Updated: Mar 6, 2018
Regarding Asset Information Management, IAM (Institute of Asset Management) and ISO 55001 state "Appropriate quality data and information are key to the management of any asset management business. It provides support for the asset management activity itself, for policy making and for supporting both operational and strategic business decisions." These standards are intended to offer guidelines on what should be done to be competent in asset information management, but don’t go into how it should be done. Guidelines are related to asset information strategy, standards, systems and data management. The intention of this article is to show a practical way to achieve effective asset information management from a strategic to an operational level.
Everyone will agree on the importance of data for asset management decisions. From a single set of data (CMMS/EAM) up to multiple sets of data (IoT, SCADA, CBM, Cost, CMMS/EAM), all sources bring capabilities of measuring asset performance that support decision making. Still, bad quality of information, data silos, culture & behavior, low visibility of data, just to mention some issues, keep us far away from where we want to be. Asset information management has a direct impact on business. IAM also stated “Improving the efficiency of how asset information is managed within the business therefore offers a significant opportunity for savings which has been estimated to be in the order of 1 – 5% of total business expenditure.”
Technology also plays a disruptive role on asset information management. New technologies emerge at an important pace, and implementation is always a challenge. But technology on its own won’t solve fundamental data problems; many companies keep jumping from technology to technology expecting to solve their problems, but usually the main issues remains unsolved and the big question comes again: How do we solve this issue? How to address this topic in a practical way?
Line of sight & objective alignment
Let’s take for granted that an organization X’s policy and strategy have been already defined. For example, part of a general company policy may be: Develop and maintain safe, efficient operation that sustainably serve the needs of customers and optimize the long-term return of investment for stakeholders. The corresponding strategy is in terms of meeting customer standards, achieving life cost efficiencies, managing overall asset risk profile, just to mention some.
For effective asset management, it is important to determine a line of sight & objective alignment at strategic, tactical and operational levels. “This line of sight helps ensure consistency between all individual performance objectives, organizational strategic objectives, policy and how individual assets or asset groups contribute to the delivery of organizational strategic objectives” (IAM). This means that asset strategy needs to be translated into objectives, these objectives need to be aligned to operational environments so that every objective is measured and linked back to the strategic plan’s performance. This line of sight can be achieved if data and information are available and support it. Asset information needs to be as well aligned between operational, tactical and strategic levels. This is when failure and condition data at an operational level can be transformed to represent risk and performance information at strategic levels that can be used for business decision making. So, the challenge is to manage asset data and information considering that data is stored in silos and not very aligned to asset objective and asset strategies.
¿How to achieve this? In Uptime Analytics, we structure a QFA (Question First Analytics) approach, which is a top-down approach upon which we base our analytics. It consists on the identification of the objectives and business questions every role has in an organization. Questions that the CEO, CFO, operation managers, maintenance managers, supervisors, reliability engineers, technicians, among others need to answer. Some examples of these questions are:
CFO – What should the budget for the next year be?
Maintenance manager – What is the overall performance of my equipment?
Reliability engineer – What is the condition of my equipment, and what will be the probability of it failing in the next 3 months?
Mapping these allows us to correlate business questions between levels and lets us build a data model that will consider all the data needed to answer the different questions and to align asset data & information to asset management strategies and plans. It increases visibility of the asset management and performance and lets data and information to travel from operational to tactical and strategic levels. This way, data is reviewed on a periodic basis to measure asset performance and objectives at all levels. Up to this point we understand why we need data and how it can be connected between levels, next question is what information is needed?
Asset information strategy
The information strategy inside an organization defines what data is needed and how it’s acquired, stored, used, assessed, improved, archived and/or deleted to maintain the data quality required to support asset management decisions. It also defines data standards and requirements, for example types of data, definitions, attributes, different uses, methods to standardize maintenance language, asset physical and logical hierarchy, among others. Some data may also be dynamic, so for asset information strategy it is important to consider data modification over time. The asset information strategy must also consider that data will probably be stored in multiple data bases and that they will help answer different business questions each role in the organization has.
¿How to achieve an efficient asset management strategy? At Uptime Analytics, we work on the equipment model concept. An Equipment Model is a data-driven approach that defines in the first place how the equipment is physically built and in second place its functions and how it should perform, be monitored and finally be operated and maintained by the organization. It will be the data model that will relate all the information associated to an asset, so that we have a 360-degree view of its performance. In a general way the equipment model contains:
Operational context and design variables
Functions and operating variables
Failure modes, effects, consequences and criticality
Condition variables (sensors, IIoT)
Critical spare parts
Maintenance tasks (description, skills, frequency, etc)
Not all information comes from a single source, so we consolidate all sources related to the equipment, including: physical asset data, location data, relationship (how assets are related to each other), work order data, performance data, condition data, cost data and any other data related to the asset. The equipment model also establishes the type of data, its format, measurement unit, attributes and frequency of use.
The Equipment Model is not only a characterization of the equipment but also a data structure that will identify and store data related to the assets, which allows us to build analytical and predictive models that help answer business questions at all levels. It’s meant to be the “single source of truth” related to assets. Now that we have data we need to answer business questions, let’s see how to use it.
Support decision making
The main reason of asset information management is to process and present asset performance related data in a business context that supports asset management decisions according to its corporate strategy and goals. Availability and visualization of asset information has high importance for analyzing performance, to define benchmarks and to audit management best practices. This is where all business questions should have an answer.
¿How to achieve proper support in decision making? Our analytics capabilities correlate all information of the equipment model and identify the best analytical object to answer every question in a direct way. Uptime predictive and advanced analytics help our customers execute periodic assessments of the performance of their assets and manage risk based on data, not on instinct. For more information on our analytics, please relate to our other articles in the Uptime Analytics blog:
Information asset strategy is important to asset management, and ISO states between its guidelines two important aspect to related to it: Line of sight & objective alignment and Asset Information Strategy. Using our QFA approach, equipment model concept and an asset management analytical application will allow organization respond to asset information management in a practical way.
Uptime with its team of maintenance engineers and data scientists has come up with a solution that not only utilizes technology but also takes into consideration the basics of asset maintenance and operation. Our solution offers a way to perform asset information management from a practical way and aligned to ISO 55001 guidelines. This is the first of a series of asset information articles in which we will cover topics as: cost of having good data, difficulties of achieving good quality data, what is good data, importance of failure data in the equipment model, among others.