Secure the Bag: Manufacturing Tech Deals Sizzle


Equity market sentiment has shifted sharply in recent weeks. Technology growth stocks in particular are getting crushed. Tiger Global is down around $17 billion on the year already. This negative market sentiment is leaking into private markets and is impacting fundraising for startups. Most significantly, the companies closest to going public have seen a massive slow down in late stage venture funding. Startup founders have taken notice of the rapid equity repricing and have begun to secure financing to thrive (or survive) through the potentially turbulent times ahead.

Exponential Industry has been diligently tracking venture activity in the industrial technology market. The last couple of weeks have seen a flurry of activity unlike any other period over the last couple years.

The biggest headline is Augury acquiring Seebo for between $100 and $200 million. Moving beyond machine health, Augury is evolving into end-to-end production health for industrial enterprises. From the post, “As one of the first unicorns in industrial AI, Augury is already a proven entity when it comes to reducing and eliminating unplanned downtime. But now with Seebo’s best-in-class process health technologies in the mix, Augury can leverage AI-driven insights across machines, processes and operations in ways that can unlock fundamental transformation across the industry.”

In late stage venture, industrial technology companies seek shelter or growth capital to ride out the storm. Mojix, a supply chain SaaS platform, secured investment from private equity firm Peak Rock Capital “to accelerate the Company’s product roadmap and pursue organic growth and strategic acquisition.” Fictiv raised a hefty $100 million Series E from Activate Capital and SkySpecs landed $80 million in Series D funding from Goldman Sachs. ProteanTecs, a global leader in deep data analytics for electronics monitoring, closed $45 million financing round to strengthen their market leadership.

Leading mid-stage startups also found capital to spare. Vention, a digital manufacturing automation platform, captured $95 million in Series C funding. Construction robotics startup, Dusty Robotics, whose solutions automate construction’s manual workflow raised a solid $45 million Series B round.

Lastly, in the earliest stages of funding, companies were able find money as well. Tignis ($7.2 MM), Voxel ($15 MM), and Voyage Foods ($36 MM) all completed successful Series A rounds. In the seed stage, Pico MES ($6.75 MM) raised to continue is mission to provide an MES that meets the needs of smaller manufacturers.

With all this talk about venture capital, I’d like to introduce Aditya Raghupathy’s newsletter Breaking the Bottleneck. Aditya, an investor at Schematic Ventures, and I collaborate regularly to give a variety of perspectives on manufacturing news, funding, and startups. Check out his latest issue for more details on manufacturing deals!

Assembly Line

Augury Acquires Process-Based AI Company Seebo, Targets $1 Trillion in Untapped Capacity for Manufacturers and Industry

📅 Date:

🏢 Organizations: Augury, Seebo

Augury, the leading provider of IoT and Industrial AI solutions that improve health and reliability of machines for manufacturing and industry, today announced it has signed a definitive agreement to acquire Seebo, a leader in AI-based process intelligence. The deal is a combination of cash and stock and is valued between $100 million and $200 million. Together the companies will provide a never-before possible, AI-driven view into the interplays between the diverse factors that influence overall production health. These correlated insights will enable customers to take actions that improve asset performance, process optimization, quality, sustainability and safety.

Read more at Businesswire

Introducing new Google Cloud manufacturing solutions: smart factories, smarter workers

📅 Date:

🔖 Topics: Cloud Computing, Machine Health

🏢 Organizations: Google, Litmus

The new manufacturing solutions from Google Cloud give manufacturing engineers and plant managers access to unified and contextualized data from across their disparate assets and processes.

Manufacturing Data Engine is the foundational cloud solution to process, contextualize and store factory data. The cloud platform can acquire data from any type of machine, supporting a wide range of data, from telemetry to image data, via a private, secure, and low cost connection between edge and cloud. With built-in data normalization and context-enrichment capabilities, it provides a common data model, with a factory-optimized data lakehouse for storage.

Manufacturing Connect is the factory edge platform co-developed with Litmus that quickly connects with nearly any manufacturing asset via an extensive library of 250-plus machine protocols. It translates machine data into a digestible dataset and sends it to the Manufacturing Data Engine for processing, contextualization and storage. By supporting containerized workloads, it allows manufacturers to run low-latency data visualization, analytics and ML capabilities directly on the edge.

Read more at Google Cloud Blog

Where And When End-To-End Analytics Works

📅 Date:

✍️ Author: Anne Meixner

🔖 Topics: Manufacturing Analytics

🏭 Vertical: Semiconductor

To control a wafer factory operation, engineering teams rely on process equipment and inspection statistical process control (SPC) charts, each representing a single parameter (i.e., univariant-based). With the complexities of some processes the interactions between multiple parameters (i.e., multi-variant) can result in yield excursions. This is when engineers leverage data to make decisions on subsequent fab or metrology steps to improve yield and quality.

“When we look at fab data today, we’re doing that same type of adaptive learning,” McIntyre said. “If I start seeing things that don’t fit my expected behavior, they could still be okay by univariate control, but they don’t fit my model in a multi-variate sense. I’ll work toward understanding that new combination. For instance, in a specific equipment my pump down pressure is high, but my gas flow is low and my chamber is cold, relatively speaking, and all (parameters) individually are in spec. But I’ve never seen that condition before, so I need to determine if this new set of process conditions has an impact. I send that material to my metrology station. Now, if that inline metrology data is smack in the center, I can probably disregard the signal.”

Read more at SemiEngineering

Factory+: a connected, smart factory driven by big data

📅 Date:

🏢 Organizations: University of Sheffield

Factory+ is an open-access digital architecture for manufacturing shop floors that simplifies the way data can be handled across an organisation. Factory+ aims to provide a synthesised way for machinery to capture and use data to solve problems; to make manufacturing more sustainable, efficient and ready for Industry 4.0 – or even 5.0. It is a truly collaborative project of Internet of Things (IoT) engineers, robotic engineers, software engineers and data scientists.

Data scientists are considered the users of the Factory+ architecture and need to be able to pull data for any project. The value of having data scientists involved in this is that, while we don’t have the domain knowledge of an engineer, we do know what should be considered when collecting useful data for an array of problems without simply trying to collect and store all available data; an endeavour quickly curtailed by storage limitations.

Read more at The Manufacturer

Konoike Transport and OSARO Team Up to Pilot Japan's First Fully Automated Warehouse

How Walmart Uses Apache Kafka for Real-Time Replenishment at Scale

📅 Date:

🔖 Topics: Inventory Optimization, Demand Planning

🏢 Organizations: Walmart, Confluent

Real-time inventory planning has become a must for Walmart in the face of rapidly changing buyer behaviors and expectations. But real-time inventory is only half of the equation. The other half is real-time replenishment, which at a high level, we define as the way we can fulfill the inventory demand at every physical node in the supply chain network. As soon as inventory gets below a certain threshold, and based on many other supply chain parameters like sales forecast, safety stock, current availability of the item at node and its parents, we need to automatically replenish that item in a way that optimizes resources and increases customer satisfaction.

On any given day, Walmart’s real-time replenishment system processes more than tens of billions of messages from close to 100 million SKUs in less than three hours. We leverage an array of processors to generate an order plan for the entire network of Walmart stores with great accuracy and at high throughputs of 85GB messages/min. While doing so, it also ensures there is no data loss through event tracking and necessary replays and retries.

Read more at Confluent Blog

Forecasting Algorithms: A Tool to Optimize Energy Consumption

📅 Date:

✍️ Authors: Dorian Grosso, Pascal Lu

🔖 Topics: Forecasting

🏢 Organizations: Metron

For example, a client connected to the main grid on a variable energy contract, with a controllable battery and solar panels, must satisfy an electricity demand. The two sources of uncertainty in the future are the electricity demand (load) and the renewable energy production. In order to avoid a black out while minimizing the total electricity cost over the time horizon, we need to forecast them.

We usually forecast both the mean value and a probability distribution. This is so that we can evaluate the level of uncertainty and assess the spectrum of all possible scenarios in the future. For example, rather than saying that the electricity production of solar panels will be 150 kWh tomorrow, it is better to make a prediction of the probability. If we say that there is a probability of 95% that the electricity production will be between 120 kWh and 180 kWh, we can be aware of the extreme values, such as in the case of high or low production.

Read more at Metron Blog

Battery Analytics: The Game Changer for Energy Storage

📅 Date:

🔖 Topics: Manufacturing Analytics

🏢 Organizations: TWAICE

Battery analytics refers to getting more out of the battery using software – not only during operation, but also when selecting the right battery cell or designing the overall system. For now, the focus will be on the possibilities to optimize the in-field operation of battery storages.

The TWAICE cloud analytics platform provides insights and solutions based on field data. The differentiation factor is the end-to-end approach with analytics at its heart. After processing and mapping the data, the platform analytics layer runs different analytical algorithms, electrical, thermal and aging models as well as machine learning models. This variety of analytical approaches is the key to balance data input quality differences and is also the basis for the wide and expanding range of solutions.

Read more at TWAICE Blog

Surge Demand

A take on China’s transformation to smart factories while Japan plans a $155 billion decarbonization fund. The history of the world’s largest industrial technology fair, Hannover Messe. US manufacturers work to address the baby formula shortage.