An Efficient Failure-Based Maintenance Decision Support System for Smalland Medium Industries - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 203

company in England. The results can provide maintenance policies in the respective
functional group in production lines, to achieve their common goal to reduce downtime.
Later, Zulkifli et al. (2008), in the third review, comprehended the model and demonstrated
the hybrid intelligent approach using the DMG and fuzzy rule-based techniques. In their
study, the DMG is employed in small and medium food processing companies to identify
their maintenance strategies. DMG is used in these study as the model is flexible, and
considers OTF, FTM, SLU, CBM, DOM, TPM and RCM strategies in the same grid. The
model is able to analyze multiple criteria and is the best choice when the number of
machines is less than fifty (Pascual et al, 2009). It can be used to detect the top ten
problematic machines on the production floor with several system conditions. This is with
regards to failures such as fatigue, imbalance, misalignment loosened assemblies, and
turbulence, which can occur in rotational or reciprocating parts such as bearings, gearboxes,
shafts, pumps, motors and engines. Identifying the top ten problematic machines is in
alignment with the 80-20 rule. The rule states that eighty percent of the problems arise from
the same twenty percent of the root causes.
ln another word, once twenty percent of the root cause had been fixed, then eighty percent
of the problem is resolved. The application of the model can have a breakthrough
performance, as it fulfils the purpose of the model to map machines into a certain grid in a
matrix and suggests the appropriate maintenance strategies to comply with.

5.1 Development of the decision-making grid model
There are many publications on CMMS and DMG applications in the area of maintenance.
Among them, there are three journal papers that are most related to this study of the DMG
model, written by Labib (1998b and 2009) and Fernandez et al. (2003). Labib (1998b)
introduced DMG as a maintenance decision-making tool to be embedded in CMMS. Later,
Fernandez et al. (2003) included DMG as a sub-module in their CMMS. They tested the
model in one of the brake pad manufacturing companies in England. Next Zulkifli et at.
(2010) integrated DMG with a fuzzy rule-based hybrid approach. The DMG model was
formed as a control chart by itself in two-dimensional matrix forms. The columns of the
matrix show the three criterions of the downtime, whilst the rows of the matrix show
another the criterions of the frequency of the failure.
A better maintenance model for quality management can be formed by handling both the
rows and columns of the matrix respectively. The matrix offers an opportunity to decide
what maintenance strategies are needed for decision-making such as to practice OTF, FTM,
SLU, CBM or DOM. The matrix also suggests maintenance concepts that are useful for each
defined cell of the matrix such as TPM or RCM approaches. The results can provide
maintenance policies in the respective functional group in production lines to achieve their
common goal, to reduce downtime. There are two basic steps to follow in the DMG model
as follows:
Step 1. Criteria Analysis: Establish a pareto analysis of two important criteria:
a.

Downtime, which is the main activity conducted by a maintenance crew; and

b. Frequency of breakdown calls, which is always a concern for a customer service

centre.
The objective of this phase is to assess how bad the worst performing machines are
over a certain period of time. The machines, as regards each criterion are sorted
and placed into a top ten list of high, medium, and low boundaries, which are
divided into three categories using the tri-quadrant approach as follows
(Burhanuddin (2009)). let x be the frequency of failures.