writes, “most of them don’t have the necessary infrastructure and oversight to leverage cognitive potential.”

 

Concluding thoughts

 

Murphy asserts numerous emerging technologies need to combine in order to create a true Industry 4.0 environment. Those technologies include:

 

  • Internet of Things (IoT): “Devices have quietly been getting smarter.”
  • Graphics Processing Units: “Powerful, flexible devices significantly smaller than their desktop and laptop predecessors.”
  • Takt Time: “The average time between the start of production of one unit to the next, determined by existing customer demand. … For many organizations, existing paper processes force takt time to be assessed at the end of stage or product line, hindering organizations’ adaptability. By introducing IoT devices capable of edge computing and onboard processing, it’s possible to measure quality parameters and time-to-completion at any workstation. From this data, organizations can glean actionable insight, effectively allowing inside- and outside-the-box thinking.”
  • Cognitive Platforms: “To maximize the impact of IoT solutions for manufacturing, companies need more than smart devices capable of acting autonomously. They need platforms capable of creating governance and management frameworks for IoT networks and combining data from IoT devices to optimize production processes at scale.”
  • Digital Twins: “Virtual versions of products and processes created using data that allow companies to manipulate and test process improvements before moving them to live production lines. While this technology shows promise, it remains in adolescence — and it’s predicated on a foundation of comprehensive cognitive IoT.”

 

According to Murphy, “Industry 4.0 — the manufacturing side of digital transformation — focuses on five key goals: connection, collection, visualization, analysis and optimization.” He reckons these goals are unachievable without cognitive technologies. “Manufacturing processes no longer exist in isolation,” he explains. “New cognitive solutions and IoT platforms can help enterprises think inside the box.”

 

Footnotes
[1] Anant Kale, “AI Expert: How the Fourth Industrial Revolution Will Impact Our Lives,” PSFK, 7 October 2016.
[2] Patrick Murphy, “Other Voices: Cognitive manufacturing and thinking inside the box,” Modern Materials Handling, 23 July 2019.
[3] Megan Ray Nichols, “5 Reasons Why Big Data Is a Game-Changer for Manufacturing,” Read IT Quik, 3 September 2018.