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Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia


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The proper functioning of warehouse processes is fundamental for operational improvement and overall logistic supply chain improvement. Order picking is considered one of the most important from the group. Throughout picking orders in warehouses, the presence of human work is highly reflected, with the main goal to reduce the process time as much as possible, that is, to the very minimum. There are several different order picking methods, and nowadays, the most common ones are being developed and are significantly dependent on the type of goods, the warehouse equipment, etc., and those that stand out are scanning and picking by voice. This paper will provide information regarding the dairy industry in the Republic of Croatia with the analysis of order picking process in the observed company. Overall research highlighted the problem and resulted in proposals of solutions.
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Promet – Trafc&Transportation, Vol. 29, 2017, No. 1, 57-65 57
J. Habazin, A. Glasnović, I. Bajor: Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia
Internal Transport
Preliminary Communication
Submitted: 20 Mar. 2016
Accepted: 16 Nov. 2016
The proper functioning of warehouse processes is fun-
damental for operational improvement and overall logistic
supply chain improvement. Order picking is considered one
of the most important from the group. Throughout picking
orders in warehouses, the presence of human work is highly
reected, with the main goal to reduce the process time as
much as possible, that is, to the very minimum. There are
several different order picking methods, and nowadays, the
most common ones are being developed and are signicant-
ly dependent on the type of goods, the warehouse equip-
ment, etc., and those that stand out are scanning and pick-
ing by voice. This paper will provide information regarding
the dairy industry in the Republic of Croatia with the analysis
of order picking process in the observed company. Overall
research highlighted the problem and resulted in proposals
of solutions.
warehousing; order picking; performance analysis; supply
chain; type of goods;
Processes which occur in warehouses are of large
importance for the circulation of goods throughout
the supply chain. Warehousing itself refers to taking
care, transportation, loading, unloading, packing and
processing of goods between the production and con-
sumption for commodity and other various functions.
According to authors Bartholdi and Hackman [1], the
warehouse process of order picking takes 70% of time
and 55% of costs which makes it a signicant process
in a warehouse. The order picking process can be
one-dimensional, two-dimensional and three-dimen-
sional. The main purpose of this paper is through a
case study to implicate how a two-dimensional order
picking in a warehouse affects the processing and
whether the observed warehouse has optimal pro-
cessing assigned.
The warehouse included in the case study stores
dairy products in diverse sizes and weights, where in
accordance to specic temperature regime, short life-
cycle, etc., the processing is dened in a way as tai-
lor-made for a company. To analyse and measure the
effectiveness, according to Richards [2], it is necessary
to approach the processes from where the deciencies
can be indicated. The same approach is conducted re-
garding the provided case study, where detailed mea-
suring and analysis are focused on the order picking at
the observed warehouse on the Croatian market with
over 9,000 storage locations.
Regarding the dairy industry in the Republic of Cro-
atia, according to the Croatian Bureau of Statistics and
the data from 2014, it is compiled of 117 companies
with 4,992 employees. The market itself includes an
assortment of packaged dairy products which com-
prises the following: cheese, drinking milk products,
yoghurt and sour milk products, and other products
such as chilled and shelf stable desserts, chilled
snacks, cream, etc. [3]
Basic warehouse processes are the following: re-
ceiving, put-away, internal replenishment, order pick-
ing, accumulating and sorting, packing, cross docking,
dispatch and shipping. Receipt and storage are con-
sidered inbound processes, while others are consid-
ered outbound processes. Previously noted is shown
in Figure 1. Beside these processes, there are also
value-added services which are not obligatory but
depend on the warehouse type and various provided
services [2].
In various warehouses, goods which usually enter
as units of a larger scale, go through reorganization
submitted to repackaging that results with units of a
smaller scale. At the observed warehouse typical ex-
ample are large packages of milk which arrive in a
warehouse as pallet quantities. Afterwards, they are
broken down into smaller quantities throughout order
(Corresponding author)
.1 University of Zagreb,
Faculty of Transport and Trafc Sciences
Vukelićeva 4, 10000 Zagreb, Croatia
58 Promet – Trafc&Transportation, Vol. 29, 2017, No. 1, 57-65
J. Habazin, A. Glasnović, I. Bajor: Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia
picking, packing and nally distribution. In this kind of
warehouses, operations which are done daily are tied
with human performance and greatly depend on it.
The smaller the handling unit, the greater the handling
cost. Smaller units require more labour and much
more processing to be delivered. Precisely, pallet ma-
nipulation at a warehouse directly inuences the time
used for picking. This results with accurately collected
units which are then forwarded to the next process [2].
2.1 Receiving
First in line of warehouse processes is receiving of
goods. This process does not take as much time as
picking, which is shown onwards, but it is as relevant
as any. Especially, if incorrect put-away occurs and
causes errors in further processing [2]. The process
of receiving can begin with the notice of the goods ar-
rival. This permits the warehouse to prepare, to sched-
ule inbound operations so there are no uncoordinated
events. With arrival, unloading begins after which units
are put away with accurate documenting before.
If there is necessity for labelling, this process oc-
curs before goods are put away. In every warehouse a
place must be preordained for these actions. If there is
no such place, but it is known that some of the arriving
products must be labelled, a temporary place must be
Products typically arrive in a warehouse in larger
units, as it has been mentioned before, on pallets. If
pallets are not arranged homogeneously they have to
be broken down into separate cartons [1]. It is neces-
sary for receiving that the method of delivery is com-
patible with the unloading equipment in the receiving
warehouse. Otherwise, the need for additional equip-
ment arises [2]. Altogether, the process of receiving
accounts for only 10% of the operating costs in a typi-
cal warehouse, but it is supposed to be reduced by the
use of Radio-frequency Identication (RFID) [1].
2.2 Put-away
Every Stock Keeping Unit (SKU) in a warehouse has
its own location, determined in advance, whether the
positioning is predened or random. Precisely, there
are several storage policies. A predened storage
policy prescribes a particular location for SKU to be
stored, but random policy leaves the decision to the
operator. Both of these storage policies can be used
in some warehouses. Furthermore, a class-based stor-
age system allocates zones to specic product which
is based upon products turnover rate as ABC zoning.
and receiving
Storage in the
reserve area
Put systemPicker-to-partsParts-to-picker
Broken case
Storage of
picking Picking robots
Sorting, accumulation and packing
Cross docking
Figure 1 – Typical warehouse processes
J. Habazin, A. Glasnović, I. Bajor: Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia
Promet – Trafc&Transportation, Vol. 29, 2017, No. 1, 57-65 59
Another storage policy includes correlated storage of
family groups, that is, storing products at nearby posi-
tions if they are often required simultaneously [4].
This step in the process is of large importance. It
can reduce time dened for picking and in the end de-
crease total duration of outbound processes. For put-
away the inventory management needs to be correct
and up to date. It must be known at all times what
storage locations are available, how much weight they
can bear, etc. In this case, the secondary inventory
management must be managed, not of products, but
of locations in order to know everything mentioned. Af-
ter the product is placed on its location, the storage
location should also be scanned to record where the
unit has been placed. This kind of information will be
of use when it is needed to pick orders [1].
There are several options for inbound SKU. First is
inbound into high-density storage like drive-in racking,
next is inbound into standard wide aisle reserve slots
such as upper levels. The last is inbound into pick slots
which are ground-level wide aisle racking. The latter
is represented in a small amount of products with no
current stock [2].
Put-away process may require a large amount of
work because SKUs must be moved over signicant
distances to their storage position. Put-away accounts
for approximately 15% of warehouse operating costs
2.3 Order picking
The process of order picking in a warehouse in-
volves selecting and gathering specied amount of
right SKUs in accordance with the order and it is com-
posed of lifting, moving, picking, putting, packing, and
other related activities [5].
During the order picking process, the orders are
generally assigned to several pickers. However, in or-
der to prevent control problems, the total pick area is
often divided into picking zones. After this, the zones
are generally served by different pickers, through the
zoning policy. Orders are picked one by one or in batch-
es in a predetermined order [4]. Further, order picking
can be manual or automated. In manual order picking,
the picker gathers units from their locations and then
transports them to a packing area. Picking time Tp is
calculated as shown below.
Tp=t1+t2+t3+t4 (1)
t1 average travelling time [s];
t2 average extracting and searching time [s]:
t2=x1·x2· ti (x1– average number of locations per
order [pieces], x2– average number of positions
per location [pieces], ti – time needed for picking
on one position (3-6) [s]);
t3information processing time [s]: t3= (10–15 s)·x1;
t4time for additional activities [s] (t4=75 – 300
s for hand pallet truck, t4= 70 – 250 s for low-
level order picker, t4= 55 – 200 s for high-reach
In the case of automated picking, that is, automat-
ed storage and retrieval systems, system retrieves one
or more unit loads and place them to a picking station.
After that, the picker takes products on orders, and the
remaining items on the unit loads are transferred to
storage again [5]. The picking systems classication is
shown in Figure 1.
2.4 Packing
The process of packing can be demanding because
every previously picked unit is generally handled sepa-
rately. At this time, the process of checking the picked
order is also required and convenient and there is less
chance for errors to occur. Order accuracy is a crucial
measure of service to a customer. If inaccurate orders
make it to the customer, there will appear new expens-
es such as returns, which are expensive to handle.
This process must be dealt with caution and aware-
ness. The basic task of packing is to prepare goods
for further transportation by any carrier in a way that
does not affect shipping costs in a negative manner. If
there is a complication with picking orders, there will
be complication with packing. Precisely, if all items
from the order are not positioned at the same time
at a packing area, it is likely that the shipment will be
delayed or/and costs will increase. The shipment can
be sent partially resulting in higher costs [1].
2.5 Shipping
The process of shipping is the nal process
amongst warehouse processes. After packing and
preparing units for shipping (consolidation), the rst
step is loading into transportation vehicles with the
assumption that the shipping methods have been pre-
viously arranged. This process is not as complex and
generally includes less labour than mentioned before,
although there can be some additional activities if the
product is being staged before being loaded [1]. Also
outbound zone can include control, which will often
occupy at least one warehouse worker to provide the
activity. Depending on the warehouse information sys-
tem, control can be done manually or using a scanner.
Of all the above mentioned warehouse process-
es, collecting orders is the most important and most
challenging for optimization. Collecting orders or or-
der picking is dened as picking from storage loca-
tions based on customer requirements. According to
Bartholdi and Hackman [1], collecting orders in the
60 Promet – Trafc&Transportation, Vol. 29, 2017, No. 1, 57-65
J. Habazin, A. Glasnović, I. Bajor: Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia
warehouse wastes the largest share of time of all
warehouse activities, approximately 70%. Therefore,
it is very important to minimize the pickers’ collection
time and picking route. Affecting the mentioned total
collection time by order decreases, and the number of
successfully collected orders per hour increases.
When collecting orders, it also appears that the
largest share of work is done by warehouse workers.
In order to meet large orders of many customers, the
warehouses have several shifts performing tasks of
collecting orders. From an economic point of view col-
lecting orders makes up to 55% of operating costs in
the warehouse. Therefore, it is important to reduce the
order picking cost by optimizing time and picking route
because it greatly reduces the overall storage costs.
Annual operating costs in warehouses are shown in
Figure 2.
Percentages of time/costs
0% Picking Shipping Receiving Storage
Time Costs
10% 10%
15% 10% 10%
Figure 2 – Percentage of annual operating costs in a
The research given in this study implicates the
problems at the observed warehouse from which cer-
tain data were collected. The methodology of research
consists of data collected from WMS and data col-
lected chronographically at the location. Also, various
authors suggest different answers to related similar
problems regarding warehouse process optimization.
According to Saif and Elhedhli [6], cold supply
chains have high levels of greenhouse gas emissions
due to high energy consumption and refrigerant gas
leaks and for this problem the solution consists of
dealing with managing inventory by using a known
policy but without explicit formulas for the inventory
cost and maximum level functions. Precisely, a novel
hybrid simulation-optimization approach is proposed
for problem solving. Lagrangian decomposition is
used to compose the model into an integer program-
ming sub-problem and sets of single variable concave
minimization sub-problems that are solved using the
simulating approach. The formula given by the named
authors combines the efciency of optimization meth-
ods with the accuracy of simulation methods. By using
it, not only is the process optimization reached, but the
warehouse processes are also turning green and sus-
Author Lu [7] conducted research for an algorithm
for dynamic order-picking in warehouses, and accord-
ing to it, the dynamic order-picking strategies that al-
low changes of pick-lists during a picked cycle are of
importance. In this paper, authors gave the routing
algorithm for optimizing the dynamic order picking
routes for a manual picker to part system, which is
also used in the warehouse described in the main re-
search of this study.
According to Thomas and Meller [8], their guide-
lines can provide a warehouse design close to the
optimal solution; precisely, they limit the decision vari-
ables and include size and layout of the shipping area,
dock door conguration, pallet shape and pallet rack
height. For developing the design guidelines, they em-
ploy a statistical-based methodology, where one set of
data is used to develop the guidelines and an indepen-
dent set of data is used to evaluate the performance
of the guidelines. The response variable is the number
of labour hours which is evaluated for each design in
the solution space, but the most impactful parameter
is using a forward area.
Authors Pazour and Carlo [9] refer to warehouse re-
shufing as a reorganization strategy and claim that it
can be optimized using a mathematical programming
formulation, based on heuristics. The given results
suggest that the proposed heuristics improves upon
a benchmarking heuristics by relaxing how cycles are
handled and incorporating double-handling. It is nec-
essary to emphasize the problem given in this, and
also include double-handling.
According to Rakesh and Adil [10], the warehouse
layout decision is important as it affects several as-
pects of a warehouse, including various costs and stor-
age capacity. Step toward warehouse optimization is
by use of their algorithm that determines lane depth,
number of storage levels, lateral depth and longitudi-
nal width of a three-dimensional order picking ware-
house. It also helps in knowing the quantum of change
in the cost due to change in different parameters,
which is difcult to predict due to the interaction of
multiple effects and trade-offs.
Authors Kang and Kim [11] also base their research
on Lagrangian relaxation, precisely on a heuristic algo-
rithm based on Lagrangian relaxation and sub-gradi-
ent optimization methods. The use of this algorithm
is planned for minimizing the sum of warehouse oper-
ation costs, inventory costs and transportation costs.
Reducing the travel distance in order picking is of
signicant importance in every warehouse. According-
ly, author Zhang [12] developed a Correlated Storage
Assignment Strategy (CSAS) in order to reduce the
travel distance in the picker-to-parts order picking sys-
tem. In the conducted study, the use of the developed
J. Habazin, A. Glasnović, I. Bajor: Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia
Promet – Trafc&Transportation, Vol. 29, 2017, No. 1, 57-65 61
algorithm, compared with the full-turnover storage,
reduces maximal 2.08% of the average travel distance
per picking.
Authors Lu et al. [7] developed an interventionist
routing algorithm for optimizing dynamic order picking.
The algorithm re-calculates the optimal route during
the picking operation and is tested using a set of stim-
ulations based on an industrial case. The results given
in this study indicate that, under a range of conditions,
the algorithm can outperform both static and heuristic
dynamic order picking routing algorithms.
The conducted research in the form of a case study
will implicate how certain organizational changes may
contribute to reducing the process time and inuence
higher quality processing. Based on warehouse pro-
cesses of dairy industry on the Croatian market, the
paper will suggest process optimization. On-site data
collection, continuous process management and con-
trol can optimize the observed warehouse process,
with the limitation of optimal process life cycle dura-
tion, regarding the possibility of upgrading in a certain
upcoming period.
In the form of a case study the research has been
conducted at the dairy industry company located in
the Republic of Croatia, a member of the large Europe-
an corporation with their products distributed to mul-
tiple countries in Europe and to other continents. The
industry has several warehouses in Croatia, while the
main warehouse included in the case study is located
in Zagreb. The aforementioned warehouse with their
9,190 storage locations is positioned next to the pro-
duction facility constantly receiving quantities of n-
ished products; also nished goods by road transport
are received from other production facilities across the
country. For the conducted research the order picking
process has been analysed, where the specications
of layout, zones, order picking methods are studied
in detail for the benet of proposing optimal process
The observed warehouse is composed of several
zones (D, G, C, M, L) which have been added over the
years. The warehouse has a total of 14 loading docks,
out of which three are located in the D zone, nine in
the G zone and two in the zone L. Zone D, or high rack
warehouse consists of 14 rows that with ve levels can
accommodate up to 3,800 pallets of nished prod-
ucts. In this zone the temperature is maintained at
about 4°C and is used to store perishable dairy prod-
ucts. D zone layout is shown in Figure 3.
GATES 01-03
Figure 3 – Layout of D Zone in Central Warehouse Zagreb
Furthermore, zone G contains a total of 990 stor-
age locations for perishable products with provided
same temperature. G zone is shown in Figure 4.
GATES 04-12
Figure 4 – G Zone layout in Central Warehouse Zagreb
Zone M is high rack warehouse with a total capac-
ity of 4,400 storage locations, which is used for Ultra
High Temperature (UHT) products, and it is not tem-
perature controlled. It is important to mention that M
zone is a fully automated storage facility, where goods
are computer system ordered, with robotic cranes ex-
empt and delivered to L zone with the help of ware-
house transporters. The observed warehouse with its
areas is shown in Figure 5.
Picking is performed in two or three shifts and all
of the mentioned zones are included in the process
of picking. All three systems of order picking are per-
formed, picking pieces, boxes and pallets. Part to pick-
er is used in the M zone and serves only to extract the
pallets that are later transported with the help of ware-
house workers to the loading docks or used as lling
for picking positions on a picker-to-part.
All positions at order picking picker-to-part intend-
ed for exempting goods according to customer orders
are placed at zero level. In zone D the pickers have at
their disposal about 80 pallet positions while in zone
G they have 330. In addition to these two zones, there
is zone C which is comprised of shelves, which, on
60 positions contain portions of bulk items that have
been returned by the truck driver as a surplus. All of
these zones and picking positions on a picker-to-part
are also shown in Figure 5.
62 Promet – Trafc&Transportation, Vol. 29, 2017, No. 1, 57-65
J. Habazin, A. Glasnović, I. Bajor: Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia
Picking is performed with a scanner controlled by
Warehouse Management System (WMS) which sys-
tematically leads the picker through the whole pro-
cess of order picking. In zone D intended for picking
positions are heavy-weight units and each order starts
right in the D zone. After zone D, WMS sends pickers
in zone G where all other units are placed and where
WMS systematically leads between 22 racks, visiting
racks in the order from G-1 to G-22. After completing
the task, the picker is required to stretch wrap a pallet,
and position it to the loading dock according to instruc-
tions of the WMS system. For lling the empty positions
in the warehouse, the company uses Hyster’s R16H
high rack forklifts. In order picking on a picker-to-part,
the pickers use electric forklifts suitable for picking,
model Linde 12.
Observing the picking process on the picker-to-part
principle, several disadvantages were noted, crucial
in maintaining KPI performances. To achieve its goal
each picker is obliged in its shift to collect a minimum
of 190 cases net per hour. The net productivity in-
cludes order picking time, i.e. the time from receiving
the order to the nal activity of issuing the certicate to
the printer at the end of order picking. The gross time
of order picking includes net time, time dedicated for
pallet wrapping and time needed to deliver pallet at
the loading docks. The average number of net boxes
per hour in 2015 is 220.89 boxes per hour and the
average number of gross boxes per hour in 2015 was
172.90 boxes.
During the second shift within a week, six different
pickers are included in research. Each of the six pick-
ers characterize different walking speeds, a variety of
picking methods and that is the reason why the out-
put results are in a wide range. During the study, the
number of boxes is recorded, as well as the number of
positions and the time required to complete each order
picking task. The values obtained during the study are
shown in the graph, data include 42 orders, with high-
lighted number of boxes and positions in the observed
warehouse. In Figure 6 the time needed for picking an
order is implicated, as the maximum time for picking
order is 00:26:34, for 82 boxes on 47 positions.
Regarding order collection, during research it was
noted that heavy products positioned between the 13th
and 15th rows in the G zone create certain problems.
When pickers arrive on positions between the 13th and
15th row they already had a 50-70% loaded pallet with
boxes from previous positions. Heavy products must
be placed rst on the pallet bottom because they can
disrupt the stability and damage lightweight products
below. During the research it was noted that every
sixth picking order includes three to ten boxes of heavy
products needed to be collected from the mentioned
positions. At this point, every picker must reorganize
his pallet for what is needed certain time. During the
reorganization, the pallet picker wastes time, occupies
transport area between the rows and unnecessarily
loses their own energy.
The proposal solution for the mentioned problem is
relocation, precisely, the relocation of heavy products
from locations G-13 to G-15 to front part of the G zone.
If heavier products were to change their location, the
problem with possible collisions and eventual bottle-
necks would be expected to stop. There would not be
any necessity for pallet reorganization, neither would
picking take as much time as it does now. Regarding
the picker and his constant need for reorganizing pal-
lets, moving and heavy lifting, this kind of repeating
actions can cause problems and decrease his work
GATES 01-03
GATES 04-12
Figure 5 – Total layout of Central Warehouse Zagreb
J. Habazin, A. Glasnović, I. Bajor: Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia
Promet – Trafc&Transportation, Vol. 29, 2017, No. 1, 57-65 63
ability, but by changing locations these actions would
be expected to be reduced. Furthermore, the picking
time would decrease for the amount of time dedicated
to reorganize units. This is proposed by Figure 7 where
for example the same order described in Figure 6, has
00:01:09 minutes of reorganization in its total picking
Relocating heavy products at the beginning of the
G zone does not require high investments, regarding
neither time nor labour. The proposal for positioning
the group of heavy products in the picking part of G
zone is shown in Figure 8.
GATES 04-12
Heavy products
Figure 8 – Picking layout of G zone with problem solution
While conducting the research, another problem
occurred during the order picking when the picker
travels to zone C that consists of units returned from
the distribution and dedicated for priority picking due
to the positioning on virtual location. When the picker
needs to collect for example 15 units and 14 are locat-
ed on G-11-7, if the same unit is positioned at C zone,
after collecting 14 pieces on G position the picker is
sent to C zone to collect one unit, then the picker con-
tinues picking. After analysing 43 orders the problem
occurred in nine orders which are shown in Table 1.
The table implicates positions at G, D and C zones with
exact location numbers, as position ‘G-13-4’ implies
that the pallet position is at G zone, 13th row, 4th po-
In practice, scanners send pickers from D to G
zone while C zone is located between these two zones.
Hence, if order consists of the product from zone C,
the scanner should have the ability to send pickers to
C positions when they are passing near the zone. This
problem can be optimized with giving position C prior-
ity over position G, through administration interface in
WMS. By adjusting like proposed, the reduction of the
picking time is expected. The mentioned observations
generally occur as a result of insufciently thorough
analysis when setting the zone picking.
Modications need to be undertaken directly to
the WMS system, rst in line for picking to be set as
D zone, C zone and in the end the G zone. This will
Picking time
Number of boxes/positions
01 3 5 7 9 11 13 15 17 19 21 25 27 29 31 33 35 37 39 41
No. of order
Figure 6 – Picking time without reorganization
Picking time/Reorganization time
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Number of boxes/positions
No. of orders
Reorganization time Linear (picking time)
Number of boxes Picking time
Number of positions
Figure 7 – Picking time with reorganization
64 Promet – Trafc&Transportation, Vol. 29, 2017, No. 1, 57-65
J. Habazin, A. Glasnović, I. Bajor: Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia
signicantly save time for the picker as stated earlier
in this paper. The layout with the proposed location vis-
it order is shown in Figure 9.
GATES 04-12
Figure 9 – Picking layout of Central Warehouse Zagreb with
picking by priority
Storing dairy products, considered sensitive goods
that need to be under special temperature regime gen-
erally implies similar processing as storing other types
of nished goods. Every process, from receiving until
shipping is as well vital, while order picking is also the
one that accounts for the majority of warehouse op-
erating costs and requires the most performing time.
Once the picker starts gathering products from orders,
they walk, move, lift, put, pack and do other related
works which take time and in that way become costs.
The dairy company researched in a form of a case
study on the Croatian market is well organized while
the analysis implicates certain possibilities for the op-
timization within order picking process which is com-
mon in every company due to the fact that every pro-
cess has a lifecycle.
To perform a suggestion of a solution, the order
picking process has been analysed, regarding detailed
process ow and time dedicated for its performance.
In accordance to its status, it has possibility to be re-
duced by different strategies. Companies willing to
optimize their processes tend to measure them but
also tend to have objective view on the core process
structure. To be open to change and to be continuous
in evaluation is of crucial matter for any process opti-
mization. Picking orders in this case and solution pro-
vided in the paper is only one of the many possibilities
to approach process evaluation. After a detailed anal-
ysis, observed on broad data that include seasonality,
KPIs, types of goods, performance and the structure of
any kind of warehouse process, it can be well modied.
By optimizing order picking as proposed, it is con-
sidered that the processes would run more efciently.
The optimization includes predening WMS data and
reorganization of dedicated storage locations directly
inuencing the time consuming order picking, which is
presented with the proposed changes in the observed
company’s warehouse layout. The evaluation of effec-
tiveness and effectivity of a certain process, such as
order picking, should be constantly supervised in the
form of analysis and chronographically measured to
be controlled and reduced.
The conducted research and research on the Cro-
atian market provided by the authors before, implicat-
ed the lack of process control. Although with a strong
position on the market the companies are focused on
the nancial aspect, while operational approach is
often marginalized. The processes structure should
be submitted to the analysis and potential change in
accordance to any criteria, increase of units stored,
customer orders, number of employees, order picking
methods, layout change, etc. Case studies should be
carried out more commonly where in the form of coop-
eration between science and market they can develop
the basis of solutions for supply chain experts.
dr. sc. IVONA BAJOR1
1 Sveučilište u Zagrebu, Fakultet prometnih znanosti
Vukelićeva 4, 10000 Zagreb, Hrvatska
Table 1 – Time spent for unnecessary travel to position C
1 - D-10-15 C-1-045 D-10-19 0:39:12
2 G-13-4 - C-1-028 G-13-06 1:16:59
3 G-02-01 - C-1-010 G-03-08 0:27:26
4 G-09-17 - C-1-012 G-10-03 1:09:11
5 - D-11-22 C-1-003 D-11-17 0:41:20
6 G-01-06 - C-1-009 G-01-10 0:35:30
7 G-11-11 - C-1-030 G-11-11 1:13:44
8 G-20-04 - C-1-013 G-21-13 1:37:40
9 G-15-17 - C-1-022 G-16-06 1:27:02
J. Habazin, A. Glasnović, I. Bajor: Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia
Promet – Trafc&Transportation, Vol. 29, 2017, No. 1, 57-65 65
Pravilno funkcioniranje skladišnih procesa važno je za
operativno poboljšanje i opći napredak u logističkom opskrb-
nom lancu. Komisioniranje se smatra jednim od važnijih
procesa iz grupe. Tijekom komisioniranja u skladištima ljud-
ski rad je značajan, s glavnim ciljem reduciranja vremena
trajanja procesa što je moguće više, odnosno na minimum.
Nekoliko je različitih metoda komisioniranja, a danas se
najčešće razvijaju i značajno ovise o vrsti robe, skladišnoj
opremi, i dr., a metode koje se izdvajaju jesu skeniranje i
komisioniranje glasom.
skladištenje; komisioniranje; analiza izvedbe; opskrbni la-
nac; vrsta robe;
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... This deep division of pallet-load requires more labor. Generally the smaller the handling unit, the greater the handling cost, see e.g., Habazin et al. (2017); Bartholdi and Hackman (2011). At the arrival, the products are received and inspected, those for which arrival coincides with customer's orders are not stored, they go immediately to next 2.3. ...
... The put-away to reserve operation requires more labor to move product until its storage space. Before put-away to reserve operation, an inventory of storage areas is performed to know occupied, available areas and where everything is stored, see e.g., Habazin et al. (2017). Storage area of a product is scanned to make easy finding when it will be ordered. ...
Full-text available
Cross-docking is a strategy originally introduced to optimize operations inside a warehouseas part of the optimization of the Supply Chain. Like traditional warehouses, productsare collected from numerous freight yards such that suppliers, factories, manufactures,etc., usingtrucks, and are moved towards processing centers named cross-docks. At cross-dock yard, productsfirst get unloaded on inbound dock doors. Afterwards, they are sorted according to theirdestinations and are immediately transferred, using handling devices, to appropriate outbounddock doors to be sometimes consolidated with other products of the same destination and arereloaded into shipping trucks. Unlike traditional warehouse where storage period of productsis indefinite, for cross-dock, goods are unloaded and reloaded the same day without waiting intemporary storage area or can wait less than one day. In this PhD thesis, we study an NP-hardoptimization problem raised by cross-dock referred to “Cross-dock Door Assignment Problem(CDAP)”. The CDAP consists in assignment of incoming and outgoing trucks to inbound andoutbound dock doors of cross-dock, respectively. The goal is to minimize the total transportationcost inside the cross-dock. The standard quadratic formulation of the CDAP includes theGeneralized Assignment Problem as subproblem. In this dissertation, we perform an extensivecross-docking literature review. Then, we carry out a broad analysis of the standard quadraticformulation as well as the standard linearization of the CDAP. From this in-deph study, wepropose several new non standard Mixed Integer Linear Programming models for the CDAP. Todetect the best linear model among those we propose and those existing, we compare the performanceof these models on instances proposed in the literature. We next propose a LagrangianRelaxation approach to produce the best new lower bounds to optimal solution value. This LagrangianRelaxation is applied to the model that produces the best LP relaxation bounds. TheLagrangian dual is solved using a subgradient algorithm. According to the experiments it seemsthat large-scale instances cannot be solved with an exact method in reasonable running times andmemory requirements. Thus, we propose and implement two heuristics based on “ProbabilisticTabu Search” to operate efficiently with larger instances of the CDAP. To assess the effectivenessof these proposed heuristics, we compare their performance, first between them and thenwith recent heuristics in the literature. The results demonstrate the efficiency of the proposedapproaches on data sets from the literature
... Order picking (OP) is a labour-intensive and timeconsuming operation. Despite increasing technical support, figures reported in the literature and statistics indicate that between 80% and 90% of OP operations are performed manually, which is mainly a result of the flexibility of human workers (De Koster, Le-Duc, and Roodbergen 2007;Habazin, Glasnović, and Bajor 2017;Napolitano 2012;Routroy and Kodali 2008;Zhao et al., 2019). According to the US Bureau of labour statistics, the total employment of order pickers is expected to grow by 3% from 2019 to 2029. ...
Order picking (OP) is a time- and labour-intensive operation in which human-system errors can lead to deficiencies in quality. This study aimed to identify human factors-related failure modes that cause human-system errors and quality deficits in OP. We conducted a systematic literature review and qualitative interviews with 38 order pickers employed by 14 different companies in four countries. The literature review found 46 papers that identified eight different failure modes related to OP system design: physical workload, physical fatigue, mental fatigue, complexity, memory demand, vision, hearing, and motivation. The interview results confirmed many of the same factors noted in the literature review but also identified communication and supervision failure modes that had not been addressed before. The results illustrate human factors-related failure modes linked to OP system design, operation, and management that ultimately increase quality deficits and pick errors. Further research on human factors and OP system design interaction is warranted, as no assessment tool has been found to support engineers and managers seeking to improve system designs to reduce pick errors.
... Process map of the picking operation[10]. ...
Full-text available
Companies have been trying continuously to reduce their logistics costs in the current competitive markets. Warehouses are important components of the logistics systems and they must be managed effectively and efficiently to reduce the production cost as well as maintain customer satisfaction. Order-picking is the core of warehouse operations and an order-picking system (OPS) is essential to meet customer needs and orders. Failure to perform the OPS process properly results in high costs and customer dissatisfaction. This research aims to investigate the state of the art in the adoption of OPS and provide a broad systemic analysis on main operating strategies such as simultaneous consideration of order assignment, batching, sequencing, tardiness, and routing need. This study reviews 92 articles, classifies combinations of tactical and operational OPS problems, and provides guidelines on how warehouse managers can benefit from combining planning problems, in order to design efficient OPS and improve customer service. Combining multiple order-picking planning problems results in substantial efficiency benefits, which are required to face new market developments.
... They created a framework and delimited decision variables, determining them to be the size and layout of the transport area, the configuration of the dock door, the shape of the pallet, and the height of the pallet warehouse. A statistical methodology was used to develop their planning guidelines [61]. ...
Full-text available
In real life situations, the material handling strategy of on-site storage systems is usually determined during the design of the storage system, which is no longer reviewed later. The strategy is typically determined by the person(s) designing the storage system, without the use of scientific methods, based on previous experience. Without a thorough periodic review of operational strategy, most companies’ warehousing systems have significant logistical losses (e.g., unnecessary material handling, waiting, operations), which also negatively affects the sustainability of the logistics operations. Therefore, eliminating these losses can increase both the competitiveness and the sustainability of companies. For this reason, the aim of this publication is to introduce a gap-filling test method that allows the selection of an optimal material handling strategy covering the total value stream in a demand-driven storage environment, using the opportunities offered by the Industry 4.0 concept, in particular in the field of big data analysis. This integrated approach has so far not emerged in the study of warehouse material handling strategies. Beyond the obvious economic benefits, the application of this method can clearly help companies to achieve a higher level of sustainability in their logistics operations, as it allows storage systems to operate more efficiently while minimizing material handling losses, ultimately resulting in a lesser demand for energy and raw materials. Moreover, this can also result in a reduction in the human and machine resources required to perform the tasks.
... It permits the warehouse to prepare to schedule inbound operations, so there are no uncoordinated events. With the arrival, unloading begins after which units are put away with accurate documenting before [7]. ...
... The warehousing reliability is closely linked to the use of modern technology, which is designed to increase productivity and quality of work. Employing modern warehouse technology ensures a more rational use of warehouse space, reduced use of energy and labour, improved control of warehousing processes, and fewer manual handling operations, which results in lower operating costs and fewer work-related accidents [2][3][4]. The reliability of warehouse processes has increased significantly with the introduction of modern technology, as its use eliminates the main risks that are inherent to the operation of a warehouse. ...
Full-text available
The purpose of the study is to determine whether the presence of risk management in a warehouse requires the implementation of modern warehouse technology. On the basis of the literature analysis, it was possible to determine that there is a correlation between the presence of the highest level of risk management and the use of modern warehouse technology in individual warehousing processes. For this purpose, a statistical analysis was carried out on a sample of companies operating in the Slovenian automotive industry. The results did not reveal a tangible correlation between the presence of risk management with the use of individual modern warehouse technology, the motivation for its use and errors in its use. The results of the study therefore, highlight the problems that are present in the warehousing system of the Slovenian companies in the automotive industry, which are related to substandard technological equipment in the warehouses and to the discrepancy between the level of manufacturing automation and the level of warehousing automation. The results are important for the Slovenian automotive industry in terms of the implementation of modern warehouse technology in the high-tech automotive industry.
... Pengelolaan gudang umumnya mengatur pelaksanaan lima kegiatan berikut: penerimaan (receiving), pengantaran barang untuk disimpan (put-away), penyimpanan (storage), pengambilan barang untuk dikeluarkan (order picking), dan pengiriman (shipping) (Bartholdi dan Hackman, 2011). Di antara lima kegiatan ini, order picking adalah kegiatan paling penting dari sisi efektifitas dan biaya (Habazin, Glasnovic dan Bajor, 2017). Kegiatan ini menghabiskan hampir 60% dari total biaya pergudangan akibat jarak perpindahan selama kegiatan berlangsung. ...
Full-text available
Kegiatan order picking mengkonsumsi hampir 60% dari total ongkos gudang. Faktor utama yang membuat hal ini terjadi adalah jarak perpindahan saat order picking dilakukan. Penelitian ini mengambil satu kasus khusus dimana tata letak gudang merupakan two-cross aisle. Untuk menemukan rute order picking dengan jarak perpindahan terpendek, lima algoritma heuristik diterapkan dan dievaluasi berdasarkan jarak perpindahan yang diberikannya. Hasil dari penelitian ini menunjukkan peningkatan efisiensi gudang melalui penurunan jarak perpindahan selama melakukan order picking. Kata kunci: algoritma heuristik, order picking, minimasi jarak perpindahan
... Intelligent warehouses support enterprises in fierce market competition, overcoming challenges such as demand fluctuation [26]. The normal operation of the warehouse process is the basis of logistics supply chain improvement [27]. With the rapid development of warehouse logistics, the routing optimization of AGVs is playing an important role in improving the efficiency of goods selection and customer satisfaction, thus attracting much attention. ...
Full-text available
In this paper, we propose a novel tabu search (NTS) algorithm that improves the efficiencies of picking goods of automated guided vehicles (AGVs) in an automatic warehouse by solving the conflicts that happen when multiple AGVs work at the same time. Relocation and exchanging operations are designed for the neighborhood searching process based on each pickup-point’s location in the warehouse, along with the initial solution generation and the termination condition in the proposed algorithm. The experimental results show that the tabu search algorithm can effectively optimize the order of pickup points, which could further reduce the total travel distance and improve the efficiencies of AGVs in automatic warehouses.
Warehouses play a significant role in cold chains as they do for regular supply chains. Although their goals are the same for both cold chains and regular supply chains, the operations of cold warehouses are more sophisticated since the cost of operation is considerably higher due to energy consumption and obsolesce of products in substandard conditions. Recently, there has been an enormous interest in the cold food supply chain to reduce food waste occurring along the chain. Hence, efficient management of cold warehouses becomes an important issue in this direction. Design and operation requirements in a cold warehouse may be different from a traditional non-cold warehouse. In this paper, we aim to provide an overview of cold chain operations, mostly by focusing on cold warehouse operations. We provide some statistics from a cold chain, design, and technology requirements for cold warehouses as well as warehouse operations shaped according to that warehouse features. It is observed that there are quite different design parameters in cold storage.
It is without a doubt that Additive Manufacturing (AM) is to radically change the way manufacturing processes take place. However, the vast majority of the extant literature deals with rather futuristic scenarios on the subject. This paper adopts a more realistic, short-term perspective by stating that new supply chain models have to be designed in a way adopting AM, but they should still incorporate Conventional Manufacturing (CM) as the de-facto means at least for a certain period. To this end, we formulate a new supply chain model where manufacturers are allowed to forward their orders to 3D shops. With some mild assumptions, our optimization model suggests that substantial cost savings could be attained when AM is adopted.
Conference Paper
Full-text available
Diverse solutions of a correlated storage assignment strategy (CSAS) are developed in this paper to reduce the travel distance in the picker-to-parts order picking system in a single-block warehouse. The correlation among stock keeping units (SKUs) is considered for the storage location assignment. Because the correlation can be used in both the item clustering and the improvement of the results of other storage assignment strategies, a methodology, which includes a pre-process and two branching processes, is firstly proposed to develop algorithms of the CSAS. For the clustering-based CSAS, the sum-seed and the static-seed clustering algorithms are presented to find the itemsets, and four algorithms of sequencing itemsets and single SKUs are developed. For the improvement-based CSAS, the insertion algorithm searches the solution iteratively. In the experiment, the average travel distance per picking is used to measure the improvement of the CSAS. Compared with the full-turnover storage, the CSAS reduces maximal 2.08% of the average travel distance per picking.
Full-text available
The order picking activities in distribution center are mainly sorting and arrangement works classified by destinations in accordance with customer's request. The activities consist of lifting, moving, picking, putting, packing and other works basically. Also it can be classified into box picking, client picking, etc. according to the shape of products, type of process, type of picking system and so forth. In some companies, picking process consists of pre-picking and auto picking. Pre-picking refers to the work before the auto picking system classification where orders received from clients are checked and products are sorted by unit of box, pallet or auto picking unit. The auto picking (or semi auto picking) is defined as the process of sorting the pre-picking-treated products according to the units of clients, order, vehicles, etc. through the auto picking system. Although these activities are very simple and easy, every warehouse has different process for picking due to the lack of standard process. In this paper, we present a standard picking process for light weight cargo & productivity analysis for warehouse picking systems considering operational processes to compare: standard process versus ad-hoc process; conventional manual picking (e.g. manual digital picking system) versus automatic picking system.
Full-text available
Abstract Warehousing has been traditionally viewed as a non-value-adding activity but in recent years a number of new developments have meant that supply chain logistics have become critical to profitability. This paper focuses specifically on order-picking which is a key factor affecting warehouse performance. Order picking is the operation of retrieving goods from specified storage locations based on customer orders. Today’s warehouses face challenges for greater responsiveness to customer orders that require more flexibility than conventional strategies can offer. Hence, dynamic order-picking strategies that allow for changes of pick-lists during a pick cycle have attracted attention recently. In this paper we introduce an interventionist routing algorithm for optimising the dynamic order-picking routes. The algorithm is tested using a set of simulations based on an industrial case example. The results indicate that under a range of conditions, the proposed interventionist routing algorithm can outperform both static and heuristic dynamic order-picking routing algorithms.
The warehouse layout decision is important as it affects several aspects of warehouse performance such as material handling cost, space cost and storage capacity. We develop an algorithm that determines lane depth, number of storage levels, lateral depth and longitudinal width of a three dimensional order picking warehouse minimizing space and material handling costs. The algorithm is illustrated with an example. Effect of variation of a set of parameters on the optimal layout decision is studied.
In response to strict regulations and increased environmental awareness, firms are striving to reduce the global warming impact of their operations. Cold supply chains have high levels of greenhouse gas emissions due to the high energy consumption and refrigerant gas leakages. We model the cold supply chain design problem as a mixed-integer concave minimization problem with dual objectives of minimizing the total cost - including capacity, transportation, and inventory costs - and the global warming impact. Demand is modeled as a general distribution, whereas inventory is managed using a known policy but without explicit formulas for the inventory cost and maximum level functions. We propose a novel hybrid simulation-optimization approach to solve the problem. Lagrangian decomposition is used to compose the model into an integer programming subproblem and sets of single variable concave minimization subproblems that are solved using simulation-optimization. We provide closed-form expressions for the Lagrangian multipliers so that the Lagrangian bound is obtained in a single iteration. Furthermore, since the solution of the integer subproblem is feasible to the original problem an upper bound is obtained immediately. To close the optimality gap, the Lagrangian approach is embedded in a branch-and-bound framework. The approach is verified through extensive numerical testing on two realistic case studies from different industries, and some managerial insights are drawn.
Warehouses can be characterized in many ways, including the number of items stored, the average number of cases per pallet, throughput and inventory requirements, and demand profile, to name a few. Thus, there is no one-size-fits-all design for case-picking warehouses, and hundreds of designs are possible. Moreover, the decision variables in warehouse design are interrelated and this further complicates the design process. The purpose of this paper is to provide a set of guidelines for arriving at a “good” design configuration for a manual, case-picking warehouse. Our goal in developing a set of guidelines is that it would provide a design that is close to the optimal solution, which could then be further analyzed and improved. We limit the decision variables considered in our analysis to include the size and layout of the forward area, dock door configuration, pallet area shape, and pallet rack height. To develop our design guidelines we employ a statistical-based methodology, whereby we use one set of data to develop the guidelines and an independent set of data to evaluate the performance of the guidelines. Our response variable is the number of labor hours. We use analytical models to evaluate labor hours for each design in the solution space. The most impactful parameter that influences our guidelines is using a forward area. Other parameters include: the ratio of SKUs to bottom-level pallet locations, ABC curve skewness, and number of lines per batch. Our results indicate that our guidelines provide a good design that minimizes labor hours.
Warehouse reshuffling is a reorganization strategy that consists of repositioning items by moving them sequentially. This study investigates how to optimize warehouse reshuffling and quantifies the effect of common assumptions. A mathematical programming formulation for the general warehouse reshuffling problem, the complexity of the problem, several heuristics based on the problem structure, a formal proof delimitating instances where double-handling can be a productive move, and managerial insights on the performance of reshuffling policies in various environments are presented. Experimental results suggest that the proposed heuristics improve upon a benchmark heuristic by relaxing how cycles are handled and incorporating double-handling.
We consider an inventory control problem in a supply chain consisting of a single supplier, with a central distribution center (CDC) and multiple regional warehouses, and multiple retailers. We focus on the problem of selecting warehouses to be used among a set of candidate warehouses, assigning each retailer to one of the selected warehouses and determining replenishment plans for the warehouses and the retailers. For the problem with the objective of minimizing the sum of warehouse operation costs, inventory holding costs at the warehouses and the retailers, and transportation costs from the CDC to warehouses as well as from warehouses to retailers, we present a non-linear mixed integer programming model and develop a heuristic algorithm based on Lagrangian relaxation and subgradient optimization methods. A series of computational experiments on randomly generated test problems shows that the heuristic algorithm gives relatively good solutions in a reasonable computation time.
The warehouse continues to play a major role within supply chains and will continue to do so for the forseeable future, although it may appear in different guises. A 4th edition has now been published.
Zagreb: Croatian Bureau of Statistics; 2014. Croatian [4] Mendes P. Demand driven supply chain: a structured and practical roadmap to increase profitability
  • Croatia
Croatia. [Basic Structural Business Indicators of Enterprises]. Zagreb: Croatian Bureau of Statistics; 2014. Croatian [4] Mendes P. Demand driven supply chain: a structured and practical roadmap to increase profitability. Berlin: Springer Berlin Heidelberg; 2011.