In this installment of the AI in Supply Chain series (#AIinSupplyChain), we explore the topic of industrial supply chains and factories of the future since this is where the AI applications we are covering will primarily be used.
According to the German Federal Ministry for Economic Affairs and Energy, “Industrie 4.0 refers to the intelligent networking of machines and processes for industry with the help of information and communication technology.” Industrie 4.0 is a term that is closely related to the terms Factory of the Future and Fourth Industrial Revolution.
What will the factories of the future look like?
Industrie 4.0 envisions a future in which: Factories produce goods in fluctuating quantities based on real-time demand rather than preset production quotas. Production lines are modularized and can be reconfigured easily to enable the production of different types of products in small lots. Production is more customer-centric. Supply chain logistics is optimized. Data is central to the production process. And, circular supply chains are ubiquitous.
The technologies that will enable the factories of the future
In The Enabling Technologies of Industry 4.0: Examining The Seeds of The Fourth Industrial Revolution, a February 2019 paper by Arrianna Martinelli, Andrea Mina and Massimo Mogi, the authors outline six disparate technologies that will come together to enable the factories of the future envisioned by the Industrie 4.0 concept.
The rest of this article is a simplified discussion of sections of the paper.
The Internet of Things (IoT): This is made up of industrial machinery that communicates with one another using standard communications protocols. If you are a truck driver using an electronic logging device that connects to your phone and sends data directly to an application that your fleet manager has immediate access to, you know what this means. This technology is core to most of the logistics optimization technologies that we have highlighted so far in this series.
Industrial analytics: This is “the process of examining large and varied data sets to uncover hidden patterns, and unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.”
Cloud manufacturing: This is a system in which “various manufacturing resources and abilities can be intelligently sensed and connected through the internet, and automatically managed and controlled using IoT technologies.” Cloud manufacturing falls into two categories: The first category is narrowly focused on “the deployment of manufacturing software on the Cloud.” The second is broader in scope, “cutting across production, management, design and engineering abilities in a manufacturing business.”
Robotics: According to the authors, industrial robots are typically classified as Selective Compliance Assembly Robot Arm (SCARA), Articulated, Cartesian, Dual Arm, and Collaborative Robots (Co-bots). Recent advancements in hardware and software technology have made humanoid robots more common. They also point out that KUKA, ABB and YASKAVA are the three major spenders on research and development, and together account for more than 70% of sales, and that these three companies are increasing their investments in advancing their capabilities.
Artificial intelligence: The authors observe that “the success of AI in industrial applications have so far been limited. However, industrial AI is fast improving as a systematic field of research, focused on developing, validating and deploying reliable machine learning algorithms for industrial applications.”
Basically, this is the observation that prompted this #AIinSupplyChain series.
Additive manufacturing: This technology is four decades in the making and originated in Japan. The authors state that “the field of 3D printing has been growing rapidly for years.” In additive manufacturing, products are made by building up layers of materials to create a finished product.
Conclusion
Fundamental advancements in, and industrial adoption of, the six technology areas that combine to form Industrie 4.0 are uneven and uncertain. The authors discuss three drivers of industrial change: First, industrial dynamics will favor those technologies that have more well-established and predictable outcomes for organizations that adopt and implement them. Second, a lack of industry standards presents a stumbling block for Industrie 4.0 technologies. Specifically, they highlight legal and technical standards as being of particular concern. Third, government policy can aid the development and diffusion of Industrie 4.0 technologies.
The authors conclude the paper with a pertinent question: How long will it take for these enabling technologies to become full-fledged general purpose technologies and revolutionize production and consumption systems?
If you are a team working on innovations that you believe have the potential to significantly refashion global supply chains, we’d love to tell your story in FreightWaves. I am easy to reach on LinkedIn and Twitter. Alternatively, you can reach out to any member of the editorial team at FreightWaves at media@freightwaves.com.
Dig deeper into the #AIinSupplyChain Series with FreightWaves
● Commentary: Optimal Dynamics – the decision layer of logistics?
● Commentary: Combine optimization, machine learning and simulation to move freight
● Commentary: SmartHop brings AI to owner-operators and brokers
● Commentary: FleetOps tries to solve data fragmentation issues in trucking
● Commentary: Bulgaria’s Transmetrics uses augmented intelligence to help customers
● Commentary: Applying AI to decision-making in shipping and commodities markets
The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates.
Author’s disclosure: I am not an investor in any early-stage startups mentioned in this article, either personally or through REFASHIOND Ventures. I have no other financial relationship with any entities mentioned in this article.
