Manufacturing companies are facing several challenges that prevent them from maintaining a competitive advantage. One of the industry’s largest obstacles is disruption; from a shrinking talent pool to difficulty tracking service cycles and maintaining positive relationships with distributors, many original equipment manufacturers (OEMs) have found themselves at a precipice. For those OEMs prepared to take a leap, there are technologies available to them that will address their disruption concerns and prepare them to withstand the changing tides of the industry with efficiency and agility.

The industry is evolving, and the streams of progress are cutting out new paths in the land. Some of the biggest business concerns for manufacturers a decade ago should no longer be the focus point, as they are easily solved with scalable automation and intelligent equipment through data collection models.

Challenge

Through many consultation experiences with OEMs, New Signature experts have found various challenges, both internally and externally, that are causing disruption to service and productivity. The three principle threats that OEMs are facing are:

  • Layering Equipment with Intelligent Service
    Heavy machinery is no longer expected to be simply a machine. Many companies are offering intelligence service models that are layered on top of the equipment itself. These insights help OEMs monitor their devices’ performance and track any machine failures.
  • Service Lifecycle Blind Spots
    All machines have a service lifecycle. Tune-ups, replaced parts, and fluid changes are just a few of the routine machine maintenance schedule. Without real-time insight and performance tracking capabilities, the maintenance schedule and service lifecycle of a machine is merely guesswork, estimated based on information the OEMs collect over time, creating less accurate and consistently dated service model.
  • Shrinking Talent in the Industry 
    Large-scale equipment companies are experiencing an industry-wide shortage of skilled employees who have the niche expertise to service these types of machines. This leads to difficulty locating service technicians, which, in turn, leads to slower repairs, strained customer relationships and time and focus removed from production and innovation.
$22B

US Machinery Manufacturing Gross Output

75M

Agriculture Equipment Devices Connected to IoT by 2020

$50B

Annual Cost of Manufacturer Unplanned Downtime

$192B

Construction Equipment Market Size

Solution

By creating data models that represent a given machine’s functions, IoT experts can predict specific events and create rules for the machine. For example, if a sensor predicts that a transmission is beginning to falter, it can automatically create a service request for that machine, creating a more fluid motion from the tremors of a system failure to a service appointment and resolution. How is this accomplished? Can experts really make machines “learn”? They can, and here’s the basic breakdown of how this is achieved:

  • Step 1: Build a Digital Twin
    Before data rules and machine learning can be implemented, the data sources are mined and the data models and formulas are built within a decoy parallel conceptual model, called the machine’s “digital twin”.  Experts in IoT and machine learning will piece data together to build a solid picture of what the machine does and how it behaves.
  • Step 2: Leverage Digital Twin SaaS
    Once the digital twin is complete, it is loaded onto a software service that helps to stack the building blocks of machine learning with ease, as opposed to manual data model builds. By leveraging technologies like Azure SQL and the Twin Thread platform to assist in the building of the data models, experts create solutions quicker than manually building the data models. This helps to break down events that occur within a machine. By assigning “red flag” events to a breakdown or system failure, end users can not only quickly know when an issue arises, but can actually predict when it may happen, opening an opportunity to prevent a failure.
  • Step 3: Begin Implementing the Models and Creating Threads
    Data modeling can be constructed quickly and easily through automation of the process. It’s a matter of creating formulas out of data sources. Experts will tell the machine if X happens, then implement Y action. They test a handful of these models and monitor the accuracy and efficiency. The more data that is collected through the models, the stronger the understanding of red flags that lead to specific events.
  • Step 4: Automate and Optimize Over Time
    The biggest benefit from automated machine learning is that over time, the models predict events and create actionable threads more and more quickly and with greater accuracy. The performance of the models improved with time, and those high-functioning models can help to guide new machine learning projects that are aligned with the overall business objectives.  Because this process is created to increase optimization over time, users can find new insights more quickly with each successive machine learning model.

Benefits/Results

In the world of manufacturing and business, there is no downside to seeing the future. There are numerous benefits of understanding your product better. When you know what could go wrong, you have the power to increase accuracy and strive toward perfection in product. Microsoft Azure, Twin Thread and Power BI are the best tools to achieve this end and deliver results with quick realization and great impact.

  • Ease of Use
    You don’t need hire a full-time team of applied innovations experts to run your machine learning capabilities. The drag-and-drop model structures and automation of the tools from the deployment make it easy for any OEM with engineers or service techs can easily utilize and find benefit in the data science models once in place. And with automated threads, the machine’s data models can create work tickets, send emails, create phone calls or respond, in many cases, to its own alerts and needs.
  • Elevated Product Quality
    By accessing data otherwise unearthed, manufacturers can get an up-close look at the functionality of their machines and work to quickly eradicate production errors, as well as work to create better machinery over time. This will decrease recalls and unnecessary servicing absorbed by the OEM. With a shrinking workforce in this industry, this can reduce costs, since many service technicians are either difficult to nail down or are expensive to contract.
  • Service Lifecycle Accuracy
    OEMs base their warranty offerings around when failures may occur within their machine. Granular data and understanding of those “red flags” and lifecycle timelines can help OEMs develop better, more succinct and profitable warranty and extended manufacturers’ warranty options. Having a stronger warranty offer set will also help to strengthen relationships with distributors and create a stickier relationship with individual machine customers.
  • Insight into Human Resources
    One final aspect into the data collected from a piece of equipment is that there are employees operating the machines. The data collected from machines does give endless insight into how the machine operates, but also into the employees who operate them and how the machines are being used. Having the ability to create a cross-section of an operator for a specific piece of equipment leads to more opportunities in account-based marketing and a stronger understanding of the target market.

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