Optimize machine metrics with a machine-as-a-service model, blockchain
Shifting to a machine-as-a-service (MaaS) model that uses blockchain helps create a secure model for greater optimization, according to representatives from the startup, Steamchain. The Industrial Internet of Things (IIoT), it seems, is everywhere in manufacturing and is changing how operations are managed. Providing better, faster data to create better decisions and higher efficiencies on the plant floor remain among priorities for those involved.
One challenge companies face is validating information received and proving that a machine or a product meets contract goals.
Steamchain seeks to help assuage those concerns with a MaaS model (Figure 1) using blockchain to provide usable, helpful data for original equipment manufacturers (OEMs) and end users. Not to be confused with one of the powering forces for the first industrial revolution, the acronym in the company’s name stands for secure transaction engine for automated machinery (steam).
Why Steamchain uses blockchain
Blockchain is a list of records linked using cryptography; each block uses a cryptographic hash of the previous block that provides data on when a transaction is recorded. It is an open ledger between two parties designed to prevent intentional or inadvertent data modification.
Blockchain has been in the news recently because of its connection to cryptocurrency, and the same principles are applied to MaaS.
“It’s about the mechanism that makes it a good data management solution,” said Michael Cromheecke, CEO of Steamchain. “It’s a great clearinghouse, and it is a secure platform.”
While there have been stories about cryptocurrencies being hacked or broken into, Cromheecke pointed out it was the user being hacked. The blockchain remains secure.
“You’d need enormous computing power possibilities to break through the blockchain,” he said. The point of MaaS, Cromheecke said, is about selling the outcome of the asset through machine performance metrics plus contract terms, and providing a financial transaction that benefits both parties in a secure environment where the data cannot be tampered with.
Machine truths, incentives
“Nobody’s able to predict failure consistently,” Cromheecke said. “Is the machine hitting its mark, uptime, producing quality? By knowing the little pieces of data, you know the operating performance of the asset.” That knowledge enables the model of companies using MaaS rather than capital investments. The correct financial model creates the right incentives for optimization rather than hitting a minimum goal in an initial specification. What’s needed, Cromheecke suggested, is a resilient, transparent record of the machine’s real-time performance.
The business process challenge, he said, is the storage and visualization of the metrics, the contract terms, the financial result calculations, and the execution and audit of the financial transaction so all parties trust what happened. For Steamchain, Cromheecke said, it’s about selling the outcome for the asset.
Steamchain’s IoT MaaS transaction platform (Figure 2) supports standard Steamchain modules by providing a:
1. Machine client that’s based on a programmable logic controller (PLC) or an open-architecture controller with an open application interface that easily supports industrial protocols.
2. Cloud-based management utility, built on Microsoft Azure for security, flexibility, and visualization of performance and financial metrics.
3. Secure transaction engine, a ledger of performance and financial data, where access is not controlled by one party, avoiding data ownership, access, or credibility issues.
There’s flexibility in how it’s applied: the Steamchain client doesn’t have to be installed at the machine level. The client could be on gateway, machine, or on an IoT platform. Steamchain’s initial goal is to pursue the original equipment manufacturer (OEM), which stands to benefit the most from this at the outset, since there’s no open platform that helps turn MaaS data into money, Cromheecke said. The initial signs have been encouraging.
Machine output, value creation
“The feedback from the OEM community has been positive,” said Christopher Zei, an advisor for Steamchain. “OEMs are using the deployment to help on the service side and to eliminate a lot of the burdens that are associated with the factory acceptance test (FAT) process because we only have to do it once.”
Zei said OEMs have been thinking about offering MaaS for a long time, but migrating may create a cashflow gap for all but the largest OEMs. Could an OEM afford to put 30 machines out the door, for instance, without getting paid within the usual period for those assets? An option might be a 50-50 ownership split for the first contract, Zei suggested. Over time, margins create continuing cash flow for the OEMs, since about 80% of revenue is for the machine and 20% for services, but margins generally are opposite.
Savings from eliminating traditional FAT can amount to hundreds of engineering hours for similar efforts in the factory and on the customer site, resulting in 50% savings on acceptance test costs and 10% improvement in delivery time, Zei said.
Machine optimization is the sweet spot for both parties. Suppose with a traditional installation, a machine initially only needs to run at 100 parts per minute (ppm), rather than the 400 ppm capacity?
Zei took the idea a step further. “Then, what if the machine could be made to run safely at 440 ppm? Traditionally, the OEM would never get paid for that and there really is no incentive for them to work towards this. They can under this model.”
At present, the company isn’t interested in solving all the problems of the factory. One machine’s data, for Steamchain, is a good starting point. Steamchain gets a percentage over time by enabling all parties to make more money over time.
“We’re focused on a narrow set of data,” Zei said. “We’re not interested in millions of bits of data, but rather looking at small amount of data that impacts the operation of machines.”
Learn more at http://Steamchain.io.