An imputation-based approach for parameter estimation in the presence of ambiguous censoring with application in industrial supply chain

TL;DR

This paper introduces a likelihood-based imputation method for estimating parameters in industrial reliability data with ambiguous censoring, improving accuracy when installation and failure times are partially observed.

Contribution

It presents a novel proportional imputation approach for parameter estimation in ambiguous censoring scenarios, addressing limitations of existing methods.

Findings

The method provides accurate parameter estimates in simulated industrial reliability data.

It is computationally efficient and applicable to real-world supply chain data.

The approach outperforms traditional methods under ambiguous censoring conditions.

Abstract

This paper describes a novel approach based on "proportional imputation" when identical units produced in a batch have random but independent installation and failure times. The current problem is motivated by a real life industrial production-delivery supply chain where identical units are shipped after production to a third party warehouse and then sold at a future date for possible installation. Due to practical limitations, at any given time point, the exact installation as well as the failure times are known for only those units which have failed within that time frame after the installation. Hence, in-house reliability engineers are presented with a very limited, as well as partial, data to estimate different model parameters related to installation and failure distributions. In reality, other units in the batch are generally not utilized due to lack of proper statistical methodology, leading to gross misspecification. In this paper we have introduced a likelihood based parametric and computationally efficient solution to overcome this problem.