Отчет по решениям в области APM (Asset Performance Management)

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ОТЧЕТ GARTNER ПО APM. ИЮНЬ 2018

Отчет аналитического агентства Gartner по решениям в области APM (Asset Performance Management) от 25 июня 2018г.

Market Guide for Asset Performance Management Software

Published 25 June 2018 - ID G00327803


APM systems help organizations achieve higher levels of operational reliability, safety and efficiency. CIOs in asset-intensive organizations can use this guide to gain insights on APM offerings and understand market direction in order to better support asset management strategies.

Overview

Key Findings

  • Interest in asset performance management (APM) is being accelerated by the realization that a key use case of the industrial Internet of Things (IIoT) is equipment reliability, which is a fundamental capability of APM.
  • APM integration with complementary technologies such as mobile and GIS solutions is a growing area of importance in enabling more efficient work practices.
  • Asset management strategies continue to mature from preventive to predictive and look toward becoming financially optimized, although at a varied pace.
  • Digital twin, a separate but complementary category of technology, modeled on operational technology (OT) data streams, can improve asset performance and utilization and has the ability to simulate asset behaviors to anticipate potential risky or critical operating conditions.

Recommendations

CIOs in asset-intensive industries looking to leverage cross-industry innovation for better equipment performance:
  • Improve tangible ROI from APM strategies by establishing a regular formal review of both legacy and new IIoT projects with your engineering and maintenance teams.
  • Source support from your IT and OT business unit leaders by tasking them to jointly create a roadmap of how APM will interact with complementary APM technologies to ensure effective and efficient integration and to maximize opportunities.
  • Identify possible data sources to support more advanced maintenance strategies, and determine what data analysis software can maximize your investment in multiple situations by collaborating with your operations team.
  • Work with business leaders to identify and prioritize digital twin use cases by identifying where digital-twin-based simulation with APM could deliver value.

Strategic Planning Assumptions

By 2020, 60% of asset-intensive organizations will rely on APM to help them optimize the performance of their mission-critical assets.
Through 2020, the preferred delivery method for APM in asset-intensive organizations will continue to be on-premises systems, with cloud quickly on the rise.

Market Definition

APM is a market of software tools and applications for optimizing operational assets (such as plants, equipment and infrastructure) essential to the operation of an enterprise. Organizations invest in APM tools and technologies to reduce unplanned repair work, increase asset availability, minimize maintenance costs and reduce the risk of failure for critical assets. These products can also improve an organization's ability to comply with regulations that prescribe how assets are inspected and maintained. APM uses data capture, integration, visualization and analytics to improve operations and maintenance timing, and to identify which maintenance and inspection activities to perform on mission-critical assets.

Market Description

There are two other related, but separate, asset management systems — enterprise asset management (EAM) and asset investment planning (AIP) — which are not assessed in this research, but it is important to understand the relationship (see Figure 1 for the relationship flow).
  • APM should not be confused with EAM, although integration between the two is common for triggering work orders in all levels of functional capabilities listed above, and many EAM vendors have invested in some level of APM. APM is designed for decision support; EAM is designed for maintenance execution. For more information on EAM, please see Note 1 or "Magic Quadrant for Enterprise Asset Management Software." Some EAM vendors have an APM product strategy; most rely on partnerships with APM vendors. Sourcing an APM solution that is compatible with your EAM solution provides more ready-to-use integration.
  • Similarly, APM also should not be confused with asset investment planning (AIP). AIP is not included in this guide as it has evolved into a separate type of software focused mainly on budget forecasting. It can be used at any level in the maintenance roadmap. AIP is used to forecast future failure characteristics across a fleet of like assets for the purpose of budget forecasts. APM is designed to support safe, reliable and efficient operation of equipment and infrastructure. AIP is designed to support both short- and long-term capital investment decisions, most often used by local governments and regulated utilities, which have to provide a forward budget of asset replacement and decisions about repair versus replace. This is also of interest to other asset-intensive industries such as oil and gas. AIP is primarily used to support long-term planning, enterprise business cases and regulatory filings. It takes data on asset condition, maintenance costs, criticality, budgets and risks, and then analyzes it to produce capital investment plans over extended time horizons (see "Technology Overview for Utility Asset Investment Planning"). The two solution types often use the same data and similar analytical techniques, but for different purposes. There is no overlap between APM and AIP solution providers.
Figure 1. Data Flow in Asset Management Systems

Source: Gartner (June 2018)

Data Flow in Asset Management Systems
APM includes specific functional capabilities (organized by APM category) that require data collection and aggregation from data historians and other operational data stores for the purpose of analysis (see Table 1).

Table 1: APM Capabilities and Categories

Capability
Processes and Tools
Asset Strategy and Risk Management
  • Data collection and aggregation from EAM systems
  • Certified integration with major EAM systems for updating maintenance plans
  • Various analysis techniques and tools for calculating risk and assessing criticality, including:
    • Weibull analysis — Statistical distribution of asset life data from a representative set of sample units to predict the life of an asset
    • Risk-based inspection (RBI) — Analysis methodology and process that requires qualitative or quantitative assessment of the probability of failure (PoF) and the consequence of failure (CoF) associated with each equipment item
    • Fault tree analysis (FTA) — A deductive failure analysis method that models the pathways within a system that can lead to failures or undesired results
    • Mechanical integrity — Management of critical process equipment to ensure it is designed and installed correctly, and that it operates and is maintained properly (that is, all elements are fit-for-service)
    • Safety integrity level (SIL) analysis — A method to indicate the tolerable failure rate of a particular safety function
    • Library of failure modes and recommended practices
Reliability-Centered Maintenance (RCM)
  • Data collection and aggregation from EAM systems
  • Root cause failure analysis — Actions taken to determine why a particular failure or issue exists and correcting those causes
  • Failure mode and effects analysis (FMEA) — A method to identify where and how an asset might fail and to assess the relative impact of different failures
Predictive Asset Management
  • Statistical modeling/regression analysis of physically observable characteristics in a piece of equipment, recorded over a period of time
  • Neural network analysis — Computational data model that can capture and represent complex input/output relationships
  • Machine learning to determine the related characteristics that are observable in a failure of equipment or parts so as to use that as the basis of future failure indicators
  • Proprietary algorithms
  • Monte Carlo simulation
Condition-Based Maintenance (CBM)
  • Rule engines — To determine and record tolerance levels for physically observable characteristics in a piece of equipment
Source: Gartner (June 2018)

Market Direction

State of the Market

APM is gathering significant interest as asset-intensive organizations realize that a key use case for IIoT and digital innovation is equipment reliability. This interest, combined with a relatively low threshold of entry into the APM market, means there are (1) a lot of vendor activity, (2) specialized products and (3) conflicting claims. Confusion looms in the market around APM capabilities and the value of technology as APM continues to be a critical investment area for asset-intensive industries and gains interest in product- and service-centric industries. CIOs look to their maintenance and operations managers to weigh APM investment versus return and struggle to see how to fully embrace the technology. With APM, organizations expect to address a variety of pain points such as:
  • Safety risks/environmental, health and safety (EH&S) incidents
  • Regulatory compliance
  • Operational/maintenance costs
  • Inventory cost
  • Employment productivity
    • Number of field technician trips to a site
    • Lack of visibility into justification and effectiveness of maintenance strategies being executed
  • Asset performance/throughput, including maximizing revenue and unscheduled callouts to planned maintenance identified with predictive analytics
  • Increasing first-time fix percentage (with the right data and understanding of the problem, send the right tech with the right tools to the site).
  • Helping to simplify the operations process to make way for a new workforce to enter the industry
  • Data silos
  • Poor quality, inconsistent and incomplete master data
  • IoT projects unaligned with APM investment
  • Inability to make timely and accurate decisions
  • Lack of governance over strategies, leading to inconsistencies in strategies being executed
Single-site deployments continue to dominate, generally with an increase in enterprisewide deployment initiatives as asset-intensive organizations seek to break down regional barriers and reduce licensing carrying costs.
Gartner surveyed APM vendors to assess their coverage of global markets. The vendors shown in Figure 2 supplied the percentages (with the overall total for each vendor equal to 100%) of 2017 APM revenue by geographic region. Clearly, there are many geographic markets where APM needs are not yet served by the vendors evaluated in this research.

Figure 2. Percentage of APM Revenue by Geographic Region
Percentage of 2017 APM revenue by region: Green: >20%; Yellow: 5% to 20%; Gray: <5%; Blank: 0%

Source: Gartner (June 2018)

Percentage of APM Revenue by Geographic Region

APM Adoption Continues to Accelerate, but at a Varied Pace

APM is a critical investment area for asset-intensive industries, including manufacturing, mining, oil and gas, transportation, and utilities (see "Asset Performance Management Transforms How Operational Assets Are Managed and Maintained"). Successful APM deployments can deliver measurable improvements in availability, as well as reduce maintenance and inventory carrying costs. Some aspects of APM have been practiced for more than 10 years, mostly by the largest companies in a handful of industries. Its broader adoption has been stalled until recently by a combination of internal and external factors, including provable ROI, budget access, skills, delegation of responsibilities and maturity of technology. In prior years, there was a need to build your own or apply complex mathematical tools to the problem.
Recently, APM has become more productized and is maturing into a more accepted part of business. This is due, in part, to rapid innovation in enabling technologies such as the IoT, advanced analytics and algorithms in asset-intensive industries. These are widening the scope and decreasing the deployment cost, aiding more widespread awareness and use of APM. The promise of reduced maintenance cost and downtime, coupled with higher levels of operational reliability, is attracting other industries. APM adoption is progressing at a varied pace among industries. Those industries that depend on the success of their assets such as manufacturing, utilities and natural resources tend to be further along in their asset management strategy and usually invest more heavily in APM. Other industries that rely on physical assets to some degree — such as healthcare, retail and public sector — are beginning to embark on this journey, but may not invest as heavily in APM solutions (see Table 2).

Table 2: APM Example Use Cases by Industry

Industry
Use Case
Utilities
  • Assess condition information such as oil analysis and power fluctuations on a transformer.
  • Assess vibration in wind turbine gearboxes to pre-empt a failure.
Oil and Gas
  • Optimize adherence to industry emissions and maintenance regulations.
  • Centralize disparate OT data systems to improve operations and reduce costs.
  • Reduce safety incidents by maximizing efficient and effective maintenance practices.
Manufacturing
  • Make process improvements, and reduce waste by using predictive analytics to measure equipment degradation.
  • Use advanced analytics to improve overall utilization across a fleet of assets such as compressors.
Transportation
  • Catch early warning of a shift in core parameters on a turbine engine (aviation).
  • Optimize work during planned shutdowns.
Facilities
  • Use remote monitoring and analytics to determine optimal service intervals for elevators based on usage and age.
Government
  • Monitor mobile assets to determine future failure rates and, therefore, forward positioning of spares.
  • Optimize maintenance activities to reduce inventory costs.
Source: Gartner (June 2018)
Not all organizations are mature enough to invest in APM. In some instances, there may be immature asset management processes and a standard EAM system of record. In that situation, the better investment may be to upgrade the existing EAM system and/or invest in a data-cleansing project. APM is not an execution system and, therefore, depends on EAM to execute its recommendations and provide feedback on the results (see Figure 3). Before investing in APM, organizations need to assess the maturity of their EAM system and have a sustainable integration plan between the two (see "Mapping a Route to Asset Management and Reliability" and "Financially Optimized Maintenance Planning Using Asset Performance Management").

Figure 3. Maintenance and Reliability Flows

Source: Gartner (June 2018)

Maintenance and Reliability Flows

APM Optimizes the Adherence to Industry Regulations and Maintenance Standards

The concept of industry-specific regulations and maintenance practice standards is not new. These provide governance on how an activity should be completed. Most standards and regulations related to asset management have been in practice for many years, if not decades, and can be specific to an activity, industry or role. For instance, an engineering department relies on the development and use of standards to help ensure the reliability, use and maintenance of its systems and assets. An example of this is the 2004 British Publicly Available Specification (PAS) 55. This became the first internationally recognized standard for physical asset management, and in 2014, became recognized and enhanced as the ISO 55000 series:
  • ISO 55000 — Overview, principles and terminology
  • ISO 55001 — Requirements
  • ISO 55002 — Guidelines on the application of ISO 55001
Previously, in asset-intensive companies, an organization would rely on its employees and simple internal governance to ensure industry regulations and maintenance practices were followed. A downside of this method is workforce accountability and the potential for process breakdowns leaving gaps, which could potentially cause significant impact on the product, on service or on quality, or lead to penalties from regulators. However, as asset management practices and technology have advanced, it is now easier to adhere to industry regulations and maintenance standards with APM solutions. APM can be leveraged to create rules, thus taking the onus of tracking off an organization's workforce. Another benefit from asset management solutions comes from the ability to focus on any number of standards an organization may have. Asset management can be configured to address an organization's specific needs and is demonstrated in seven deceptively simple questions:
  1. What do we own and where is it?
  2. What is it worth?
  3. What condition is it in?
  4. What do we need to do to it?
  5. When do we need to do it?
  6. How much money do we need?
  7. How do we achieve sustainability?

Cloud-Based Deployments of APM Systems Will Increase Over the Next Five Years as Buyers Accept Cloud as an Option and Technology Misconceptions Are Addressed

Many enterprise application markets such as CRM and HR management have witnessed significant adoption of cloud deployments over the last decade. In contrast, the APM market has seen a slower rate and limited cloud deployments. Reasons for this include:
  • Some regulated industries operate on models that have a preference for capital expenditure (capex) rather than operating expenditure (opex).
  • Asset-intensive organizations have more complex requirements.
  • There has been significant customization of existing systems.
  • There is a misconception among asset-intensive industries that data security is lessened if based in the cloud.
Changing user attitudes and expanding vendor offerings are creating new market dynamics. While on-premises deployments still dominate the market, most vendors offer cloud-based APM solutions and have APM-as-a-service offerings. With the expanded set of cloud-based APM options, some buyers such as smaller organizations are implementing these type of solutions. It is becoming increasingly rare for organizations that are in the market to buy, upgrade or replace an APM system to not have a dialogue on the possibility of a cloud deployment. This openness to discussing cloud as an option is a leading indicator for future cloud adoption.
Cloud benefits include:
  • Not having to manage upgrades every two to five years
  • The need for fewer resources to maintain on-premises infrastructures
  • Lower cost to deploy
  • Reduction in costs as part of a broader move to reduce investments in infrastructure and support resources
It is also important to note that the benefits and changing attitudes vary among industries, and the style, architecture and security of cloud requirements will vary due to compliance, regulations and other requirements. Gartner surveyed vendors for the percentage of APM customer deployments by delivery model. Tables 3 and 4 summarize the responses from 13 leading APM vendors.

Table 3: Percentage of Utility APM Customer Deployments by Delivery Model

Delivery Model Option
Percentage of Customer Base
On-Premises
67%
Hosted
33%
Source: Gartner (June 2018)

Table 4: Percentage of APM Customer Deployments in Cloud

Delivery Model Option
Percentage of Customer Base
Public Cloud
65%
Private Cloud
35%
Source: Gartner (June 2018)

Advanced Analytics Are Being Leveraged to Achieve More Reliable Maintenance Strategies

Most asset-intensive organizations are investing in, or plan to invest in, advanced analytics for use in their maintenance strategies in an effort to help predict the failure of mission-critical assets. This shift is mainly due to pain points that include the following:
  • There is a significant cost to unplanned downtime.
  • Traditional approaches to ensuring high levels of equipment utilization have reached their limits.
  • Collecting and managing equipment data are complicated by legacy data formats.
  • Effectively inferring equipment failure often involves analyzing large volumes of extraneous data.
Predictive forecasting based on analytics has long been the reliability goal for engineers and others responsible for managing asset performance. Until recently, it has been an elusive goal due to technology limitations, project costs and lack of good quality data. However, as organizations are more prepared for digital transformation and advances in sensor technology, communications technology, information management, and analytics make it possible to better predict equipment failure, investments in advanced analytics are more obtainable and attractive to end-user organizations.
Vendors in the APM space have taken note. Both small, independent software vendors (ISVs) and large original equipment manufacturers (OEMs) such as GE and Siemens have made substantial investments to provide these capabilities through their APM offerings. And it will continue to be a major investment area for the coming years as globalization, regulatory oversight, social media scrutiny and complex value chains reduce the room for error in managing operational assets.

Digital Twins Improve Decision Making

Digital twins represent a separate, but complementary, category of technology that may include consideration of maintenance, reliability and production simulation and can be leveraged at any level of maintenance planning. Digital twins modeled on OT data streams and IoT extensions are improving asset performance and utilization in asset-intensive organizations such as utilities, oil and gas, and manufacturing. Recently, the emergence and proliferation of the IoT, coupled with cloud computing and advanced analytics, has given rise to digital twins. A digital twin is a virtual counterpart of a real object, which enables other software/systems and operators to interact with it — rather than with the real object directly — to improve maintenance, upgrades, repairs and operation of the actual object. The minimum elements of a digital twin include the model of the object, data from the object, a unique one-to-one correspondence to the object and the ability to monitor the object. A key benefit of a digital twin comes from the ability to simulate object or asset behaviors to anticipate potential risky or critical operating conditions.
Much of the current development in digital twins has come from the manufacturing sector (e.g., Industry 4.0), as cross-industry use cases continue to develop. Digital twins have been of high interest in a few industries such as asset-intensive organizations in recent years. As these organizations continue to seek greater efficiencies and further progress toward digital transformation, the ability to predict an asset's performance, evaluate different scenarios, understand trade-offs and enhance efficiency, places digital twins high on the list of investment inclusions.
Digital twins enable new methods of enhanced data insights to improve decision making. Digital twins can model a complex asset such as a generator or transformer, and by leveraging sensor and historical data, they can be used to predict when and how a failure could occur. This would allow the organization to operate more efficiently. Additional benefits could include the ability to maximize reliability, optimize operating and maintenance costs, and manage assets more effectively.

Financial Information Helps Optimize Maintenance

Managing maintenance in the context of operations and cost impacts can be done scientifically. For most companies, maintenance planning uses the simple measure of availability of equipment at minimal cost, optimizing mean time between failures (MTBF) and mean time to repair. This doesn't take into account the value of that lost production or the impact that less or more maintenance has on the production output and how to budget for that. A more important measure is overall production or mean time between production loss, which could include not just a failure, but degradation of quality or decrease in efficiency. Sometimes, this risk is mitigated by having expensive spare equipment idle. It should be noted that the cost of repair is usually much higher if equipment fails in operation, as there will be collateral damage, safety problems and lost production. There may also be "inefficient" equipment utilization as expensive assets are kept on standby mode. In some industries, this may also involve environmental impacts, where the failure of equipment may contribute to environmental breaches for which penalties are levied and which can be quantified as risks.
This is a more advanced strategy, and few organizations are currently leveraging it. However, the combination of financial information (maintenance cost and production value), equipment health data (from programmable logic computing devices and supervisory control and data acquisition) and failure probability (end of life) data demonstrates high value. It allows organizations to optimize maintenance strategies. Because of the inclusion of financial data, APM solutions are linked not just to EAM to trigger work, but also from EAM to gather maintenance cost data. APM is also interfaced with the ERP/manufacturing execution systems (see "Financially Optimized Maintenance Planning Using Asset Performance Management").
There are four main forms of financial optimization of maintenance using APM:
  • Risk-based inspections and maintenance based on value of production assets to tune maintenance schedules for the most important assets
  • Integrated production versus maintenance scheduling so that production value and maintenance interruption are analyzed and balanced
  • APM for analysis of lowering equipment rate/activity/frequency to delay failure
  • APM tools to calculate the value of improving efficiency or reducing fuel or materials consumption by analyzing the impact of equipment degradation on the output efficiency of an asset

Enabling Technologies and Market Opportunities Are Expanding Capabilities

Recent advances in enabling and complementary technologies have spurred concurrent advances in the APM market and are furthering use cases (see Table 5).

Table 5: Enabling Technology Use Cases With APM Solutions

Technology
Example Use Cases
Artificial Intelligence (AI)
  • Can be used to train deep-learning algorithms to recognize and classify different machine usage or parametric limits such as with sounds, temperature and vibrations that can identify early warning signs for failure and performance issues. For example, a wind farm operator could use AI with acoustic monitoring to troubleshoot turbines and gearboxes in the field remotely.
  • Can be applied in the data integration process to determine correlations between siloed data sources.
Machine Learning (ML)
  • Can be used to identify production assets trending toward potential failure such as with autodisposition and autocase hazardous gas and thermocouple detection.
  • Can identify the correlation between maintenance and operation data, along with other data that may contain clues about equipment usage and degradation.
AR/VR/MR
  • Inspections.
  • Rounds.
  • Asset condition assessments.
Mobility
  • Used for real-time access to condition and operational events and data.
  • Collection of field data where there are no sensors monitoring conditions.
  • Remote diagnostics and repair.
GIS
  • Tracking geospatial coordinates when taking readings via rounds, to ensure the user was actually in the field when taking the measurement or noting the condition.
  • Linking to network of fault-detection sensors and other devices for prescriptive and preventative maintenance across territories.
  • Can enable grid analytics in order to understand number of customers that could be impacted in case of asset failure.
Unmanned Aerial Vehicles
  • Remote inspection of assets in hazardous or difficult-to-reach locations such as with pipeline or solar panel inspections.
  • Assessing the threat of natural disasters such as wildfires to assets in rural or mountainous areas.
Source: Gartner (June 2018)

Additionally, technology integration is important to furthering the capabilities of APM solutions and contributes to more efficient work practices. For example:
  • Mobility system integration is a growing area of importance in asset management, and organizations should have good mobile capabilities. Complementary to the data collected through the IoT, mobile technologies are employed for the capture of inspection data in the field in the form of operating statistics and condition data. This is a crucial building block for RCM analysis. The integration with mobile solutions allows the capture and use of real-time data in the field.
  • Geospatial capabilities improve data collection in the field. These capabilities equip field workers with access to real-time mapping data and data to help them understand what is happening with an asset — as it relates to the associated location and context — and why it is happening. For example, in a gas line leak, a technician can more easily find a specific asset component like a shut-off valve and view other layers of infrastructure like underground cables or sewer lines that provide access to repair the gas line. GIS is embedded for a graphical interface, which assists with better planning and routing. Some uses include field mapping and design for asset inspection, maintenance and procurement, and for decision making in the field. Other capabilities (such as geofencing, dynamic rerouting and lightweight apps for geocentric data reporting processes) can present utility buyers with more options and flexibility. Location-aware and context-aware applications are essential for long-term productivity improvement.

What's Under Development on Vendor Roadmaps

The following are representative development activities from vendor product roadmaps. It is important to note that not all vendors are developing these capabilities, and those that are will be at various stages of development:
  • Leveraging advanced technologies such as AI and machine learning
  • More ubiquitous connectivity to IoT data
  • SaaS delivery models to reduce implementation times and costs
  • APM algorithms for edge computing
  • AR/VR support for viewing nonvisible infrastructure in the field, for overlaying asset information on the worker's view and for workforce training
  • Digital twin
  • Integration with mobile and GIS solutions for access to real-time asset data to improve service delivery
  • Open platform integration for single source of the truth
  • Prepackage interface expansions (e.g., AIP, OMS, EAM, SCADA, data historians)

Market Analysis

The APM market is composed of two distinct, but overlapping, submarkets. One is a market of APM platform vendors. The other is a market of asset analysis solutions used to support specific analytical approaches or, in some instances, specific classes of assets.

Platform Vendors

Platform vendors deliver comprehensive APM platforms for:
  • Aggregating asset data
  • Analyzing the data
  • Creating an asset management strategy based on risk factors, criticality and predicted outcomes
APM platform vendors support a comprehensive range of risk assessment and management methodologies (as described in the Market Definition section). They also provide integration with EAM systems and operational data stores, such as data historians. If deployed as an enterprise system, an APM platform becomes the focal point for an organization's asset management strategy and, where applicable, its adherence to the ISO 55000 standard for physical asset management.
An organization should not automatically select a platform vendor because it has a comprehensive platform. These offerings should be considered only if your organization is in the market for an integrated product set based on the needs of your organization.

Asset Analysis Vendors

Asset analysis vendors typically provide a suite of capabilities for predicting equipment failure that includes:
  • Aggregation of data from various operational data sources
  • Application of advanced analytics to discern patterns from the data
  • Visualization to identify potential failure patterns
These vendors may also include creation of alerts and workflow to support decision process.
These capabilities can be applied to a broad spectrum of different types of equipment. Most asset analysis tools are general in nature, while a few target a specific class of asset. They can be used strategically or tactically as part of a more comprehensive asset management strategy.

Other APM Ecosystem Vendors

The market is also supported by vendor products that aren't widely used as APM solutions per se, but serve an essential role in the APM ecosystem. Some — such as asset performance technologies — primarily provide APM content to other solution providers. Others — such as the data historian OSIsoft — primarily provide the operational data necessary to support APM. The historian's data infrastructure also has been used by customers as a platform to build unique CBM solutions. (Note: Given the large number of existing and potential CBM solutions in the market, we have chosen not to highlight vendors that do only CBM. However, a number of the vendors represented in this Market Guide can support a CBM project.)
The decision on which APM solutions and techniques to use is driven by the types of assets an organization needs to manage, as well as available solutions. While some APM vendors can provide all of the capabilities required to support APM strategies, no APM vendor can manage all classes of assets across all industries. Gartner collected data through vendor surveys and client interactions for the purpose of demonstrating vendor capabilities in the four functional APM categories. Figure 4 illustrates the field-proven capabilities of vendors represented in this Market Guide. It is not a complete list of existing solutions, but it does include the major APM vendors, indicated as follow:
  • APM vendor categories colored green have a proven capability supported by both vendor and client claims.
  • APM vendor categories colored yellow have a product available; however, it may have limited deployments or the specific customer number is not available.
  • APM vendor categories with no coloring indicate a vendor does not have a capability.

Figure 4. Capabilities of Representative APM Vendors

Source: Gartner (June 2018)

Capabilities of Representative APM Vendors

Representative Vendors

The vendors listed in this Market Guide do not imply an exhaustive list. This section is intended to provide more understanding of the market and its offerings.

Market Introduction

Table 6: Representative Vendors in Asset Performance Management

Vendor
Web Address
Headquarters
Product Name(s)
Current Version
Industry Focus
ABB
Switzerland
Ability Ellipse APM
5.0
Utilities, mining and minerals, and oil and gas
ARMS Reliability
Australia
OnePM
4.7
Oil and gas, mining, utilities, manufacturing, process industries, transportation, and infrastructure
AspenTech
U.S.
aspenONE
10
Chemicals, oil and gas, CPG, pharma, transportation, mining, utilities, paper and pulp
AVEVA
U.K.
Predictive Asset Analytics and Condition Management
Predictive Asset Analytics version 2.9.4 and Condition Management 3.1
Energy, electric utilities, banking and finance, food and beverage, healthcare, oil and gas, mining
Bentley Systems
U.S.
AssetWise Suite
AssetWise Connect
Buildings and facilities, communications, discrete manufacturing, electricity and gas, government, mapping and surveying, mining, oil and gas, power generation, process manufacturing, roads and highways, rail and transit, and water and wastewater
Detechtion Technologies
U.S.
Enalysis and Enbase
Version 3.2
Oil and gas
DNV GL
Norway
Cascade
N/A
Maritime, oil and gas, power and renewables
GE Digital
U.S.
Predix APM
Predix APM
Manufacturing, oil and gas, chemicals, life sciences, consumer products, food and beverage, transportation, public sector, mining, forest products, and utilities
IBM
U.S.
IBM Asset Performance Management
N/A
Mining, oil and gas, utilities, power, transportation, chemicals, life science, consumer products, food and beverage, manufacturing, and transportation, public sector, and facilities
IPS-Intelligent Process Solutions
Germany
IPS-SYSTEMS Asset Performance Management
1.9.3
Utilities
MaxGrip
Netherlands
APMSmartApps, Optimizer+, strEAM+
Optimizer+ 8.2 and Stream+ 4.1.2
Infrastructure and utilities, food and beverage, and oil and gas
SAP
Germany
SAP Asset Strategy and Performance Management and SAP Predictive Maintenance and Service
SAP Asset Strategy and Performance Management 1802 and SAP Predictive Maintenance and Service CE 1802
Energy and natural resources, financial services, consumer industries, discrete industries, service industries, and public service
SAS
U.S.
SAS Asset Performance Analytics
SAS Asset Performance Analytics 6.2
All
Siemens
Germany
MindSphere and others
3
Oil and gas, utilities, and transportation
Uptake
U.S.
Asset Strategy Library, Preventance APM
Preventance APM Version 2.5 released in November 2017
Oil and gas, power, mining, and steel
OSIsoft
U.S.
PI System
PI Server 2017 R2
Oil and gas, power and utilities, chemicals and petrochemicals, metal, and mining
Source: Gartner (June 2018)

Vendor Profiles

Platform Vendors

Bentley Systems
Bentley is an ISV based in Exton, Pennsylvania. Bentley offers its AssetWise Suite of APM products, including the acquisitions Ivara and C3global. Most AssetWise customers are in North America and Western Europe and span a range of industries.
Bentley's latest release is AssetWise CONNECT Edition. AssetWise Asset Lifecycle Information Management (ALIM) provides visualization, geospatial referencing and structured control of asset information and managed change throughout the asset life cycle beyond just APM. AssetWise Asset Reliability supports core APM functions with a map-based, mobile offering for inspections. AssetWise Operational Analytics is an operational intelligence/predictive analytics product that serves three primary functions — operational data capture, data analysis and visualization/reporting. It is complementary to AssetWise Asset Reliability with limited overlap. AssetWise Enterprise Interoperability facilitates the interoperation of multiple data sources and includes predefined connectors for many third-party systems such as EAMs. It supports a number of exchange standards.
In 2017, Bentley announced a new partnership with Siemens that focuses on digital workflows and digital cities. This development augments previous partnerships between the two vendors, specifically with combining Bentley's APM, BIM and reality modeling software with Siemens' product design and production process engineering solutions, and integrates Siemens' COMOS with Bentley's OpenPlant. Further investment between the two companies will continue through 2018. This includes collaboration in APM within Siemens' Power Generation Services Division and Bentley's AssetWise.
MaxGrip
MaxGrip is an APM ISV and service provider headquartered in Utrecht, Netherlands, with offices in the Americas and Asia. Founded in 1997 as a maintenance and reliability service organization, it expanded into a software business in 2000. The majority of its APM customers are located in North America, Western Europe and Asia/Pacific. MaxGrip's APM customers span a broad cross-section of industries, with a concentration in oil and gas, chemicals, utilities, and food and beverage.
The company offers three APM software products. These include (1) Optimizer+, which interfaces with SAP, Infor, IBM and Ultimo Software Solutions' EAM systems; (2) strEAM+, which embeds APM functionality in IBM Maximo; and (3) APMSmartApps, which are mobile, light apps to simplify and enable APM in SAP EAM.
The latest version of Optimizer+ is 8.2 and Stream+ 4.1.2. Optimizer+ incorporates FMECA/RCM and Root Cause Analysis (RCA) functionalities, in combination with simulation, spare-part optimization, dashboard and reporting functions. Optimizer+ also offers access to MaxGrip Asset Libraries and can be used in offline mode. MaxGrip strEAM+ includes six modules that vary from RCA, RCM and RBI models to maintenance and compliance management. The APMSmartApps are built to work on all versions of SAP.
In late 2017, a partnership was announced with AVEVA to enhance assessment services and risk-based maintenance capabilities.

Asset Analysis Vendors

ABB
ABB is a public global power and industrial automation company based in Zurich, Switzerland. Its APM clients are located in North America, Latin America, Western Europe and China and are mainly in the utilities and transportation industries.
Its Enterprise Software product group provides a suite of software products, including APM products — ABB Ability Asset Suite Equipment Reliability (ER) and ABB Ability Ellipse APM (formerly Asset Health Center). ABB announced in January 2018 a new unified solution for integrated EAM, APM and workforce management (WFM) under the ABB Ability Ellipse, connected asset life cycle management (CALM) banner. ABB Ability Ellipse APM is one of the three primary components of ABB Ability Ellipse, apart from EAM and WFM.
ABB's latest release is ABB Ability Ellipse APM version 5.0. ABB Ability Ellipse was launched in late 2017, along with ABB Ability Ellipse connected asset life cycle management solution (CALM) strategy. The product is available either on-premises or in the Microsoft Azure cloud and can be delivered as a SaaS solution. ABB Ability Asset Suite ER was designed as a solution to help enforce standards related to equipment reliability and work management, specifically to support INPO AP-913 compliance in the nuclear power industry. It has been folded under the Asset Suite banner.
ARMS Reliability
ARMS Reliability is a privately held asset management software and advisory services vendor established in 1995, with headquarters in Australia. The majority of clients are located in North America, Western Europe and Australia. The company has a client base spanning a range of industries.
ARMS Reliability's latest release is OnePM 4.7, which supports devising and enhancing asset strategies by capturing data from all sources and reviewing the same, and together with Asset Management Solutions, augmenting proactive strategy formulation and optimizing plant maintenance and reliability.
In partnership with Isograph, the company also supports a range of reliability tools, like Isograph's Availability Workbench, Reliability Workbench, and FaultTree+, for detailed reliability, risk and availability assessment. ARMS Reliability has also partnered with Copperleaf to deliver AIP solutions to asset-intensive industries in Australia and New Zealand.
AspenTech
AspenTech is a public company providing asset optimization software and services. It was founded in 1981 and is headquartered in Bedford, Massachusetts. In recent years, AspenTech has made a number of APM-related acquisitions. These include Fidelis Group (reliability management solution that takes process flows into account) in 2016; ProSensus ProMV in 2016 (offline multivariate data analysis software); Mtell in late 2016; RtTech Software (IIoT cloud-based software) in 2017; and Apex Optimisation in early 2018.
The majority of clients are located in North America, Latin America, Western Europe and the Middle East and span a range of industries.
AspenTech's latest release is aspenONE Version 10, and includes both Aspen Mtell and Aspen Fidelis Reliability, as well as Aspen ProMV and Aspen Asset Analytics. Aspen Mtell is a predictive analytics and prescriptive maintenance asset management solution that can be applied to a broad range of assets. It leverages machine learning, which indicates impending failure and sends notifications. Aspen Fidelis Reliability performs asset risk management by identifying significant causes of lost operational availability that limit system production.
In early 2018, AspenTech announced a partnership with technology and engineering automation company Emerson to deliver asset optimization software solutions, along with global automation technologies.
AVEVA (Schneider Electric)
AVEVA is a publicly held engineering and industrial software company headquartered in Cambridge, U.K. The recent merger with Schneider Electric's industrial software business expands the company's engineering, planning and operations, asset performance, and monitoring and control solution portfolio. The majority of its APM customers are located in North America, Western Europe, the Middle East and India and span a range of industries.
AVEVA offers an integrated APM portfolio with several native AVEVA APM products. These include PRiSM Predictive Asset Analytics (latest release 2.9.4) and Condition Management (latest release 3.1) and capabilities that support RCM with fault diagnostics and asset risk management through AVEVA's Control of Work and Performance Manager products.
In late 2017, a partnership was announced with MaxGrip, leveraging MaxGrip's Optimizer+ product, which enables a more comprehensive asset life cycle management offering that includes asset risk management and RCM capabilities.
Detechtion Technologies
Detechtion Technologies is a private APM software and service provider based in Houston, Texas. It offers its Enalysis product for gas compression fleet monitoring, alerting and optimization as a cloud-based service only. The product was built over many years of gas compressor troubleshooting service engagements and has few direct competitors. Its customers are mostly in the upstream and midstream sectors of the oil and gas industry. The majority of its business is in North America and Australia.
Detechtion has two APM products, Enalysis and Enbase. The latest releases are Enalysis version 3.21 and Enbase version 3.2. The vendor's APM offering uses proprietary algorithms to determine the exact operating status of compressors from both production and maintenance viewpoints. This can be delivered through multiple platforms, including computers, tablets and phones and in offline mode. It also offers remote workers with no access to the internet a comprehensive set of PDF reports delivered by email for review as they travel through their oil and gas routes. Detechtion also offers the complementary mobility product Fieldlink.
In October 2016, Detechtion acquired Enbase, which provides an industrial IoT platform, mobile applications and predictive analytics for the oil and gas industry. Enbase was integrated with Enalysis in late 2017 and extends the capabilities for wellhead chemical injection and compression assets.
GE Digital
GE Digital is the software division within publicly held global industrial conglomerate GE, which has a diverse set of businesses across the energy, aviation, healthcare, transportation and oil and gas industries. GE Digital is headquartered in San Ramon, California. The majority of APM customers are located in North America with presence in all regions globally. The APM product is deployed across a range of industries, including manufacturing, oil and gas, chemicals, transportation, mining, and utilities.
GE has streamlined APM to be a horizontal solution built on the Predix Platform, which can be contextualized for industry verticals. GE offers its Predix APM, which was launched in early 2018. The new product offers capabilities of APM Health, Reliability, Strategy and Integrity in a Predix cloud environment and updates for APM Classic on-premises (including Meridium and SmartSignal). Predix APM offers prepackaged integration to GE's field service management solution Predix ServiceMax and offers an offline mode of use.
In recent years, GE Digital has acquired a number of companies. In 2016, it acquired Meridium, Bit Stew Systems (data management and integration), and Wise.io (machine learning). GE offers a number of APM-focused solutions acquired over several years, including SmartSignal, Bently Nevada System 1 and GE Digital Historian, as well as new Predix APM applications.
IBM
IBM is a publicly held global technology and consulting corporation with headquarters in Armonk, New York. IBM has two products that offer APM capabilities: IBM Maximo Enterprise Asset Management and IBM Asset Performance Management. The latter was an early 2018 consolidation of several APM offerings geared toward providing a more comprehensive APM-focused functionality set. The Maximo Asset Management product is positioned in IBM's Watson IoT group. The products are deployed in a broad cross-section of asset-intensive subsectors around the globe, including mining, oil and gas, utilities, power, transportation, manufacturing, and aviation.
The latest IBM product, Maximo Enterprise Asset Management v.7.6, provides basic condition monitoring, asset health assessment, risk and criticality analysis, and FMECA tools, in addition to the full set of core EAM functionality. The company has predictive and AI-assisted maintenance capabilities and is partnering with MaxGrip and GE Digital for aspects of APM. For the energy and utility industry, IBM has a full set of predictive models for utilities equipment to assist with asset replacement strategies.
In 2016, IBM acquired the rights to resell the SCHAD Automatic Meter Reading (AMR) product to connect APM solutions to industrial automation systems.
IPS-Intelligent Process Solutions
IPS-Intelligent Process Solutions is a privately held software company headquartered in Germany. The APM product is primarily deployed in the utility sector, with a majority of the company's APM clients located in North America, Western Europe, Eastern Europe and Australia.
The company's APM product, IPS-SYSTEMS Asset Performance Management version 1.9.3, is part of the IPS-SYSTEMS platform. The functionalities offered include maintenance decision support, maintenance concept definition, integrated planning, user configurable maintenance analysis, and mobile workforce management with offline mode capabilities and mobile access to technical documentation and a geolocation interface. The product can be deployed on-premises or in the cloud and offers comprehensive types, models and analytical libraries shared across the entire customer base. The product is aligned to ISO 55000 and PAS 55, Common Information Model (CIM) and IEC 61970 standards.
SAP
SAP, is a publicly held global enterprise application software vendor based in Walldorf, Germany. SAP Predictive Maintenance and Service, SAP Asset Strategy and Performance Management, SAP Predictive Engineering Insights, and SAP Asset Intelligence Network are integrated with SAP EAM. The company's primary client base is located in North America and Western Europe with presence in most regions globally. Clients span a range of industries.
In November 2014, SAP launched SAP Predictive Maintenance and Service, cloud edition, which allows reliability engineers and data scientists to leverage a set of prediction models and machine learning algorithms in SAP HANA. In February 2018, SAP announced the release of SAP Asset Strategy and Performance Management. The new product leverages SAP Leonardo IoT technology. SAP Asset Strategy and Performance Management is the latest addition to cloud solutions for asset management from SAP, which include SAP Asset Intelligence Network, SAP Predictive Engineering Insights and SAP Predictive Maintenance and Service.
SAP offers integration with OSIsoft's PI System product, which is used to load asset data collected via sensors to be stored in SAP HANA, the company's in-memory DBMS, and has certified interfaces with OSI data historian. SAP's CBM capability is provided through the SAP Plant Maintenance (PM) module (EAM solution), which has been available for many years, as well as through SAP Predictive Maintenance and Service.
In early 2016, SAP acquired Fedem Technology and, through that acquisition, released SAP Predictive Engineering Insights Solution, a digital-twin-based technology.
SAS
SAS is a global business intelligence, analytics and data science ISV. It is based in Cary, North Carolina. SAS was founded in 1976, and can claim a long history of helping businesses apply advanced analytics to discover patterns in large data and complex sets. While it has clients globally, its primary customer base is located in North America, Western Europe and Australia. SAS's APM business is distributed across the energy, oil and gas, and manufacturing industries.
SAS offers an APM solution — SAS Asset Performance Analytics (first released under the name SAS Predictive Asset Maintenance, which is now part of the SAS Quality Analytic Suite). The latest version is 6.2M1. Asset Performance Analytics uses a variety of analytical approaches, including time series regression and neural networks to model failure modes for specific assets. The solution offers an offline mode of use.
In late 2017, SAS partnered with GE Transportation to analyze IoT data to optimize equipment operation in the IIoT.
Siemens
Siemens is a publicly held global industrial conglomerate headquartered in Germany. The majority of its APM customers are located in North America, Western Europe, Latin America, the Middle East and Asia, with clients spanning a range of industries.
Siemens offers several APM products across different divisions, which interface with the MindSphere IoT Digitalization platform. They include native cloud applications, based on MindSphere (latest release is version 3) and utility-specific Power Diagnostics Center (PDC) (latest release is PDC Light) and Condition Monitoring and Diagnostics (latest release PDC for Program Units). The MindSphere product offers condition-based maintenance capabilities through its Visualizer App, predictive analysis through its Advanced Analytics App, RCM in its Product Intelligence App and RBI in the RBI App. MindSphere is deployed primarily as a hosted solution. The PDC product focuses on early detection of abnormal operating conditions of power equipment to help improve plant availability and operation equipment to help improve plant availability and operations. An offline mode of use is offered in the products. The products are aligned with industry standards such as ISO 55000 and ISO 27001.
In 2016, a corporate strategic alliance was announced with Bentley, focusing on digital workflows to advance infrastructure project delivery and asset performance. This development augments previous partnerships between the two vendors, and further investment will continue through 2018, including collaboration in APM within different Siemens divisions and Bentley's AssetWise.

Other APM Ecosystem Vendors

Uptake (Asset Performance Technologies)
Asset Performance Technologies (a provider of APM content and software) was acquired by Uptake in April 2018. Uptake is an industrial data science and AI vendor headquartered in Chicago, Illinois. Uptake offers advanced analytics, including predictive and prescriptive maintenance, through its Asset Perform offering. Its content offering (based on the acquisition) is Asset Strategy Library (ASL), consisting of an extensive library of reliability and preventive maintenance (PM) information, failure modes and asset strategies for industrial equipment. The vendor has strategic partnership with GE Digital and Bentley APM as its resellers and is also currently working with Oracle Utilities to develop an interface with its WAM product. In addition, it offers an APM software product — Preventance APM — that leverages the ASL. The ASL is currently available in its new web-based platform, called Preventance APM. Although neither as comprehensive, nor as well-established, as other APM products, it is has been successfully utilized by small-to-midsize asset-intensive businesses with an interest in utilizing the recommendations in ASL, in lieu of a major RCM project. The majority of acquired customers are in the power generation sector, while existing Uptake customers span other industries such as wind, rail, manufacturing, oil and gas, transportation, and mining.
DNV GL
DNV GL, headquartered in Norway, is a provider of classification, technical assurance, software and advisory services. Its utility-oriented software Cascade, for technical asset management and predictive maintenance, consolidates equipment diagnostics, nameplate and real-time data, providing equipment statuses. It can be integrated with most leading ERP and EAM systems. The utility-specific software interfaces with a wide variety of test equipment, as well as real-time and asset repository data, and allows SCADA and online-monitoring data to be automatically evaluated and captured as equipment reads.
OSIsoft
OSIsoft is a large, privately held, process data infrastructure vendor based in San Leandro, California. Its PI System is widely used by asset-intensive organizations to aggregate and manage their process data for the IIoT. OSIsoft's latest release is PI Server 2017 R2. Many of the vendors in this Market Guide rely on PI System for the operational data required for APM. PI System has also been used to develop custom CBM solutions in agriculture, chemicals, oil and gas, and utilities. These custom CBM applications typically use PI System's Asset Framework (AF). This can be used to define hierarchies of assets and process flows between assets for process data, event-framed data, performing calculations on assets, and integration with EAM systems to trigger maintenance work orders based on predefined conditions. The PI System can also be linked to off-the-shelf CBM solutions. In 2015, OSIsoft released PI Integrator for Business Analytics, a software integration product that enables sensor-based data captured within the PI System to integrate with other existing advanced analytics and visualization tools an organization may have. That was followed by release of PI Integrator for Microsoft Azure in 2016. In January 2016, OSIsoft announced a global reseller agreement with SAP. SAP will resell the OSIsoft solution as the SAP HANA IoT Connector by OSIsoft through its global network of affiliated customers and business partners.
In July 2017, OSIsoft unveiled a new version of PI Integrator for Esri ArcGIS, which includes a new time-enabled technology that lets users rapidly explore the history of their operations within ArcGIS web maps.

Market Recommendations

While a comprehensive APM platform solution would deliver value for almost any organization, all the components of these types of solutions may not be needed for most organizations. The value should be weighed against the investment and total cost of ownership. More specifically, when evaluating APM options, consider:
  • The importance of good quality data in your EAM systems. Assess your data quality, and if there are deficiencies, then invest in upgrading your EAM systems and/or an asset data-cleansing project before investing in APM.
  • The importance of integration with EAM. Ensure there is an interface to your EAM to be able to execute APM recommendations directly in the transactional EAM system.
  • Consider the value of integration with other complementary technologies such as mobile and GIS products.
  • The importance of sufficient, secure, sustainable and relevant OT data. If the data doesn't exist or is not accessible, then invest in deploying sensors and process data management infrastructure before embarking on an APM project. Additionally, look at the underlying governance of the OT systems to ensure you have a documented, secure and stable basis for OT data architecture. If you are considering APM-as-a-service options, determine if outsourcing the core competency of data analysis is beneficial in your long-term plan, or alternatively, if you should build competencies in-house.
  • The APM vendor's experience with your specific use case. Does it have customers already using the product to manage the performance of similar assets? Most APM vendors serve a variety of industries and asset types, but some are quite specific and are developing "domain expertise" offerings. Pick a solution that fits your scope and budget.
  • The alignment between the vendor's APM product roadmap and your own long-term equipment reliability strategy (assuming you have one). Not all APM vendors have an expansive product strategy. If your long-term plan includes expanding the scope of the solution to encompass different assets and different approaches to managing their performance, then invest in an APM platform vendor solution. If your organization only needs to support specific analytical approaches or specific classes of assets, then an APM asset analysis vendor may be more suitable.
  • The ability of the solution to support collaboration across the organization, as well as with external business partners such as OEMs. Cloud technology is changing asset management collaboration dynamics and opening the door to new asset management business models. If you are considering a more collaborative asset management model, invest in APM solutions that support, or will support, the necessary collaboration.

Acronym Key and Glossary Terms

EAM
Enterprise asset management includes planning and scheduling, work order creation, maintenance history, and inventory and procurement, as well as equipment, component and asset tracking for assemblies of equipment. In some instances, the functionality is extended by the addition of basic financial management modules such as accounts payable, cost recording in ledgers and HR functions such as a maintenance skills database (see "Magic Quadrant for Enterprise Asset Management Software"). While some EAM vendors have an APM product strategy, most rely on partnerships.
ISO 55000
This is an international standard covering management of physical assets. Initially a Publicly Available Specification (PAS 55) published by the British Standards Institution in 2004, the ISO 55000 series of asset management standards was launched in January 2014.

Evidence

Gartner received vendor briefings and associated material from 15 APM vendors (most have global reach, but some are only regional) from March through April 2018. Gartner also surveyed secondary research sources for information on market trends and vendor activity.

Note 1 Representative Vendor Selection

The 16 vendors named in this guide were selected to represent the three market segments as discussed in the Market Analysis section: platform vendors, asset analysis vendors and other ecosystem offerings. For each of these three categories, we list vendors in which Gartner has received the most client interest.

Note 2 Gartner's Initial Market Coverage

This Market Guide provides Gartner's initial coverage of the market and focuses on the market definition, rationale for the market and market dynamics.