Maintworld 3/2019

RESEARCH AND DEVELOPMENT
Measuring the Value
of Data in Maintenance
It is easy for organizations to assume that more data equals more value in
maintenance. However the value of data is case dependent and should be
assessed to ensure that the benefits from the data exceed the additional costs.
DR. SALLA
MARTTONEN-AROLA,
University of
Sunderland
PROF. DAVID BAGLEE,
University of
Sunderland
MANY MAINTENANCE organizations
have been tempted by the big data hype
into collecting excessive amounts of
data without specific business cases or
data exploitation plans. When following
the hype, it is easy to forget that the additional
value created by the data must
exceed the costs of resources used to collect
and analyze it (Günther et al. 2017).
Achieving significant value from big
data tends to require extensive resource
use, however, many companies do not
have the necessary resources and competence
to keep experimenting with big
data technologies, especially within the
manufacturing and maintenance industries
where the technology maturity is
currently quite low (Diez-Olivan et al.
2019; Kans 2013). It has been acknowledged
that the optimal amount of data in
maintenance decision-making depends
on the size, business, competences, and
complexity of assets and processes within
the organization (BS ISO 55001 2014).
The value of maintenance data also
depends on the situation: for instance, in
corrective maintenance, data are mostly
used to detect failures and to decide
whether to repair the asset immediately
or at a later date, whereas in conditionbased
maintenance the data are considerably
more complex and used to define
measurement parameters, techniques
and locations, maintenance action limits
and maintenance actions.
Value of data
Value of data can be defined as having the
right information, in the right amount,
quality, format, time, place, and for an appropriate
price (Bucherer & Uckelmann
2011). Familiar mostly as a production
philosophy, lean management emphasizes
increasing value through eliminating
waste (Gupta et al. 2016).
Adapted to data management, lean
could help maintenance organizations
in assessing and maximizing the value of
their data-based decision making (Marttonen-Arola
& Baglee 2019a). The waste
types in data management include:
• Unnecessary data (duplicate,
non-relevant or too detailed data
which can cause an information
overload),
• Unnecessary transfer of data (nonvalue
adding transfer between people,
systems or organizations),
• Unnecessary processing of data
(non-value adding processing, e.g.
changing format, ensuring access,
copying, unnecessary summarizing),
• Underutilization of data management
resources (for example unused
IT systems or personnel),
• Poor quality data and/or analyses
(can lead to suboptimal decision
making), and
• Waiting for data or missing data
(waiting or looking for data items)
(Marttonen-Arola & Baglee
2019b).
To assess the value of maintenance
data, quantifying the changes in the
aforementioned wastes is beneficial. Figure
1 shows how the value of additional
maintenance data can be modelled based
on various types of decreasing waste in
the data management process. A number
of the waste types can be quantified in
terms of time, which makes evaluating
the overall value quicker. The quality
of data and analyses can be taken into
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