Last week we started a roundup of some digital manufacturing trends based on a recent ‘‘Trends in Digital Manufacturing” survey, that was jointly conducted by SME, a manufacturing association promoting advanced manufacturing technologies, and Plataine, a provider of Industrial IoT (IIOT) and AI-based manufacturing optimization solutions, that shows key insights on plans for factory digitization.
While we agree with most of the issues raised in the report, they represent pretty broad strokes when real digital manufacturing trends are considered. However, we do see several positive trends occurring and one glaring negative one (but there is hope) in these trends:
Blockchain Migrates To Manufacturing
One of the most interesting, but mysterious and most misunderstood technologies in the digital realm are blockchain and bitcoin. Blockchain, specifically, is also the technology with great potential for securing data and transactions that demand trust. Although it requires quite a bit of space to adequately explain, this time around, I’ll focus on a few aspects of blockchain and possible implications for manufacturing.
Blockchain combines the openness of the Internet (that is, until Net Neutrality goes away) with the security of cryptography to give companies a faster way to verify vital information and establish trust without the need for third parties and other intermediaries. It was initially developed more than a decade ago to provide the technical underpinnings for Bitcoin, the cryptocurrency with which it is sometimes mistaken. As Pat Bakey, president of SAP Industries, noted, “Early horror stories about bitcoin, the most famous digital currency to use blockchain, prompted its mainstream dismissal as a dubious tool of the dark web.”
Manufacturing Cloud Services (Source: Researchgate.net)
At its core however, blockchain is simply an open and secure method of recording transactions, just like a traditional ledger. Because blockchains establish trust, they provide a simple, paperless way to establish and track ownership of money, information, and objects by individuals, companies, and other organizations.
By design, blockchains are inherently resistant to modification. The data stored in a blockchain exists as a shared and continually reconciled database hosted on millions of computers around the world, so that no single version of it exists in a single place. In addition, each block of data in a blockchain is linked and secured to the next in a sequence using cryptography.This makes it virtually impossible to add, remove, or change data without alerting others in the chain.
Understanding what blockchain can do and enable is part of the process of understanding both the challenges and opportunities for innovation, afforded by digital transformation.
Data (more about that below) is at the core of this transformation, and has become the biggest resource for business. The companies that survive and thrive in this new hyper-competitive environment will collect and curate Big Data using IoT sensors and other tools, process that data to discover patterns and insights through machine learning and analytics, and secure and streamline their operations using blockchains.
Blockchain may just be getting started in manufacturing, but is almost certain to be one of the most disruptive technologies taking it into the future.
However, before we do our blockchain happy dance, let’s briefly touch on the potential downside.
Even though its been around a while, there is still a significant amount of confusion and debate what a blockchain even is. Some would argue that it’s become just another meaningless marketing buzzword, but the most commonly accepted definition describes a shared, decentralized, cryptographically secure, immutable digital ledger. In theory (and it’s only theory), provides new opportunities to solve complex coordination problems without letting ingrained coordinators so much value in the process. This same philosophy was one of the initial tenets of the internet. Eventually, the open collaborative potential succumbed to a massive takeover of “trusted” third parties – Amazon, Facebook, and Google. So much for decentralization. Will blockchain ultimately go the same way? That’s hard to say or predict now, but nothing should surprise us – there’s just too much at stake for somebody not to try.
As long as we’re speaking of blockchain in the context of manufacturing, last week General Electric announced that it had filed a patent application for using the technology of blockchain in validating and verifying 3D printed objects on their supply chain. The application which was filed in December 2017 and recently released by the U.S. Patent & Trademark Office, discusses methods for implementing a distributed ledger system into additive manufacturing. GE proposes to use blockchain and the distributed ledger system, which would keep a record of the historical data on the additive manufacturing process with proper verification and validation of the devices used for 3D printing and the stakeholders who push the product along in the supply chain. This patent has far-reaching consequences and gives GE a good start into the world of blockchain.
Data and Analytics
It is predicted that by 2020, there will be as much as 50 times the digital content compared to what exists today. Big data analysis becomes increasingly difficult and time-consuming as digitized manufacturers struggle to manage, update, and analyze product and consumer information. As such, many businesses are opting to move content to the cloud as well as house on-site for a hybrid approach to their storing, managing, and processing needs. Information about things like supply, delivery, customer support used to be difficult to find or cumbersome to work with. In the digital era, that data is streamlined and collaboration-friendly, increasing accessibility for all stakeholders. Because production teams and consumers are growing accustomed to the immediacy and intuitiveness of IoT, they now expect the same from their processes and products, requiring faster innovation from manufacturers. To keep up with these expectations, digital transformation changes the way businesses manage and share product information across the enterprise, increasing production and transparency and decreasing cost and down time.
Most manufacturers have already made the most obvious changes to streamline their operations, using traditional methods to eke as much productivity out of their supply chains and plants as possible. To do even more with less in a slow-growth and uncertain environment, however, companies must look for new ways to boost the productivity and profitability of their operations.
There’s one significant asset that manufacturers have not yet optimized: their own data. Process industries generate enormous volumes of data, but many have failed to make use of this mountain of potential intelligence. Historically, manufacturers have lagged other industries in their IT capabilities. However, thanks to cheaper computational power and rapidly advancing analytics opportunities, process manufacturers can put that data to work, gathering information from multiple data sources and taking advantage of machine learning models and visualization platforms to uncover new ways to optimize their processes from the sourcing of raw materials to the sale of their finished products.
Advanced analytics also help manufacturers solve previously impenetrable problems and reveal those that they never knew about, such as hidden bottlenecks or unprofitable production lines. There are three applications of advanced analytics in particular that together are powerful tools for maximizing the physical and financial performance of manufacturers’ assets and often-complex supply chains.
Advanced analytics approaches can deliver earnings before interest, taxes, depreciation, and amortization (EBITDA) margin improvements of as much as 4 to 10 percent. They can also boost ongoing continuous improvement efforts at a time when manufacturers have seemingly exhausted other options for increasing productivity. Moreover, they offer a lever for competitive advantage, even for companies with overcapacity, by helping them better manage their production systems and optimally reallocate resources in real time.
Data-driven manufacturing can be realized by applying advanced analytics to manufacturers’ data can produce insights to optimize the productivity of individual assets as well as the total manufacturing operation. Deployed in conjunction with each other, these tools enable operators to maximize their productivity and profitability.
In an increasingly complex manufacturing environment, this ongoing data-driven transformation can enable companies to dynamically optimize their tactical planning and make better strategic decisions for the long term. However, advanced analytics tools alone will not magically transform process manufacturing. The value of these new tools is only realized when they complement human skills and expertise. These new approaches make it possible for manufacturing professionals to engage in more fact-based discussions, comparing the real impact of different parameters on business outcomes before making decisions and, in many cases, to consider counterintuitive actions that might improve productivity or profitability.
Cyber Threats Increase
A huge issue that affects all of the aforementioned trends is how do you keep all of this stuff safe and secure? From Equifax to WannaCry to Russia’s manipulation of U.S. social media, 2017 was the most challenging year ever for cyber threats. An IT security firm, AV-TEST Institute, says it now registers more than 350,000 new malicious programs every day, and believes 2018 may turn out to be worse (with up to 780+ million malware instances), as the number of sensor/internet-connected devices increases.
It could be particularly challenging for those in the industrial IoT space: cyber expert Shachar Daniel warns that as manufacturers increasingly embrace cyber-physical systems, the vulnerability of their operations will become far more vulnerable.
The good news is, while there are more opportunities to make life miserable for businesses, advances in AI and machine learning offer solutions that will help predict and ward off random cyberattacks.
All of these trends point to the fact that there has never been a greater need for highly skilled workers with STEM talents – a tall order given the lack of urgency and the state of education in too many schools. However, the manufacturing organizations that can hire qualified employees, and who continue to use technological advancements to move their companies forward are the ones that will thrive and remain relevant, competitive, and profitable.
