A framework to estimate the cost of No-Fault Found events

Authored by John Ahmet Erkoyuncu, Samir Khan, Syed Mohammed Fazal Hussain, Rajkumar Roy

Date Published: 2016

DOI: 10.1016/j.ijpe.2015.12.013

Sponsors: United Kingdom Engineering and Physical Sciences Research Council (EPSRC)

Platforms: AnyLogic

Model Documentation: Other Narrative Flow charts

Model Code URLs: Model code not found

Abstract

The article investigates a generic framework to estimate maintenance costs attributed to the No Fault Found (NFF) phenomenon. Such overhead costs are particularly difficult to quantify due to potentially serviceable equipment being returned for repair. Other factors, such as a reduction in the availability of the system, compromising reliability of high value assets, and logistical factors, can all contribute to the cost of resolving an unknown fault. Here we apply the soft systems methodology to capture the critical cost drivers of NFF across the supply chain and build a framework to estimate the cost of NFF. We use a multi-method design including an online survey, workshops and semi-structured interviews to study NFF related cost practices based on information from 12 key participants across 7 UK organisations. The study identifies the major NFF cost drivers across the supply chain (e.g. transportation), the OEM (e.g. inventory) and the customer (e.g. lost man hours). An agent based model is used to evaluate the impact of these cost drivers on the overall NFF cost. The analysis shows how the most appropriate drivers can be selected to represent the cumulative costs due to NFF events and their impacts across the supply network. From the academic perspective, the generic framework for NFF cost estimation demonstrates how qualitative and quantitative information can be used together to achieve maintenance objectives. From a practical perspective, by applying the framework on one component, an organisation has the liberty to analyse the cost of NFF for that particular unit only. (C) 2016 The Authors. Published by Elsevier B.V.

Tags

Management models Decision-Making systems Failures Asset maintenance Warranty