The second Action Research cycle as shown in Exhibit 4.1 commenced on 28^^
January 2011.
Based on the evaluation done in Action Research cycle 1, there was still no inventory management process in place. Therefore, the diagnosis was obvious other options needed to be considered.
During the stage of proposing action the researcher held an informal meeting with the Director Maintenance and the Purchasing and Logistics Manager on 28^^ January 2011.
With the knowledge of statistical forecasting methods gained during the literature review, the researcher suggested to use such methods to determine the company’s spare parts stock.
This suggestion was refused by both the Director Maintenance and the Purchasing and Logistics Manager.
“This ju st doesn’t work. ’’ (Director Maintenance, 28.01.2011)
“I tried it in the past and it was a disaster. ’’ (Purchasing and Logistics Manager, 28.01.2011)
It turned out that in previous professional positions, both had applied such methods without success. As pointed out in Chapter 2.5, statistical forecasting methods are meant to detect underlying patterns in data and to exclude randomness. To allow these methods to function best, long histories of historical data is necessary to feed them (Goodwin and Fildes 2011).
Furthermore literature suggests that forecasts for spare parts in the aviation industry are generally based on historical data (Ghobbar and Friend 2003). In case of XYZ Cargo there was no historical data available. In the past both the Director Maintenance and the Purchasing and Logistics Manager had worked with airlines that operated different aircraft types and were therefore not familiar with the B747 operated by XYZ Cargo in terms of spare part failure rates etc.
These data could also not be obtained from the industry. The aircraft Original Equipment Manufacturer would only provide the Recommended Spare Parts List which is mainly based on airlines and Maintenance, Repair and Overhaul facilities’ feedback regarding component failure rates. As discussed in Chapter 1.3, there are service providers such as Lufthansa Technik who offer
component support to airlines (Lufthansa Technik 2009a, 2009b, 2010).
Flowever, Lufthansa Technik would not publish their historical data, but rather would only sell them as part of one of their component support products based on their data base. The researcher approached a contact at Lufthansa Technik who she knew from a former job. This contact confirmed in a telephone call that Lufthansa Technik would not consider making such data available to 3’’^ parties outside a support contract, as this data constituted the main ingredient for its component support products.
The researcher was not able to convince the airline’s representatives to pursue her proposal to apply statistical forecasting methods. The reason was that the researcher was not able to provide examples where the use of statistical
forecasting had actually increased the Technical Dispatch Reliability. Due to the focus on the company’s requirements which was necessary to ensure access to data, she decided to give in. At a later stage of the project the airline’s
representatives would have agreed to benchmark the statistical forecasting methods with the approach developed in this study. But by then the ownership of XYZ Cargo had changed and there was no possibility to resume this
approach. What actually caused both the Director Maintenance and the Purchasing and Logistics Manager to change their minds regarding statistical forecasting has not become ultimately understandable for the researcher. It can only be assumed that they considered a benchmark with statistical forecasting methods to be helpful, especially in terms of obtaining management board approval to invest in a software development that would quantitatively model the operational factor approach.
However, the findings from e.g. Siddique and Choudhary (2009) support the view that skepticism towards statistical forecasting methods is common in the aviation industry with only 9-10% of airlines participating in a study applying mathematical forecasting models. This resistance was also expressed by two aviation experts the researcher approached to peer review the findings of the field work. One of them, a very experienced Procurement Manager with more than 30 years procurement expertise responsible in four different airlines
worldwide reported that she had applied different statistical forecasting methods such as single exponential smoothing or Croston (Croston 1972). The Technical Dispatch Reliability had not reached a satisfactory level which was set at 99%
plus. Therefore she supported the operational factor approach detailed in the following chapter. The other expert, a very experienced aviation consultant, compared the XYZ Cargo case with one of his own projects. He had worked for an airline start up that had faced the same problems regarding the Technical Dispatch Reliability development as XYZ Cargo. In his project, the consultant had applied Croston as forecasting method to determine the client’s spare parts stock. The Technical Dispatch Reliability stayed below the one achieved for
XYZ Cargo after application of the operational factor approach. As the consultant perceived the basic settings of his client airline and XYZ Cargo as very similar, he clearly supported the operational factor approach developed in this study.
Even with the application of statistical forecasting methods being refused by XYZ Cargo the question has to be raised whether a simple Decision Support System could have been introduced. As discussed before inventory
management is by nature, risk management. The risk of not having a spare part available when required has to be compared with the cost arising from the missing spare part. Here a Decision Support System as discussed by Allen and Emmelhainz (1984), Carter and Narasimhan (1994), Nagarur et al (1994) and Katok et al (2001) would help to decide whether keeping a certain spare part on stock outweighs emergency procurements.
The Recommended Spare Parts List provided by the aircraft manufacturer would deliver the required input to feed such Decision Support System. The Recommended Spare Parts List is tailored by the Original Equipment
Manufacturer for each customer. The airline provides forecasts about their flying activity. The aircraft manufacturer combines the airline forecast with its own data about component failure rates. Based on this analysis the aircraft manufacturer then provides a recommendation which quantity should be kept on stock for each component. However, this quantity recommendation is derived from very limited data. The component failure rates the aircraft
manufacturer considers are only based on flying hours implying wear and tear through standard usage. Other factors such as rough climate influencing equipment functionality and failure likelihood are not considered. Furthermore, the recommendation does not take market availability of spare parts into account. If procurement lead times are extremely long it could be better to have more spare parts on stock than actually recommended.
A Decision Support System that only considering the Recommended Spare Parts List as given by the Original Equipment Manufacturer would therefore only be of very limited use for the airline.
This Action Research cycle 2 can be regarded as an “aborted” cycle. The diagnosis was done; the action to be taken was proposed, but refused.
Therefore the stages of taking and evaluating action were not passed through.
Instead, Action Research cycle 3 was started.