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Big Data – Automation World

If they’ve used Big Data to any degree, it’s hard
to find a company that doesn’t extoll its virtues
for predictive and real-time insights. Industrial
Big Data in particular—specifically the large
and diversified time-series data emanating from
Internet-connected automation equipment from
sensors to plant floor machinery—has notable and
demonstrable business value for companies looking
to distill data into insights that drive better business
and plant performance.

As trendy as it may sound, it’s only the name—Big Data—that’s of recent vintage. Companies
have been collecting time series data from factory
floor and field assets for decades. However, much
of this data remains unused, trapped in siloed, proprietary
historians and supervisory control and data
acquisition (SCADA) systems that often aren’t
readily accessible much less available to meld with
other relevant business data to drive broad-based
actionable insights. And while plant managers and
maintenance workers have long analyzed data from
specific plant floor assets—mostly with spreadsheets—
this was rarely, if ever, done with wide
business transformation in mind.

That’s all changed thanks to the scale of what’s
possible fueled by technology advances in edge and
cloud computing, data acquisition and historian
tools, and artificial intelligence (AI) and machine
learning (ML) analytics. With these technologies,
manufacturers can transform siloed data and proprietary
systems into flexible and intelligent factory
floor and industrial operations that can be holistically
automated and optimized in near-real-time.
They are building next-generation systems able to
sift through the volume and variety of industrial
Big Data to up level operations and optimize performance,
including reducing maintenance costs,
ensuring near-zero downtime, boosting product
quality, and driving additional revenue streams
through the introduction of new services.

Orchestrating a successful industrial Big Data
analytics program requires investment in a number
of central technology components. Critical to
the mix are:

1Data acquisition systems: The first step in a Big
Data initiative is identifying and capturing all relevant
data. One way to do this is with a data acquisition
(DAQ) system, which processes sampling
signals that measure real-world physical entities
and converts them to a digital form for use by
various computing systems. The digital acquisition
components typically comprise sensors that measure
phenomena like temperature, voltage, fluid
flow, strain and pressure, and shock and vibration
along with signal conditioning devices and analog-to-
digital signal converters. The conditioning and
converter devices work together to filter the analog
signals from sensors, converting them to a digital
formal that is compatible with standard computing
resources for further analysis.

Most DAQ systems record, store, and visualize
the data in addition to providing some base level
analysis and reporting capabilities. More recently,
data acquisition systems are being integrated with
real-time control applications, pairing the ability to
acquire data quickly with the possibility of reacting to
events using highly deterministic data. These new capabilities
also allow manufacturers to track and measure
current performance against historical trends.

2Historians: One of the more popular data collection
technologies are historians. Since their initial
development in the early 1980s, data historians
have found wide use across every manufacturing
and processing vertical. Essentially, a data historian
is software that logs production data and makes the
data it collects easily accessible for analysis.

Though its function is not that different from a
data acquisition system, the difference lies in how
they’re designed to collect data. DAQs collect
both high- and low-speed data from a variety of
sensors, and commonly have a very high-powered
computing system either built-in or connected to
them. DAQs can also handle signal conditioning—an essential capability for processing sensor data.

While historians can collect direct sensor data
like DAQs, they can also collect data from larger
systems and equipment; however they tend to do
so at a slower rate than a DAQ system. While historians
can collect data in real time, their main purpose
is to collect data over longer periods of time—
days, weeks, or months—rather than for specific, short time periods.

The two basic types of historians used in manufacturing
are:

  • Time-series databases, where data are logged
    with time stamps to simplify tracking and
    monitoring; and
  • Operational historians, which combine historian
    software and time-series databases for fast retrieval
    of the large amounts of data collected for
    Industry 4.0 and Internet of Things applications.

3CMMS (computerized maintenance management
systems):
This software is widely used for its
ability to centralize maintenance information and
automate or direct maintenance workforce tasks. A
capable CMMS can contain all of a manufacturer’s
or processor’s maintenance information such as
work orders, preventive maintenance schedules,
assets, logs, work histories, parts inventory,
vendors, purchase orders, and maintenance
reports. Algorithms used in the CMMS can
recognize and organize maintenance-related data
to provide insights on asset condition, lifecycle, and
maintenance tasks. A CMMS also typically notes
the equipment, materials and resources required for
the specific maintenance tasks it recommends.

Like other data collection technologies, a database
is at the core of any CMMS, but it is more than
a field data capture tool. The insights it provides help
reduce downtime and aid troubleshooting. That’s
why CMMS technology is often used for creating
reports and identifying key performance indicators.

Edge and cloud computing: As more sensors,
PLCs, and other devices are connected to the Industrial
Internet of Things (IIoT), there is a requirement
to move additional computing power closer
to the source where data is generated. Bringing
computing capabilities closer to the edge of a network—directly on the shop floor, for example, or
at a remote oil rig or wind turbine site—enables
efficient processing of real-time data about the
condition and performance of the industrial asset
without the traditional latency challenges that arise
when channeling data to a central network system
for monitoring, analysis, and even real-time automation. Edge capabilities also enable IIoT-enabled
use cases in instances where high-bandwidth connectivity
is not readily available, such as in rural areas
or remote plant sites.

Edge capabilities are central to empowering new
data-driven use cases such as predictive maintenance
or real-time quality management. In these
instances, plant floor equipment and industrial assets
are continually monitored and analyzed at the
edge, empowering corrective actions that could
include diagnostic checks, initiating maintenance
work orders, even enabling a specific condition based
maintenance action.

In most IIoT-enabled industrial Big Data use
cases, it’s not a matter of deciding between edge or
cloud-based systems to support predictive maintenance
or asset optimization applications. Typically,
companies turn to a combination of edge and cloud
functionality, with the cloud poised to deliver additional
storage and compute scalability while providing
a way to aggregate data from myriad industrial
assets and edge computing systems, or even multiple
plant floor operations for analysis.

4Advanced analytics: It’s not enough to collect and
process industrial Big Data—there also needs to be
capabilities in place to analyze and interpret the
data, potentially in near-real-time, to drive intelligent
insights and initiate closed-loop corrective
actions. The incoming generation of analytics tools
are powered by AI and its subset of machine learning
technologies, making them much more capable
of finding patterns in voluminous time series and
other IIoT data for use cases such as predictive
maintenance, real-time quality control, and scenario
testing for root cause analysis.

While operations personnel have had early successes
building analytical models, typically in Excel
spreadsheets for a single machine in a factory or
for a particular use case, they have less experience
scaling those efforts to bring intelligence to the entire
factory, let alone to operations that span factories
across the globe. The unique nature of data
collected in historians and SCADA systems is also
different than the more familiar enterprise analytics.
While enterprise data in financial or customer
systems is usually well structured, time-series data
lacks context for understanding how the raw data
can and should be applied to understand the state
of a specific process or the condition of materials
on a manufacturing line. Next-generation analytics
tools add modeling and machine learning capabilities
to uncover patterns, classify and contextualize digital twin of a factory so anomalies can be spotted
and downtime issues readily addressed.

Without the technologies that deliver such context,
manufacturers will be hard pressed to fully leverage
data to drive Industry 4.0 applications such as
continuous operational performance improvements,
condition monitoring, or predictive or prescriptive
maintenance applications.

Analytics software can be used a stand-alone technology,
receiving data from a variety of connected
devices. It can also be included in edge and cloud
computing technologies wherein data collection and
analysis are conducted in one system. Many manufacturers
employ a hybrid approach using edge and
cloud technology for analytics—with edge computing
analyzing real-time data for day-to-day operational
insights and cloud computing for longer-term analysis
for strategic operations and business applications.

5End user and integrator insights

To better understand how industry—across discrete
manufacturing and processing—is collecting and analyzing
data, the business factors driving their decisions
around data, the types of technologies they use, and
their recommendations around the selection and use
of data collection and analysis technologies, Automation
World conducted research to gather data from
end users and system integrators.

While most end user respondents (86%) indicate
they collect data from equipment and devices specifically
for production improvement initiatives, most have
begun doing so only within the past five years. Only
27% of respondents indicate that they have been collecting
data for such purposes for more than six years.

In line with these end user results, 71% of responding
industrial system integrators report seeing a noticeable
increase in interest around data collection
and analysis from their clients.

An interesting point among the end user responses is
that 98% plan to gather even more data from their production
systems in the next two years. However, only
30% plan to do so for specific operational improvements.

This could indicate that, of those who have been
collecting and analyzing data for a few years, many
may have already discovered numerous ways of improving
their production operations and could be
looking to leverage the data they collect for other,
strategic business purposes.

If you’re among those companies not actively aggregating
and analyzing data for production improvement,
most of your industry peers are also relatively
new at it too. However, with so many businesses
actively pursuing this, those companies not already
looking to improve production via data analysis are in
a small enough minority to be considered laggards in
this area. Meaning that it will only become more difficult
to compete as your data-analyzing competitors
discover ways to improve their production operations
before you can do the same.

Level of data collection

Looking at the depth of data collection activities
across industry, 53% of end users say that less than
50% of their production systems or related devices
are connected for data collection purposes. In contrast,
system integrators note that, among their clients,
only 42% collect data from less than 50% of
their production systems or devices.

At the other end of the spectrum, integrator respondents
note that 29% of their clients collect data
from all of their production systems/devices. This may
a bit of overestimation on the part of the integrator
respondents, as only 5% of end user respondents report
having more than 90% of their systems/devices
connected for data collection.

There is also a significant difference among respondents
in the middle tier—in which 51%-90% of systems
are connected. Just 29% of integrators indicate their
clients have 51%-99% of the systems/devices connected,
while 42% of end users estimate that 51%-90% of
their systems/devices are connected for data collection.

6Driving factors

Among end users, the top three reasons cited for data
collection and analysis is the improvement of specific
line or equipment operations (70%), improving maintenance
operations (65%) and as part of an overall
Industry 4.0/digital transformation initiative (54%).

System integrators also cited these applications
as the top three reasons they see industrial clients
collecting and analyzing data, but the order differs
somewhat. Integrators say 100% of their clients’ data
collection and analysis is put toward to maintenance
operations improvement, while just 40% cite both Industry
4.0/digital transformation initiatives and specific
line/equipment improvements.

Another reason cited by 40% of integrators is
FOMO (fear of missing out) on potential unidentified
improvements. One integrator respondent said
the emphasis now being placed on remote operations
is making organizations “not want to miss any data
points that could lead to erroneous decision making.”

Types of technology used

Talk of digital transformation, Internet of Things,
Industry 4.0, and Smart Manufacturing have dominated
industrial technology discussions for more than
a decade. But the reality is that most manufacturers
rely on data collection and analysis technologies that
existed long before the advance of the technologies
receiving the bulk of attention today.

That’s not to say newer collection and analysis
technologies are going unused—as that is certainly not the case. Edge and cloud technologies, for example, are being used widely
across industry. Even so, there remains much headroom left for their growth.
Only 29% of system integrator clients use hybrid cloud/edge technology and
14% use stand-alone cloud systems.

According to our survey results, most manufacturers rely on three methods
of data collection and analysis—and only two of them would be considered automation
technologies.

System integrator respondents say that 57% of the clients still rely on handwritten
data collection that is then entered into spreadsheet software. The integrators
also note that 57% of their clients also use historians, a technology that
emerged in 1980. (Editor’s note: respondents were allowed to check multiple
categories of data collection types to more accurately reflect the use of multiple
technologies in a facility; thus the percentages will exceed 100%).

Clocking in at 43% are computerized maintenance management systems
(CMMS), a technology that debuted in the mid-1960s. Integrator respondents
also noted the use of data acquisition technologies, another technology that’s
been in use since the 1960s.

End users responding to our survey still ranked CMMS and handwritten/
spreadsheet as the top two data collection and analysis methods used, though
their percentages differed from the integrators. According to end user respondents,
44% use CMMS—largely in line with the 43% reported by integrators.
However, end users report only 29% relying on a combination of handwritten and
spreadsheet use, which differs dramatically from the integrators’ response of 57%.
Though this discrepancy could be attributed to numerous factors, it would be difficult
to ignore the possibility that many end users probably prefer not to mention
their reliance on handwritten data collection in 2022.

Of note, end user respondents cited broader use of cloud and edge technologies
than did integrators. Though no integrators cited use of stand-alone edge
technologies in their clients’ facilities, 27% of end users report its use. Also, 26% of
end users cite use of stand-alone cloud technologies, compared to the integrators’
response of 14%. End users’ response about use of hybrid edge/cloud technologies
is more in line with the integrators’ response—with 23% of end users citing its use
compared to the 29% noted by integrators.

Looking more closely at end user responses around cloud and edge technology
use, 37% cite use of a hybrid edge/cloud environment for data analysis specifically. In
comparison, 32% use edge technology for analytics while 28% use cloud computing.

7Recommendations

As a final step in our research, we asked integrator and end user respondents to
offer their best advice about selecting and using data collection and analytics
technology based on their experience with both.

Key points highlighted by integrators include:

  • Start small but ensure the technology you’re using for these small-scale
    applications can scale across the plant and enterprise.
  • Focus on improving the process, not on trying to achieve a specific number.
  • Don’t overlook data management and data governance, as data standardization
    is the key to successful downstream analytics.
  • Begin by examining our operations to determine where you should start collecting and analyzing data for meaningful decision-making strategies.
  • As you review the technology landscape, carefully assess whether you
    have the internal resources to support this effectively in-house or if you’ll
    need outside help.

End users’ recommendations tended to focus more on gathering input from
different groups in the workforce:

  • Realize that data analysis, on its own, can often be an indirect measure
    of what is needed to improve operations. Therefore, plan to be inventive
    around key performance indicator determination and involve process experts
    as much, if not more so, than data specialists.
  • Be sure to include workers directly involved with your production processes.
    Managers won’t always have the correct point of view to detect areas of
    opportunity in data.
  • Don’t discount your in-house engineering and operating expertise, as they
    can provide added benefits beyond that delivered by the technology.

This UrIoTNews article is syndicated fromGoogle News

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