Goods-to-Person versus Person-to-Goods Systems: A Human Factors Based Comparison

Abstract

Traditionally order picking involves the order picker walking up and down the aisle to pick out items from the shelves and bring them back for processing the order. However, studies reported that the most time-consuming component of such order-picking strategies is walking. To overcome this and increase the efficiency of order picking, warehouses have started employing auto-retrieval and storage systems. These systems coupled with strict picking targets, for example, one pick per 10 seconds, increase the efficiency of the process and productivity of the picker however pointers suggest that such systems increase the possibility of the picker developing musculoskeletal diseases due to increase in the frequency of actions such as bending, twisting, etc. than in the case of traditional person-to-goods order picking strategy. Hence this study aims to quantify the impact the impacts and provide a means of comparative analysis between goods-to-person (GtP) and person-to-goods (PtG) strategies. The study utilizes validated human factor’s simulation software, and it is supplemented by motion capture to translate the natural posture assumed by pickers while picking the items from shelves. The analysis is performed for 5th, 50th, and 95th percentile anthropometry males from the Asian_Indian database. The results are obtained across five different matrices namely, static strength prediction, metabolic energy expenditure, lower back analysis, cumulative lower back analysis, and RULA scores. The results indicate that GtP strategy leads to higher and more frequent compressive as well as shear loading on the lower back as well as more frequent torque on upper body especially arms. However, PtG leads to more energy expenditure, which is attribute to increase in walking. This study not only present a comparative analysis of two order-picking strategy but also as a framework for evaluating manual tasks using human factor’s simulation software and motion capture.

Date of Award	Dec 2022
Original language	American English
Supervisor	Saed Amer (Supervisor)