Research Article
Improving order‑picking response time at retail warehouse: a case
of sugar company
Ammar Y. Alqahtani1
Received: 8 April 2022 / Accepted: 18 November 2022
© The Author(s) 2022 OPEN
Abstract
Reducing customer response time, lowering overall logistic costs, and raising customer service standards are all partly
attributable to innovative methods for increasing order-picking efficiency. This paper focuses on the storage allocation
issue to determine the optimal stock-keeping-unit (SKU) inventory level and the storage assignment problem to place
SKUs in the most efficient locations. In this research, we describe a hierarchical top-down technique for merging separate
decision-making processes related to allocation and assignment. In this example, we use the suggested method, and
show how the Analytic Hierarchical Process (AHP) yields useful insights for solving the case study at hand. The Radio
Shuttle, a rack system with the best evaluation and the shortest product retrieval time, was put forth as an alternative.
The proposed solution was estimated to save 50 h of travel time and 48 h of lost work time.
Article highlights
• We focus on the order picking process optimization.
• An analytic hierarchical process model is formulated.
• Some managerial implications for warehouses manag-
ers are proposed.
Keywords ABC analysis · Racking systems · Storage policy · Order picking · AHP technique
1 Introduction
Nowadays, a competitive environment and the need for
supply chain integration have put huge pressure on warehouses to increase the rate of throughput and decrease
operating costs to maximize profits. Many factors need to
be considered to achieve this objective. The direct labor
costs stemming from the movement of products in different stages of production are considered a prime example of an area that can be optimized to achieve a large
cost reduction and efficiency improvement. Warehouse
operations such as order picking, loading, unloading, and
stocking account for 40% of the total direct labor activities
[1], whereas the other 60% is consumed in transporting
the products. Order picking is one of the main operations
in the warehouse and it contributes to a large portion of
the warehouse’s total operational cost. It is the most laborintensive operation in internal logistics.
During the last few decades, many researchers have
developed mathematical and simulation planning models to help increase the efficiency of order picking systems
in different areas such as storage assignments policies,
picking routing, storage retrieval systems, and inventory
management, and they have suggested various strategies
* Ammar Y. Alqahtani, aaylqahtani@kau.edu.sa | 1Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University,
P.O. Box 80204, Jeddah 21589, Saudi Arabia.
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to increase efficiency and reduce costs. In this paper, the
retail warehouse of a sugar company is considered. It is the
leading company in sugar manufacturing in the Middle
East and North Africa (MENA) region and one of the main
vendors of sugar for many countries in Asia and Africa.
One of the main problems facing the retail warehouse
is the high loading time. This retail warehouse has many
customers inside and outside the MENA region and many
orders that must be fulfilled on time; thus, it requires a
high-efficiency warehouse system. This paper analyzes the
loading time in the retail warehouse of the sugar company,
identifies the main causes of excessive loading time, and
provides some solutions. The main focus is on reducing
the travel time by reducing the forklift travel distance [2],
which will result in reducing the total loading time.
The specific solutions suggested by this paper are to
change the storage policy, using ABC analysis, to one that
is based on product picking frequency and to modify the
current storage system. Therefore, formulas for different
storage policies and systems are compared. It is suggested
that class-based storage policies be applied rather than
the random policies currently in place. In addition, an
Analytic Hierarchy Process (AHP) analysis is constructed
to compare different rack systems based on several criteria. The rack system with the lowest product retrieval time
is proposed, which is the radio shuttle. The approximated
savings in total travel time is around 50 h and 48 h per
month for the proposed solutions, respectively.
The proposed work combines different techniques in
order picking and time response reduction to reduce the
response time in order picking processes.
Fig. 1 Current layout of the retail warehouse
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1.1 Problem statement
This section will highlight the different aspects of the
problem in the retail warehouse of the sugar company
considered in this paper. The problem will be explained
and analyzed from different angles, including layout,
stock-keeping units (SKUs), and order processing. Solutions and recommendations will be proposed later in the
paper that apply specifically to this company. However, a
warehouse could have other problems that require different strategies to improve efficiency and maximize profits.
Figure 1 shows the layout of the warehouse under
study. The diagram also shows the entrances and exits, as
well as any hallways or corridors that can be used to move
goods. It can be seen that the warehouse has a capacity
to store 10,800 pallets. Through Gate 10, the pallets are
received from the packaging department to be stored in
the warehouse. In addition, there are four gates (namely,
Gates 1, 2, 3, and 4) for shipping or receiving the SKUs
made by the external factory, but only Gate 1 is currently
used because it is the nearest gate to the preparation area.
The forklift trucks transit outside the warehouse near the
shipping door. The warehouse has three main zones (A,
B, and C); each zone is divided into two areas (A1, A2, B1,
B2, C1, and C2). All products in these areas are stored in
block stacking systems, except two lines of selective storage racks, which exist in Zone A1. The block-stacking system allows a maximum of 3 pallets to be stacked, while up
to 4 levels can be stored in the selective racks. Currently,
the SKUs are stored in the warehouse based on a random
storage policy. Based on the design of the warehouse, the
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routing policy used is the return strategy, which means
that the forklift operator goes to the pallet location, picks
up the pallet, and comes back using the same path.
The retail warehouse of a sugar company produces
many types of sugar, which can be variously categorized.
For example, the SKUs for sugar can be divided into five
groups, based on the type of sugar: fine white sugar,
coarse white sugar, brown sugar, icing sugar, and diet
sugar. They can also be divided into four groups on the
basis of their packaging material: polyethylene bags, polypropylene bags, paper sachets, and cartons. Other methods for categorizing SKUs include weight, pallet type, and
brand name. However, the retail warehouse of the sugar
company under study contains 40 different SKUs, which
are stored randomly in the warehouse. Each SKU has an
ID number, an estimated demand, and a pallet position
number.
The SKUs are received, stored, and shipped on pallets
as a unit load. There are four different types of pallets: normal, CHEP, plastic, and heated pallets, but the most commonly used pallets are normal and CHEP pallets. The pallet dimensions (LengthxWidthxHeight) without load are
120 × 100x20 centimeters and with load, 120 × 100x143
centimeters.
A sugar company’s loading operations consist of two
main processes: picking and shipping. The process that
consumes most of the loading time is the picking process.
This process includes receiving the order, determining the
SKUs and the picking lines, forklift traveling to the lines,
picking the SKU, transferring it to the preparation area
where the shipping process will start, and updating the
inventory system. The shipping process includes loading
the SKUs onto the trucks; checking that the order is fulfilled accurately; confirming the pick in the Oracle system;
documenting the delivery to the truck driver; and allowing
the truck to move to the weighbridge, which is the last
shipping station. Table 1 shows the information summary
of the loading process.
The rest of the paper is organized as follows: Sect. 2
provides a literature review on warehouse operations.
Table 1 Loading process information summary
No
Title
Value
1
2
3
4
Average loading time
Average no. of trucks loaded/day
No. of pallets/truck
No. of trucks loaded at same time
5
No. of forklifts working in the
loading process
21 min
60 Trucks
22 Pallets
Normal: 2 Trucks
Exceptional: 3 Trucks
Total: 6 Forklifts: 2 are
for loading. 4 are for
preparing
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Section 3 presents the methodology of this study, and
Sect. 4 presents the data collection. Results and analysis
are given in Sect. 5. Finally, a discussion and conclusions
appear in Sects. 6 and 7, respectively.
2 Literature review
This section covers essential terminology pertaining to
the warehouse, picking time, and the techniques used to
study and analyze the warehouse layout. Also included in
this section are the results of similar studies that have been
conducted on optimizing warehouse operations.
2.1 Warehouse operations
A warehouse is defined as "a planned space for the efficient storage and handling of goods and materials" [3].
In other words, it is a place for storing goods in an efficient, systematic way. The warehouse operations’ main
goal is to satisfy customers’ needs by delivering products
in good condition at the right time [4]. Achieving this goal
required continuous planning and ongoing modifications
involving inspection and repair. Many different classifications for warehouse operations have been formulated.
However, the basic warehouse operations consist of
receiving, storing, order picking, and shipping, and each
operation involves different sub-operations and tasks [4].
Some researchers, such as the authors in [5], consider a
wide range of warehouse functions and classify them as
optional or elective, depending on the situation. Altogether, 10 functions are often identified. The first function,
receiving, mainly focuses on the receipt of materials coming into the warehouse. In repackaging, another function,
goods may be processed at once or separately. Put-away
refers to the transfer of goods in storage. The function of
storing goods refers to physically placing the goods and
waiting for demands. Order picking occurs when goods
are processed to fulfill customer orders. Packaging and
pricing are elective functions within the picking process.
Sorting batches is a function aimed at accommodating
customer orders containing multiple items. Cross-docking
is for receiving, shipping, and packing. Finally, replenishment of goods is an elective function, carried out if needed
[6]. In a traditional warehouse that holds inventory, the
goods are received and put away in storage; later, they are
picked and shipped through shipping docks.
As warehouse operations are becoming more complex
to manage, issues regarding various functions have arisen
over time. These issues have implications for warehouse
locations and layout, and they pertain to the whole supply chain process, which researchers have studied in an
attempt to increase overall productivity [7]. The availability
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of information is critical when making decisions regarding operations such as incoming shipments, customer
demands, warehouse dock layout, and the available material handling resources [8]. These inputs help to determine
the assignment of inbound and outbound carriers to
docks, which is critical in defining the aggregate internal
material flows. They also determine the schedule of the
carriers at each dock, and they determine the allocation
and dispatching of material handling resources, such as
labor and material handling equipment.
2.2 The picking process
Although the picking process is defined differently, all definitions focus on satisfying customer orders. The authors in
[9] have defined the picking process as recalling the items
from the storage zone to fill customer requirements. Likewise, the researchers in [10] have defined it as a process
within the warehouse to achieve customer orders at the
right time and quantity. Order picking is known as the
most time-consuming among the functions, representing
about 70% of the operating time and about 55% of warehouse operating costs [11]. Thus, this function is essential
to study due to its impact on warehouse operations and
customer service. The authors in [4] have intensively discussed the three picking policies introduced by [12]: zone,
wave, and batch picking. They divide zone picking into
sequential zoning and parallel zoning, the former taking
place when one or more orders move sequentially across
the zones, and the latter occurring when the orders move
simultaneously. Wave picking occurs when waves of orders
are picked up and shipped together. Finally, batch picking
takes place when a group of orders is picked up in one
trip. Aside from these three picking policies, the picking
process itself might be one-dimensional, two-dimensional,
or three-dimensional [13]. These complex systems reflect
the value and importance of the picking process on warehouse operations, and they explain why researchers are
especially interested in the picking process.
2.3 Optimizing the picking time and travel time
Optimizing the picking time activities may involve redesigning the warehouse layout to help the company optimize the warehousing process. Focusing on two stages
in the automotive industry, the researchers in [14] used a
mathematical model and stochastic evolutionary optimization approach to optimize the system of order picking
and storage location. In the first stage, integer programming was performed to reduce warehouse transmissions
and solve the storage location assignment problem. In
the second stage, the problems of batching and routing were considered to minimize the cost of travel. The
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integer-programming model was implemented to analyze
the warehouse. To speed up the process of obtaining a
solution, the researchers developed a genetic algorithm
to solve the warehouse layout problem and improve the
real-time application response to production orders, optimizing batches and routes for the order picking. In another
study [15], the authors proposed an integrated framework
supported by a simulation tool to find the best design for
an order picking system that optimized the picking process and its efficiency for two companies. The framework
was found to be effective, improving the performance of
both companies. These studies show that redesigning the
warehousing process can improve the picking time and
reduce time and waste, which reflects directly in customer
services.
As mentioned earlier, the picking process is well known
to cause difficulties and problems and remains one of the
main factors that impacts efficiency in warehouses. The
SKU’s movement constitutes more than half of the total
order preparation time [16]. Since movement reduction
should be the main goal that companies strive toward in
order to stay competitive [17], reducing the picking time
is a priority task for every warehouse management. The
total picking duration depends on many factors, such as
the applied storage system, the level of automation, and
the strategy of order completion [11]. It is important for
the order to be completed in quantity and assortment and
shipped on time (the deadline should not be exceeded).
The order picking process consumes the highest share of
warehouse resources and costs, accounting for approximately 55% of total warehousing costs [18]. Considering
the related operations such as packing and loading, this
figure could even reach 61% [19].
2.4 Storage location assignment problem (SLAP)
The Storage Location Assignment Problem deals with how
the SKUs are put away in the warehouse so as to optimize
the performance measures [20]. Generally, order-picking
time represents a warehouse’s most important performance measure [20]. Picking performance is directly
affected by the process of storage applied in the warehouse. Therefore, warehouse designers try to consider it
in the design phase [21]. Roodbergen and de Koster have
discussed four approaches to reducing the order picking
travel distance or time: 1) dividing the warehouse into
zones; 2) picking up orders in groups; 3) picking good
routes for picking up orders; and 4) choosing the best
way to assign storage locations [22]. The fourth approach
to order picking effectiveness focuses on optimizing the
SKU’s storage assignment, which decreases the order
picking travel distance or time [23]. If the wrong storage
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location assignment strategy is used, the cost of moving
things around will also be high and space will be wasted
[24].
2.4.1 Storage policies
Products can be assigned to storage locations in different ways, which can be summarized as follows: The first
method is called the "Haphazard Policy," which arbitrarily
assigns the SKUs to different locations. The second method
is called "Dedicated Storage," which assigns the products
based on defined criteria [25]. The class-based storage
policy falls between these two strategies.
Haphazard storage considers all SKUs in a single class,
whereas dedicated storage has one class for each SKU. A
dedicated policy stores the higher demand SKUs near the
input/output (I/O) point, which is considered more efficient in material handling than a haphazard policy. On the
other hand, a dedicated policy lowers space utilization by
requiring more space to accommodate the maximum
levels of inventory of each SKU in their predetermined
locations. Thus, the class-based storage policy merges
the advantages of both policies [26]. Figure 2 shows classbased and haphazard storage policies, both known as
"Shared Storage" [27].
Haphazard storage is a simple procedure. To store an
SKU, the warehouse manager only needs to know if the
storage location is empty or not. The most common types
of this storage method are the following: closest open
location, random assignment, longest open location, and
farthest open location [26]. Haphazard storage is a common practical policy due to its many advantages, including simple implementation, space utilization, immunity
to assortment and demand fluctuations, and low congestion due to the uniform use of aisles. On the other hand,
there are some pitfalls to this policy, such as the fact that
it causes difficulties and confusion in positioning the
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tracking system since products do not have predetermined locations [28]. In addition, the lack of a systemic
view of strategic storage location results in declining warehouse performance as product information is not utilized
or considered as consecutive processes [29].
In dedicated storage policies, the storage locations are
reversed and allocated for SKUs over the planning horizon based on an appropriate criterion. In 1976, Kallina
and Lynn defined four main criteria for dedicated storage procedures: complementarity, space, compatibility,
and popularity. Complementarity refers to the practice of
keeping SKUs that are likely to be ordered close together.
Space refers to the practice of assigning locations near the
(I/O) doors to the less bulky products. Compatibility mandates that products be stored close together only if they
do not contain risks of infection, contamination, corrosion, or other damage; incompatible products should not
be stored close together. Finally, popularity means storing popular products with high demand near (I/O) doors
to reduce the total travel distance, as these products are
the largest contributor to the distance [30]. Altogether,
part number, turnover, cube per order, correlation, and
length of stay are the most common criteria for dedicated
storage.
The researchers in [31] have proposed that class-based
storage products be classified on the basis of a suitable
criterion such as usage rate or volume. Then, each class can
be assigned to a storage area in the warehouse. Products
can be positioned in a class based on a simple haphazard rule, such as the nearest open location. If the number
of classes equals three, the method of storage is often
called ABC storage. Considered in terms of class, products
stored under the haphazard method belong to one class,
whereas under the dedicated policy the number of classes
are equal to the number of SKUs.
The class-based policy is more commonly used than
these two alternatives, however, due to its several advantages, such as manageable maintenance, simple implementation, and the ability to cope with demand variations and product mix [32]. This policy is much easier for
administration as it does not require a full-sorted list of
SKUs for implementation as compared to dedicated storage. A class-based storage policy is also better in terms
of travel distance and travel time than a random storage
policy. Furthermore, Muppani and Adil show that when
a system faces high demand fluctuations, a class-based
policy performs better than a dedicated policy [33]. Most
previous studies have chosen turnover rate as the criterion
for classifying products [23]. However, all other criteria displayed in dedicated storage can also be applied to classbased learning. In this paper, the Cube-per-Order Index
(COI) class-based storage policy is applied.
Fig. 2 Classification of storage policies
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2.5 SKUs classification using ABC analysis
ABC analysis is a well-known single-criterion analysis tool
that classifies products and is widely used in warehouses
due to its ease of implementation, maintenance, and handling of changes in picking frequency and assortments
[32]. In addition, a class-based storage strategy is preferable to a full-turnover dedicated storage policy [34]. The
practice of ABC analysis divides the products into three
classes with 80%, 15%, and 5% participation for classes A,
B, and C, respectively [35]. However, these percentages can
be modified to permit more than three classes. In addition,
the analysis can be executed using various criteria [36].
Some examples of ABC classification criteria are the number of times an SKU is picked, its volume, its sales value,
and its COI [37].
2.5.1 Rack systems
Storage improvement should be ongoing as the number
of customers increases. Since warehouses have limited
space, this space must be utilized to the full. There are
various types of racking systems for storing products, and
each type has a different storage function. The type of system chosen will depend on the needs of the warehouse.
Increasing the number of pallet positions and organizing
the high, medium, and low demand products to make
higher demand products more accessible will improve
warehouse optimization and decrease travel time for
workers. The latter is essential, since movement is the most
important factor to consider in determining which storing method to use [38]. Once the proper type is chosen, a
sequence of actions will be needed to take advantage of
the new system, such as designing the forklifts and other
machinery [39]. The radio shuttle, which can be applied to
all warehouses, is designed to cut distance and improve
the utilization of the warehouse.
While rack systems make efficient use of space and
reduce distance, they are also costly to implement. When
a rack system is implemented, warehouse systems such as
electricity, sprinklers, and gates must be redesigned [40].
In [41], the researcher studied the suitable selection of
storage rack systems in an e-commerce clothing industry
using AHP by comparing storage rack systems on criteria
such as cost, volume utilization, height utilization, ease of
order picking, and stock cycle speed. A consulting group
first compared different storage systems based on cost,
volume utilization, height utilization, ease of order picking,
and stock cycle speed. Then, considering the usage areas,
features, and advantages and disadvantages of different
systems, they decided to include Back-to-Back, Narrow
Aisle, and Automatic Storage systems, based on the AHP
analysis. Back-to-Back racking was the most appropriate
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storage system with a 36.2 percent ratio. Automated storage systems came next due to their cost disadvantage, and
the Narrow Aisle Rack System fell behind other systems.
However, because of the speed of the inventory cycle, the
Narrow Aisle Rack System was less expensive than Automatic Storage.
2.5.2 Use of AHP in layout design
The plant layout is critical in terms of warehouse profitability and effectiveness. Because of the importance of
the layout in industrial companies, it has been a focus
of research for many decades. Several formulations have
been developed to solve the plant layout problem. When
a production plant site is divided into discrete rectangular
grids and each facility adopts one or more of these grids,
the layout is often referred to as a Quadratic Assignment
Problem (QAP). Recent research has introduced the easiest
solution to the plant layout problem by using FLD analysis
to locate grid cells in facilities while aiming to minimize the
total cost of material handling.
Even though many algorithmic and exact approaches
have been suggested to estimate the solution to plant layout problems, they are NP-hard; there is no exact solution,
especially when the essential and crucial requirements
affect FLD and lead in constrained time environments to
complexity regarding significant issues. In recent work, a
MIP statistical equation was developed for incorporated
production system planning. The economic decision model
takes product portfolio, capacity planning, process planning, and facility layout into account, and this study used a
similar model in which equipment dimensions and places
were different factors and numeric factors were introduced
to enforce the non-overlap constraint. All facilities in the
formulation of layout plans may be located anywhere on
the linear site [14], but they should not coincide [13].
Order processing, the most time-consuming task in warehouses, is crucial in the fast expansion of the online retail
business since customer orders must be fulfilled within constrained time frames. Assigning the right goods to the right
storage locations is one of the fundamental strategies to
improving order-picking operations. The storing zone problem is typically NP-hard and is primarily solved by heuristics,
which either have poor solution quality or require extensive
effort, especially for complex problems.
3 Methodology
All the methods and calculations used in the research
will be explained step-by-step, ending with the expected
results if those methods have been used. Figure 3 shows
a summary of all the methodology steps.
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3.1 Models and formulas assumptions
3.2 Cube per order index (COI) index
1. The warehouse handles 40 different SKUs, but only 32
of them are considered in these calculations.
2. The number of pallet positions required by SKU is
known.
3. The pallet positions are pre-established, so the number of storage lines and columns do not vary.
4. The storage line cannot be assigned to more than one
SKU.
5. There is only one I/O available for shipping, and the
distance from each line to the I/O point is known and
rectilinear.
6. The forklift movement starts from a specific point and
ends by coming back to the same point.
7. The forklift speed is known and fixed.
8. There is no shortage in order fulfillment.
9. Time and distance measurements focus on retrieving pallets from the storage locations using forklifts
and delivering the picked pallets to the shipping area.
Some other activities, such as managerial activities, are
excluded.
The COI is applied by introducing the COI index of an item.
The COI index for an item is the ratio of the required number of pallet positions (i.e., storage space) to the number of
movements per period. Then, the COI could be calculated
using Eq. (1) with the previous notations.
COIi =
Si
Pi
(1)
where: Si = number of storage locations required for product i, Pi = number of trips in/out of storage for product i
(e.g., throughput of product i).
3.3 ABC analysis
ABC analysis is a very simple technique to classify SKUs
used to optimize the warehouse or inventory according
to their degree of importance [42]. It aims to organize the
stocked SKUs to reduce the time needed to manage them
(e.g., time to put away, search, pick or move items in the
warehouse).
Fig. 3 Methodology steps summary
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Table 2 Products classification
rules based on ABC analysis
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Class
Importance
Classification rule
A
B
C
Most importance (High)
Secondary importance (Moderate)
Least importance (Low)
15–20% of SKUs represent 70–80% of the usage value
25–30% of SKUs represent 15–20% of the usage value
The remaining SKUs represent 5–10% of the usage value
3.3.1 ABC analysis concept
SKUs are classified into three classes based on specific
criteria in ABC analysis. Those criteria could be picking
frequency, COI, turnover, annual profit, or any other criteria, and the SKUs will be assigned based on their contribution to those criteria. Table 2 summarizes the product
classification.
3.3.2 ABC analysis procedures
ABC analysis procedural steps are as follows [43]:
1. Determine the value of the chosen criteria for each
item (e.g., demand or picking frequency).
2. Calculate the summation of the values of the individual items.
3. Calculate the percentage value of each item concerning the total volume of the selected criteria.
4. Sort the items based on their percentage values from
the best to worst (usually in descending order).
5. Calculate the cumulative percentage values.
6. Examine the cumulative percentage values and group
the items into three classes based on the classification
rule.
3.4 Travel distance and travel time calculations
Considering the current layout of the retail warehouse
shown in Fig. 1, the distances of the forklift routes are
determined based on the assumption of rectilinear distance. Therefore, the total distance that the forklift travel
can be divided into two components: (1) horizontal travel
distance (i.e., on the x-axis) and (2) vertical travel distance
(i.e., on the y-axis). The travel distance of the forklift’s forks
to the item height will be neglected because it does not
differ for all products and storage policies. However, the
distance calculations will be calculated from the center
of each aisle. The pallets’ location areas are determined
based on the pallet’s dimension. Based on the distance
calculation, the total order retrieval time can be expressed
using the forklift speed and distance traveled per time unit
relationship as follows:
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Total Order Retrieval Time (T ) =
Total Travel Distance (TD)
Forklift Speed (V )
(2)
3.5 Models formulation
3.5.1 Random storage policy
A random storage policy is a currently applied policy in
the retail warehouse, random means that there is an equal
probability for each storage location to be accessed at any
point in time. Therefore, the expected picking travel distance for the random storage policy is the simple average
of the sum of all distances [44] and can be calculated as
follows:
TDRandom =
M
�
i=1
Pi ×
∑S
j=1
Si
dij
(3)
where: dij = distance, time or cost between products i and
j, Si = number of storage locations required for product i,
Pi = number of trips in/out of storage for product i (e.g.,
throughput of product i).
3.5.2 Class‑based policy
The storage area is divided into several classes in a classbased storage policy. While the classes are arranged based
on some criteria, the products within a class are put away
randomly. Assume the case where the warehouse is
divided into three classes; the partitions between classes
are noted as R, which is the last storage location index in
the class. All products that belong to class 1 (i.e., class A)
are arranged randomly in the storage locations that are at
location index j with j ≤ R1. The products assigned to class
2 (i.e., class B) are arranged at storage location index j with
R1 < j ≤ R2 . In the same way, in class 3 (i.e., class C), the
products are arranged at the storage location index with
R2 < j ≤ S . Let Pc1 , Pc2 , andPc3 referred to total pallet movements for SKUs assigned to classes 1, 2 and 3, respectively.
Therefore, the expected one-way picking travel distance
can be expressed as [45]:
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TDclass−based = TDclassA + TDclassB + TDclassC
∑ R2
∑S
∑ R1
d
d
d
j=R1 +1 j
j=R2 +1 j
j=1 j
TDclass−based = Pc1
+ Pc2
+ Pc3
R1
R2 − R1
S − R2
(4)
Since the current storage lines ID does not represent the
actual order of the distance, the lines should be reported
in ascending order of distance, and this rank represents
the classes’ partitions.
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Table 3 Comparison matrix for evaluating the alternatives
Compared to the 2nd alternative, the 1st alternative is
Numerical
rating
Extremely preferred
Very strongly preferred
Strongly preferred
Moderately preferred
Equally preferred
9
7
5
3
1
3.6 Analytic hierarchy process (AHP)
The AHP, introduced by Thomas Saaty [46], is a procedure
designed to quantify managerial judgments of the relative importance of several conflicting criteria used in the
decision-making process. The AHP method describes the
problem in three parts. The first part is related to the issue
that needs to resolve. The second part describes the alternative solutions available in order to solve the problem.
The third part and, most importantly, the AHP method
criteria are considered and used to evaluate the alternative solutions.
AHP can be defined as a dynamic and useful tool due
to the final scores obtained from the pair-wise comparison
evaluations between criteria and alternative solutions. The
experience of the decision-maker guides the calculations
made in the AHP, and the AHP can be recognized as a tool
that can turn the assessments made by the decision-maker
into a ranking of multi-criteria. Additionally, the AHP is
clear because there is no need to create a complicated system structure with decision-makers included in it. On the
other hand, the AHP may need many evaluations by users,
mainly if the problems include many criteria and alternatives. Evaluating every single option is simple because it
requires the decision-maker to express two criteria or alternatives by comparing others. In order to conduct the AHP,
there are sets of steps that need to follow:
Step 1: List the overall goal, criteria, and decision alternatives.
Step 2: Develop a pair-wise comparison matrix.
Step 3: Develop a normalized matrix.
Step 4: Develop the priority vector.
Step 5: Calculate a consistency ratio.
Step 6: Develop a priority matrix.
Step 7: Develop criteria pair-wise development matrix.
Step 8: Develop an overall priority vector.
The first step is to make a hierarchy shape for the overall
goal, criteria, and decision alternatives. Then, the second
step is to rate the relative importance of each pair of decision alternatives.
The matrix lists the alternatives horizontally and vertically and has the numerical ratings comparing the horizontal (first) alternative with the vertical (second) alternative comparison matrix are shown in Table 3. Furthermore,
Intermediate numeric ratings of 8, 6, 4, and 2 can be
assigned. The value of 1 is always assigned when comparing an alternative with itself. In the third step, each number
in a pair-wise comparison matrix column will be divided by
its column sum. In the fourth step, the average of each row
of the normalized matrix is calculated. These row averages
form the priority vector of alternative preferences with
respect to the criterion—the values in this vector sum to
1. In the fifth step, calculation of the consistency ratio of
the subjective input in the pair-wise comparison matrix
is performed. A consistency ratio of less than 0.1 is good.
For ratios greater than 0.1, the subjective input should be
re-evaluated. In order to calculate the consistency ratio,
several points need to follow:
• For each row of the pair-wise comparison matrix, deter-
mine a weighted sum by summing the multiples of the
entries by the priority of its corresponding (column)
alternative.
• For each row, divide its weighted sum by the priority of
its corresponding (row) alternative.
• Determine the average, λ max that results from step 2.
• Compute the consistency index (CI), of the n alternatives by:
CI =
(𝜆 max − n)
(n − 1)
(5)
• Determine the random index (RI), as shown in Table 4.
• Compute the consistency ratio (CR):
CR =
CI
RI
(6)
In the sixth step, after steps 2 through 5 have been performed for all criteria, the results of step 4 are summarized
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Table 4 Random index scores
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Number of alterna- Random
tives (n)
index (RI)
3
4
5
6
7
8
0.58
0.90
1.12
1.24
1.32
1.41
in a priority matrix by listing the decision alternatives horizontally and the criteria vertically. The column entries are
the priority vectors for each criterion. The seventh step is
done in the same manner as that used to construct alternative pair-wise comparison matrices using subjective ratings (step 2). Similarly, normalize the matrix (step 3) and
develop a criteria priority vector (step 4). The criteria priority vector (from step 7) will multiply by the priority matrix
(from step 6) in the last step.
4 Data collection
The gathering of data is an essential part of every research
project. In order to reach your goals, you must take action.
Data must be gathered with care, and it should be neither
too huge nor too tiny to be of use. Having an idea of what
sort of data you need and how long you need it for Rather
of focusing on sheer quantity, we should instead concentrate on high-quality output. Using several tools, we want
to shorten the loading time as much as feasible.
4.1 Data collection for ABC analysis
There are 40 different items to choose from. Rack systems
with eight SKUs are available. Each SKU’s month-to-month
fluctuation is shown in the next column. The proportion
of each SKU’s displacement from the others is shown in
the following column. This might reveal which SKUs are
most popular. Each SKU’s lines are listed in the next column. Lines form the structure of the warehouse. 24 rows
and three layers make up each line. In the next column, the
total number of pallet locations is obtained by multiplying the number of lines by 24 and then by 3. The number
of SKUs with a pallet location is shown in the last column.
Finally, the COI index for every SKU is calculated. Divide the
pallet position over the movement to do this.
4.2 Data collection for AHP analysis
During the AHP study, a warehouse manager was consulted for his thoughts on the criteria for assessment. The
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Table 5 Evaluation of the alternative
Alternatives
Cost
Utilization
Load accessibility
Stock cycle
speed
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground floor
150
220
1000
500
150
300
120
60
75
30
75
60
90
50
Moderate
Hard
Hard
Easy
Moderate
Easy
Hard
Moderate
Slow
Slow
Fast
Moderate
Fast
Moderate
other warehouse employees gave the following criteria:
cost, utilization, loading accessibility, and stock cycle
speed. Table 5 displays the results of the examination of
a consulting firm for the various possibilities. The data
received from the warehouse manager for further evaluation purpose.
5 Results and analysis
In this section, all the methods that have been discussed
in the methodology section will be applied.
5.1 COI index and ABC analysis
In this analysis, only 32 of the 40 SKUs, which represent 97 percent of the overall storage need, are considered. The COI for the 32 SKUs is sorted in ascending order (i.e., from lowest to highest). Many SKUs use
the same storage locations, despite minor variations in
demand, because of a requirement in the retail warehouse demanding that each storage line be allocated to
just one item (i.e., picking frequency). COI-based allocation may not be suited for ABC analysis; thus, it may be
changed based on pure demand or choosing frequency.
The number of pallets sent per unit of time is known as
the picking frequency.
It is best to use the ABC categorization rule because of
the frequency of selection. Table 6 shows the results of the
ABC analysis. The SKUs will be assigned to storage locations in subsequent sections based on this categorization.
5.2 Travel distance calculations
Based on the warehouse drawings provided by the retail
warehouse management, the distances of the aisles
where the forklifts are moved are calculated. The results
are shown in Fig. 4. Distance is separated on the basis
of the aisles’ interaction. The travel distance calculations
determine the distance between each storage line and the
preparation area, and the storage lines are rated in the
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Table 6 SKUs classification based on ABC analysis
Class
Level of picking
frequency
% of Items
% of
picking
frequency
A
B
C
High
Moderate
Low
21.9
31.2
46.9
78.51
95.03
4.97
following order, from left to right: A1, B1, C1, A2, B2, and
C2. To classify these storage lines, the distances must first
be sorted ascendingly.
This section shows the outcomes of using the formulae explained and referenced in Sect. 4. Current storage
policies were compared to those under consideration.
Each policy’s total distance was first computed, and then
the total picking time was established based on the safe
forklift speed of 10 km/h (or 10,000 m/h). Finally, time and
percentage savings were calculated. Table 7 provides an
overview of the findings. As this table shows, switching
from the existing random storage strategy to a class-based
strategy would reduce journey distance by 499,172 m and
transit time by 50 h, saving a total of 2,249,907 m.
When the class-based policy is implemented, the SKU’s
storage location assignment will be altered, since the SKU’s
allocation to the storage lines will then be dependent on
its class classification. As a consequence, the configuration of the warehouse will alter, as seen in Fig. 5. The suggested arrangement does not fit the zone dividers since
Research Article
Table 7 Results of the performance comparisons (class-based policy vs random policy)
Policy
Class-based
Random
Unit
Total travel distance
Total travel time
Saved distance
Saved time
% of savings
1,750,734.521
175.0734521
499,172.4036
49.91724036
22.18%
2,249,906.925
224.9906925
Meter
Hour
Meter
Hour
the partitions are intended to meet fire safety standards.
However, colored signals and line numbers may be used
to prevent allocation problems.
5.3 AHP analysis and results
The rack system was selected as the best alternative option
using the AHP method. As shown in Fig. 6, a warehouse
retailing rack system selection model has been developed.
Goals, criteria, and options are the three aspects of the
model. Choosing the optimum rack system in this scenario involves four criteria: cost, utilization, load access,
and stock cycle speed. Drive-in, push-back, double-reach,
radio shuttle, and ground storage are all included in these
selected rack systems.
Among the four criteria mentioned above, cost is calculated per pallet position. Utilization refers to how many pallet
positions of the warehouse have been covered or used. Load
accessibility refers to the ability to transfer the product easily
Fig. 4 The retail layout with aisles distances
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Fig. 5 Retail layout based on class-based policy
Fig. 6 AHP hierarchy model for warehouse retailing
from receiving to shipping, and stock speed cycle is the time
needed to move the products outside the warehouse.
Tables 4 and 5 illustrate the results of the warehouse
manager’s evaluations based on the comparison matrix.
For each criterion, the weights are shown in Table A-1
(Appendix A). It is apparent that among the other criteria,
utilization has a considerable weight. Equation (6) yields
a consistency ratio of 0.08, indicating a satisfactory level
of consistency has been achieved.
5.3.1 Cost criterion
Table A-2 (Appendix-A) compares various rack system alternatives based on the cost criterion using the comparison
matrix. Table A-3 (Appendix A) shows the normalized matrix
of the averaged values of each alternative in terms of cost
criterion and the weights calculated by averaging each
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row. The results show that ground storage with a weight
of 24.82% is more beneficial in terms of cost than other
rack systems. The more expensive rack system is push-back,
with a weight of 1.93%. The consistency ratio equals 0.05,
which is within an acceptable range of consistency.
5.3.2 Utilization criterion
Table A-4 compares the different alternatives among rack
systems according to the utilization criterion. This criterion, as calculated earlier, has a higher weight among
the other criteria. Table A-5 (Appendix A) shows the normalized matrix of each alternative’s averaged values and
storage rack system weight according to the utilization
criterion. Radio shuttle has a higher utilization rate than
the others, with a ratio of 51.21%. Although ground storage is beneficial in terms of cost, it is calculated to have
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10.35% utilization. The consistency ratio equals 0.06, which
is within an acceptable range of consistency.
5.3.3 Load accessibility criterion
Table A-6 (Appendix A) compares various rack system
alternatives based on the load accessibility criterion. This
criterion affects the workers directly in terms of safety.
Because accessibility was difficult for the workers, high
injuries could result. Table A-7 (Appendix A) shows the normalized matrix of the averaged values of each alternative
and the weight of storage rack systems according to the
load accessibility criterion. It was calculated that the load
accessibility in the radio shuttle system is easier than in the
other storage rack systems, with a weight of 38.91%. The
load accessibility in the current storage (ground storage)
seems difficult with a weight of 8.60%. The consistency
ratio equals 0.05.
5.3.4 Stock cycle speed criterion
Table A-8 (Appendix A) compares various rack system
alternatives based on the stock cycle speed criterion. This
criterion represents the loading time. Table A-9 (Appendix A) shows the average weight of storage rack systems
according to the stock cycle speed. The higher contribution
weight is for radio shuttle, which is considered the fastest rack system among the other systems, with a weight
of 50.23%. The push-back system is considered the slowest system, with a weight of 2.45%. The consistency ratio
equals 0.07.
5.3.5 AHP final score
Table A-10 (Appendix A) shows the final scores for the rack
systems calculated. The scores are obtained by multiplying
the weights of the criteria by the weights of each alternative, then taking the summation of each row. Radio shuttle
has a higher score than the other rack systems, with a ratio
of 47.11%, which makes it the best choice for the retail
warehouse.
6 Discussion
The results of the AHP show the most appropriate storage rack type that can be implemented in this case study.
Radio shuttle has the highest score of all types. Choosing
the right rack system has some limitations. The space utilization of the warehouse should increase or remain the
same to accept the rack system. If the top-rated rack system
Research Article
decreases space utilization, it should not be considered.
The second limitation is the cycle speed, or how fast the
stock can go out of the warehouse. Time is limited in this
case, so with space utilization, the stock cycle speed should
increase.
In this study, radio shuttle improved the warehouse
operation by storing different types of SKUs in one line,
whereas the current storing method did not allow for more
than one type in a line. This can happen when each level
of the rack is treated as an independent line, reducing the
space of the aisles. Radio shuttle can do the job of transferring the pallets to the empty location from the first position of the line.
Forklifts cause many accidents while transferring the
pallets from one point to another. The damage is huge and
occurs when a full stack collapses because of a small accident. This rack system minimizes human involvement by
taking the pallets from one point and storing them. When
the pallets are ready for shipping, it also moves them to
the shipping forklift, so there is no need for forklifts to
move between the pallets.
The speed of filling a truck or removing stock is critical
to the warehouse. Governmental regulation allows the
trucks to move in the streets only for a specific time, so
trucks need to be filled in a short time and as fast as possible. In this study, the radio shuttle cut the distance for
the forklift to move inside the warehouse.
In order to assess the advantage of the proposed solution, we measured the distance and speed in the current
situation and compared these figures to those obtained in
the proposed situation. This comparison was conducted to
help the company decide whether the improvements provided by the new system are worth the changes required.
The distance was calculated for all storage lines. Each line
has a number of pallet positions. In the proposed situation, the distance traveled by forklift through the lines was
done by the radio shuttle to save forklift time. The distance
from each pallet position to the aisle was calculated. The
overall saved distance was about 242 km. The average
maximum speed of the forklift inside the warehouse is
10 km/h, while the forklift speed inside the line is 5 km/h.
The radio shuttle cut the time required for the forklift to
move inside the line, saving about 48 working hours. This
impacted the total load, which was minimized as the forklift travel distance was reduced. In addition, this choice
increased space utilization as it both reduced the space
requirements and increased the freedom of using the pallets’ position levels (height), which are currently forced to
store only one SKU in a storage line.
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7 Conclusions
Warehouses have complex integrated systems that must be
kept in mind when attempting to make changes. Reducing
the loading time, which includes the put-away process and
the shipping process, requires rearranging the warehouse.
The warehouse arrangement indicates the position of each
SKU. With the random storage policy, low demand items
may be stored in a place where there are high demand
items, which will lead to unnecessary time in order picking. Moreover, when space is limited, increasing utilization
becomes very important to save costs. In this study, the
sugar company was struggling and needed some solutions.
Our analysis indicated that three fundamental problems
affected the loading time: layout, SKU arrangement, and process time. All these problems affected the loading time in a way
that can be modified. The layout had some issues with the input
and output gates as well as the height of the block: only three
pallets were stacked, though there was room to increase it to
four pallets. The second problem was the haphazard arrangement of SKUs inside the warehouse, which we arranged according to demand. The last problem was with the process that the
product follows from receiving to shipping, which needed to
be simplified to reduce the time in the process.
Different tools were used in this research to investigate
and simplify the process. The use of Microsoft Excel was
implemented to track the process through the control charts.
Microsoft Excel was also used to determine the COI index and
build the ABC analysis. The last tool used was the AHP, which
was built and designed using the same Microsoft software.
One of the proposed solutions in this paper was to rearrange the SKUs and change the current storage policy in
the warehouse to reduce the travel distance in order picking. The literature on SLAP and different storage policies
was reviewed, which included comparisons between different storage policies. On the basis of this review, a classbased storage policy was proposed in the warehouse as an
improvement over its current random storage policy. ABC
analysis was used to classify the SKUs based on picking
| https://doi.org/10.1007/s42452-022-05230-6
frequency. In addition, different analytical formulas were
established (based on the literature review) to compute
the total travel distance and travel time under both policies. The results showed that a saving of approximately
50 h per month could be gained, accounting for 22.18% of
the total travel time under the random policy.
The next solution in this paper was to choose the best
rack system among different types in order to redesign the
warehouse. The type chosen was selected on the basis of
the criteria important to the company. The AHP technique
was also used to find the highest score among the racking
scores, taking into consideration these criteria. Compared
to the current system, the proposed radio shuttle saved
242 km of unnecessary distance, which translates into
about 48 working hours per month.
The work was carried out with known input data in
order picking and shipping process in the sugar industry,
and the proposed method applied to a fixed layout with
basic parameters was only considered for evaluation. The
work can be extended with fuzzy AHP to make better decisions in the order picking process.
Funding The author received no financial support for this article’s
research, authorship, and/or publication.
Data availability The data supporting this study’s findings are available from the corresponding author upon reasonable request.
Declarations
Conflict of interest The author declares that he has no conflict of interest.
Ethical approval This article does not contain any studies with human
participants or animals performed by the author.
Appendix
See Tables 8, 9, 10, 11, 12, 13, 14, 15, 16, 17.
Table 8 Comparison between the alternatives in term of cost
Cost
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Sum
1
1/2
1/9
1/6
1
1/4
1
4
2
1
1/8
1/5
2
1/2
3
8 5/6
9
8
1
5
9
7
9
48
6
5
1/5
1
6
4
5
27 1/5
1
1/2
1/9
1/6
1
1/3
1
4 1/9
4
2
1/7
1/4
3
1
4
14 2/5
1
1/3
1/9
1/5
1
1/4
1
3 8/9
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Table 9 Normalized matrix for the cost criterion
Cost
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Weights
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Sum
36/145
18/145
4/145
6/145
36/145
9/145
36/145
1.00
80/353
40/353
5/353
8/353
80/353
20/353
20/353
1.00
3/16
1/6
1/48
5/48
3/16
7/48
3/16
1.00
15/68
25/136
1/136
5/136
15/68
5/34
25/136
1.00
9/37
9/74
1/37
3/74
9/37
3/37
9/37
1.00
12/403
56/403
4/403
7/403
84/403
28/403
112/403
1.00
180/701
60/701
20/701
36/701
180/701
45/701
180/701
1.00
23.73%
13.34%
1.93%
4.49%
22.73%
8.95%
24.82%
Table 10 Comparison between the alternatives in term of utilization
Utilization
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Sum
1
3
1/5
3
1
8
2
18 1/5
1/3
1
1/7
1
1/3
7
1
10 4/5
5
7
1
7
5
9
6
40
1/3
1
1/7
1
1/3
7
1
10 4/5
1
3
1/5
3
1
8
2
18 1/5
1/8
1/7
1/9
1/7
1/8
1
1/7
1 4/5
1/2
1
1/6
1
1/2
7
1
11 1/6
Table 11 Normalized matrix for the utilization criterion
Utilization
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Weights
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Sum
5/91
15/91
1/91
15/91
5/91
40/91
10/91
1
7/227
21/227
3/227
21/227
7/227
47/227
21/227
1
1/8
7/40
1/40
7/40
1/8
9/40
3/20
1
7/227
21/227
3/227
21/227
7/227
47/227
21/227
1
5/91
15/91
1/91
15/91
5/91
40/91
10/91
1
63/902
36/451
28/451
36/451
63/902
252/451
36/451
1
3/67
6/67
1/67
6/67
3/67
42/67
6/67
1
5.87%
12.27%
2.15%
12.27%
5.87%
51.21%
10.35%
Table 12 Comparison between the alternatives in term of load accessibility
Load accessibility
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Sum
1
1/5
1/6
5
1
5
1
13 3/8
5
1
1
8
5
9
5
34
6
1
1
7
5
9
5
34
1/5
1/8
1/7
1
1/4
2
1/4
4
1
1/5
1/5
4
1
5
1/2
12
1/5
1/9
1/9
1/2
1/5
1
1/7
2 1/4
1
1/5
1/5
4
2
7
1
15 2/5
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Table 13 Normalized matrix for the load accessibility criterion
Load accessibility
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Weights
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Sum
30/401
6/401
5/401
150/401
30/401
150/401
30/401
1
5/34
1/34
1/34
4/17
5/34
9/34
5/34
1
3/17
1/34
1/34
7/34
5/34
9/34
5/34
1
25/496
4/127
9/250
249/988
39/619
249/494
39/619
1
10/119
2/119
2/119
40/119
10/119
50/119
5/119
1
83/940
13/265
13/265
83/376
83/940
83/188
7/111
1
5/77
1/77
1/77
20/77
10/77
5/11
5/77
1
9.80%
2.63%
2.66%
26.91%
10.49%
38.91%
8.60%
Table 14 Comparison between the alternatives in term of stock cycle speed
Stock cycle speed
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Sum
1
1/3
1/5
3
1
7
1
13 1/2
3
1
1/2
7
3
8
3
25 1/2
5
2
1
8
5
9
5
35
1/3
1/7
1/8
1
1/3
7
3
12
1
1/3
1/5
1/3
1
7
1
10 7/8
1/7
1/8
1/9
1/7
1/7
1
1/7
1 4/5
1
1/3
1/5
1/3
1
7
1
10 7/8
Table 15 Normalized matrix for the stock cycle speed criterion
Stock cycle speed
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
Weights
Selective
Drive-in
Push Back
Pallet Flow
Double-Reach
Radio Shuttle
Ground Storage
Sum
15/203
5/203
3/203
45/203
15/203
15/29
15/203
1
2/17
2/51
1/51
14/51
2/17
16/51
2/17
1
1/7
2/35
1/35
8/35
1/7
9/35
1/7
1
5/179
11/919
2/191
61/728
5/179
61/104
229/911
1
15/163
5/163
3/163
5/163
15/163
105/163
15/163
1
72/911
63/911
56/911
72/911
72/911
504/911
72/911
1
15/163
5/163
3/163
5/163
15/163
105/163
15/163
1
8.93%
3.76%
2.45%
13.56%
8.93%
50.23%
12.13%
Table 16 AHP final score
Criteria
Cost
Utilization
Load accessibility
Stock cycle speed
Weight
Alternatives
Selective
Drive-in
Push back
Pallet flow
Double-reach
Radio shuttle
Ground storage
0.04
0.57
0.17
0.22
0.24
0.13
0.02
0.04
0.23
0.09
0.25
0.06
0.12
0.02
0.12
0.06
0.51
0.10
0.10
0.03
0.03
0.27
0.10
0.39
0.09
0.09
0.04
0.02
0.14
0.09
0.50
0.12
Vol:.(1234567890)
Final score (%)
7.98
8.82
2.29
14.67
8.06
47.11
11.08
% Items
3.1
6.3
9.4
12.5
15.6
18.8
21.9
25.0
28.1
31.3
34.4
37.5
40.6
43.8
46.9
50.0
53.1
56.3
59.4
62.5
65.6
68.8
71.9
75.0
78.1
81.3
84.4
87.5
90.6
93.8
96.9
100.0
SKU ID
6
10
21
1
11
5
22
9
12
20
29
14
19
4
18
28
40
26
24
23
31
13
27
30
17
15
8
25
2
7
16
3
Total
11,002
5738
4890
4109
2173
1665
1230
1229
1221
849
849
544
544
315
313
310
309
305
147
141
136
135
134
134
132
130
127
126
122
112
40
30
39,241
Demand (picking frequency)
Table 17 SKUs full data
18
13
12
11
8
7
6
6
6
5
5
4
4
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
1
1
146
Storage lines
1296
936
864
792
576
504
432
432
432
360
360
288
288
216
216
216
216
216
144
144
144
144
144
144
144
144
144
144
144
144
72
72
10,512
Pallet positions
12.33
8.90
8.22
7.53
5.48
4.79
4.11
4.11
4.11
3.42
3.42
2.74
2.74
2.05
2.05
2.05
2.05
2.05
1.37
1.37
1.37
1.37
1.37
1.37
1.37
1.37
1.37
1.37
1.37
1.37
0.68
0.68
100.00
12.33
21.23
29.45
36.99
42.47
47.26
51.37
55.48
59.59
63.01
66.44
69.18
71.92
73.97
76.03
78.08
80.14
82.19
83.56
84.93
86.30
87.67
89.04
90.41
91.78
93.15
94.52
95.89
97.26
98.63
99.32
100.00
Pallet positions Cum. pallet posipercentage
tion percentage
28.04
14.62
12.46
10.47
5.54
4.24
3.13
3.13
3.11
2.16
2.16
1.39
1.39
0.80
0.80
0.79
0.79
0.78
0.37
0.36
0.35
0.34
0.34
0.34
0.34
0.33
0.32
0.32
0.31
0.29
0.10
0.08
100.00
Movements
percentage
28.04
42.66
55.12
65.59
71.13
75.37
78.51
81.64
84.75
86.91
89.08
90.46
91.85
92.65
93.45
94.24
95.03
95.81
96.18
96.54
96.89
97.23
97.57
97.91
98.25
98.58
98.90
99.23
99.54
99.82
99.92
100.00
Cum. movements percentage
0.118
0.163
0.177
0.193
0.265
0.303
0.351
0.352
0.354
0.424
0.424
0.529
0.529
0.686
0.690
0.697
0.699
0.708
0.980
1.021
1.059
1.067
1.075
1.075
1.091
1.108
1.134
1.143
1.180
1.286
1.800
2.400
25.08
COI index
0.47
0.65
0.70
0.77
1.06
1.21
1.40
1.40
1.41
1.69
1.69
2.11
2.11
2.73
2.75
2.78
2.79
2.82
3.91
4.07
4.22
4.25
4.29
4.29
4.35
4.42
4.52
4.56
4.71
5.13
7.18
9.57
100.00
COI index
percentage
0.47
1.12
1.82
2.59
3.65
4.86
6.26
7.66
9.07
10.76
12.45
14.56
16.67
19.41
22.16
24.94
27.73
30.55
34.46
38.53
42.75
47.00
51.29
55.57
59.92
64.34
68.86
73.42
78.13
83.25
90.43
100.00
Cum. COI
index percentage
SN Applied Sciences
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