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In this piece, we’ll explore how IIoT drives productivity in the plastics industry and provide an example of implementing an IIoT solution for monitoring equipment effectiveness at a plastic parts manufacturing enterprise.
Volatile raw material prices, the lack of skilled labor, and the complexity of the global supply chain — these and other issues contribute to a decrease in the plastics segment’s efficiency. It has been reported that over the past year the industry’s productivity has declined by 3.3 percent. To change the situation for the better, a number of plastics manufacturers have turned to modern tech and have started adopting the Industrial Internet of Things (IIoT) for monitoring and analyzing shop floor performance.
As the experience of early adopters can be helpful for the
manufacturers who are at the start of their IIoT journeys, in this article, we
will share an example from our IoT consulting practice and show how a U.S.-based
plastics manufacturer leveraged IIoT to tackle the issue of insufficient
equipment effectiveness. We will also examine the benefits they’ve achieved and
the struggles they’ve encountered on the way to a connected shop floor.
The IIoT-Driven Approach to Monitoring Equipment Performance
With IIoT in place, manufacturers get the opportunity to
view equipment effectiveness metrics (e.g., runtime, downtime, cycle time, the
number of parts produced) in real-time without physical access to machines. For
that, the data about equipment’s operational parameters (e.g., a machine reset
signal, pen drop, pen lift) is fetched automatically from machines’ PLCs via serial
or Ethernet interfaces.
Equipment’s operational data is relayed over to the cloud software — the core of an IIoT solution — for storing and analysis. The analytics module of the cloud software turns equipment’s operational parameters into informative insights about machines’ availability and performance (e.g., uptime, downtime, cycle time, total parts count, etc.). The insights obtained with the analysis are visualized and presented to the plant’s staff upon request via web or mobile applications.
The Implementation Example
To illustrate how the described approach works in practice, we’ll
discuss an example of a plastics parts manufacturer who has leveraged IIoT to
monitor shop floor performance in three geographically distributed
manufacturing facilities across two manufacturing divisions: die-cutting and
machined plastics. The manufacturer rolled out an IIoT solution to address the
issue of unreliable machine performance data arising from manual data
collection. Below, we describe the implemented IIoT solution from the
perspectives of machine connectivity, data analytics, and communication with
In both manufacturing divisions, the enterprise uses machinery
with computer numerical control (CNC). At the machined plastics division, the
enterprise employs CNC milling machines, CNC lathe machines, and CNC routers.
At the die-cutting division, the manufacturer uses hydraulic, traveling head,
beam cutting, and receding head presses, all with computer numerical control. The
equipment comprises both legacy and newer machines, which have different
connectivity interfaces. This influences the way in which the machines are
connected to the IIoT solution.
- For legacy equipment lacking Ethernet connectivity, the operational data is sent from a machine’s PLC through a serial port (RS-232, RS-422, RS-485) whenever there’s cycle start/end, spindle on/off, etc. The serial port is connected to the serial-to-LAN converter, which forwards the data to a cloud platform via an IoT gateway.
- For CNC machines supporting Ethernet communication, the operational data is sent from the machines’ PLCs to the cloud software through an Ethernet port via an IoT gateway over a wireless network
The enterprise wanted to conveniently see equipment availability
and performance metrics (e.g., operating time, downtime, total parts count,
etc.) across the machined plastics and the die-cutting division for varied
timeframes and to be able to compare machine effectiveness across production
lines and factories. Along with it, the manufacturer wanted to be informed
about possible critical production issues, such as machine failures, as soon as
To meet the requirements of the enterprise, the IIoT solution was geared with two types of data analytics: batch and near real-time.
With batch analysis,
the operational data collected from a machine’s PLC via a serial or an Ethernet
interface is transmitted to a cloud data storage, where it is aggregated for an
appropriate period of time (e.g., hour, shift, day, etc.) before it is
analyzed. For instance, the operational parameters of a CNC milling machine are
aggregated for an 8-hour shift and analyzed at the end of it to generate an equipment
effectiveness report per shift, featuring such metrics as total power-on time,
total motion time, average cycle time, and the total number of tool changes.
analytics implies collecting and analyzing equipment data immediately after
it is generated. Near real-time data analytics is used to provide a fast output
in the form of an alarm informing responsible staff of potentially critical
situations — for instance, a traveling head press abruptly stopping during an
Communication with Users
The insights obtained with the analysis are communicated to
the shop floor and factory managers via web and mobile applications. The output
the solution provides takes the form of reports and near real-time alerts.
The solution allows factory and enterprise managers to
generate basic and extended reports. Basic reports display the data about CNC machines’
statuses, uptime, downtime, the number and the duration of cycles, and other
availability and performance metrics for a selected period on a dashboard.
The extended reports provide the ability to explore an
entire machine’s event history for any period and see every machine’s
effectiveness dynamics. For instance, a COO can view a monthly availability and
performance report for the die-cutting division and compare the obtained
metrics with those of the previous month, the dynamics conveniently visualized
in the form of a line chart.
Alerts are generated when equipment data shows patterns
critical for the cutting or pressing operations. The equipment operational data
is analyzed in near real-time against the rules defined by data analysts in
cooperation with manufacturers. The rules determine potentially critical
situations and respective actions that should be taken whenever such a
situation arises. For instance, if a CNC milling machine rejects the start of a
new cycle, an alert is triggered and sent to a maintenance specialist and a shop
floor manager via a web or mobile application.
The Benefits Gained with IIoT
The solution has driven substantial operational improvements
across the enterprise, the most important of which include:
- Instant Access
to Shop Floor Data: Due to the improved collection, aggregation, and
processing of data, equipment effectiveness reports become available to the
enterprise and shop floor managers in a matter of minutes, so that they are
always supplied with accurate equipment effectiveness KPIs.
- Precise Visibility
into the Shop Floor Operations: At the shop floor level, IIoT has provided
shop floor managers with the ability to see the current level of equipment
availability and performance, as well as get notified about potentially
critical situations in near real-time. At the enterprise level, the
implementation of IIoT has given enterprise managers visibility into how well
each manufacturing division is performing and provided an opportunity to see
performance dynamics across divisions.
- Detailed Analytics:
The insights obtained with IIoT go well beyond the information about a
specific machine’s uptime and downtime. IIoT allows you to see detailed
performance metrics for each machine, production line, factory, and
manufacturing division. For instance, for CNC routers, it is now possible to
obtain the data about the average cycle time, the last cycle time, the number
of tool changes, the number of tools in use, the number of parts produced, and
more. This has enabled enterprise managers to leave behind the guessing game
and start making data-driven decisions about improving equipment effectiveness
across the enterprise.
- The Ability
to Monitor Labor Effectiveness: By correlating equipment effectiveness data
with the data about operators working on particular machines, it is now possible
to get insights into the performance of each operator and get a better
understanding of the overall labor effectiveness. A shift manager can, for
instance, see that during a shift, John operating CNC lathe X has produced 12
parts more than Jim operating CNC lathe Y has.
The Challenge on the Way to a Connected Shop Floor
A challenge that substantially complicated rolling out an
IIoT solution for this manufacturer was the need to connect legacy equipment to
the cloud software. Some of the enterprise’s hydraulic and receding head
presses were designed several decades ago, so they do not support modern
connectivity methods. To make the presses communicate with the cloud, it was
decided to equip them with sensors, which in their turn, were connected to an
external controller. The sensors would gather the operational metrics (e.g., press
power on/off, table lift, etc.) and pass them over to the cloud software
through an external controller via an IoT gateway.
Outcomes and Future Plans
The automatic collection and analysis of equipment data
allowed this manufacturer to tackle the issues they had struggled with when
collecting and analyzing equipment utilization data with manual methods — data
inconsistency and its availability with delays. The implementation of an IIoT
solution for monitoring equipment effectiveness allowed the enterprise to see how
well the machines were performing across both die-cutting and machined plastics
division. The enterprise has since experienced considerable operational
improvements and plans to extend the solution’s functional scope to be able to
track yield quality.
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