Optimized Maintenance and Physical Asset Data Quality

December 2, 2010 by · Comments Off on Optimized Maintenance and Physical Asset Data Quality
Filed under: Business Impacts, Business Intelligence, Data Analysis, Data Quality, Master Data, Metrics, Performance Measures 

It would be unusual for there to be a company that does not use some physical facility from which business is conducted. Even the leaders and managers of home-based and virtual businesses have to sit down at some point, whether to access the internet, make a phone call, check email, or pack an order and arrange for its delivery. Consequently, every company eventually must incur some overhead and administrative costs associated with running the business, such as rent and facility maintenance, as well as telephones, internet, furniture, hardware, and software purchase/leasing and maintenance.

Today’s thoughts are about that last item: the costs associated with building, furniture, machinery, software, and grounds maintenance. There is a balance required for effective asset maintenance – one would like to essentially optimize the program to allocate the most judicious amount of resources to provide the longest lifetime to acquired or managed assets.

As an example, how often do offices need to be painted? When you deal with one or two rooms, that is not a significant question, but when you manage a global corporation with hundreds of office buildings in scores of countries, the “office painting schedule” influences a number of other decisions regarding bulk purchasing of required materials (e.g. paint and brushes), competitive engagement of contractors to do the work, temporary office space for staff as offices are being painted, etc., which provide a wide opportunity for cost reduction and increased productivity.

And data quality fits in as a byproduct of the data associated with both the inventory of assets requiring maintenance and the information used for managing the maintenance program. In fact, this presents an interesting master data management opportunity, since it involves the consolidation of a significant amount of data from potentially many sources regarding commonly-used and shared data concepts such as “Asset.” The “Asset” concept can be hierarchically organized in relation to the different types of assets, each of which exists in a variety of representations and each of which is subject to analysis for maintenance optimization. Here are some examples:

  • Fixed assets (real property, office buildings, grounds, motor vehicles, large manufacturing machinery, other plant/facility items)
  • Computer assets (desktops, printers, laptops, scanners)
  • Telephony (PBX, handsets, mobile phones)
  • Furniture (desks, bookcases, chairs, couches, tables)

I think you see where I am going here: errors in asset data lead to improper analyses with respect to maintenance of those assets, such as arranging for a delivery truck’s oil to be changed twice in the same week, or painting some offices twice in a six month period while other office remain unpainted for years. Therefore, there is a direct dependence between the quality of asset data and the costs associated with asset maintenance.