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Author: Vivek Shah

  • 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

  • Machine Learning and Predictive Analytics Are Reshaping Manufacturing

    Machine Learning and Predictive Analytics Are Reshaping Manufacturing

    Regardless of industry or vertical, companies everywhere are encountering a new generation of customers with ever-evolving expectations. These new demands are forcing brands to redefine the way they do business.

    Today’s customers overwhelmingly favor the simplicity of subscription models, in which they pay a flat monthly fee for access to a product in the form of a service. For example, consumers no longer buy or rent DVDs; they subscribe to Netflix. And more prefer to use the services of Uber or Lyft over traditional car ownership. As these digital-age consumers enter the industrial workforce in large numbers, they are causing business models to be redefined, leading to an increasing trend toward subscription-based services in manufacturing industries.

    This shift toward a subscription-based business model in the manufacturing industry is being referred to as servitization, wherein manufacturers no longer strictly sell new products but instead sell access to and the outcome those products deliver. This new way of delivering services is forcing manufacturers to shift to subscription-based pricing models, where product-as-a-service is the norm. Manufacturers of complex industrial equipment, because they operate in a B2B environment, can attribute this shift to another major cause: Their customers are also dealing with complex operating paradigms, forcing them to improve productivity and capital utilization. In these scenarios, customers are looking to simplify their businesses and increasingly favor suppliers that can help minimize their risks in operating a profitable business.

    The full realization of servitization, which could take up to 15 years, especially impacts manufacturers’ after-sales service organizations (the service delivered after the initial sale of a product). In today’s current reactive, break-fix service model, the responsibility to maintain and ensure equipment availability falls on the customer. In a servitization model, however, manufacturers own the responsibility of maintenance and repairs and need to focus on maximizing product uptime, since revenue can only be earned when their products are available to generate output in the field.

    Machine Learning and Predictive Analytics in Manufacturing

    The use cases of machine learning and predictive analytics are as varied as the industries within manufacturing. However, there are a few common use cases that apply to most manufacturing verticals, typically grouped under terms such as smart manufacturing, industry 4.0 or industrial internet of things (IIoT).

    1. Predictive Maintenance: Predictive maintenance is the most well-understood and varied use case in most manufacturing industries. Here, data from process monitoring sensors such as temperatures, pressures, flows, vibrations and more are captured in real-time and used in pattern recognition software to detect the earliest symptoms of wear and tear predictive of eventual functional failures. Early detection and prediction can help prevent failures or at least plan for eventual corrective actions leading to minimized downtime. Downtime—especially unplanned downtime—can be a very expensive event, possibly leading to millions of dollars in losses. Some analysts estimate unplanned downtime in certain industries is worth $20 billion.
    2. Process Optimization: Process optimization, in which existing processes are updated and optimized based on historical data, is a critical use case, especially in industries such as power generation, oil and gas refining, petrochemicals and chemicals. In this instance, sensor data feed machine learning algorithms for yield and quality optimization of output components for different combinations and quality of input raw material feedstocks. This also helps with energy efficiency, thus improving sustainability and profitability for these process manufacturers. In the global airline fleet, for example, a 1% fuel savings would save $30 billion over the next 15 years.
    3. Supply Chain and Inventory Management: High levels of raw material, work-in-process and finished goods (e.g., replacement service parts) inventory are one of the highest contributors to inefficient capital utilization for discrete manufacturing industries. Using machine learning to improve raw material and demand forecasts while meeting dynamically changing production goals helps improve capital utilization and supports lean and just-in-time manufacturing production goals.

    In today’s customer-driven world, manufacturers no longer can rely on selling expensive spare parts and services, since these become costs of supporting a single-price subscription. This shift 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 replacing ones developed over decades and centuries. They will have to become data-driven organizations, investing in technologies to connect and track products, collect data and efficiently analyze these massive amounts of operational and service data, using technologies such as 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 who can successfully adapt to these paradigm shifts will be able to gain a significant competitive advantage over their competition.

    — Vivek Shah