By: Jagmeet Singh
Blockchain has already disrupted business processes in the financial sector, and is poised to impact companies across industries. Because the technology can provide an immutable digital record of contractual interactions and transactions across an ecosystem, we believe that manufacturing is likely next in line.
Blockchain is a mutually shared ledger of all transactions in a given transactional relationship. Combined with its consensus mechanisms and use of public key infrastructure (PKI) to verify and authenticate all changes made to the ledger, blockchain can enable the network itself to ensure trust among participants. The result: a whole new way to support distributed manufacturing across the value chain.
The Importance of Trust
Consider, for example, the ways in which blockchain can simplify how trust is developed within a manufacturing ecosystem. In the traditional manufacturing world, parties transacting with each other spend considerable time and money on establishing external mechanisms to ensure trust, in the form of contracts, service-level agreements, quality checks, inspections, audits, scanning, escrows and regulatory compliance reviews, to name a few. As the number of parties increases, so does the complexity. Reconciling separate ledgers, enforcing contracts, ensuring supply chain transparency and protecting intellectual property when multiple entities are involved are all laborious and burdensome processes, prone to error and vulnerable to fraud.
Research shows that companies that build a culture of trust can fuel stronger performance by enabling departments to interact better and perform better across multiple dimensions. Establishing trust betweencontracted parties has similar positive effects. All these measures, however, amount to a costly “trust tax.”
For participants in a blockchain network – product designers, production shops, 3-D printers, logistics partners, sales and customer service – that tax is greatly reduced. A secure, distributed ledger infrastructure accessible to multiple parties enables a new level of real-time transparency and efficiency for transactions involving the transfer of anything of value – whether that means ideas, money or ownership.
In our recent global study that included 281 manufacturing professionals, in fact, “trust” was a top driver for blockchain adoption.
Ensuring Transparency, Security, Auditability
Blockchain ledgers are:
- Shared: Separate entities share a common source of truth.
- Distributed: Blockchain relies on peer-to-peer collaboration, with no central ownership.
- Secure: Cryptographic algorithms verify, authenticate and secure transactions.
- Time-sequenced: Data is written consecutively and is time-stamped.
- Immutable: Once written on the blockchain, data cannot be changed, tampered with or deleted.
Through smart contracts with supply chain partners on the blockchain network – programmed agreements that are independently verifiable and automatically executed when predefined conditions are met – companies can minimize human intervention and ensure performance transparency, transaction certainty and auditability.
Within industries and even across interlocked, tiered manufacturing sectors, distributed ledger systems allow companies to develop new, platform-based process flows. A user might execute a smart contract for a custom-configured order, for example, combining designs from multiple sources. The encrypted design data would be recorded on the shared platform; materials and services could be autonomously sourced; and a shared factory could produce the customized product. Payments, including royalties to designers, would be issued when the product is delivered. A record of all transactions, from design selection to payment, remains on the blockchain.
A Rising Tide Lifts All Boats
Blockchain technology thus enables distributed manufacturing, offering participants unprecedented opportunities to develop new product and service lines, create new customer segments, enter new markets and find new ways to use and share assets:
- Through supply chain transparency. All parties transact on a common platform, gaining real-time visibility into processes in the value chain, and simplifying materials sourcing and the interaction of design, manufacturers and other service providers. Supply chain processes, including payments and trade finance, can be streamlined and automated using smart contracts.
- Through digital product memories. Immutable records of asset provenance, materials, production data, ownership and other data ensure authenticity and minimize transaction risk.
- Through secure digital intellectual property. Parties to a transaction can be assured that their intellectual property is protected. Using blockchain to manage a contracted production run from a 3-D printer of ceramic components, for example, would allow a manufacturer to encrypt proprietary 3-D print files from end to end while creating an immutable history of the transaction. Similarly, escrows and royalty accounting would protect designers and other owners of IP.
There are many more circumstances in which adopting blockchain technology can deliver value. Participants can slash inventory costs and service times. They can eliminate reconciliation, and automate and speed financial and process flows. They can reduce manual interventions and reduce fraud. And they can create new ways to extend the lifecycle of products and optimize the use of assets.
What’s Next? Evaluating Readiness
As manufacturers move toward a shared and distributed model, business leaders can consider four questions when evaluating readiness:
- Where in the value chain, internally and externally, are we paying the highest “trust tax” in terms of excess cost, effort or lack of agility?
- How would the availability of a digital product memory drive value for our company, our customers and our business partners?
- Which types of partners, in what geographies and with what expertise, could we work with if transaction costs and efforts were lower?
- Which information assets (e.g., manufacturing, maintenance, operational and usage data) about our products could we monetize if there were a secure way to do so?
A blockchain-enabled, collaborative database is optimal for ensuring agreement between all participants in a value chain. It’s time for manufacturers to examine the implications for their business model. Organizations that gain hands-on experience with blockchain technology thorugh pilot projects will have an advantage as consortia start to form, and will be better equipped to lead the effort and make key decisions around structure and governance, prepare for the corresponding cultural shift, build skills and capabilities, and understand how it will impact business strategy going forward.
Get in the blocks. The race starts now.
Olesya Gorbunova, a Senior Consultant in Cognizant’s Blockchain & Distributed Ledger Practice, contributed to this blog.
This article originally appeared on the Digitally Cognizant Blog
Cognizant (Nasdaq: CTSH) is dedicated to helping the world’s leading companies build stronger businesses — helping them go from doing digital to being digital.
The State of the Union for IoT Intelligence
A digital transformation revolution in manufacturing is underway, and data is the primary currency paving the way for more efficient ways of doing business.
By Frank Antonysamy
Frank Antonysamy is Vice President of Cognizant’s Global IoT and Engineering Services
A digital transformation revolution in manufacturing is underway, and data is the primary currency paving the way for more efficient ways of doing business. Gone are the days when data analysis was left to Monday night quarterbacking by poring over static results. Today, thanks to a central wireless ecosystem which links relevant mobile devices, Internet of Things (IoT) connected machines and connected employees, data gathering and analysis in a smart factory is immediate and real-time optimization drives significant efficiencies.
But not all smart factories are created equal.
Given that enterprises are all on different points on the path to complete digital maturity, it helps to take stock of the state of IoT intelligence — where we are now and where we are headed — and what industrial organizations need to be successful.
Laying the foundation for intelligence
One of the key advantages of Industry 4.0 is the promise of the Internet of Things (IoT) or Industrial Internet of Things (IIoT). Sensors connected to raw materials, factory floor equipment and final products can relay information, over a Wi-Fi connection, about their health and usage patterns to wider enterprise asset management software and enterprise resource planning systems.
Equally important, stakeholders can access this data in real-time and on-demand. Companies can leverage this data to deliver insights across three channels:
- Product intelligence
- Service intelligence
- Operational intelligence
There is significant overlap across these three pillars but their power to deliver a smart factory with new avenues for monetization, is revolutionary.
Here’s an overview of the IoT landscape with respect to its three core pillars of intelligence.
Product intelligence shakes up traditional PLM
The smart factory runs on smart products whose intelligence can be leveraged to read the tea leaves of market demand. At its core, product intelligence is defined as intelligence derived from an intelligent (read…IoT-enabled) product. In the IoT world, traditional rules of product lifecycle management (PLM) no longer apply. Gone are the rinse-and-repeat cycles of concept, design, manufacture, marketing and sales. In traditional manufacturing, the ideation-sale stage took years if not decades and slight changes in market demand had a whiplash effect on the process.
IoT has rebooted the PLM conversation to move it away from the product and make it more about the customer. IoT-enabled products can now deliver intelligence post sales about how the product is being used (or not), how it is being disposed of, and a whole host of other downstream information. Such product intelligence is useful in two primary ways: as a method of refining the product to make it more agile and responsive to consumer needs (thereby leading to potentially more sales) and as new avenues of monetizing such product intelligence.
The future of product intelligence is a complete “closed-loop” product development, with real-time customer feedback woven into the process. It bears stressing that while customer focus groups and behaviors have always been part of the design and manufacture process, IoT has effectively compressed that time cycle and expanded the scale of parameters that might be considered — and monetized.
Service intelligence delivers customer-focused monetization
Monetization in the new smart factory landscape need not be restricted to product intelligence alone. Service intelligence, for example, is about delivering aftermarket intelligence in the form of added services to an existing or expanding customer base. A customer who buys Widget A from a manufacturing company might also be interested in understanding how to optimize the use of that widget for their own tailored environments.
While aftermarket services are not entirely new, the addition of IoT has the capability of delivering service intelligence on steroids. In the future, service intelligence providers will use IoT to tailor measurements of key performance indicators (KPIs) and delivery of data insights depending on exactly what the end customer is looking for. Tailoring service intelligence to the customer potentially leads to greater client stickiness. What’s more, IoT is capable of slicing and dicing intelligence for each and every customer, making the net results that much more insightful and leading to more bountiful monetization opportunities.
Operations intelligence squeezes the most out of machines
Monetization also comes from picking the low-hanging fruit in production processes. Arguably one of the best ways to squeeze the most out of IoT is to use it to increase manufacturing uptime. IoT is also favorably impacting the ability to fine-tune production processes by being able to connect, visualize and analyze data from a whole host of new players such as machines on the plant floor. RFID and computer vision layers also add to such intelligence.
IoT-embedded devices on the plant floor can spit out data that measures machine health, which can be fed into machine learning algorithms for predictive maintenance. If a rotor heats up past a preset temperature setting, for example, it can trigger the algorithm to send an alert to a plant worker or even proactively shut the machine down. Machine learning capabilities derived from IoT enhance KPIs such as manufacturing uptime.
In the future, expect a move toward increasingly segmented manufacturing, possibly sliced and diced into ever smaller batches. Operations intelligence will allow manufacturers to segment the production process — and fine-tune each — to fulfill a variety of specialty orders at the same time.
What it takes to deliver on the promise of IoT
While IoT intelligence in its various forms promises a truly smart factory with a wealth of monetization opportunities, it needs a robust infrastructure to truly deliver. Elements of this winning infrastructure include, among others: a C-suite willing to address negative attitudes of incumbency; standardization of data aggregation and analytics processes such as machine learning; and future-proofing technologies through increasing reliance on open-source models.
Since data is the lifeblood of IoT, enterprises need to ensure that they don’t get mired in the data lake — that the data they’re working with is clean and structured, relevant to the KPIs they want measured, and fed to algorithms in a consistent format. Once data is clean and uniform, smart factories can leverage IoT to feed machine learning algorithms that learn from the data and eventually deliver an almost lights-out production stream.
Since the future of intelligence also involves its monetization — vendors up and down the digital supply network will pay for insights — it will be important to connect stakeholders to the central nervous system of the smart factory in new ways. Customer service agents (or even customers themselves) for example should be able to see where product orders are in the production process and fine-tune their forecasts accordingly. IoT delivers transparency to all stakeholders — within reason, keeping intellectual property concerns in mind.
IoT in manufacturing is not limited to the production floor either. IoT sensors in warehouses can detect when supplies are going bad, when inventory is low and beef up accordingly. Remote weather events that can affect vendor delivery can trigger automated backups. The IoT-driven smart factory touches many processes and products much beyond the plant floor.
Until true digitization from start to finish is a total reality, companies are figuring out stop-gap measures that will leverage the promise of IoT. A “nerve center,” which serves as a central repository for data gathering and analytics can serve to overcome the problem of data connectivity across locations and devices.
The ripple effect from IoT intelligence is not limited to the manufacturing floor alone. By placing the digital core at the center, it reshapes processes up and down key constituencies such as supply chain and asset management.
How tomorrow’s tech might impact IoT intelligence
IoT is already being incorporated in the smart factory of today. Tomorrow, expect acceleration with respect to monetizing closed-loop product intelligence, an increased focus on the customer through service intelligence and using operations intelligence by improving businesses processes on the way to a truly smart factory.
The road is expected to get even smoother with the advent of 5G technology which will decrease latency of IoT for edge computing devices. 5G will deliver even faster access to data in real time which will make real-time analysis even more accurate. The technology has special ramifications for production processes where time is of the essence. Devastating machine shutdowns can be averted in split seconds by machine learning algorithms fed through 5G connections from IoT-enabled equipment. This means smart factories of faster computing speeds and greater agility. The state of the union for IoT intelligence is strong and only expected to grow stronger as new technologies such as 5G make data competencies that much more robust.
Cognizant (Nasdaq: CTSH) is dedicated to helping the world’s leading companies build stronger businesses — helping them go from doing digital to being digital.
Improving working conditions with blockchain
Blockchain is more often spoken about as an external tool for businesses to help secure supply chain. In a new pilot, blockchain is to be used to help improve health and safety within the workplace – at a Levi Strauss factory.
The testing out of blockchain as an internal health and safety auditing tool is being run as a collaboration between Harvard University’s public health graduate school, U.S. think-tank New America and the U.S. denim jeans company Levi Strauss & Co. The three have declared a project to design, build and operate a blockhain-based system for health and safety at work.
The new technology will be designed to augment outside auditors of factory health and safety with a system that will allow factory workers to self-report issues of concern. The factories that will test out the technology are based in Mexico, where three manufacturing sites in total employ 5,000 workers.
Mexico’s regulations for health and safety laws are exclusively federal in content. Under this legislation employers must obey standards, maintain safety programs, maintain compliance systems, ensure proper equipment and hazardous substance control. However, the level of safety is often subject to criticism (as with the International Labor Organization), such as in terms of accident rates and occupational illnesses like respiratory diseases.
The new project is designed to provide an alternate avenue for worker health and safety to be addressed, outside of periodic audit, and the mechanism enables a U.S. based company to ensure that clothes manufactured for the U.S. market are produced under conditions that are safe for workers.
The aim of the scheme is to input an annual worker survey on the blockchain. Once inputted the company’s site-based managers will be unable to alter it, and the findings will be made available to the workforce. The findings will be available for Mexican authorities to review as well as U.S.-based Levi Strauss managers. The blockchain will be provided by ConsenSys, the blockchain company founded by Joseph Lubin, once of Ethereum.
Tesla wants its factory workers to wear futuristic augmented reality glasses on the assembly line
- Tesla patent filings reveal plans for augmented reality glasses to assist with manufacturing.
- Factory employees has previously used Google Glass in its factory as recently as 2016.
To cut down on the number of fit and finish issues — like the “significant inconsistencies” found by UBS— Tesla employees on the assembly line could soon use augmented reality glasses similar to Google Glass to help with car production, according to new patent filings.
Last week, Tesla filed two augmented reality patents that outline a futuristic vision for the relationship between humans and robots when it comes to manufacturing. The “smart glasses” would double as safety glasses, and would help workers identify places for joints, spot welds, and more, the filings say.
Here’s how it works:
And here’s the specific technical jargon outlining the invention (emphasis ours):
The AR device captures a live view of an object of interest, for example, a view of one or more automotive parts. The AR device determines the location of the device as well as the location and type of the object of interest. For example, the AR device identifies that the object of interest is a right hand front shock tower of a vehicle. The AR device then overlays data corresponding to features of the object of interest, such as mechanical joints, interfaces with other parts, thickness of e-coating, etc. on top of the view of the object of interest. Examples of the joint features include spot welds, self-pierced rivets, laser welds, structural adhesive, and sealers, among others. As the user moves around the object, the view of the object from the perspective of the AR device and the overlaid data of the detected features adjust accordingly.
As Electrek points out, Tesla has previously been employing Google Glass Enterprise as early as 2016, though it’s not clear how long it was in use.
Tesla has a tricky relationship with robotics in its factory. In April, CEO Elon Musk admitted its Fremont, California factory had relied too heavily on automated processes. Those comments, to CBS This Morning, came after criticism from a Bernstein analyst who said “We believe Tesla has been too ambitious with automation on the Model 3 line.”
Still, the company seems to be hoping for a more harmonious relationship between human and machine this time around.
“Applying computer vision and augmented reality tools to the manufacturing process can significantly increase the speed and efficiency related to manufacturing and in particular to the manufacturing of automobile parts and vehicles,” the patent application reads.
This article was originally published on Business Insider. Copyright 2018.
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