A device to monitor fatigue level in order-picking
| Published date | 14 May 2018 |
| DOI | https://doi.org/10.1108/IMDS-05-2017-0182 |
| Date | 14 May 2018 |
| Pages | 714-727 |
| Author | Martina Calzavara,Alessandro Persona,Fabio Sgarbossa,Valentina Visentin |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
A device to monitor fatigue level
in order-picking
Martina Calzavara, Alessandro Persona, Fabio Sgarbossa and
Valentina Visentin
Department of Management and Engineering, University of Padua, Padua, Italy
Abstract
Purpose –In order-picking activities, the performance of the system can be influenced by different variables
such as the order to be fulfilled, the distance to be covered or the experience of operators. Usually, this kind of
activity is performed by operators rather than machines to assure flexibility. Consequently, their fatigue
accumulation can decrease the performance of the overall system. The purpose of this paper is to define the
kind of device to be used in an order-picking context, to obtain data which can be utilized for the evaluation of
the level of fatigue and to improve the performance of the picking system.
Design/methodology/approach –The paper presents a comparison between existing fatigue methods
which can be applied in a picking context. In addition, an analysis of the physiological literature for the
evaluation of a new device for the monitoring of fatigue level is carried on and its practical use is shown.
Findings –The proposed research identifies in the heart rate monitor the device that, thanks to its
advantages, can be the best one to be used in an industrial context for monitoring the physical fatigue
of operators.
Originality/value –This study considers the importance of human factors in picking activities such as
physical fatigue of operators and the need to have validated tools to monitor and to define the level of fatigue
accumulation in each activity of different rate and duration.
Keywords Fatigue, Human factor, Energy expenditure, Heart rate, Order picking, Oxygen consumption
Paper type Research paper
1. Introduction
Order picking is defined as the process of retrieving items from their storage locations in a
warehouse to fulfill customers’orders (Tompkins et al.,2010).Usually,thisactivityis
carried out by operators rather than machines (Grosse et al., 2015). In fact, in Napolitano
(2012) and De Koster et al. (2007) it is confirmed that manual order picking warehouses are
the dominant type consisting in about 80 percent of all the order picking warehouses.
Order picking is not only labor-intensive, but also it implies more than 50 percent of the
operating costs of a warehouse (Frazelle, 2002; Tompkins et al., 2010). Considering that the
time taken to perform this activity can be high, and that it is not defined as an activity
with the same added value as an usual production process, the aim for warehouse picking
systems design is usually focused on improving their efficiency and their throughput (De
Koster et al., 2007). Then, this goal often turns out on reducing the time needed to perform
a picking tour (Battini et al., 2015). During this activity, the operator first has to have the
time necessary to receive and understand the picking list, defined as the setup time. After
that, during the search time and the pick time, he or she identifies the right item to be
picked and picks the item from its storage location. Besides this, he or she needs sufficient
time to travel from one storage location to the next one. Up to now, the literature has
mainly focused on how to improve the overall performance of the system (Gunasekaran
et al., 1999; Grosse et al., 2015) by reducing firstly the traveling time and then the setup,
picking, and search times (Grosse et al., 2015). Travel time reduction is often related to the
choice of the best warehouse design and on the establishment of the best storage
assignment or the best routing and batching method (Chackelson et al., 2013; Pan et al.,
2014; Yang and Nguyen, 2016). In addition, methods and techniques for preventing errors
inpickingthewrongitemorthewrongquantity have also been analyzed (Brynzér and
Johansson, 1996; Battini et al., 2015).
Industrial Management & Data
Systems
Vol. 118 No. 4, 2018
pp. 714-727
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-05-2017-0182
Received 10 May 2017
Revised 27 July 2017
6 September 2017
Accepted 27 September 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
714
IMDS
118,4
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