Management criteria of automated order picking systems in high‐rotation high‐volume distribution centers
| DOI | https://doi.org/10.1108/02635570610712627 |
| Date | 01 December 2006 |
| Pages | 1359-1383 |
| Published date | 01 December 2006 |
| Author | Antonio C. Caputo,Pacifico M. Pelagagge |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
Management criteria of
automated order picking systems
in high-rotation high-volume
distribution centers
Antonio C. Caputo and Pacifico M. Pelagagge
Department of Mechanical, Energy and Management Engineering,
University of L’Aquila, L’Aquila, Italy
Abstract
Purpose – To develop a decision support system (DSS) and improved management criteria for
operating dispenser-based single-piece automatic order picking systems (AOPS) in distribution
centers, able to reduce the need for manual decision making based on personal experience or subjective
judgement.
Design/methodology/approach – Simulation was utilized to analyze the relationships between
stochastic demand, setup parameters and performances of an AOPS. A set of rules was then defined to
cost-effectively select the values of setup parameters. A DSS was built incorporating the heuristic rules
to dynamically update the equipment setup.
Findings – Manual management of an AOPS can be poorly efficient even if largely practiced.
Significant economic benefits may result from rule-based equipment setup instead of the traditional
manual decision approach. This was verified resorting to a case study referring to the distribution
center of a leading pharmaceuticals distributor in Italy. Major performances improvements resulted
regarding manual operation by an experienced logistic manager, including a 40 per cent reduction of
the cost per picked order line.
Practical implications – The proposed DSS is able to monitor the system behaviour over a
specified time window and automatically set the values of the state variables for the next period. It is
able to automatically define the set of items to be allocated on to the machine, to select the number of
storage locations allocated to each item and set reorder levels and maximum picking quantities for
each item, thus greatly simplifying the task of the logistic manager. Utilization of this DSS enables one
to maintain a high level of picking automation efficiency while drastically cutting the required support
personnel, thus significantly improving profit margins of high-volume high-rotation distribution
centers.
Originality/value – The paper addresses, with original methodology, a practically relevant issue
which is neglected in the literature. The paper is aimed at distribution centers managers seeking to
improve the performances of AOPS and reduce their operating costs.
Keywords Decision supportsystems, Case studies, Distributioncentres, Warehouses, Automation,
Pharmaceuticalsindustry
Paper type Research paper
Nomenclature
A¼set of product codes subject to
automated picking
a¼numerical constant
AE ¼automation efficiency
AL ¼automation level of the
warehouse
AM1 ¼automated machine no. 1
AM2 ¼automated machine no. 2
AOPS ¼automated order picking system
CAP ¼maximum throughput per shift
CCB ¼maximum product stock per
dispensing channel
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
Automated order
picking systems
1359
Industrial Management & Data
Systems
Vol. 106 No. 9, 2006
pp. 1359-1383
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635570610712627
CCBT ¼maximum on board stock
CONS ¼forecastof demand for nextperiod
d¼numerical constant
E¼efficiency
LC ¼channel length
LD ¼product length
LS ¼reorder level
NC ¼number of dispensingchannels
ND ¼number of unfulfiledorder lines
o.l. ¼order line
Q
max
¼maximum quantityallowed to be
picked per order
QTY
max
¼maximum number of items
requested by a singleorder line
QTY
DvSt
¼standard deviation of maximum
number of itemsrequested by a
single order line
RO ¼number of order lines
SL ¼resource saturationlevel
Subscripts
AP ¼actually picked
AUT ¼automatic
j¼product index
M¼average
TOT ¼total
Greek symbols
a
¼numerical constant
b
¼numerical constant
m
¼emptying coefficient
f
¼minimum per cent valueof
reorder level
Introduction
A warehouse provides temporary storage and protection of goods, also performing
value added services such as fulfilment of individual customer orders, packaging, after
sales service, etc. (Bartholdi and Hackman, 2005; Frazelle, 2001; Heragu, 1997). The
increased pressure on industrial logistic systems caused by fierce market competition
and rising service level expectations has radically changed the way that warehousing
operations are carried out. Orders have become gradually smaller, and split-case piece
picking operations instead of full cases handling are required with increasing frequency,
while customers demand 100 per cent order accuracy. Managers strive to reduce
warehousing costs by increasing operational productivity, but an ever increasing array
of products ask for different order filling technologies and methodologies which need to
be carefully balanced and integrated. Furthermore, warehouse order profiles change
rapidly, therefore, asking agile and flexible order filling solutions (Rouwenhorst et al.,
2000). This also imposes the ability of rapid redesign of the distribution chains (Ma and
Davidrajuh, 2005). Such circumstances are changing the role of distribution centers (DC)
which, not only have to maximize productivity and throughput to remain cost
competitive, but are also required to be more market sensitive in terms of order
fulfilment, customer requirements, and response time. DC, in fact, are shifting their
mission from large inventory storage areas to facilities where value adding activities are
performed. DC must compensate for the fact that retailers do not want to maintain
inventories but will not tolerate missed sales owing to out-of-stock products, and do not
want to sort orders when this can be made right at the DC, while inaccurate orders and
shipping mistakes are hardly accepted and can have significant financial consequences.
Therefore, accuracy, single-piece picking capability, sorting ability and response time,
all become a further issue besides productivity and throughput for DC operations. This
process is even more critical in e-commerce fulfilment and on line retailing operations
(Caputo et al., 2004; Duffy and Dale, 2002; Ho et al., 2003; So et al., 2005; Soliman and
Youssef, 2003; Ting et al., 2004) which, respect traditional DC operations, require almost
100 per cent single-piece pick with few lines per order on each order. The design and
operation of a warehouse is therefore a complex problem with a large number of
interrelated decisions among processes, resources and organization (Heragu et al., 2005)
IMDS
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1360
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