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Fig. 4 - Main effect plots from
sensitivity analysis

5. Conclusions and next steps • to defne in which felds one policy is better
than the other;
of the research • to deepen this analysis to give general
results;
The management of spare parts is critical for • to study the behavior of these policies
many aspects. In fact, their availability infuences varying the demand distribution;
the availability of production systems where they • to link each policy to demand patterns
are used but on the other hand their holding costs classifcation, according to Ghobbar et al
are high due to their high-technological value and (2002).
their high risk of obsolescence.
For these reasons, many
Their behaviors researches have been carried References
have been deeply out to better forecast the spare Axsäter S.: Inventory Control (2nd edition) -
investigated, varying parts demand and to improve Springer-Verlag, New York, 2006
the main spare parts their inventory management. Dekker R., Kleijn M.J., De Rooij P.J.: A Spare Parts
In this paper, the research
parameters, such as focuses on this latter aspect, Stocking Policy Based on Equipment Criticality -
MTBF, holding and comparing two different International Journal of Production Economics, v.
stock-out costs, supply methodologies recently 56-57, p. 69-77, 1998
lead time, inventory introduced in literature. The
levels. It has been studied policies are the (s, S) Gamberini R., Lolli F., Rimini B., Sgarbossa F.:
one with delayed ordering,
Forecasting of Sporadic Demand Patterns with
highlighted that no introduced by Teunter et al Seasonality and Trend Components: An Empirical
model is better than (2012) and policy based on Comparison between Holt-Winters and (S) Arima
the other. Generally, binomial distribution and total Methods - Mathematical Problems in Engineering,
the policy with delayed cost function, developed by vol. 2010, article ID 579010, 14 pages, 2010.
ordering has a more Persona et al (2006). doi:10.1155/2010/579010
Their behaviors have been
economical result, but deeply investigated, varying the Ghobbar A.A., Friend C.H.: Sources of Intermittent
it fails in a very critical main spare parts parameters, Demand for Aircraft Spare Parts Within Airline
manner in several such as MTBF, holding and Operations - Journal of Air Transport Management
cases stock-out costs, supply lead 8, 221–231, 2002
time, inventory levels.
It has been highlighted that Gutierrez R.S., Solis A.O., Mukhopadhyay S.:
no model is better than the other. Generally, Lumpy Demand Forecasting Using Neural
the policy with delayed ordering has a more Networks - International Journal of Production
economical result, but it fails in a very critical Economics, v. 111, p. 409-420, 2008
manner in several cases. Starting from this frst
phase of the research, it will be interesting to Jin T., Liao H.: Spare Parts Inventory Control
carry out further studies as follows: Considering Stochastic Growth of an Installed

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