2. Resource planning & sourcing
Once a product design is finalized, the next step is planning how it will be made at production scale. Typically, this requires gathering a web of parts suppliers, basic materials makers, and contract manufacturers to fulfill a large-scale build of the product. But finding suppliers and gaining trust is a difficult and time-consuming process.
The vacuum maker Dyson, for example, took up to two years to find suppliers for its new push into the auto industry: “Whether you’re a Dyson or a Toyota it takes 18 months to tool for headlights,” a worker on their project reported.
In 2018, assembly lines are so lean they’re integrating a nearly real-time inflow of parts and assembling them as fast as they arrive. Honda’s UK-based assembly factory, for example, only keeps one hour’s worth of parts ready to go. After Brexit, the company reported longer holdups for incoming parts at the border, and said that each 15 minute delay translates to £850,000 per year.
We looked at how technology is improving this complicated sourcing process.
DECENTRALIZED PARTS MANUFACTURING
Decentralized manufacturing may be one impending change that helps manufacturers handle demand for parts orders.
Distributed or decentralized manufacturing employs a network of geographically dispersed facilities that are coordinated with IT. Parts orders, especially for making medium- or small-run items like 3D printed parts, can be fulfilled at scale using distributed manufacturing platforms.
Companies like Xometry and Maketime offer on-demand additive manufacturing and CNC-milling (a subtractive method that carves an object out of a block), fulfilling parts orders across its networks of workshops.
Xometry’s site allows users to simply upload a 3D file and get quotes on milling, 3D printing, or even injection molding for parts. Right now, the company allows up to 10,000 injection-molded parts to be ordered on-demand, so it can handle builds done by larger manufacturers.
Xometry isn’t alone in offering printing services: UPS is also embracing the movement, offering services for 3D printed plastic parts like nozzles and brackets in 60 locations and using its logistics network to deliver orders globally.
As mass-customization takes off, so could the reliance on decentralized network of parts suppliers.
BLOCKCHAIN FOR RESOURCE TRACKING
Enterprise resource planning (ERP) software tracks resource allocation from raw material procurement all the way through customer relationship management (CRM).
Yet a manufacturing business can have so many disparate ERP systems and siloed data that, ironically, the ERP “stack” (which is intended to simplify things) can itself become a tangled mess of cobbled-together software.
In fact, a recent PwC report found that many large industrial manufacturers have as many as 100 different ERP systems.
Blockchain and distributed ledger technologies (DLT) projects aim to unite data from a company’s various processes and stakeholders into a universal data structure. Many corporate giants are piloting blockchain projects, often specifically aiming to reduce the complexity and disparities of their siloed databases.
Last year, for example, British Airways tested blockchain technology to maintain a unified database of information on flights and stop conflicting flight information from appearing at gates, on airport monitors, at airline websites, and in customer apps.
When it comes to keeping track of the sourcing of parts and raw materials, blockchain can manage the disparate inflows to a factory. With blockchain, as products change hands across a supply chain from manufacture to sale, the transactions can be documented on a permanent decentralized record — reducing time delays, added costs, and human errors.
Viant, a project out of the Ethereum-based startup studio Consensys, works on a number of capital-intensive areas that serve manufacturers. And Provenance is building a traceability system for materials and products, enabling businesses to engage consumers at the point of sale with information gathered collaboratively from suppliers all along the supply chain.
Going forward, we can expect more blockchain projects to build supply chain management (SCM) software, handle machine-to-machine (M2M) communication and payments, and promote cybersecurity by keeping a company’s data footprint smaller.
3. Operations technology: Monitoring & machine data
Presumably, tomorrow’s manufacturing process will eventually look like one huge, self-sustaining cyber-physical organism that only intermittently requires human intervention. But across sectors, the manufacturing process has a long way to go before we get there.
According to lean manufacturing metrics (measured by overall equipment effectiveness, or OEE), world-class manufacturing sites are working at 85% of their theoretical capacity. Yet the average factory is only at about 60%, meaning there’s vast room for improvement in terms of how activities are streamlined.
Industry 4.0’s maturation over the next two decades will first require basic digitization.
Initially, we’ll see a wave of machines become more digital-friendly. Later, that digitization could translate into predictive maintenance and true predictive intelligence.
Large capital goods have evolved to a “power by the hour” business model that guarantees uptime. Power by the hour (or performance-based contracting) is now fairly common in the manufacturing world, especially in mission-critical areas like semiconductors, aerospace, and defense.
The idea dates back to the 1960s, when jet engine manufacturers like GE Aviation, Rolls Royce, and Pratt & Whitney began selling “thrust hours,” as opposed to one-off engine sales. This allows engine makers to escape the commodity trap and to focus on high-margin maintenance and digital platforms. Nowadays, GE is incentivized to track every detail of its engine, because it only gets paid if the engine is working properly.
Despite a guarantee of uptime, a machine’s owner is responsible for optimizing usage (just like airlines that buy jet engines still need to put them to good use). In short, factory owners still “own” the output risk between the chain of machines.
Without digitizing every step, efficiency is being left on the table. Yet there are serious barriers for manufacturers to take on the new burden of analytics.
Shop floors typically contain old machines that still have decades of production left in them. In addition to significant cost, sensors tracking temperature and vibration aren’t made with a typical machine in mind, lengthening the calibration period and efficacy.
When Harley-Davidson’s manufacturing plant went through an IIoT sensor retrofit, Mike Fisher, a general manager at the company, said sensors “make the equipment more complicated, and they are themselves complicated. But with the complexity comes opportunity.”
FROM INITIAL DIGITIZATION TO PREDICTIVE
To put it simply, operational technology (or OT) is similar to traditional IT, but tailored for the “uncarpeted areas.” Where the typical IT stack includes desktops, laptops, and connectivity for knowledge work and proprietary data, OT manages the direct control or monitoring of physical devices.
For manufacturers, the OT stack typically includes:
Connected manufacturing equipment (often with retrofitted industrial IoT sensors)
Supervisory control and data acquisition (SCADA) systems and human machine interfaces (HMI), which provide industrial monitoring for operations analysts
Programmable logic controllers (PLCs), the ruggedized computers that grab data on factory machines
3D printers (additive manufacturing) and computer numerical control (CNC) machines for subtractive manufacturing (like whittling away a block)
In a way, IT and OT are two sides to the same tech stack token, and as manufacturing gets better digitized, the boundaries will continue to blur.
Today, the “brain” for most industrial machines is in the programmable logic controller (PLC), which are ruggedized computers. Industrial giants like Siemens, ABB, Schneider, and Rockwell Automation all offer high-priced PLCs, but these can be unnecessarily expensive for smaller manufacturing firms.
This has created an opportunity for startups like Oden Technologies to bring off-the-shelf computing hardware that can plug into most machines directly, or integrate existing PLCs. This, in turn, allows small- and medium-sized businesses to be leaner and analyze their efficiency in real time.
As digitization becomes ubiquitous, the next wave in tech efficiency improvements will be about predictive analytics. Today’s narrative around the Internet of Things has suggested that everything — every conveyor and robotic actuator — will have a sensor, but not all factory functions are of equal value.
Slapping cheap IoT sensors on everything isn’t a cure-all, and it’s entirely possible that more value gets created from a smaller number of more specialized, highly accurate IoT sensors. Augury, for example, uses AI-equipped sensors to listen to machines and predict failure.
Cost-conscious factory owners will recognize that highly accurate sensors will deliver greater ROI than needless IoT.
NEW ARCHITECTURE AT THE EDGE
Computing done at the “edge,” or closer to the sensor, is a new trend within IIoT architecture.
Drafting on innovations in AI, and smarter hardware, Peter Levine of a16z anticipates an end to cloud computing for AVs, drones, and advanced IoT objects.
Connected machines in future factories should be no different.
Companies like Saguna Networks specialize in edge computing (close to the point of collection), whereas a company like Foghorn Systems does fog computing (think a lower-hanging cloud that’s done on-site like a LAN). Both methods allow mission-critical devices to operate safely without the latency of transmitting all data to a cloud, a process that can save big on bandwidth.
In the near future, advances in AI and hardware will allow IoT as we know it to be nearly independent of centralized clouds.
This is important because in the short term, it means that rural factories don’t need to send 10,000 machine messages relaying “I’m OK,” which expends costly bandwidth and compute. Instead, they can just send anomalies to a centralized server and mostly handle the decision-making locally.
Additionally, cloud computing latency has drastic downsides in manufacturing. Mission critical-systems such as connected factories can’t afford the delay of sending packets to off-site cloud databases. Cutting power to a machine split-seconds too late is the difference between avoiding and incurring physical damage.
And in the longer term, edge computing lays down the rails for the autonomous factory. The AI software underpinning the edge will be the infrastructure that allows factory machines to make decisions independently.
In sum, devices that leverage greater computing at the edge of the network are poised to usher in a new, decentralized wave of factory devices.
CYBERSECURITY IS A PRIORITY
One paradox of IIoT is that factories bear significant downside risk, yet are barely investing in protection: 28% of the manufacturers in a recent survey said they saw a loss of revenue due to cybersecurity attacks in the past year, but only 30% of executives said they’ll increase IT spend.
Cyber attacks can be devastating to heavy industry, where cyber-physical systems can be compromised. The WannaCry ransomware attack caused shutdowns at the Renault-Nissan auto plants in Europe. And in 2014, a sophisticated cyber attack resulted in physical damage at a German steel plant when an outage prevented a blast furnace from being shut down correctly.
Consequently, critical infrastructure is a growing segment within cybersecurity, and many startups like Bayshore Networks are offering IoT gateways (which bridge the disparate protocols for connected sensors) to allow manufacturers across many verticals to monitor their IIoT networks. Other gateway-based security companies like Xage are even employing blockchain’s tamperproof ledgers so industrial sensors can share data securely.
28% of the manufacturers in a recent survey cited a loss of revenue due to cybersecurity attacks in the past year. But only 30% of executives said they’ll increase IT spend.
Similarly, adding connected IoT objects and Industrial Control System (ICS) sensors has opened up new vulnerabilities at the endpoint.
Additionally, several of the most active enterprise cybersecurity investors are corporates with interests in OT computing. The venture arms of Dell (which makes industrial IoT gateways), as well as Google, GE, Samsung, and Intel are among the most active in this space.
Managing the ICS and IIoT systems securely will continue to be a critical area for investment, especially as hack after hack proves OT’s vulnerability.
Source : CBinsights