Origins of the Bionic Supply Chain
This week, Garvis, raised “€3.5 million euros for a bionic AI platform to optimize supply chain management.” Furthermore Piet Buyck, the CEO, describes “with Garvis, the planner communicates directly through Artificial Intelligence with what we call a bionic interface.” I don’t think a GUI with some predictive algorithms qualifies as bionic or “having artificial body parts, especially electromechanical ones” but the term was peculiar enough to investigate its origins.
So what is the Bionic Supply Chain?
The Boston Consulting Group (BCG) has done the most work popularizing bionic transformation. They describe bionic supply chains as:
Bionic supply chains use technology to augment human decision making. By empowering people with technology, companies can change how they operate in their supply chains - enabling faster, better integrated, and more precise decisions. In a bionic supply chain, the operating model enables integrated decisions across silos, taking into account impact over the entire supply chain.
In that spirit, Garvis is certainly developing bionic supply chain technology. However, during my research I could not find any connections between the Garvis team and the BCG authors leading me to investigate further.
A Short Lineage of Bionic Industry
The phrase “bionic supply chain” returns no Google results before 2014.
The first mention of the bionic supply chain concept comes in a text on “Materials Management: An Integrated Systems Approach” by Prem Vrat dated August 2014. In chapter 9.2, “A Perfect Example of JIT (Zero-Inventory) System” he uses the following analogy:
A perfect example of JIT or zero-inventory system is presented here by drawing an analogy of bionic supply chain in a human body. This example unfolds the conditions under which a zero-inventory system is possible. As indicated in introductory sections of the book, supply of oxygen to human body is taken as an analogy which exemplifies a perfect JIT or zero-inventory system. As we know, every living human body consumes oxygen as the most vital material input to sustain the life in the body but we do not need to carry any inventory of oxygen with us nor we experience shortages because, in this situation, we have a very benevolent and dependable supplier of oxygen in the form of nature (or whatever name one wishes to give). The supplier is 100 % dependable; is a local supplier with pull-based consumption, frequent deliveries and uninterrupted flow through dedicated flow routes (nostrils). There is no additional transportation cost for frequent deliveries and over-capacity of supplier (nature) and it does not pass on its carrying cost to the consumer. Being a fast-moving, vital material, there is no possibility of accepting delayed supplies or stockouts (a delay of more than 2–3 minutes in meeting oxygen requirements of the body can be catastrophic). Thus it is a truly JIT or Zero-Inventory system.
The connection between the human body and JIT supply chain systems is apt. Flashing forward a couple years to 2016, the term bionic design begins to appear. ELISE “is a systemized approach to design optimization that uses lightweight, multifunctional structures from nature to achieve weight savings of 50% and shortened development times.” In 2017, this was extended to additive manufacturing in a piece by Kevin Michaels on LinkedIn:
Another recent development is the emergence of “bionic design”—using designs found in nature. Bird bones and lily pads, for example, have impressive strength-to-weight ratios and cannot be made by traditional subtractive manufacturing techniques. Airbus thinks bionic design could someday reduce aerostructure weight 20-30% or more. It has evaluated applying bionic design to thousands of parts and printed more than 100 demonstrators.
The additive-bionic combination, which was not on the radar at Paris in 2013, is analogous to the introduction of composites four decades ago. Early composite aerostructure designs, dubbed “black aluminum,” looked like metallic aircraft. It took several decades for designs to catch up to the unique properties of composites. “The mental mindset of engineers is one of the biggest obstacles for AM—particularly with bionic design taking hold,” says Mueller. “OEMs need to educate their engineers now if they plan to use bionics—especially if they are going to compete for future programs like the [Boeing] 797 in the next decade.”
2017 is also when bionic thinking began to influence business strategy consulting. BCG wrote an article on “ Global Retail Banking 2017: Accelerating Bionic Transformation” and PricewaterhouseCoopers (PwC) discusses “The Bionic Company.”
By 2018, PwC was claiming the “bionic supply chain is already a reality”. While “Leading a Bionic Transformation” outlines a ‘bionic’ strategy based on the ideas of behavioural, cognitive, and network capital.
Bionic transformation was quiet throughout 2019, but was revived in 2020 by BCG. Their work on “Building the Bionic Supply Chain” remains the top Google search result for ‘bionic supply chain.’ Their definition remains as the most relevant to the meaning of bionic supply chain today:
The bionic supply chain operating model, a major step forward from the digital supply chain, addresses these issues. Digital capabilities are fully deployed E2E. A platform organization, which consists of a cross-functional team with digital and supply chain capabilities, designs automated E2E processes and manages exceptions. Companies use KPIs to steer E2E supply chain processes while balancing tradeoffs to best support the business strategy.
As we move further into the 2020s we will see if Garvis, BCG, and PwC continue to bring bionic thinking to mass adoption in industry.
Capturing this week's trending industry 4.0 and emerging industrial technology media
Building sustainability into operations
The path discrete manufacturing companies have taken to make their operations carbon neutral has important lessons for any business pursuing the dual mission of profitability and sustainability. Today, manufacturers of physical products find themselves on the front lines of sustainability. In part, that’s because their customers demand cleaner, lower-carbon products right now. In the high-tech sector, for example, Apple’s targets for reducing Scope 1 and Scope 2 emissions far exceed the minimum requirements of the Science Based Target Initiative’s (SBTi) 1.5° pathway, and the company is committed to achieving Scope 3 carbon neutrality by 2030. The electric carmaker Polestar has established a “striving for net zero” mission that aims to create a truly climate-neutral car by 2030 through intense collaboration with suppliers, entrepreneurs, and innovators.
Discrete manufacturing organizations are also well positioned to understand, manage, and mitigate their environmental impact, thanks to their progress in digitizing operations and supply chains over the past decade. To recognize manufacturing enterprises that embrace both sustainability and the Fourth Industrial Revolution (4IR) at scale, last year the World Economic Forum (WEF) announced a new category in its Global Lighthouse Network program: the Sustainability Lighthouse. These businesses are applying 4IR technologies to reduce their environmental footprint significantly.
Development of New Technology for Wastewater Treatment for Semiconductor Production
Alcohols are used to remove impurities on the surface of semiconductors or electronics during the manufacturing process, and wastewater containing alcohols is treated using reverse osmosis, ozone, and biological decomposition. Although such methods can lower the alcohol concentration in wastewater, they are ineffective at completely decomposing alcohols in wastewater with a low alcohol concentration. This is because alcohol is miscible in water, making it impossible to completely separate from alcohol using physical methods, while chemical or biological treatments are highly inefficient. For this reason, wastewater with a low alcohol concentration is primarily treated by diluting it with a large amount of clean water before its discharge.
The research team employed Fenton oxidation that uses oxidizing agents and catalysts during the advanced oxidation process for water treatment. Usually alcohols were used as reagents to verify radical production during Fenton oxidation in other advanced oxidation process (AOP) studies, they were the target for removal from semiconductor wastewater in this research. This water treatment technology is expected to dramatically reduce the cost and water resources invested into the treatment of semiconductor wastewater. In the past, clean water with a volume 10 times higher than that of the wastewater under treatment was required for dilution of the wastewater in order to reduce the alcohol concentration of 10 ppm in the wastewater to less than 1 ppm.
AMP Robotics Develops Industry’s First AI-Powered System for Recovery of Film and Flexible Packaging
AMP Robotics Corp. (“AMP”), a pioneer in artificial intelligence (AI), robotics, and infrastructure for the waste and recycling industry, is developing an AI-powered automation solution to improve recovery of film and flexible packaging. This first-of-its-kind innovation for materials recovery facilities (MRFs) aims to tackle the persistent challenge of film contamination.
A mere 1 percent of U.S. households have curbside access for recycling film and flexible packaging today, estimates The Recycling Partnership. Yet film and flexibles comprise the fast-growing and second-largest valued packaging segment, behind only corrugated containers and ahead of bottles and other rigid plastic packaging. Close to 95 pounds of these materials, including grocery and storage bags, pouches, and wrappers, are found in the average U.S. home each year. AMP’s solution, AMP Vortex™, is the industry’s first AI-powered automation system for film removal and recovery in MRF environments. AMP’s system targets film contamination and is initially optimized for quality control on fiber lines. Vortex provides the industry with the most flexible and adaptable solution targeting film; it can be deployed as a retrofit solution in various configurations to accommodate different belt sizes and inclines.
Table Tennis: A Research Platform for Agile Robotics
Robot learning has been applied to a wide range of challenging real world tasks, including dexterous manipulation, legged locomotion, and grasping. It is less common to see robot learning applied to dynamic, high-acceleration tasks requiring tight-loop human-robot interactions, such as table tennis. There are two complementary properties of the table tennis task that make it interesting for robotic learning research. First, the task requires both speed and precision, which puts significant demands on a learning algorithm. At the same time, the problem is highly-structured (with a fixed, predictable environment) and naturally multi-agent (the robot can play with humans or another robot), making it a desirable testbed to investigate questions about human-robot interaction and reinforcement learning. These properties have led to several research groups developing table tennis research platforms.
Walking Robot Designed for in-Space Maintenance Missions
The autonomous walking robot can move across a surface, performing tasks at any stage of its journey, and is hoped to provide novel opportunities in in-orbit infrastructure assembly and space station maintenance. The design has already been tested on Earth for the assembly of an 82 foot Large Aperture Space Telescope – something it is hoped to ultimately be able to achieve in-space, while a smaller version of the design has also been tested for large-scale construction purposes back on Earth, such as construction and maintenance of energy infrastructure such as wind turbines.
U.S. Navy Takes Falkonry AI to the High Seas for Increased Equipment Reliability and Performance
Falkonry today announced a big leap for Falkonry AI with the Office of Naval Research deploying its AI applications to advance equipment reliability on the high seas. This AI deployment is carried out with a Falkonry-designed reference architecture using NVIDIA accelerated computing and Oracle Cloud Infrastructure’s (OCI’s) distributed cloud. It enables better performance and reliability awareness using electrical and mechanical time series data from thousands of sensors at ultra-high speed.
Falkonry has designed its automated anomaly detection application, Falkonry Insight, to take advantage of Edge computing capabilities that are now available for high security and edge-to-cloud connectivity. Falkonry Insight includes a patent-pending, high-throughput time series AI engine that inspects every sensor data point to identify reliability and performance anomalies along with their contributing factors. Falkonry Insight organizes the information needed by operations teams to determine root causes and automatically informs operations teams to take rapid action. By inserting an edge device into the US Navy’s operational environment that can process data continuously, increasingly sophisticated naval platforms can maintain high reliability and performance out at sea.
How United Manufacturing Hub Is Introducing Open Source to Manufacturing and Using Time-Series Data for Predictive Maintenance
The United Manufacturing Hub is an open-source Helm chart for Kubernetes, which combines state-of-the-art IT/OT tools and technologies and brings them into the hands of the engineer. This allows us to standardize the IT/OT infrastructure across customers and makes the entire infrastructure easy to integrate and maintain. We typically deploy it on the edge and on-premise using k3s as light Kubernetes. In the cloud, we use managed Kubernetes services like AKS. If the customer is scaling out and okay with using the cloud, we recommend services like Timescale Cloud. We are using TimescaleDB with MQTT, Kafka, and Grafana. We have microservices to subscribe to the messages from the message brokers MQTT and Kafka and insert the data into TimescaleDB, as well as a microservice that reads out data and processes it before sending it to a Grafana plugin, which then allows for visualization.
We are currently positioning the United Manufacturing Hub with TimescaleDB as an open-source Historian. To achieve this, we are currently developing a user interface on top of the UMH so that OT engineers can use it and IT can still maintain it.
Tracking this week's major mergers, partnerships, and funding events in manufacturing and supply chain
Garvis Raises €3.5 Million Euros for a Bionic AI Platform to Optimize Supply Chain Management
A year after the launch of its AI platform for demand and inventory planning, Belgian startup Garvis raised € 3.5 million to develop the platform. Investors include British investment funds Superseed and Scalebridge Capital, and the German group Bosch Ventures. Garvis’ technology enables companies to respond at lightning speed to upheavals and evolutions that influence customer purchase behavior. One unique feature: even non-specialists are able to use the platform within a day.
Businesses need a radical new planning method that uses risk profiles, insight and real-time data. The platform Garvis developed, maps relevant environmental factors and provides transparent, explainable insights and predictions of demand patterns for various industries such as automotive, semiconductor, retail, and food and beverage. It uses open-box AI to respond to global fluctuations in buying behavior. Unexpected changes in demand patterns are recognized early, allowing planners to immediately adjust their forecasts and keep schedules up to date. Garvis works with the University of Antwerp (Belgium) to continuously optimize the predictive algorithms.
Ambi Robotics Raises $32 Million for New Kind of Warehouse Robot
The Ambisort can plow through about 400 parcels per hour; humans do the same work at about one-third the pace and usually make more mistakes. Ambi Robotics, the company that developed the system and the accompanying machine-learning algorithms that allow the robot to recognize each parcel and select the right way to grasp it, has deployed 80 of these systems and plans to surpass 100 in the field next year. On Monday, Ambi is announcing that it raised $32 million from Tiger Global, Bow Capital and the UK’s Ahren Innovation Capital. Pitney Bowes, the postage meter maker turned e-commerce logistics firm with 55 warehouses around the country, is another investor in the round as well as a customer.
Makersite secures $18M investment
We are happy to announce the completion of an $18M funding round. The investment is led by Hitachi Ventures, the global venture capital arm of Hitachi, Ltd., and Translink Capital, a Silicon Valley-based VC fund, with participation from KOMPAS, an EU-based venture capital fund, and seed investor Planet A.
When looking at the sustainability space right now, there’s a great buzz about reporting standards, ESG, Science Based Targets, GHG-Protocol, etc. But in 3-5 years’ time, no one will care about any now-implemented corporate reporting. What counts are the changes implemented across an organization. Makersite helps enterprises to take the right decisions today, not tomorrow. The investment will help us to grow our sales and marketing teams in Europe and the U.S along with increasing our delivery capacities. This way, the investment supports both our old and new customers.
ShipIn Systems Announces Series A Funding with Zeev Ventures, Raising $24 Million
ShipIn Systems, the world’s first Visual Fleet Management Platform, today announced Series A funding led by Zeev Ventures, a leading Silicon Valley venture capital firm. Investors at.inc/, Hyperplane, and Munich Re Ventures also participated, bringing the total funding to $24 million.
With this investment, ShipIn will focus on significantly scaling across the industry, and expand its visual analytics platform capabilities. Using AI and computer vision technology, ShipIn’s FleetVision™ platform detects events onboard ships in real-time, alerting crew at sea and teams ashore to safety or security hazards, operational anomalies, machinery concerns, and more. A recent study with one of its customers highlighted a 40% reduction in losses onboard cargo ships, while increasing cargo operations productivity by 8%.