A body of research explicitly deals with Decision Making for spare parts
Inventory Management. There are studies looking at the organisational context, namely by whom Inventory Management decisions are taken in a company.
Here findings from manufacturing industries reveal that decentralised Decision Making processes lead to overstocking of parts (Cohen et al 1997, Kisperska- Moron 2003).
Other studies investigate the Decision Making process itself and suggest methods for improvement. A focus lies on multi criteria decision making.
Duchessi et al (1988) suggest a top-down approach for spare parts management that comprises the following steps:
(1) A classification methodology is developed by means of multi criteria decision making. The criteria used are inventory cost and part criticality. Criticality is determined by downtime cost, lead time and reliability.
(2) Control mechanisms are associated to the categories.
(3) Appropriate systems are selected, and (4) implemented.
Almeida (2001) proposes a multi criteria decision making model for spare parts provisioning considering total spare part cost and the risk of item non-supply.
The model assumes that there is at least one item on stock that can be used in case of failure while the defective item is repaired. Here the decision needs to be taken how many additional spare parts shall be provided.
Braglia et al (2004) develop a multi attribute spare part tree analysis based on two different methods: the reliability centered maintenance technique which determines a suitable maintenance strategy for component within a system, and the Analytic Hierarchy Process. In a first step spare parts are clustered in terms of criticality. Afterwards these clusters are crossed with possible Inventory Management strategies. The most appropriate strategy is derived by applying
the Analytic Hierarchy Process, in their case study conducted in a tissue
production plant, the authors were able to significantly reduce stock levels. They claim that their approach can be used in other industries as well.
Literature also reveals studies dealing with aviation spare parts.
Tedone (1989) reports on the Retables Allocation and Planning System introduced at American Airlines. The Retables Allocation and Planning System has similarities with the Multi-Echelon Technique for Recoverable Item Control system (Bathoun et al 2003). Demand forecasts are generated by linear regression assuming a Poisson distribution of demand. The system seeks to minimise the sum of the inventory and shortage costs. According to Tedone (1989) the Retables Allocation and Planning System provided a multi-million dollar improvement in American Airlines Inventory Management system.
In the study conducted by Zain (1990), the author develops recommendations to improve the Inventory Management of Malaysia Airlines. These are mainly focused on the introduction of organisational changes such as improved supplier management, automated ordering and logistics network optimisation.
Batchoun et al (2003) propose a genetic algorithm approach to allocate spare parts to stations within a given network of airlines working together and sharing spare parts via pooling. Delay costs and removal estimates are considered to compute the optimal distribution of parts to the stations. The authors claim that genetic algorithms seem to be very appropriate in this setting but that the required computation is intensive and expensive.
A body of literature deals with spare parts availability optimisation for military operations (Parsons and Goodwin 1986, Kang et al 1998, Kumar et al 2000, Raivio et al 2001, Nickel et al 2006). As the military context is excluded from this work, these studies will not be discussed.
2.21 Summary and Conclusions
This chapter provided the literature review conducted within this research project. In the following the key findings are summarised.
The literature review on spare parts Inventory Management revealed that forecasting of the future demand of spare parts is an essential requirement for inventory decisions. This statement is true for all industries operating equipment that needs to work properly anytime and where unplanned malfunction of such equipment can lead to major disruptions of the production process.
Furthermore, businesses that maintain high value machinery need sophisticated forecasting to keep the value of their spare parts stock at a reasonable level.
For the aviation industry these baselines apply. Aircraft are high value equipment requiring expensive maintenance and spare parts. A disruption of the planned flight schedule due to malfunctioning aircraft can have serious impacts on time-critical freight as well as on customer satisfaction.
The demand for spare parts is classified as lumpy, i.e. a regular demand pattern is missing. Periods of low or even zero demand may be followed by high
demand periods. A prediction of both the demand levels and the length of the low versus high demand periods is difficult. Statistical forecasting models recognising the lumpy demand pattern of spare parts have been developed.
However, in the aviation industry only a minority of airlines applies such statistical forecasting models. Research conducted among airlines has shown that most airlines either rely on operational experience or on rather simple forecasting methods factoring seasonal changes in the models.
Several authors emphasise the need for managerial judgment in forecasting.
They were able to show that the accuracy of forecasts generated with statistical methods could be improved if managers adjusted the results. Recognising that forecasting is an essential tool for taking inventory decisions and that
managerial judgment can improve forecasting accuracy triggered the literature review on decision making.
Decision Making can be understood as choosing between existing or identified alternatives. Early models of the decision making process assumed decision makers to have complete knowledge of the situation in which the decision making was embedded. People making decisions were supposed to behave rational. However, research has shown that in most cases humans have only limited time and knowledge for decision making. This leads to people making inferences about the situation. Models of bounded rationality and heuristics reflect this understanding of how humans make decisions.
Evidence suggests that successful decision makers rely on intuition or gut- feeling. This confirms the findings that managerial judgment can improve forecasting accuracy. However, literature also shows that these decision makers also face common traps in decision making that can negatively impact the result.
The literature findings about aviation decision makers not making use of sophisticated statistical forecasting methods and Inventory Management methods supports the research question that motivates this study.
If airline professionals apparently do not consider the available methods to be sufficient in supporting their Inventory Management decisions, how should they then decide in which way critical spare parts inventory should be build up?
The literature review also forms the basis to justify the applied research methodology which will be presented in the following chapter.