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“...companies who do not adopt a Business Intelligence strategy
in the next five years will be at a competitive disadvantage in the marketplace.” 
                                                                                    - per Gartner Inc.

Business analytics (BA) will be a key factor in reaching these goals. Informed decisions must be made at every level – production, maintenance, purchasing, engineering and IT –enabling the management team to take decisive action and predict the future.

Strategies for business analytics are very similar to maintenance strategies. One approach is the “what happened” strategy. The business plan is: “Let’s look at the data we have and find out what bad thing just happened and why.” This is very similar to a run-to-failure strategy in maintenance. Something went wrong, now let’s try to find out why. Here we are focusing on making business and maintenance decisions using historical data.

Another strategy focuses on performance to plan. This is a decision making or performance management strategy. Decisions become more real time, in the moment. This business strategy is comparative to using preventative maintenance strategies.

The final strategy is predictive insight. This is when we start to ask what will happen next and how can we influence a positive outcome. We can now use business analytics as predictive tools to anticipate future events and avoid them or take advantage of them. This is when a maintenance program can start moving toward reliability centered maintenance (RCM).

So why is it important to compare business analytic strategies to maintenance strategies? Because in the end, they are all connected. Sales and marketing are using business analytics to predict market trends and opportunities. Production has to be prepared to adapt to these changes. Purchasing has to have materials and parts in the pipeline to support the production schedule. And maintenance has to provide the uptime and capacity. All of these different groups within the organization have to work together to take advantage of opportunities in the market and create a new future.


To accomplish these goals, many companies are using enterprise asset management (EAM) systems. An EAM solution manages the entire optimal life of physical assets to maximize value. Enterprise refers to the entire operation of a company and the management of assets across departments, locations, facilities and even business units. The goal of an EAM solution is to improve utilization and performance, reduce costs, extend asset life and improve return on assets (ROA).

An effective EAM implementation includes whole life planning, lifecycle costing and planned maintenance that leads to industry best practices. Companies can now see the impact and relationships between operations, engineering, maintenance, personnel and lifecycle costs.

Without high quality and consistent data delivered on an established schedule, the return on investment of the EAM is lessened. The furnished data must be provided automatically from SCADA systems, a PLC network, a distributed control system (DCS), on-line condition monitoring networks and/or other types of control systems that can consistently feed machine health data to an EAM or computerized maintenance management system (CMMS). Real time or near real time data is essential to the monitoring of machine health.


“More simply, the more you raise the quality of the data your organization interacts with, the higher the statistical probability your organization will make a better decision.”

- R. Taggs, P. Sage, M. Osana, D. Tepora, J. Mark de Asis,
TEAM Global, The Maximo Manager’s Guide to
Business Performance Management

Step one is to set goals for the machinery health monitoring program and determine what machine condition data needs to be in the maintenance software for optimal decision making. The data required can vary depending on the type of machine. Pressure, flow, temperature, ultrasound, oil analysis and vibration data and/or a combination of all these or other inputs can provide the data required to assess machine health.

Step two is to figure out how the data will be collected and how often. Depending on the criticality of the machine to your production, you might want to collect data more often. High speed, critical machines (e.g., steam or gas turbines) with fast failure modes may need to be monitored continuously.

On-line data provides the most consistent and ubiquitous information as it can be automatically fed to the EAM or CMMS. Manually collected data is, by nature, collected less often and the task of manually moving the data to an EAM or CMMS is time consuming and expensive.

There is significant value to having good data in the reliability maintenance software. Dave Bertolini of People and Processes, Inc., writes, “Perhaps it will surprise you to learn that 90% of Computerized Maintenance Management Systems contain little data worth trying to utilize for sound maintenance management decision making.” In other words, it’s not bad software – it’s incomplete or irrelevant data.

  • Good data increases production uptime.
  • Good data protects against environmental issues and regulatory fines.
  • Good data allows the reliability maintenance team to work more efficiently.
  • Good data reduces spare parts inventory while ensuring that parts that are required are on hand.

High quality and consistent asset condition data is needed to effectively transition from a reactive to a predictive maintenance culture. To date, the asset management revolution is focused on high-end assets. Online integrated condition monitoring solutions have traditionally been too expensive to deploy on hundreds of assets and could not be justified.

The costs of technical tools (e.g., smart phones, laptops, tablet computers) are coming down and those economies of scale are allowing providers to offer affordable solutions for condition monitoring. Modern networks allow the owner to outsource data analysis in real time and “expert” software programs have improved over the years.

Wireless technology and advanced networks that are being adopted as industry standards in other business models (probably within your company) are being applied as reliability maintenance solutions, eliminating the old, expensive, hardware-intensive solutions of the past. New systems are now scalable to enterprise and deliver ubiquitous data.

Today’s condition-based maintenance (CBM) software allows machine faults to be readily identified and prioritized from machines that do not currently have problems. Trends can be built faster with on-line monitoring than with route-based programs. These new solutions are also scalable – from one machine to an enterprise level – on a single platform.

The combination of a well-implemented reliability maintenance software package and high quality, consistent data can help any maintenance program that is on the reliability path to achieve world-class reliability centered maintenance status.

Are you and others in your organization getting the data you need to make good decisions? Start today to find ways to add high quality, consistent machine health data to your reliability maintenance software program.

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