ARC STRATEGIES
By Ralph Rio
SEPTEMBER 2015
APM 2.0 with Industrial IoT
Executive Overview .................................................................... 3
Asset Performance Management Scope .......................................... 4
APM 2.0: Impact of Industrial IoT ................................................ 7
Getting to APM 2.0 ....................................................................11
APM 2.0 Business Process Automation ..........................................16
Recommendations .....................................................................19
VISION, EXPERIENCE, ANSWERS FOR INDUSTRY
ARC Strategies • September 2015
On-time Shipments
Production
Equipment
uptime
Asset Management
High quality
Both Prod. &
Asset Mgt.
Cost control
Both Prod. &
Asset Mgt.
Less Inventory
Production
Equipment
uptime
Asset Management
ROA; Less fixed
assets
Asset longevity
Asset Management
Risk Mitigation
Safety
Both Prod. &
Asset Mgt.
Revenue
improvement
P&L
Drivers
Cost reduction
Cash conservation
Balance
Sheet
Governance
Domain in
Annual Report
Objective
KPI
Function
within APM
C-Suite Drivers and Related KPIs for APM 2.0
Approach
Method
Application
Cost/Benefit
Reactive
Run to failure, and
then repair
Failure is unlikely, easily
fixed and/or non-critical
10X plus when
failure occurs
Preventive
Service in a fixed
time or cycle interval
Probability of failure
increases with asset use
2X maintenance costs
Predictive
Monitor a single process data value for
bad trends and alert
prior to failure
Assets with a random or
unpredictable failure
pattern
1X maintenance costs
Prescriptive
Multiple variables
with engineered
algorithms and/or
machine learning
Longer range prediction
of failure with high confidence
Unscheduled
downtime
approaches
zero
Maintenance Strategies and Maturity
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ARC Strategies • September 2015
Executive Overview
ARC Advisory Group developed its Asset Performance Management
(APM) concept almost a decade ago to provide our clients with a framework to analyze their asset management needs, develop effective strategies,
and ultimately optimize their asset availability and utilization.
Asset Performance Management 2.0, which now incorporates emerging Industrial IoT (IIoT) approaches and technologies and new analytics
solutions, uses information from production management and control sysIIoT and analytics enables the next
generation of APM 2.0. This ARC
Strategy Report provides insights that
can help asset management
professionals improve KPIs.
tems in asset management applications to provide
new opportunities to optimize asset availability and
operational performance.
Reliability studies (pg. 14) show that, on average, 82
percent of assets have a random failure pattern.
Preventive maintenance assumes that the probability of equipment failure
increases with use, which applies to just 18 percent of assets. IIoT and analytics, using engineered algorithms and/or machine learning techniques,
provide a new means to predict failures. This can enable organizations to
drive down unscheduled downtime to near zero – with a positive effect on
a broad range of KPIs.
Consumer electronics, including more than a billion mobile phones, drove
the creation of infrastructure and provided economies of scale for the sensors, networking and cloud computing used in IIoT. APM 2.0 leverages
this new capability to enable new business processes and business models
in industrial organizations.
APM 2.0 incorporates IIoT, analytics, and other predictive and prescriptive
technologies to bring performance to a higher level. It provides a means to
systematically improve key metrics like uptime, mean time to repair
(MTTR), asset longevity, cost, quality/yield and safety for maintenance;
and on-time shipment, quality, and inventory for operations. This optimization goes beyond functional silos and occurs between silos where
significant inefficiency, waste and sometimes dysfunction often reside. Rather than accepting waste among APM functions, ARC recommends that
industrial organizations develop a disciplined approach for improvements.
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ARC Strategies • September 2015
Key findings in this report for adopting APM 2.0 include:
•
Start proactive maintenance projects using IIoT and analytics
•
Target critical assets that have a random failure pattern
•
Use smaller pilot projects to build confidence within the organization
•
Consider an IIoT platform that can grow to build on success
•
Adopt mobile devices to eliminate paper-based work order processing
•
Use business process automation between systems - particularly PdM
and EAM
Asset Performance Management Scope
Asset performance management (APM) involves improved integration of
production management (making the product) with asset management (ensuring the capability to produce).
The resulting alignment enables
increased visibility, collaboration, and communication for higher productivity, reduced risk, and improved return on assets (ROA).
Goals and
objectives become more clearly communicated and shared. The ramifications extend into business processes, technology, and organizational
structure.
APM 2.0
Production
Maintenance
EAM, MOM, PAM
Integration
Quality
EH&S
Scheduling
Inventory
Low-cost
Sensors
Edge
Intelligence
Reliability
Dispatch
Network,
Cloud
Asset Integrity
Workflow
Remote
Monitoring
Asset Info.
Tools
Advanced
Analytics
Fleet
Mobile
Devices
3D Scan for
Upgrades
APM Synchronizes Production and Maintenance
An APM 2.0 strategy includes information sharing and application integration among enterprise asset management (EAM), manufacturing operations
management (MOM), plant asset management (PAM), and other solutions
to provide a comprehensive view of production and asset performance. It
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ARC Strategies • September 2015
incorporates IIoT, advanced analytics, and other predictive and prescriptive
technologies to reveal new opportunities to optimize asset availability and
production. It includes an improved understanding of risk, with fact-based
risk assessment. Integrating information from production and asset management applications provides new opportunities for organizations to
balance operational constraints (capacity, inventory, labor, etc.) and improve ROA. APM applies to the long, “operate and maintain” phase of an
asset’s lifecycle where margins and profits are determined. With a combined view of asset availability and operational constraints, workers and
managers have a richer set of data to enable information-driven decisions.
APM 2.0 Collaboration
With a good APM strategy, operations and maintenance groups become
more collaborative, exchanging information to manage critical issues and
operational constraints while improving overall operating performance.
Combining the information from the traditionally separate operations and
maintenance solutions improves the effectiveness of both areas, and offers
new opportunities for managing risk and optimizing performance.
On-time Shipments
Revenue improvement
P&L Drivers
Equipment
uptime
High quality
Cost reduction
Cost control
Less Inventory
Cash conservation
Balance Sheet
Equipment
uptime
ROA; Less fixed assets
Asset longevity
Governance
Risk Mitigation
Safety
Annual
Report
Objective
KPI in APM
C-Suite Drivers and Related KPIs for Asset Performance Management
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ARC Strategies • September 2015
Business Drivers for APM 2.0
C-suite metrics are included in the company’s annual report, i.e. the P&L
statement, balance sheet, and risk assessments. These metrics drive the key
objectives of production and maintenance. ARC has surveyed end users
several times during the past decade. Each annual survey has “improve
uptime” in the top position, followed by asset longevity and safety. Typically, the KPIs for operations focus on on-time delivery, quality, and lower
cost of production.
APM Supports Executive Metrics
In the chart for “C-Suite Drivers”, executives tend to manage from left to
right and “drill down” to identify issues and/or opportunities for improvement. When writing the business justification for an APM-related
proposal, managers and engineers can “drill-up” by going from the KPIs on
the right and connect to the P&L statement and balance sheet on the left.
This approach provides the foundation for a good business case that is
more likely to gain executive support by relating to their KPIs. The project
justification and executive support helps assure allocation of the needed
resources and management attention for successful implementation.
P&L: Uptime improves equipment availability so production can meet its
schedule with on-time shipments – which directly effects revenue and customer satisfaction. Improved production and maintenance effectiveness
helps control and reduce the related labor, material, and equipment losses.
Balance Sheet: When assets last longer, the company retains cash and
avoids using it to purchase replacement assets. Higher uptime also increases capacity, which can avoid expenditures for more assets and
conserves cash. Conserving cash improves the financial ratios used by Wall
Street to measure the value of a company.
Governance: Either intense competition or regulatory compliance drive
executives to carefully manage risk. The goal of safety and risk management aligns with this concern.
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ARC Strategies • September 2015
APM 2.0: Impact of Industrial IoT
Industrial IoT provides the enabler for next generation APM 2.0.
The
wealth of data provided through IIoT, combined with analytics, opens new
opportunities for improving asset performance.
Industrial IoT Essentials
IIoT solutions have an architecture that can be simplified into three major
components:
•
Data acquisition from various systems, equipment, devices or sensors:
In the case of an owner/operator, the data usually comes from sensors
attached to the control system and passes into an historian or other
time-stamped database. For an OEM monitoring its installed equipment, the sensor is usually part of an intelligent device with a
processor, memory, and small software applications. In both cases, the
data can be process values (pressure, temperature, flow,
etc.) or asset health information.
Analytics
•
Communications, networking and security: Usually, hierarchal transfer of information occurs from sensor to
control system to cloud applications. However, peer-to-
Network
peer communications among machines has interesting
possibilities (energy management or process coordination)
Historian &
Controls
that have yet to be applied broadly.
•
Cloud applications: Currently, the dominate use of the
data involves analytics to predict equipment failure so
Industrial IoT Architecture
repairs can be made prior to a fault with unscheduled
downtime. Some applications identify production or
operating issues that require attention. When conditions warrant, an
alert goes to operations or maintenance.
Ideally, business process
automation initiates appropriate workflow in the applications used by
these functions.
Why Now?
Consumer electronics – particularly smartphones – provided economies of
scale to lower the costs of sensors, networking, cloud computing and
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ARC Strategies • September 2015
application development.
This cost advantage combined with high
business benefit drives the current rapid IIoT adoption.
New Class of PaaS Applications with Analytics and Big Data
Historian and analytics software solutions have been around for at least
three decades. In that time, the effect of Moore's Law has vastly improved
data storage and computing capacity.
Also, the number of intelligent
sensors and devices feeding data has exploded. This confluence of Big
Data, low-cost cloud computing, and advanced analytics enables a new
class of applications.
Analytics have become pervasive for finding value that was "hidden" in the
data. Standardized platforms - like IBM's BlueMix, PTC’s ThingWorx, GE’s
Predix, and Microsoft’s Azure (plus many other more focused IoT
applications) - reduce the investments in terms of engineering and
programming
investments
needed
to
implement
a
solution.
Implementation costs and ease-of-use for these PaaS (platform as a service)
solutions have significantly improved, further driving acceptance.
Fail Fast, Learn, and Move On
Project risk has been lowered with inexpensive IT resources via the cloud.
Organizations can start with a free entry-level service to prototype and
even pilot a small application. If it doesn't work, scrapping the project has
low losses and organizations can “fail fast, learn, and move on.” Costs
would only begin to accrue with scale up following success, and these costs
would be offset by the business benefits.
Industrial IoT for Avoiding Unplanned Downtime
A time-stamped data repository like an historian brings pervasive access to
operating data for IIoT applications. Most industrial organizations have
critical equipment for which unplanned downtime would disrupt
operations. A small undetected problem can cascade into a much bigger
issue - much like how failure to spend $25 for engine oil could lead to your
car's engine seizing, requiring a $5,000 repair. IIoT enables new techniques
for condition monitoring and predictive maintenance (PdM) to enable
organizations to identify issues that could otherwise lead to costly
unplanned downtime, negatively impacting KPIs and executive metrics.
Using process data to alarm and drive maintenance work (like the oil
pressure light on a car’s dashboard) is a well-recognized concept. PdM –
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ARC Strategies • September 2015
which is often the “low hanging fruit” for IIoT applications - is a proven
approach for improving uptime while reducing maintenance costs.
Role of Analytics
The software to analyze the data provided by IIoT is segmented into two
general categories: engineered algorithms and machine learning.
Engineered Algorithms
Many types of assets (like large power transmission transformers) have
well-researched and well-understood failure patterns and an extensive
knowledgebase of the attributes related to failures. For specific classes of
equipment, this research has been used to create algorithms to predict
failures. Parameters collected from an asset go through engineered
algorithms (i.e., predetermined formulas, Boolean logic, rules and/or
decision trees) to determine the health of an asset.
Packaged software algorithms can often provide a viable approach for
developing, executing, and managing multiple types of assets and their
algorithms. This software helps select process data, configure diagnostic
algorithms, and create alerts that drive the appropriate actions by
maintenance or operations. The process data typically comes from the
plant historian and/or other systems typically incorporating OPC
communications.
Machine Learning
An emerging technology for the analytics involves advanced patternrecognition and other types of machine learning. Open source code for various types of machine learning approaches has fueled its recent rapid speed
of adoption. This technology generates an empirical model by “learning”
from an asset’s unique operating history during various stable and dynamic process conditions. This model becomes the baseline profile for normal
operations for a specific piece of installed equipment or a broader processing unit. The learning system automatically compares an asset’s model
with real-time operating data to detect subtle changes. These changes provide early warning signs of impending equipment failure before they reach
alarm levels and possibly an unplanned shutdown.
Though machine learning can operate with a limited number of existing
sensors, IIoT offers a richer set of process data (variety, volume, and veloci-
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ARC Strategies • September 2015
ty) for a higher fidelity model with improved condition monitoring and
asset reliability. The combination of these two emerging technologies offers
an opportunity to take prescriptive maintenance to a new level. Case stories indicate that the combination of machine learning with a large number
of IIoT sensors provides more advanced notice of a pending failure than
traditional, single-variable condition monitoring systems.
Positioning Engineered Algorithm and Machine Learning
With many of the same asset and well-understood operating parameters,
the engineered algorithm usually provides a good fit. A machine manufacturer with standard products has the economies of scale to create software
for monitoring its equipment via the internet and sell aftermarket services
for predictive maintenance and reliability. In some cases, an end-user has
many of the same type of asset, providing similar economies of scale. As
discussed in a previous ARC report, the Sporveien case story (for doors on
passenger trains) provides a good example.
Machine learning provides a good fit with more unique equipment or processes. Usually this involves an end user with a critical process step that
could result in expensive unplanned downtime. A common approach has
the machine learning application reading data from the historian. Initially,
many false-positive alerts will occur. The software must be “trained” to
improve the proportion of positive alerts. This can take from as little as a
few days to as much as six months, depending on the sophistication of the
machine learning software and the scope of the process being monitored.
Critical factors for success include:
•
Authorized trainers with deep expertise and can provide accurate
guidance to the machine learning algorithm
•
Prescriptive information when an alert is generated to help humans assess priority and diagnose the issue (otherwise, they will ignore it)
Industrial vs. Consumer IoT Adoption
Including Industrial IoT in a project involves decision makers who usually
require a clear benefit and corresponding business case.
Frequently, a
sponsor or “recommender” investigates, determines how the benefit will be
derived, and includes IIoT in a proposal. Management reviews and either
approves or denies the project. This leads to a rational, business-driven
decision. Industrial IoT involves projects with clear business benefits.
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ARC Strategies • September 2015
Consumer IoT includes use in products like smartphones, wearable fitness
trackers, smart thermostats, connected cars, and many other product
categories.
Consumer IoT typically has a low price-per-unit and high
volume compared to industrial applications.
With the low price, we
sometimes see fad-like buying patterns.
Getting to APM 2.0
Asset Management Strategies
Asset management strategies can generally be classified into four types:
reactive, preventive, predictive, and prescriptive. We’ve intentionally kept
the examples included here basic and generic.
Reactive – Run to Failure
Reactive is the most common approach to equipment maintenance since the
majority of assets have a very low probability of failure and are non-critical.
This is appropriate in many cases and helps control maintenance costs.
However, when a failure does occur, the broken component can cascade
into other components and become a major expense. Much like not servicing the engine oil in your car, running to failure, and having a $5,000 engine
replacement because the bearings seized. This approach is only appropriate for non-critical assets.
Preventive Maintenance
Manufacturers often employ a preventive maintenance approach. Here,
maintenance is performed based either on time (replacing the batteries in
your household smoke detectors once a year), or usage (changing your car's
oil every 5,000 miles). Preventive maintenance fits when wear with age,
run time, or number of cycles provides the driver for failure (i.e., assets
with an age-related failure pattern). Periodic inspections and condition
evaluation are often used for stationary plant equipment such as steam
boilers, piping, and heat exchangers.
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ARC Strategies • September 2015
Approach
Method
Application
Cost/Benefit
Reactive
Run to failure, and
then repair
Failure is unlikely, easily
fixed and/or non-critical
10X plus when
failure occurs
Preventive
Service in a fixed
time or cycle interval
Probability of failure
increases with asset use
2X maintenance costs
Predictive
Monitor a single process data value for
bad trends and alert
prior to failure
Assets with a random or
unpredictable failure
pattern
1X maintenance costs
Prescriptive
Multiple variables
with engineered
algorithms and/or
machine learning
Longer range prediction
of failure with high confidence
Unscheduled
downtime
approaches
zero
Asset Management Maintenance Strategies
Predictive Maintenance
Predictive Maintenance (PdM) uses condition monitoring to predict when
something bad is about to happen and provide a warning well in advance
of failure so appropriate maintenance can be performed to prevent unplanned downtime. The applications involve the more critical assets for
which failure would significantly impact uptime, asset longevity, safety,
product quality, or involve major repairs. Typically, the monitoring involves a single asset attribute, such as vibration or temperature.
One PdM approach uses process or device data to assess the current condition of the equipment. This data often comes from the plant historian and
is used with an algorithm to predict failure. Another approach for PdM
involves a separate plant asset management (PAM) system for condition
monitoring. Commonly used sensors include vibration, infrared, ultrasonic, oil analysis, and corrosion. PAM systems are usually applied to rotating
equipment (pumps, electric motors, fans, internal combustion engines, and
presses) and related automation assets.
Prescriptive Maintenance
Prescriptive maintenance combines data (usually from a particular device
or system) with algorithms that model that type of equipment (virtual
equipment or “digital twin”) to monitor condition and raise an appropriate
alert when appropriate. The data from a particular device, combined with
algorithms engineered for that type of equipment, provide a means to assess condition and identify a problem before it cascades into a much larger
problem that could impact business performance.
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ARC Strategies • September 2015
One benefit of the specific algorithm or model is the ability to replicate it
like a template across many similar devices in a large network – like doors
on a passenger train or transformers in power transmission lines. Another
major benefit of using a model is the longer time horizon provided for notification of an issue. Here, integrating the alerts into other applications and
business process automation (BPA) becomes important since humans tend
to forget long-term issues (more on this BPA later). Also, technicians must
be provided with specific information to help them understand and diagnose the problem, so they won’t just ignore the alert.
Proactive Approaches Currently
Preventive
Lead
23%
Advancing asset management maturity
Inspections
is not a smooth road, and requires
14%
56%
Proactive
Safety
10%
leadership.
Most organizations have
avoided devolving into a run-to-failure
maintenance strategy with reactive
Predictive
work practices using corrective or
10%
emergency work assignments.
Corrective
19%
30%
Reactive
Emergency
11%
Based on the results of a recent ARC
survey of 141 users on enterprise asset
management, most organizations currently employ preventive maintenance
Project
6%
approaches.
The maturity varies by
industry with those having a higher
Administrative
business and safety risk with a failure
5%
0%
5%
10%
15%
20%
(as with continuous processes) adopt-
25%
Categories of Maintenance Work Orders
Source: EAM User Survey, March 2015 by ARC
ing more predictive and proactive
maintenance.
Benefits with APM 2.0 and Higher Maintenance Maturity
As one moves along the maturing curve from run to failure and towards
preventive, predictive, and prescriptive maintenance; improvements occur
in the core KPIs for asset management and maintenance: uptime, asset longevity, cost control, yield/quality, and safety. Note that some industries,
such as refining, rank safety higher. These KPIs relate directly to executive
metrics for the C-suite – hence their importance.
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ARC Strategies • September 2015
Reduced Maintenance Costs
Predictive maintenance allows maintenance staffs to anticipate failures,
schedule work orders, and prevent failures. A study by a major petroleum
company showed that, compared to calendar-based preventive maintenance, a predictive approach reduces maintenance costs by 50 percent. The
specific benefits reported:
•
Maintenance costs reduced by 50 percent
•
Unexpected failures reduced by 55 percent
•
Mean Time Between Failures (MTBF) increased by 30 percent
•
Machinery availability increased by 30 percent
Improved Maintenance Execution
At the lower level of asset management maturity, the EAM system is used
primarily for record keeping to document past reactive activities. It provides supervisors a means to explain how resources were expended, and
perhaps justify the next budget. This is akin to "managing through the rear
view mirror.” With higher maturity and less reactive activity, the EAM system becomes a proactive management tool for planning, scheduling, and
prioritizing resources to prevent failures and unplanned downtime.
Mitigating Aging Workforce
The aging workforce issues include the difficulty hiring replacements willing to work in an industrial setting.
Some have forecasted double-digit
reductions in the available workforce for industrial companies in developed
countries. With the combination of fewer people and continually aging assets, more effective maintenance practices are required.
Performing
maintenance when conditions warrant (prescriptive) rather than periodically (preventive) requires less labor and can help mitigate issues associated
with the aging workforce.
APM 2.0 Needed for 82 Percent of Assets
Preventive Maintenance Fits 18 Percent of Assets
Preventive maintenance assumes the probability of equipment failure
increases with use, and schedules maintenance based on calendar time, run
time, or cycle count. However, data on failure patterns from four different
studies show that (on average) only 18 percent of assets have an age-related
failure pattern (reference the next chart). Thus, preventive maintenance
provides a benefit for just 18 percent of assets.
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ARC Strategies • September 2015
Prescriptive Maintenance for the Other 82 Percent
Doing a preventive maintenance on the other 82 percent may well cause
failures by placing some assets at the beginning of the Type B curve for
early life failures. APM 2.0 with prescriptive strategies using IIoT and
analytics to identify randomly occurring failures provides an appropriate
maintenance strategy for the other 82 percent of assets.
Age Related Failures
SUBMEPP
2001
Average
2%
3%
10%
8%
17%
7%
Age Related Failures: Ave. = 18%
Random Failures
9%
8%
56%
32%
6%
42%
Random Failures: Average = 82%
Failure Patterns of Simple and Complex Items
Sources: RCM Guide, NASA, Sept. 2008, and
U.S. Navy Analysis of Submarine Maintenance Data 2006
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ARC Strategies • September 2015
APM 2.0 Business Process Automation
Most organizations have operations, maintenance, and inspection in separate departments with different managers and metrics.
Usually, each
organizational silo has been optimized to meet its metrics – if not the manager would have a job continuity issue.
However, cross-functional
activities between the organizations often get less attention.
Disconnects between the Silos within APM
The difference in metrics among the functions within APM often leads to
disconnects, delays, and sub-optimal overall performance. The key metrics
for maintenance typically include equipment uptime, asset longevity, reliability, and cost control.
Production has a different set of metrics that
includes on-time delivery, quality/yield, quantity, and control of production costs. While complementary, these
Ad hoc human
communications
goals are distinctly different, encourag-
65%
ing
the
two
groups
to
operate
independently and thus sub-optimally.
Separate without
info. sharing
49%
Each functional area in APM has an
No predictive
maintenance
ecosystem of users, applications, sup-
40%
pliers, and integrators. The associated
management systems developed inde-
Auto email to
maintenance
25%
Auto create work
order in EAM
23%
pendently and, to a large part, remain
separate silos.
Unfortunately for the
business, there is little or no focus on
the gaps between silos and associated
Auto email to
operations
18%
Auto email to
engineering
17%
inefficiencies. For example, a delay between the time an operator or condition
0%
monitoring
application
identifies
a
problem and the maintenance group
20%
40%
60%
Predictive Maintenance and EAM Integration
Source: EAM User Survey, March 2015 by ARC
80%
resolves it, can result in unnecessary
unplanned downtime.
This needs to
change.
Business Process Automation Connects the Silos
It does no good to identify a pending failure, but not take appropriate
preventive action. Unfortunately, most organizations operate in silos, with
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ARC Strategies • September 2015
only ad-hoc communications between operations, reliability, maintenance
management and technicians. Too often, identified problems become lost,
resulting in a failure to take action and, ultimately, unplanned downtime.
Workflow improvements involving application integration for business
process automation avoids lost alerts. Combining IIoT with application
integration helps assure fewer “lost” issues providing higher uptime and
asset longevity.
Control
System
Process
Data
PdM with
Historian,
CBM, or
Analytics
Mobile
EAM
System
Alerts
or Flags
Planner
Work
Orders
Process
Execute Work Order
IIoT with Business Process Automation and Workflow Improvement
PdM = Predictive Maintenance; CBM = Condition Based Maintenance
High Data Quality with Mobile Devices
Paper-based processes tend to create delay and data quality problems that
negatively impact system integrity. With paper-based work orders, the
busy technician writes up the work order on paper, but typically doesn’t
enter the data into the system until the end of a shift when he or she can
find the time. As a result, data quality suffers. In other cases, the paper
goes to a data entry person who must interpret handwriting and again data
quality suffers.
Manual Data Entry Errors Undermine IT Systems
In the ‘70s, as part of the evaluation of newly introduced barcode systems, a
study found that data entry using a form with just 80 characters had a 10
percent error rate. More recently, findings show that manual data entry
error rates range from 10 to 40 percent. 1 Also, paper cannot be tagged,
“Quality Management for Organizations Using Lean Six Sigma Techniques” 2014
by Erick Jones, page 520
1
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ARC Strategies • September 2015
searched, or automated. Paper, with its inherent data integrity and manual
processing issues, undermines the effectiveness of IT systems. When people don’t trust the system, they create workarounds and manage activities
manually; resulting in declining productivity and increasing waste. Since
the data cannot be trusted, the corresponding system devolves into a historical record rather than a tool for proactive planning and management.
Data Collection at the Point of Action
Mobility for asset management applies to maintenance and field service
workers whose daily workflow involves going to where the equipment is
located to perform their assignments. These mobile workers
include technicians, craftspeople, dispatchers, maintenance, and
other personnel who physically move about the facility or
among customers.
Accuracy improves when technicians use a mobile device to
process work orders and enter data as part of their workflow
while doing the work. This dramatically improves the timeliness of work order processing. The mobile device also provides
the opportunity to check for errors and validate data. In addiMobile Technician
Source: Schneider Electric
tion, with mobility solutions, technicians can be given work
order information, asset history, spare parts inventory, repair
instructions, location, travel directions, maps, and make queries. Managers
use mobile solutions to assess KPIs, assess status, and make decisions.
Mobility solutions for processing work orders within the technician’s normal work flow extend asset management business processes to the point of
action. This creates enormous opportunities to improve data integrity and
Application
Mobility’s Impact
optimize the business. Mobility solu-
Plant Equipment
Maintenance
Work order execution and
system data integrity
exchange, including information dis-
Field Service
Management
Route optimization, customer
satisfaction, and up selling
Fleet Maintenance
Vehicle longevity and availability
for on-time deliveries
PAM and Condition
Monitoring
Data collection, accuracy, and
display
Environmental, Health
& Safety (EH&S)
Execution and compliance
enforcement
Applications Using Mobility for Asset Management
18 • Copyright © ARC Advisory Group • ARCweb.com
tions
provide
bi-directional
data
play, data entry, and confirmation for
work orders, data collection, and
documentation while the technician
is at the asset’s location. Applications
used by mobile workers include inplant
maintenance;
field
service
management; facilities management;
fleet maintenance; PAM and condition monitoring; and environmental,
ARC Strategies • September 2015
health & safety. Accurate data and work order status elevates the integrity
of the application to provide a viable, accepted, and trusted tool for management.
Master Data Management
Data inconsistency, incompleteness, ambiguities, and latency will occur between silos. This drives a need for some form of data standardization,
which can range from simple translation look-up tables to cross-silo naming
conventions. For initial pilot projects, it’s best to keep this data management methodology simple.
When the pilot projects succeed and are
extended to a broader enterprise, organizations should consider including
an appropriate level of master data management.
Recommendations
APM 2.0 incorporates the potential of IIoT and provides a strategy for systematically improving key metrics like uptime, mean time to repair
(MTTR), asset longevity, cost, quality/yield, and safety. Rather than accepting waste among APM functions, ARC recommends that industrial
organizations develop a disciplined approach for improvements. Based on
research and analysis, ARC recommends the following actions for owneroperators:
•
Start predictive and prescriptive maintenance projects using IIoT and
analytics. Consider targeting those critical assets that have a random
failure pattern – for which preventive maintenance is ineffective and
can be counterproductive. Focus on the “small data” for a specific type
of equipment.
•
Use smaller pilot projects to build confidence within the organization
and get the skeptics on board. Avoid going directly to a large, expensive, and high-risk Big Data initiative that will take too long, resulting
in decaying executive support.
•
Start with an IIoT platform and architecture that can grow so that you
can continue past the first project completion and build on success.
Copyright © ARC Advisory Group • ARCweb.com • 19
ARC Strategies • September 2015
•
Wherever possible, eliminate paper-based business processes – particularly those involving data collection by technicians and operators – to
assure data integrity and confidence in the associated APM systems.
Adopt mobile devices for the mobile technicians.
•
Automate the business process to connect the alerts generated by the
predictive maintenance application with the EAM system so that issues
get attention. Avoid dependence on ad hoc communications among
functional groups.
•
As part of the supplier selection project, include a review of the vendor’s IIoT strategy. Those with a solid strategy will have a first-mover
advantage, and a more sustainable business.
20 • Copyright © ARC Advisory Group • ARCweb.com
ARC Strategies • September 2015
Analyst: Ralph Rio
Editor: Paul Miller
Distribution: MAS and EAS Clients
Acronym Reference: For a complete list of industry acronyms, refer to our
web page at www.arcweb.com/Research/Lists/IndustryTerms/
KPI
APM Application Performance
BPA
Key Performance Indicator
MOM Manufacturing Operations
Management
Management
Business- Process Automation
BPM Business Process Management
MTTR Mean Time to Repair
CBM Condition-based Maintenance
OEM Original Equipment Manufacturer
EAM Enterprise Asset Management
PAM Plant Asset Management
PdM
Predictive Maintenance
IIoT Industrial IoT
PLM
Product Lifecycle Management
IoT
Internet of Things
RCM Reliability-centered Maintenance
IT
Information Technology
ROA Return on Assets
ERP
Enterprise Resource Planning
Founded in 1986, ARC Advisory Group is the leading research and advisory
firm for industry. Our coverage of technology from business systems to product and asset lifecycle management, supply chain management, operations
management, and automation systems makes us the go-to firm for business
and IT executives around the world. For the complex business issues facing
organizations today, our analysts have the industry knowledge and first-hand
experience to help our clients find the best answers.
ARC Strategies is published monthly by ARC. All information in this report is
proprietary to and copyrighted by ARC. No part of it may be reproduced without prior permission from ARC.
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of our staff members through our Advisory Services. ARC’s Advisory Services
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call, fax, or write to:
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Copyright © ARC Advisory Group • ARCweb.com • 21
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