Change the oil every 3,000 miles or when the national deficit increases by a trillion dollars
Brittany Detamore 270006ARTU email@example.com | 2013-06-20 12:22:56.0 | Tags:  predictive-analytics business-analytics big-data predictive-maintenance analytics government | 1 Comments | 6,632 Visits
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Government organizations, funded by your tax dollars, require a wide range of physical assets to provide the services that we come to expect from the world's largest national economy.
Let’s start with some numbers.
In addition to vehicles, the government manages buildings, infrastructure (water, sewer, gas, electric, roads, bridges, etc.), wind turbines, dams, oil rigs…and the list goes on and on. All of these assets need to be serviced and maintained to provide continued functionality and service. Politics aside, the United States is not in a financial position to toss away an asset after it has provided a few months of useful service. In the case of a building or infrastructure system, that is physically impossible.
The multitude of items listed above requires trained, skilled, specialized technicians to do the work: everything from plumbers and mechanics to engineers and nuclear physicists. Your standard mail truck is probably the least complex asset owned by Uncle Sam.
So how is the government optimally keeping the ship afloat (pun intended)?
By analyzing the data that is stored by most organizations in the form of maintenance logs, repair tickets, temperature sensors, and flow sensors to name a few, predictive maintenance is carried out when needed. IBM Business Analytics Predictive Maintenance enables clients to identify maintenance requirements & operational issues that are critical to preventing production interruptions, improving usability & service levels for customers, to meet or exceed service-level agreement expectations
It is all based on how the asset is being used and under what conditions. In contrast to scheduled maintenance that is conducted on routine intervals whether the asset needs it or not, this leads to expense and waste. The worse case scenario is something failing in the middle of a task.
For example, one agency implemented the solution to schedule maintenance items while the asset was not being used for a mission, ensuring that the proper materials and technicians were on site and avoiding a breakdown while deployed. Another example is a major US city that modernized their water infrastructure management by predicting potential problems and occurrences based on location, time, weather and historical events.
For more information, we encourage you to attend our upcoming webinar Thursday, June 27,2013 at 1.00 PM ET: Saving money, preserving assets and improving operational efficiency with Predictive Maintenance – A Roundtable featuring Forrester Research. The expert panel includes Industry analyst Holger Kisker of Forrester Research, who will explain the market forces driving adoption of predictive maintenance; Erez Daly, Manager of the Gas Turbines & Combine Cycles department at Israel Electric Corporation, who will provide insights on the challenges they faced and the benefits realized and representatives of both IBM and Genius Systems, answering questions about the implementation process.