Often maintenance systems don't reap the benefits that they promise
through no fault of their own. How can you expect a system to improve
underlying data? The answer is that you can't. What you need is to
have good data in the system so that it can be accessed, processed and
used to provide practical information for the organization.
Let
me illustrate the cost of not having good data with an example. A
multi-site manufacturer has four locations, three of which are in fairly
close proximity to each other. Each site has its own autonomous
storeroom with inventory parts. At each site, there is a part time
catalog manager responsible for all database activity. Because the
plant is unionized and positions often change, the catalog manager may
be replaced every few months.
The resulting inventory catalogs
reflect this: inconsistent manufacturer naming; missing manufacturer
part numbers; inconsistent use of symbols/abbreviations; spelling
mistakes; incomplete descriptions and; duplicate items. System word
searches are next to impossible and finding a part is a frustrating,
challenging, usually unsuccessful experience.
Maintenance workers
at all locations had long lost faith in stores; each kept a stash of
parts hidden somewhere for his own use. To plan for a repair job, they
would attempt to find parts through the system, but if unable to locate
what they needed, they would abandon the search and just order the part
directly; in the case of an emergency, they might call another location
to request the loan of a part. Inventory value across the company
topped $80 million.
Recognizing that something had to be done, the
company attempted to undertake the data cleaning themselves. They
established a team of nineteen internal people comprised of maintenance
workers (Electrical, Mechanical, Instrumentation & Pipe Fitters)
from all four sites as well as two support people and one Inventory
Specialist.
After more than a year of effort, and with only half
the database cleaned, they decided to engage outside data cleaning
experts to revitalize the effort. Systematically, the data from each
site was cleaned. In conjunction with maintenance workers from all
sites, a common layout for item descriptions with acceptable
noun/modifier pairs was developed; the order of attributes was
negotiated to satisfy all locations; terminology, symbols, abbreviations
and industry nomenclature were agreed upon. It took six months to
rework the entire database.
Having good data brings with it
measurable rewards. Duplicates within sites were revealed to be in the
10% range. Common items across sites were identified in the 25% range.
Merging the three regional stores into a central warehouse reduced
overall stocking levels and allowed sites to share common critical
spares. It also freed up millions in cash savings.
Item searches
successfully revealed part information that maintenance workers could
count on. As confidence in the central stores grew, additional stock
from private caches was repatriated, further adding to the savings
realized. Overall, across the company, inventory was reduced by more
than 20%.
The data cleansing effort clearly paid for itself
several times over. It also became the impetus for other corporate
initiatives. The company went on to improve its item-equipment links to
further enhance the maintenance system. In addition, it consolidated
items along product lines and reduced its supplier base for volume
discounts.
