Modeling errors in parts supply processes for assembly lines feeding

Abstract

Purpose The purpose of this paper is to develop a quantitative model to assess probability of errors and errors correction costs in parts feeding systems for assembly lines. Design/methodology/approach Event trees are adopted to model errors in the picking-handling-delivery-utilization of materials containers from the warehouse to assembly stations. Error probabilities and quality costs functions are developed to compare alternative feeding policies including kitting, line stocking and just-in-time delivery. A numerical case study is included. Findings This paper confirms with quantitative evidence the economic relevance of logistic errors (LEs) in parts feeding processes, a problem neglected in the existing literature. It also points out the most frequent or relevant error types and identifies specific corrective measures. Research limitations/implications While the model is general purpose, conclusions are specific to each applicative case and are not generalizable, and some modifications may be required to adapt it to specific industrial cases. When no experimental data are available, human error analysis should be used to estimate event probabilities based on underlying modes and causes of human error. Practical implications Production managers are given a quantitative decision tool to assess errors probability and errors correction costs in assembly lines parts feeding systems. This allows better comparing of alternative parts feeding policies and identifying corrective measures. Originality/value This is the first paper to develop quantitative models for estimating LEs and related quality cost, allowing a comparison between alternative parts feeding policies.

Modelling time efficiency of cobot-supported kit preparation

“…This is because the operator is relieved of activities associated with sorting components into kits. Furthermore, cobotsupported sorting can remove the risk of human errors with respect to misplacing components between kits, which is important when kitting is used in industry [5,6].…”

“…With kitting, the process by which kits are preparedthe kit preparationis typically reported as labour-intensive, and time efficiency is key for keeping running costs low [4]. Moreover, manual kit prepara-tion is prone to human errors that lead to errors in kits, which are disruptive and costly at assembly processes [5,6]. Recent research has shown that robots can support time-efficient kit preparation by relieving pickers of some of the tasks in kit preparation while simultaneously supporting kit quality by removing the risk of human errors [7][8][9].…”

Links between kit quality and kit preparation design

“…In two recent studies, Caputo, Pelagagge, and Salini (2017a) and Caputo, Pelagagge, and Salini (2017b) developed comprehensive models for estimating the costs of kit errors in kit preparation. They defined five error types: missing component, wrong component, damaged component, wrong number of components, and wrong component position, and developed event trees for estimating quality-related costs based on how severe the error types were for the assembly process.…”

“…An example is to improve response time for supplying the right component by using a location closer to the assembly process, as identified by Hanson, Johansson, and Medbo (2011). Such links can help address the propagating errors in the model by Battini et al (2015), and the severity of various error types in Caputo, Pelagagge, and Salini (2017a) and Caputo, Pelagagge, and Salini (2017b).…”

“…Some reports maintain that using kitting can improve the quality of the assembly process by making it easier for the assembler to find the right components (Medbo 2003). However, other reports suggest that kitting can compromise the quality of the assembly process, as kit errors can occur during kit preparation (Caputo, Pelagagge, and Salini 2017a;Caputo, Pelagagge, and Salini 2017b;Hanson and Brolin 2013). Kit errors can lead to costly disruptions if they reach the assembly process, for example requiring the assembly process to stop unless the right components are supplied in time, or that the wrong components are assembled onto end products (Boysen et al 2015).…”