Improving automotive garage operations by categorical forecasts using a large number of variables

Shixuan Wang, Aris A. Syntetos, Ying Liu, Carla Di Cairano-Gilfedder and Mohamed M. Naim

European Journal of Operational Research, 2023, vol. 306, issue 2, 893-908

Abstract: Cost effective job scheduling for garage management relies upon assigning repair times into appropriate categories rather than using exact repair time lengths. In this paper, we employ an ordinal logit model with least absolute shrinkage and selection operator (LASSO) to forecast such repair time categories for automotive engines. Our study is based on a unique dataset of maintenance records from the network of 64 UK garages of BT Fleet Solutions, and we consider a large number of predictor variables, with condition, manufacturing, geographical, and calendar-related information. The application of LASSO enables the identification of relevant predictor variables for forecasting purposes. Based on the Brier score and the ranked probability score (and their skill scores), we document substantial predictive ability of our method which outperforms five benchmarks, including the method used by the company. More importantly, we demonstrate explicitly how to associate the predicted probabilities with a loss function in order to make operational decisions in garages. We find that the best choice of job scheduling does not always correspond to the predicted categories, especially when the loss function is asymmetric. We show that scheduling jobs on the basis of our method can help the company reduce loss value. Finally, we identify opportunities for further improvements in the operations of the company and for garage maintenance operations in general.

Keywords: Forecasting; Maintenance; Repair time; LASSO; Automotive garage (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221722005525
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:306:y:2023:i:2:p:893-908

DOI: 10.1016/j.ejor.2022.06.062

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().