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Tag: manufacturing industry

  • How to Get the Supply Chain Back to (Better than) Normal

    How to Get the Supply Chain Back to (Better than) Normal

    If a chain is only as strong as its weakest link, today’s supply chain is in dire straits. A recent infographic from The New York Times shows all too clearly that the international supply chain is in upheaval, due directly or indirectly to the outbreak of COVID-19. At the same time, reams of data generated along the supply chain are going to waste. The internet of things (IoT) can’t single-handedly fix the supply chain, but IoT technology combined with the cloud, AI and automation has the potential to bring the supply chain (and society) back to something resembling normal—actually, something better than normal.

    Indeed, even during “normal” times, the supply chain was stretched to the limit by the challenge of constantly reducing costs and improving margins, as well as optimizing delivery times for just-in-time (JIT) inventory. With that said, the last two years have been especially grim, with factories, stores and restaurants literally shutting down for lack of materials. Then there’s the computer chip shortage, which is doing nothing less than “dragging down the economy,” according to a White House briefing room document,

    The problem is now multifaceted, so the solution is not going to be as simple as adding a better monitoring mechanism.

    At a minimum, companies need better visibility into and transparency of their supply chains. This gives them more control over the entire manufacturing process, from sourcing raw materials to delivery of final products. Technologies like IoT, AI/ML, automation and edge computing can optimize supply chain management by providing visibility and metrics so companies can make real-time decisions.

    In manufacturing, for example, IoT can bring greater visibility into the production workflow. Data from the digital twins of machinery—analyzed in real-time and fed back to control systems—can lead to improved levels of operational and business efficiency. When manufactured goods are ready to be shipped, IoT technology can guide the logistics and transportation of goods as they move from manufacturers to end users. In addition to managing supply, companies also need to get better at predicting customer demand so it’s a self-sustaining feedback loop.

    However, companies adopting these technologies need to be aware that they will not be effective on their own. As with any digital transformation initiative, people and processes are as important as technology. To ease or even overcome today’s supply chain challenges, companies must not only agree on common data exchange formats and APIs across the supply chain but also dedicate themselves to ongoing cooperation and motivation to achieve the common goal of streamlining and hardening the supply chain. To these ends, open source will be critical to the ongoing development of common, industry-wide standards that enable companies to future-proof their solutions.

    For example, carmakers such as Tesla are simplifying their supply chain by using a higher-end processor that can perform multiple functions rather than using dozens of dedicated microcontrollers for each function, with updates pushed out over the air. Organizations need to think along similar lines when it comes to their IoT architecture. Developers have historically thought about IoT applications in a discrete way—creating custom frameworks for creating apps for low-power devices. Rather, developers should be considering IoT applications as an extension of their application portfolio—using the same languages, frameworks and tools for IoT apps that they use for cloud applications.

    Post-Pandemic Supply Chain Issues

    As we move into the next phase of the pandemic, it might be tempting to believe that all will be right with the supply chain world. However, beyond COVID-19, there are several macro trends that will affect the supply chain, including climate change, cybersecurity and the changing dynamics of global trade.

    To combat climate change, for example, companies will need to reduce their Scope 3 emissions. The EPA defines Scope 3 emissions as the result of activities from assets not owned or controlled by companies themselves but by their supply chain (or value chain). Scope 3 emissions are often the majority of a company’s greenhouse gas emissions. To reach sustainability goals, companies will need better resource management of their supply chains.

    Supply chains also are increasingly being used as a cyberattack vector. This includes both physical entities, like building operations (such as the hack of Target systems via a third-party HVAC vendor) or the software supply chain (such as the SolarWinds hack). In fact, it’s no longer a matter of if an organization gets attacked, but when.

    The war on Ukraine has magnified the intricacies of our hyperconnected global economy. The U.S. government and its allies have imposed severe sanctions on Russia, a move that will have widespread repercussions. The New York Times reported that the “response that U.S. officials have promised could roil major economies, particularly those in Europe, and even threaten the stability of the global financial system.” The same could be said for the global supply chain, if Russia responds by, for example, cutting off shipments of materials needed to build semiconductors or mounting new cyberattacks.

    Conclusion

    COVID-19, a steady and stealthy rise in cyberattacks, trade barriers, war … None of these factors caused the problems we’re seeing with both physical and technological supply chains; rather, they have exposed existing weaknesses for all the world to see—and experience. Organizations must work together to increase resiliency and security through the use of key technologies such as IoT, AI/ML and edge, as well as a commitment to open, standards-based solutions.

  • Unifying Partner Ecosystems With a Distributed Ledger

    Unifying Partner Ecosystems With a Distributed Ledger

    Blockchain has become famous in recent years for powering the cryptocurrency craze. But the fundamentals behind blockchain could prove helpful in other areas of the data economy, too. Because, as the value of data rises, so does the need for accurate, real-time data sharing across multiple partners.

    As we saw recently, global trade is surprisingly fickle. If a 1,300 ft long cargo ship can end up blocking the Suez Canal for an entire week, imagine how many data record incongruities the shipping and logistics industries have to sort out each day. Similarly, other partner networks, such as food distribution or airline affiliates, for example, must constantly share data back and forth to remain in business.

    Since a distributed ledger is external yet sharable with partners, it opens some interesting data-sharing possibilities. Similar to merges for collaborative software, partners would retrieve and manipulate the distributed ledger, which would act as a source of truth for status on a physical object or a piece of data. Essentially, this process could reduce manual reconciliation efforts and avoid disputes when something goes wrong.

    I recently spoke with “father of serverless” and AWS Lambda inventor Tim Wagner. Wagner, now heading Vendia, shared with me why he foresees distributed ledgers as the future for real-time, business-to-business data sharing. “Supply chains are surprisingly brittle,” Wagner said. To solve this problem, we need an “automation of that workflow that isn’t centralized any longer,” he said.

    Role of a Distributed Ledger

    A distributed ledger could be especially relevant for a multi-party supply chain. Within multi-partner agreements, data tracking is complex, and many partners struggle to come together. “Companies are struggling for a lack of truth,” said Wagner. “Everyone wants their own copy of the data, but they want it to be locked to what everyone is seeing at the same time.”

    For example, take supply chain management. Suppose an automotive parts manufacturer produces a chassis. They send it to a shipping logistics provider, who then sends it to an automaker to complete assembly of an automobile. If the chassis breaks at some point during these handoffs, whose fault is it? Without a system of record, you open a big problem of distrust, Wagner explained.

    Or, consider an airline ecosystem. Partner airlines must often coordinate multi-carrier flight plans, apply discounts or settle unused travel vouchers. Often, it’s difficult to track the journey of these transactions between organizations. An externalized ledger could help partner airlines follow the route of a single passenger, helping to ease financial settlements.

    “It’s not just about sharing, which is hard enough, but it’s really about sharing with control,” said Wagner. These days, tracing the lineage of data is critical, as is having real-time visibility into an object’s state. A distributed ledger could help these partner ecosystems have a record they can all trust. Wagner describes this as a tamper-proof, immutable record — an “unassailable source of truth.”

    Would This Replace Partner APIs?

    What’s especially interesting to the integration-minded is that the distributed ledger concept could supplant established business-to-business communication models. Over the last couple of decades, most partner integrations have moved to RESTful API connections. One company opens up data access, and another authenticated partner makes HTTP API calls to integrate data into their own application.

    Yet, the issue here is that the API is only a layer on top of a private database owned by the provider. Located behind closed doors, there’s no industry-wide real-time data synchronization. API calls are good for data retrieval and manipulation, but stringing a sequence of thousands of transactions together is difficult, described Wagner. Retaining the time-series integrity of a complex database is a little too much for synchronous calls to handle.

    Instead of opening access to an internal platform via API, Wagner believes that a distributed ledger is a better fit for certain business-to-business machine automation. “Any place where you see API-level protocols developed to maturity is a good sign; there is a good chance distributed ledgers will find greater speed,” he said. “Trying to pull that information through APIs and pray you’re finding the right data is difficult to manage.”

    For example, Wagner referred to how some animal shelters are creating a global repository to avoid siloed records. In doing so, they are making a global animal rescue schema for a unified database that could hopefully help every pet find a home.

    Communicating with the ledger would still involve calls, but likely not your typical RESTful requests. Wagner advocates for the use of GraphQL as a better method to involve clients around changes. With a GraphQL API, stakeholders can query, mutate and subscribe against changes in a distributed system. GraphQL isn’t perfect, but its easy data mechanics make it a Goldilocks-style middle ground between REST and SQL, said Wagner.

    Getting Buy-In

    A synchronized distributed ledger could help resolve disputes and reduce manual checks. But, how do we get buy-in? How can you convince an entire partner ecosystem to align around a distributed ledger as a source of truth?

    Wagner says there is usually a leader or single manufacturer leading the charge. This figure is likely a more prominent, well-respected player in the process. But for a digital ledger system to be successful, it must also satisfy other criteria too, Wagner said:

    • Data schema forethought: Agreeing upon a shared data schema may be a challenge. Ecosystems will require forethought when designing a data model for the long haul.
    • Compliance: There are many new data regulations to consider, such as GDPR, CCPA and PCI. “You can’t blindly copy data the way you used to,” said Wagner.
    • Governance: As enterprises deal with data crossing boundaries, they must apply governance measures to satisfy compliance and maintain security. This governance needs to be tightly associated with data and offer fine-grained control.
    • High performance: A distributed ledger requires operational flexibility for large objects, such as a 5 TB file, noted Wagner.
    • Redaction capabilities: Whereas some partners must see all fields, others may, by law, be limited in what they can view. Thus, a ledger must include the ability to redact fields. Wagner described this as “intentional redaction or visibility control of individual customer records.”

    Of course, different industries are at very different points along their digital journey, some more advanced than others. Change may be easier for, say, financial services supporting ACH payment technology, which Wagner described as being “ripe for natural organic expansion into a data-centric methodology.”

    The Poor Man’s Blockchain

    Tapped into the same feed, an extensive partner ecosystem could use a blockchain-esque arrangement to stay on the same page. This could help reduce manual reconciliation typically handled through email or over the phone.

    Yet, as distributed systems experts are few and far between, implementing such technologies will most likely involve a cloud platform solution, said Wagner. He jokingly calls this the “poor man’s blockchain.” Such a solution must be cloud-friendly, enterprise-focused and easy to integrate and deploy.

    As more and more critical data begins to reside outside company walls, “organizations have to reconstitute that data to work with partners,” said Wagner. A distributed ledger could, in effect, act as a sort of outsourced centralized IT, enabling a data model for serverless environments.

  • Manufacturing Workers Eager to Digitally Upskill

    Manufacturing Workers Eager to Digitally Upskill

    Anyone who has worked in machining within the past 70 years knew that the future of machining was automation. In the 1940s, computer pioneer John T. Parsons and engineer Frank L. Stulen invented, via a contract with the U.S. Air Force, numerical control milling machines. The machines were designed to help the Air Force produce geometrically complex parts more economically and accurately.

    Those machines were the precursor to today’s computer numerical control (CNC) machining tools that use programming to automatically process some material, such as metal or wood, to specification. By the late 1980s, such machines were standard within tool and die, machine shops and manufacturing floors. Today, such digitization of manufacturing is accelerating.

    Workers, for the most part, embrace the change and would do more to meet the technical skills demands needed for their jobs – if only their employers supported them. That’s the primary finding from a survey, Digital Illiteracy in the Factory: Providing Workers with Low-Code Tools, commissioned by low-code platform provider Mendix.

    Interestingly, today, low-code development tools are finding a home in manufacturing. According to the survey, nearly 9 out of 10 said they want to learn low-code software development, while only 3% currently use low-code in their jobs. More than two-thirds (67%) say they want to create a software app that would solve work issues. According to this survey and a parallel survey conducted in Europe, U.S. workers are more willing to welcome and contribute to workplace digitalization than their counterparts in Germany (78% versus 61%) — a surprising result, given that German manufacturing workers enjoy a reputation as some of the most skilled in the world.

    “Incorporating digital applications is vital to the ongoing competitiveness of manufacturing in the U.S., and it is heartening to see such a large percentage of the manufacturing workforce ready to contribute to this effort,” Gardner Carrick, vice president of strategic initiatives at The Manufacturing Institute said in a statement. “Low-code applications provide our workforce with the tools to contribute to this digital transformation, and we should prioritize training in these skills.”

    The survey found that 87% of U.S. workers want to learn low-code development, and within those respondents, 88% of women who responded indicated being very interested in learning low-code, while 51% of men responded in kind. Perhaps not surprisingly, younger workers are more interested in learning low-code than those nearing retirement.

    That may be so, but U.S. manufacturing employers aren’t getting the memo. The survey found that U.S. manufacturers are giving their workers little help in developing their digital skills, and only 18% of workers are happy with their digital skills. “That only 18% are happy with their digital skills is just one sign that more training is required. At a time when the country’s manufacturing industry remains mired in a decades-long slump and when managers clamor for more useful software applications, news reports indicate that the nation’s workforce is ill-equipped to deal with writing code,” Mendix said in a statement.

    “The United States needs to move now to get their workers into the digital game,” said Derek Roos, Mendix CEO. “If the country wants ‘Made in the USA’ to mean something again, and if it wants to reduce unemployment and keep its economy vital and thriving, manufacturing and industry should provide low-code and other tools that help bridge the digital divide.”

    The survey was conducted from Jan. 29 through Feb. 11, among 250 full-time manufacturing workers in the U.S. and 250 within Germany. The survey’s objectives included learning workers’ attitudes and motivations, as well as obstacles that prevent them from acquiring low-code and other digital skills.

  • 4 Challenges OEMs Face When Implementing New Technology

    4 Challenges OEMs Face When Implementing New Technology

    Like many others, the manufacturing industry is moving to a subscription-based business model. This new way of doing business is being referred to as servitization and it is forcing manufacturers to no longer strictly sell new products, but instead sell access to and the outcome those products deliver. This is fundamentally changing the way manufacturers have traditionally done business and is not only encouraging a shift to subscription-based pricing models, but also Products-as-a-Service.

    As manufacturers implement the processes and resources necessary to make servitization a reality, implementing new technology will be key to success–laying the foundation for success today and in the future. More specifically, machine learning and predictive analytics will become especially important for predictive maintenance, process optimization and supply chain management. While there are opportunities to capture a significant competitive edge using these technologies, there are also significant challenges specific to the manufacturing industry. Some of these noteworthy challenges include:

    Efficient and Secure Data Connectivity

    Complex, capital equipment (think construction equipment, agricultural equipment or industrial machinery) is built and designed to last, on average, 30 to 40 years. As a result, these machines must be very reliable. Today, however, the majority of equipment in service has upwards of 20 to 30 years remaining in its lifespan. However, almost none of these products were built or designed for IoT connectivity–creating the challenge of retroactively fitting existing equipment with IoT-enabled parts.

    In addition, a majority of this equipment operates in some of the harshest and most rugged environments in the world. Whether it’s an electro-submersible pump 3,000 feet below the surface of the ocean or a mining truck in a remote mine with little to no cellular connectivity, efficiently connecting this type of equipment to collect relevant data is a difficult challenge and oftentimes cost prohibitive.

    Even if manufacturers manage to connect this equipment, there is an added challenge of doing it in a way that ensures cybersecurity. Rogue elements that could gain malicious access to this equipment could have serious safety and economic consequences–and could even implicate national security in some instances.

    Sparse Data

    Newer machine learning algorithms, such as Deep Learning and Recurrent Neural Nets, have been groundbreaking for capabilities like image and voice recognition. However, these algorithms are also very data hungry, requiring millions–if not billions–of similar data points from millions of users.

    Because complex industrial equipment is intended to last for decades, failures are generally rare. Think about it: There have been just a handful of actual engine failures during flights for the entire global commercial fleet, which amounts to nearly 30,000 aircraft. Assuming an average of 2.5 engines per plane, the global commercial aircraft engine fleet is less than 100,000. While certainly a large number by itself, it is relatively small when considering these engines vary greatly in design and thus any single engine type that could be modeled together would be even smaller.

    Manufacturers of industrial equipment or durable goods also tend to be reluctant to share data, especially if their competition could potentially benefit from it. The challenges of efficient connectivity, rare failures and relatively small numbers of similar equipment in a fleet leads to the sparse data problems in the Industrial IoT (IIoT) realm.

    Domain Expertise

    While domain expertise to successfully apply machine learning and predictive analytics is required in the consumer world, in the manufacturing space, it is much more critical. Machines and processes are designed using physics and engineering principles. And, the context in which machines operate and behave is very important for accurate training models and analyzing results. Without appropriate context, there becomes a “garbage in, garbage out” problem as sensor data and machines alone do not capture or provide any context. This is where domain experts who can make sense of historical data for accurate model training as well as interpreting results are critical.

    The impact of making an incorrect decision can have expensive–and sometimes catastrophic–results that aren’t seen in the consumer-facing domain. For example, re-routing an aircraft mid-flight could cost millions of dollars for an airline. If that decision is made because of bad data and an incorrect prediction, it would be a very costly mistake. For us as consumers, we have some aspect of domain expertise for problems in the consumer world, whereas a trained pool of engineers with the relevant design, operational and maintenance expertise is a very small pool. And this, combined with the problem of baby-boomer retirement where expertise is lost at a significant rate, there is a real challenge to capture the knowledge and best practices required to deliver value.

    Data Ownership

    Currently in the manufacturing sector, there aren’t industry standards for data security, ownership or accessibility. Currently, many manufacturers are wrestling with who owns and has access to the data. Is it the manufacturer who designs and builds the equipment? Or, is it the third-party entity that rents and operates it? Or, is it the financial entity that may be the legal owner? What about the supplier that provides a component that is part of a larger piece of equipment (e.g. an aircraft engine that works in conjunction with the aircraft, with each component manufactured by a different entity)?

    Manufacturers being risk-averse in nature, are being very conservative in interpreting and applying the myriad patchwork of regulations being brought in other industries, including data security standards. This is limiting them in adopting more connected processes and strategies. The industry must accelerate efforts to establish industry-wide standards as well as regulatory frameworks for data ownership, data-sharing, interoperability and testing of systems and end-to-end security to overcome the roadblocks for faster adoption of these technologies and realize the promised benefits.

    The shift to servitization will require manufacturers to completely re-think how they operate–new organization structures and skilled resources, new incentive models, new KPIs to measure success and new processes. They will have to become data-driven organizations investing in technologies to connect and track products, collect data and efficiently analyze massive amounts of operational and service data, using technologies like IoT, machine learning and predictive analytics. This will strain manufacturers’ existing organizations and IT infrastructures, necessitating investment in highly scalable, cloud-based solutions to lay the foundation for a successful future. Manufacturers that embrace these changes will be the winners, while others will struggle to stay relevant. In fact, the ones that can successfully adapt to these paradigm shifts will be able to gain significant competitive advantage and market share.

    — Vivek Shah