Order-picking Methods and Technologies for Greener Warehousing

Abstract

Green supply chain management is a concept that is gaining popularity all over the world. Besides, it is a way to demonstrate commitment to sustainability and to be fully adopted by the organizations it should contribute to better economic performances and competitiveness. Recently there have been many incentives for more sustainable warehousing in supply chains. In order to improve efficiency of order-picking in warehouses, there are many methods, models and technologies developed and used. This paper presents, after a brief overview of green supply chain management, an overview of order-picking methods and technologies and their potentials in improving order-picking efficiency, based mainly on reducing traveling distances. In this way energy consumption is reduced, influencing also greening of warehousing too.

Full text

Strojarstvo 52 (1) 23-31 (2010) G. ĐUKIĆ et. al., Order-picking Methods and Technologies... 23Order-picking Methods and Technologies... 23 23
CODEN STJSAO ISSN 0562-1887
ZX470/1428 UDK 658.786.012.34
Original scientic paper
Green supply chain management is a concept that is gaining popularity
all over the world. Besides, it is a way to demonstrate commitment to
sustainability and to be fully adopted by the organizations it should
contribute to better economic performances and competitiveness. Recently
there have been many incentives for more sustainable warehousing
in supply chains. In order to improve efciency of order-picking in
warehouses, there are many methods, models and technologies developed
and used. This paper presents, after a brief overview of green supply chain
management, an overview of order-picking methods and technologies and
their potentials in improving order-picking efciency, based mainly on
reducing traveling distances. In this way energy consumption is reduced,
inuencing also greening of warehousing too.
Metode i tehnologije komisioniranja za “zelenije” skladištenje
Izvornoznanstveni članak
“Zeleni” menadžment lanca opskrbe (Green supply chain management)
je koncept koji dobija na popularnosti širom svijeta. Osim što je način
demonstriranja posvećenosti održivosti, da bi bio u potpunosti prihvaćen od
strane poduzeća nužno mora pridonijeti boljim ekonomskim pokazateljima
i konkurentnosti. U posljednje vrijeme ima mnogo inicijativa za održivije
skladištenje u opskrbnim lancima. S ciljem povećanja učinkovitosti
komisioniranja u skladištima razvijene su i koriste se mnoge metode,
modeli i tehnologije. U ovom radu se, nakon kratkog pregleda “zelenog”
menadžmenta lanaca opskrbe, daje pregled metoda i tehnologija
komisioniranja te njihovih potencijala u poboljšanju učinkovitosti,
temeljenih uglavnom na skraćenju vožnje prilikom komisioniranja. Time
se smanjuje potrošnja energije, te utječe i na “zelenije” skladištenje.
Goran ĐUKIĆ1), Vedran ČESNIK2) and
Tihomir OPETUK3)
1) Fakultet strojarstva i brodogradnje, Sveučilište
u Zagrebu (Faculty of Mechanical Engineering
and Naval Architecture, University of Zagreb)
Ivana Lučića 1, HR - 10000 Zagreb,
Republic of Croatia
1) P.Z. Auto d.o.o.
Zagrebačka bb, HR - 10410 Velika Gorica,
Republic of Croatia
1) Končar D&ST d.d.
Josipa Mokrovića 8, HR - 10000 Zagreb,
Republic of Croatia
Keywords
Green supply shain management
Green warehousing
Order-picking methods and technologies
Ključne riječi
Metode i tehnologije komisioniranja
“Zeleni” menadžment lanaca opskrbe
“Zeleno” skladištenje
Received (primljeno): 2009-09-20
Accepted (prihvaćeno):2009-12-15
Order-picking Methods and Technologies for Greener
Warehousing
goran.dukic@fsb.hr
1. Introduction
Nowadays there is more and more ecological
awareness among people and every day there are more
people who think and act green. There are many ways
how one can go green. For example, going green means
buying products that are produced from recycled material,
other people want to buy products that are more energy
efcient or in their production use resources that are
friendlier to the environment.
Apart from end consumers, green thinking emerged
also in the various initiatives adopted by companies.
There are three main reasons why companies implement
the greening process into their businesses [1]. They have
to comply with environmental regulations (legislation),
address environmental concerns of their customers
(marketing), and mitigate the environmental impact of
their production activities (ecological awareness). The
concept that encompasses environmental initiatives in
all stages of supply chain is called Green Supply Chain
Management (GSCM), dened in [2] as integrating
environment thinking into supply chain management,
including product design, material sourcing and selection,
manufacturing processes, delivery of the nal product to
consumers, and end-of-life management of the product
after its useful life. Despite GSCM evidently gaining

24 G. ĐUKIĆ et. al., Order-picking Methods and Technologies... Strojarstvo 52 (1) 23-31 (2010)
popularity all over the world, the goal of every company
is to make a prot. To be fully adopted by organizations,
the greening of supply chains should contribute to better
the denition – sourcing and procurement, operations
(conversion) and logistics activities, green supply chain
management could be illustrated as in Figure 1.
economic performances and competitiveness
(economy). Therefore it is necessary to understand
the elements and roots of the concept, with positive
linkage between environmental impact and
economical performances and competitiveness.
Green warehousing is denitely a part
of the broader picture of green supply chain
management, and lately many distribution centers
and warehouses around the world are aiming
green, “faced with a lot of tough choices between
economy or eco-friendliness, discovering that
much of good logistics engineering is not only
compatible with greener business practices, it’s
actually synonymous with it.” [3]
2. Green warehousing
2.1. Green warehousing as a segment of green SCM
Environmental awareness and ecology are not so
new in industrial systems and supply chains. The term
and eld “Industrial ecology” is now almost 40 years
old, concerned with tracking the ows and stocks of
substance and material, especially those whose cycles
are heavily inuenced by industrial activities, as the basis
for reducing the impact of the production process on the
environment [4]. Comparing it with green supply chain
management, we could conclude that Industrial ecology
and green supply chain management are practically the
same thing with a difference in scope. We can say that
Industrial ecology is mainly a eld of study and research
for a cleaner manufacturing process while GSCM is a
eld of implementation of green thinking in all segments
of companies’ supply chain activities.
For the purpose of highlighting segments of
GSCM as they often appear in literature as methods or
approaches to sustainability in supply chains, one formal
denition of Supply Chain Management (SCM) is used.
According to an American professional association
(Council of Supply Chain Professionals), “supply
chain management encompasses the planning and
management of all activities involved in sourcing and
procurement, conversion, and all logistics management
activities. Importantly, it also includes coordination
and collaboration with channel partners, which can be
suppliers, intermediaries, third party service providers,
and customers. In essence, supply chain management
integrates supply and demand management within
and across companies.” Making SCM green is simply
implementing environmental thinking into its activities.
Focusing on the three basic groups of activities from
Figure 1. Segments of Green Supply Chain Management
Slika 1. Segmenti “zelenog” menadžmenta lanca opskrbe
2.2. Elements of green warehousing
Green warehousing is a relatively new approach
which implements greening into warehouses and
distribution centers. There are many elements that you
can implement in a warehouse, but in short, each element
which reduces energy consumptions or material usage/
waste is a greening element. Some elements which
are frequently mentioned in literature and also used in
practical examples are [5]:
Implementation of paperless warehouse management •
system (WMS),
Using an energy efcient lightening,•
Using doors with sensor which automatically close,•
Using wind turbines or/and solar energy,•
Using ventilators to push hot air from the top to the •
bottom of a warehouse,
Using sensors for lightening so the light is turned on •
only in the passage/area where needed,
Using building materials which are better insulator,•
Using equipment with less carbon emission and less •
energy consumption,
Using returnable/recyclable containers and •
packaging materials,
Forklift eet improvements, etc.•
There are many benets to be derived simply by
revamping the lighting in your facility. A shift to more
current uorescent lighting technologies could help
reduce your light-related electricity requirements by as
much as 70 %. Also you could reduce your consumption
even further by placing motion sensors in key areas of
your warehouse to trigger lights to turn on only when
needed. [3]
You can use better insulation material for your roof
and you will reduce energy losses. In addition you can

Strojarstvo 52 (1) 23-31 (2010) G. ĐUKIĆ et. al., Order-picking Methods and Technologies... 25Order-picking Methods and Technologies... 25 25
put ventilators on top of warehouse which can push hot
air to the ground. In this way, you can use hot air, which
naturally would not be used.
There are now many new forklifts available on the
market that could be used for forklift eet improvement
in terms of greening. There are plenty of new technologies
for propane forklift eet that allow the vehicles to burn
cleaner and be more fuel-efcient. AC powered electric
lift truck are more energy efcient then DC powered.
Electric hybrid forklifts (like Komatsu “New ARION”
series [6]) and forklifts powered by hydrogen fuel cells [7]
are also great examples of using new forklift technologies
available on the market, solving environmental concerns
and providing better energy efciency and/or operational
performances.
Implementing Warehouse Management System in
distribution center or warehouse could greatly reduce
overall warehouse costs, which is achieved mainly by
optimizing various activities. Optimizing activities
performed with transportation equipments could have
signicant impact on reducing energy consumption and
CO2 emission. Paperless WMS, providing even higher
possibilities for more efcient operations, reduces also
paper consumption thus giving even more green image
to warehouses.
Of course, not all green initiatives focus on reducing
energy consumption. Some are focused on creating
energy from what is already readily available — sun
and wind. For example, many newly constructed DCs
are increasing their use of skylights and windows so
they can use natural light in many parts of their facility.
And those that are especially visionary are placing solar
panels on portions of their roofs, an ideal scenario since
warehouses are so at. [2]
3. Order-picking efciency for greener
warehousing
3.1 Basic characteristics of order-picking process
It is well known that logistic costs have an important
inuence on the nal success of any company. According
to the Logistics Cost and Service 2007 study [8], in
western countries, these costs represent almost 10 %
of sales. Warehousing, along with transportation and
inventory carrying, is one of the three major drivers
of total logistics cost, with 21 % in US and 37 % in
EU. Order-picking process, dened as the process of
retrieving items from storage locations in response to a
specic customer request, is the most laborious and the
most costly activity in a typical warehouse, with up to 55
% of warehouse total operating costs [9]. With a direct
link with speed of delivery, it also inuences the service
level. Therefore, it is very important to put some effort
into reducing order-picking costs and cycle time, i.e., to
improve order-picking efciency.
It is possible to improve operational efciency of
order-picking using appropriate operating policies. The
research in this area has grown rapidly recently and
considerable literature exists on various methods of
picking an order as efciently as possible [10]. The time
to pick an order can be divided into three components:
time for traveling between items, time for picking
the items and time for remaining activities. The fact
that about 50 % of total order-picking time is spent on
travelling [9] gives a potential to improve order-picking
efciency by reducing travelling distances. Most methods
of improving operational efciency of order-picking
focuses on reducing travel times, and can be categorized
into one of three groups of operating policies: routing,
storage and batching [11].
Analysis of those methods showed non negligible
inuence of layouts on performances of particular method
or mix of methods. Additionally to various traditional
layouts of order-picking systems, some radically new,
innovative warehouse layouts that could reduce retrieval
times in pallet picking were proposed [12].
There are also technology achievements that
signically aid the order-picking process. Using WMS
with RF handheld terminals, voice technology terminals
or pick-to-light system enable further improvements in
efciency – raising productivity by reducing search time
and travel time, while greatly improving accuracy.
The overview of order-picking methods and
technologies given further aims to present the possibilities
and potentials in reducing routes for order-pickers,
simultaneously implementing greening process into
warehouses. The inuence of reduced traveling distances
for order-picking in warehouses on green level depends
naturally on type of forklifts used, and could be expressed
in saved energy or reduced CO2 emission.
3.2. Order-picking methods
As already mentioned, several order-picking methods
could be used with the goal of reducing travel time.
Routing methods determine the sequences and routes of
traveling, trying to minimize total travel distances. Storage
methods, assigning items to storage locations based on
some rule, could also reduce travel distances compared to
random assignment. Order batching methods, grouping
two or more customer orders in one picking order, are
also very efcient in reducing total travel distances.
All methods mentioned are well known and proven
in improving order-picking efciency. However, the
performances depend greatly on the layout and size of
the warehouse, the size and characteristics of orders and
the order-picker capacity. Additionally, the performance

26 G. ĐUKIĆ et. al., Order-picking Methods and Technologies... Strojarstvo 52 (1) 23-31 (2010)
of a particular method depends also on the other methods
used, therefore it is important to understand their mutual
interactions [13].
3.2.1. Routing methods
There are several routing methods (policies) developed
and used in practice. They range from the very simple
to the slightly more complex. The performance of these
heuristics depends on the particular operating conditions
of the system under study due to their denitions. The
simplest routing heuristic is S-shape policy. When this
policy is used, the order picker enters every aisle where
an item has to be picked and traverses the entire aisle.
Aisles where nothing has to be picked are skipped. An
exception is made for the last aisle visited in case the
number of aisles to be visited is odd. In that case a return
travel is performed in the last aisle visited. Another very
simple routing heuristic is Return policy. The order-picker
enters and leaves aisles containing item(s) to be picked
from the front aisle. A Midpoint routing policy, also
one simple heuristic, looks like a return method on two
halves of a warehouse. Only the rst and last aisle visited
are traversed entirely. Similarly to the last heuristic, with
Largest Gap policy all aisles that contain even one item
to be picked are also left at the same side as they were
entered, except the rst and last visited which are traversed
entirely. The gap represents the separation between any
two adjacent picks, between the rst pick in the aisle
and front aisle, or between the last pick in the aisle and
the back aisle. If the largest gap is between two adjacent
picks, the picker performs a return route from both ends
of the aisle. Otherwise, a return route from either the
front or back aisle is used. The largest gap is therefore the
portion of the aisle that the order picker does not traverse.
This policy is a slightly more complex routing heuristic
than the rst three mentioned. The resulting route is
somehow similar, but denitely at least equal or better
than the route dened by Midpoint policy in all possible
situations. Two relatively new policies developed are
Composite policy and Combined policy. Composite
routing heuristic combines features of the S-shape and
Return heuristics, minimizing travel distance between
the farthest picks in two adjacent aisles for each aisle
individually. Combined heuristics is also a combination
of S-shape and Return policies, but a small component
of dynamic programming gives it the possibility to look
one aisle ahead. The decision about return or traversal
route in the aisle depends not only on minimized travel
in that aisle, but also on a better starting point for the
next aisle. This in turn leads to a better overall result than
Composite heuristic. All routing policies described above
by their denitions have some restrictions of creating
a route. An optimal algorithm [14], combining a graph
theory and dynamic programming, results in a shortest
possible, thus optimal route. Examples of routes created
by mentioned routing heuristics and an optimal algorithm
are given in Figure 2.
According to one case study [15], with routing
order-pickers efciently using routing methods it is
possible to obtain a reduction between 17 and 34 % in
traveling distance. The amount of reduction depends on
the particular method used. Although algorithm for an
optimal route has been invented, in practice heuristics
are predominantly used to route the fork lift trucks [10].
Figure 2. Examples of routes by routing heuristics and optimal algorithm
Slika 2. Primjeri ruta heuristikama usmjeravanja i optimalnim algoritmom

Strojarstvo 52 (1) 23-31 (2010) G. ĐUKIĆ et. al., Order-picking Methods and Technologies... 27Order-picking Methods and Technologies... 27 27
The reason for that is that heuristic policies may provide
near optimal solutions and avoid the confusion inherent
in optimal solutions. It is true that a specic heuristic
policy could in some situations results in near optimal
route, but in some other situations it could perform badly.
Therefore, it is important to know in what situations
some heuristics are good or bad. Even more, which are
better than another and how much better in particular
situations. A more detailed overview of routing methods
and analysis of performances are given in [16-17].
3.2.2. Storage methods
Storage methods assign items to warehouse storage
locations, based on popularity, demand, size, hazard etc.
In order-picking systems, storage methods are usually
based on rule of assigning the frequently accessed items
to the locations near depot [18]. Volume-based storage
policy assigns items to storage locations based on the
expected order or picking volume [19], while Cube-per-
order index (COI) based storage policy assign items to
the locations based on the ratio of the item’s required
storage space to the item’s order frequency. The items
with the lowest COI are stored in the locations nearest
to the depot. In case items are stored in any available
location (randomized rule), such a method is called
random storage or oating slot storage.
There are several different types (patterns) of storage
used in practice, most relevant for order-picking shown in
Figure 3. Items with a higher volume (or smaller COI) are
stored in darker locations. They are all proven in reducing
the total travel distances in order-picking compared to
random storage assignment, but the performance of a
particular storage type greatly depends on the routing
method implemented. The question is which type of
storage suits the best particular routing method.
Figure 3. The types of volume-based storage
Slika 3. Tipovi odlaganja prema protoku
More details of the evaluation of storage methods
and analysis of performances of routing methods in
combination with different types of storage are given
in [17, 20-21]. The result of analysis showed that large
savings are possible using storage methods, with even
in some cases 45-55 % of travel distance reduction
compared to random storage.
3.2.3. Order-batching methods
Methods of organization of order-picking, called
also pick strategies, determine how orders are picked in
warehouses. Most basic method is single order-picking.
Pickers pick one customer order at a time (in one route).
This method can work well in operations with a small
total number of orders and a high number of picks per
order. Operations with low picks per order will nd the
travel time excessive. In batch picking, multiple customer
orders are grouped into batches – picking orders.
Therefore, the items from several customer orders are
picked in one route, which generally reduces the travel
distances per order.
There are several orderbatching methods (algorithms)
developed and used in practice, which could be divided
into three main groups: simple, seed and savings
algorithms. First-Come First-Serve (FCFS) is the most
obvious of the simple orderbatching algorithms. This
algorithm adds orders to a group in the sequence they
arrive. If the picker is full (capacity reached), a new
group is started. Seed algorithms consist of two steps.
First, the initial order is selected based on some seed
selection rule. Second, the remaining orders are added
to a group based on some seed order addition rule, up
to the picker’s capacity. Savings algorithms, variants of
Clarke and Wright routing algorithm, are based on travel
savings that can be obtained by combining two particular
orders in one route as compared to the situation where
both orders are collected individually. For an overview
of many different seed and savings algorithms readers
are referred to [22], while for analysis of various order-
batching algorithms in combination with different routing
methods and storage methods to [13, 23]. The results
showed that the potential savings using orderbatching
in comparison with single order-picking (picking
by order) depend mostly on the number of customer
orders per group, and ranged from cca. 40 to 70 % in
conducted simulations. The analysis of orderbatching
algorithms with volume-based storage showed that
savings are cumulative, with potential savings in travel
distances up to 80 % compared to random storage and
single order-picking.
3.2.4. Warehouse layouts
Traditional warehouse/order-picking area layouts are
layouts we could nd today in the majority of warehouses.
The basic form is with parallel aisles, a central depot (pick
up/delivery point), and two possibilities for changing
aisles, at the front and rear of warehouse, shown in Figure
4 left. Modications of this basic form are usually with
adding one or more additional cross aisles. In this case
we refer to a layout with multiple cross-aisles. The layout
with one middle cross aisle is shown in Figure 4 right.

28 G. ĐUKIĆ et. al., Order-picking Methods and Technologies... Strojarstvo 52 (1) 23-31 (2010)
Figure 4. Basic traditional layout (left) and traditional layout
with one (middle) cross aisle (right)
Slika 4. Osnovni tradicionalni prostorni raspored (lijevo)
i tradicionalni prostorni raspored s jednim (središnjim)
poprečnim prolazom (desno)
As already stated, evaluation of routing policies
showed that layouts of order-picking area have signicant
inuence on resulting traveling distances. For a given
storage capacity, one can nd optimal layout regarding
number and length of aisles [11]. Results of previous
researches showed also that adding one or more cross
aisles could benet the total traveling distances, and
that it is also possible to nd an optimal number of cross
aisles [24]. Although please note that adding additional
cross aisles increases required storage area (and therefore
related costs).
The traditional design of warehouse layout is based
on a number of unspoken, and unnecessary, assumptions.
The two most restrictive are that cross aisles are straight
and must meet picking aisles only at right angles, and that
picking aisles are straight and are oriented in the same
direction. In Gue and Meller [12] the authors show that
those design assumptions, neither of which is necessary
from a construction point of view, limit efciency and
productivity because they require workers to travel longer
distances and less-direct routes to retrieve products from
racks and deliver them to designated pickup-and-deposit
points. In layout that maintains parallel picking aisles,
but allows the cross aisle to take different shape, the
expected distance to retrieve a single pallet is 8-12 %
less than in an equivalent traditional design, depending
on the dimensions of the warehouse. They named such
layout Flying-V layout. Relaxing a second assumption
that picking aisles must be parallel, they derived so called
shbone layout. The shbone layout also incorporates
the V-shaped cross aisles, with the V extending across
the entire warehouse. The picking aisles below the V are
horizontal, while the aisles above the V are vertical. The
expected travel distance in a shbone design can be more
then 20 % less than in a traditional warehouse. Similarly
to traditional layouts with cross aisles, these alternative
layouts also require a facility 3-5 % larger than the basic
traditional layout, which was designed to minimize the
footprint of a warehouse.
Despite the great potential of new innovative unit-load
warehouse designs in reducing traveling distance in pallet
picking (single command), the question is what would be
the distances of routes for case and item picking from
multiple locations in such layouts (multiple command),
compared to the traditional layouts. To address this
question, an analysis was done in [25] with the simplest
and commonly used in practice S-shape routing method,
and also extended for this paper with more complex
Composite routing method. Figure 5 illustrates one
example of a routing using S-shape method modied to
be adapted for analyzed shbone layout.
Figure 5. Example of picking route in examined shbone
layout
Slika 5. Primjer rute komisioniranja u analiziranom
prostornom rasporedu “riblja kost”
The simulation was conducted on tree warehouse
layouts with 576 locations per layer: basic traditional,
traditional with one (middle) cross-aisle and shbone.
Due to the simplicity of distance calculation, dimension
of a location is 1x1 meter and the width of all aisles is 2
meters. The traditional layout was with 12 main aisles
(total width across aisles 48 meters) and the length of
main aisles 24 meters (24 locations per row). With the
location of a depot in the middle, it is the optimal layout
for single command picking. Comparable shbone design
is shown in Figure 5. Order size was set to 10 picks per
travel and 30 picks per travel. The simulation results
showing average distance travel of picker is shown in
Table 1.

Strojarstvo 52 (1) 23-31 (2010) G. ĐUKIĆ et. al., Order-picking Methods and Technologies... 29Order-picking Methods and Technologies... 29 29
Table 1. Simulation results of average travel distance (in meters)
Tablica 1. Rezultati simulacija za prosječne duljine puteva (u metrima)
S – shape routing method /
S-oblik metoda
Order size / Veličina
narudžbe Composite routing method /
Kompozitna metoda usmjeravanja
Order size / Veličina
narudžbe
10 30 10 30
Warehouse layout /
Prostorni raspored
skladišta
Traditional (basic)
/ Tradicionalni
(osnovni)
258,7 375,8
Warehouse layout /
Prostorni raspored
skladišta
Traditional (basic)
/ Tradicionalni
(osnovni)
228,2 363,9
Traditional (one
cross-aisle) /
Tradicionalni (jedan
prprečni prolaz)
193,9 329
Traditional (one
cross-aisle) /
Tradicionalni (jedan
prprečni prolaz)
182,8 309
Fishbone / Riblja
kost 227,5 351,9 Fishbone / Riblja
kost 213,1 317,3
The simulation results show that the traditional layout
(without cross-aisles) produces the largest travel distances.
The shbone layout will give less travel distance (around
9.5 % in conducted simulations, depending on routing
method and order size), while the traditional layout with
one cross-aisle is going to shorten the travel even more
(around 18 % less travel time than a traditional layout
without cross-aisles in conducted simulations).
The shbone layout is without any doubt an excellent
layout for pallet picking, already implemented in real
warehouses. However, the presented analysis leads to the
conclusion that in a warehouse with case and item picking
from multiple locations the shbone layout results in
larger routes than the traditional layout with a straight,
right angled cross aisle. Finding the optimal number or
cross aisles in a traditional layout would probably result
in an even shorter route. According to [24], the addition
of an optimal number of cross aisles generally decreases
the picking travel distance by 20-30 % of associated route
in layout with no cross aisles.
3.3. Technologies for order-picking
The technology that is obviously necessary to be able
to implement mentioned methods is information system
in warehouse, apropos Warehouse Management System
(WMS) with built-in algorithms for routing, storage
and orderbatching. Although we mentioned paperless
WMS as an element of greening warehousing, paper-
based WMS would be satisfactory. One could imply that
paperless WMS encourages greening of warehousing
mainly by reducing paper usage in warehouses.
However, technologies enabling paperless WMS have
also additional capability of greening through increased
efciency, explained hereafter. The 3 technologies that
are used in order-picking systems inherent with paper-
less directing order-pickers are RF scanning, Voice
Technology and Pick to light systems. RF scanning and
Voice systems link to the WMS via a Radio Frequency
network. RF scanning is based on barcode scanning, thus
every picker has a bar code reader. Voice technology
pickers have a small, portable computer and a head-set
with a microphone through which a picker communicates
with the WMS. Pick-to-light technology has a light
signaling system on every single location and also an
interface port where a picker updates his picks. All three
systems can offer increased speed and accuracy compared
to paper-based WMS, but choice depends on the nature of
the business and products being handled [26]. Increased
speed directly improves productivity. For instance, RF
scanning orders are issued at the terminal, eliminating
the need to physically take the pick-list, while improved
accuracy will reduce the number of returns in order
to correct picking errors. Voice Technology provides
signicant benets over RF scanning because it is “hands-
and eyes-free”. Voice operators visually focus on assigned
tasks, so errors due to keying in RF hand held terminal
are eliminated. RF scanning device limits freedom of
hands and makes picking heavy and awkward items more
difcult, thus slowing down picking. Pick to Light can
provide even better productivity than Voice, because a
picker can see simultaneously all the items to be picked,
rather than being given sequential instructions. However,
please note that those systems are more likely used for
picking small fast-moving items from a relatively small
area, while RF scanning and Voice are more suitable for
case picking. Figure 6 shows the characteristics of those
three technologies in terms of accuracy and productivity
gains over paper-based picking (data from [26]), which
are also interesting from the “greening your warehouse”
point of view.

30 G. ĐUKIĆ et. al., Order-picking Methods and Technologies... Strojarstvo 52 (1) 23-31 (2010)
Figure 6. Comparison of order-picking technologies
Slika 6. Usporedba tehnologija za komisioniranje
4. Conclusion
Making warehouses more green denitely is worth
the effort from an ecological point of view. But for
warehouse managers and company owners, it can not
be viewed without implications for operational and
economic performances as well competitiveness. In this
paper we showed that improving efciency of order-
picking process in warehouses using operational methods
and advanced technologies is not in confrontation with
greening. Moreover, since gains in productivity are
mostly achieved by reduction of travelling distances,
the implications on saved energy for order-picker trucks
could be trendmenous. Large savings are possible by
setting a suitable routing method, storing and picking
strategy, choosing the right picking technology and most
suitable layout, all together making warehouse work
more efcient and greener at the same time.
Although the greening process is a relatively modern
philosophy, example of order-picking methods and
technologies showed that well-known techniques of
improving business processes could be in compliance
with greening. There are many similar examples, which
are probably the reason why green is becoming more
popular every day.
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