Revista Facultad de Ingeniería, Universidad de Antioquia, No.111, pp. 88-104, Apr-Jun 2024
Including operational costs in warehouse
location problems: A case study in USA
La importancia de incluir los costos operacionales en la localización de centros de
distribution: un caso de estudio en USA
Luis Felipe Cardona 1*Leonardo Rivera-Cadavid 2
1Logistics and Distribution Institute, University of Louisville. 2301 S 3rd. Louisville. KY 40292, United States.
2Departamento de Ingeniería Industrial, Universidad del Valle. Calle 13 # 100-00. Cali, Colombia.
CITE THIS ARTICLE AS:
L. F. Cardona and L.
Rivera-Cadavid. ”Including
operational costs in
warehouse location problems:
A case study in USA”, Revista
Facultad de Ingeniería
Universidad de Antioquia, no.
111, pp. 88-104, Apr-Jun 2024.
[Online]. Available: https:
//doi.org/10.17533/udea.
redin.20231132
ARTICLE INFO:
Received: August 29, 2022
Accepted: November 14, 2023
Available online: November
14, 2023
KEYWORDS:
Economic evaluation; Systems
design, Optimization,
Management operations
Valoración económica; Diseño
de sistemas; Optimización;
Operación administrativa
ABSTRACT: There is vast research on particular aspects of warehouse design - layout,
material handling, order picking, and operating policies - when, in fact, the decisions
involved in the process are interrelated. In this paper, we develop an engineering
economics framework for warehouse operations that decomposes the cost structure of
the operation and lays out the relationships between them. Our framework decomposes
operational costs into four exogenous characteristics (wages, leasing costs, cost of
capital, and access to technology) that depend on the geographic location of the
warehouse and two operational requirements (throughput and storage capacity). Using
these six parameters, and publicly available information, practitioners can estimate the
total operational cost of the warehouse for potential locations in facility location analysis,
which have been traditionally limited to transportation costs. In our case study, we
use our framework to establish a rank of preferable warehouse locations in terms of
operational costs among logistics clusters in the United States of America.
RESUMEN: Existe una amplia literatura sobre elementos del diseño de almacenes
(distribución, manejo de materiales, selección de pedidos y políticas operativas) de
manera independiente. Sin embargo, las decisiones involucradas en el proceso están
interrelacionadas. En este artículo, desarrollamos un marco de ingeniería económica
para operaciones de almacén que descompone la estructura de costos de la operación y
establece las relaciones entre ellos. Nuestro marco descompone los costos operativos
en cuatro características exógenas (salarios, arrendamiento, costo del capital y acceso
a la tecnología) que dependen de la ubicación geográfica del almacén y dos requisitos
operativos (capacidad de procesamiento y capacidad de almacenamiento). Usando
estos seis parámetros e información pública, los profesionales pueden estimar el costo
operativo total del almacén para ubicaciones potenciales en el análisis de localización
de instalaciones.
En nuestro caso de estudio, usamos nuestro marco
para establecer un ranking de ubicaciones preferibles
en términos de costos operativos entre los clústeres de
logística en los EE. UU.
1. Introduction
Warehousing is around 22 percent of logistics costs;
therefore, managers are under constant pressure to
reduce costs [1]. There are many interdependent decisions
in warehouse design, which make the optimization of its
process a complex task [2]. In practice, the efforts are often
focused on one cost driver regardless of the interrelations
between warehousing activities [3]. Traditional approaches
“fail to support a joint decision-making process in
warehouse location selection.” [4]
”The risk is the tragedy of the commons effect, where a
positive return for economic activities in isolation could
lead to a negative collective result in the long-term.
Therefore, there is a need to elevate the systemic view of
logistics to the macroeconomic realm” [3]. In the absence
of a comprehensive scientific method [5], practitioners
“must consider complex trade-offs, many of which are not
88
* Corresponding author: Luis Felipe Cardona
E-mail: luis.cardonaolarte@louisville.edu
ISSN 0120-6230
e-ISSN 2422-2844
DOI: 10.17533/udea.redin.20231132
88
Including operational costs in warehouse
location problems: A case study in USA
La importancia de incluir los costos operacionales en la localización de centros de
distribution: un caso de estudio en USA
Luis Felipe Cardona 1*Leonardo Rivera-Cadavid 2
1Logistics and Distribution Institute, University of Louisville. 2301 S 3rd. Louisville. KY 40292, United States.
2Departamento de Ingeniería Industrial, Universidad del Valle. Calle 13 # 100-00. Cali, Colombia.
CITE THIS ARTICLE AS:
L. F. Cardona and L.
Rivera-Cadavid. ”Including
operational costs in
warehouse location problems:
A case study in USA”, Revista
Facultad de Ingeniería
Universidad de Antioquia, no.
111, pp. 88-104, Apr-Jun 2024.
[Online]. Available: https:
//doi.org/10.17533/udea.
redin.20231132
ARTICLE INFO:
Received: August 29, 2022
Accepted: November 14, 2023
Available online: November
14, 2023
KEYWORDS:
Economic evaluation; Systems
design, Optimization,
Management operations
Valoración económica; Diseño
de sistemas; Optimización;
Operación administrativa
ABSTRACT: There is vast research on particular aspects of warehouse design - layout,
material handling, order picking, and operating policies - when, in fact, the decisions
involved in the process are interrelated. In this paper, we develop an engineering
economics framework for warehouse operations that decomposes the cost structure of
the operation and lays out the relationships between them. Our framework decomposes
operational costs into four exogenous characteristics (wages, leasing costs, cost of
capital, and access to technology) that depend on the geographic location of the
warehouse and two operational requirements (throughput and storage capacity). Using
these six parameters, and publicly available information, practitioners can estimate the
total operational cost of the warehouse for potential locations in facility location analysis,
which have been traditionally limited to transportation costs. In our case study, we
use our framework to establish a rank of preferable warehouse locations in terms of
operational costs among logistics clusters in the United States of America.
RESUMEN: Existe una amplia literatura sobre elementos del diseño de almacenes
(distribución, manejo de materiales, selección de pedidos y políticas operativas) de
manera independiente. Sin embargo, las decisiones involucradas en el proceso están
interrelacionadas. En este artículo, desarrollamos un marco de ingeniería económica
para operaciones de almacén que descompone la estructura de costos de la operación y
establece las relaciones entre ellos. Nuestro marco descompone los costos operativos
en cuatro características exógenas (salarios, arrendamiento, costo del capital y acceso
a la tecnología) que dependen de la ubicación geográfica del almacén y dos requisitos
operativos (capacidad de procesamiento y capacidad de almacenamiento). Usando
estos seis parámetros e información pública, los profesionales pueden estimar el costo
operativo total del almacén para ubicaciones potenciales en el análisis de localización
de instalaciones.
En nuestro caso de estudio, usamos nuestro marco
para establecer un ranking de ubicaciones preferibles
en términos de costos operativos entre los clústeres de
logística en los EE. UU.
1. Introduction
Warehousing is around 22 percent of logistics costs;
therefore, managers are under constant pressure to
reduce costs [1]. There are many interdependent decisions
in warehouse design, which make the optimization of its
process a complex task [2]. In practice, the efforts are often
focused on one cost driver regardless of the interrelations
between warehousing activities [3]. Traditional approaches
“fail to support a joint decision-making process in
warehouse location selection.” [4]
”The risk is the tragedy of the commons effect, where a
positive return for economic activities in isolation could
lead to a negative collective result in the long-term.
Therefore, there is a need to elevate the systemic view of
logistics to the macroeconomic realm” [3]. In the absence
of a comprehensive scientific method [5], practitioners
“must consider complex trade-offs, many of which are not
88
* Corresponding author: Luis Felipe Cardona
E-mail: luis.cardonaolarte@louisville.edu
ISSN 0120-6230
e-ISSN 2422-2844
DOI: 10.17533/udea.redin.20231132
88
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
yet fully articulated”, and “based on intuition, experience,
and judgment, make some initial design decisions about
the overall system architecture” [5]. Much of current
order-picking operations and warehousing practice relies
on rules-of-thumb [6–11].
Even if [2] and [12] provide more comprehensive
approaches to the warehouse design problem, there
are still many complex trade-offs that the designer needs
to face. When deciding the number of cross aisles in a
traditional warehouse layout, scientific methods focus on
minimizing the travel time of pickers [13–15], when, in
fact, there is a trade-off between labor costs and space
costs. The more cross aisles a warehouse has, the lower
the travel times, but also the lower the space utilization.
Non-traditional geometries for unit-load warehouses
use diagonal aisles to achieve 20% lower travel times
[16]; however, their storage density is lower, requiring
about 5% more footprint for the same number of storage
locations. Slotting strategies are primarily focused on
minimizing material handling costs [17]. Allocating
high-moving products to prime slots without consideration
of dimensional data can create wasted space within
slots, which in turn affects the space costs. Warehouse
operational costs are already affecting the industrial
real estate in Los Angeles, CA [18], where companies
moved their warehouse to urban periphery areas, because
“the gains from lower land prices and scale operation
outweigh the increase in transport costs.” Parallel, there
are many qualitative factors to consider when choosing
a warehouse location in dynamic environments. When
there are significant qualitative business drivers, experts
resort to multi-criteria decision-making methods [19, 20].
However, assessing the importance of the objectives
relative to each other is challenging [21], because the
decision factors are subjective, vague, and imprecise
[22, 23]. Alternatively, [24] discusses methods aimed at
maximizing decision-makers’ satisfaction using utility
functions from qualitative and quantitative factors.
Trade-offs are also common with other functional areas.
When procurement responsibilities are independent
of warehouse management, it is often the case that
purchasing decisions are made regardless of storage
capacity. Procurement is interested in reducing costs per
unit, buying in bulk without considering holding costs [25].
Another example comes from marketing strategies, where
service agreements with clients are changed without
consideration for their impacts on inventory levels and
throughput requirements of the warehouse [26]. We hope
these examples show the pitfalls of focusing on one cost
driver, while ignoring the impact on others.
All warehouses incur similar costs, but they are considered
differently from company to company [27]. Speh
[27] proposes a cost structure based on functions:
handling, storage, operations administration, and
general administrative expenses. However, to show
the interrelation of design decisions, we find it more
convenient to have a cost structure based on cost drivers,
because the sources of costs are easier to differentiate for
resources with multiple functions. Ackerman [28] provides
the most comprehensive framework of warehouse costs up
to date based on cost drivers. It brakes down operational
costs on costs of goods at rest and costs of goods in
motion. Then, the author establishes the effects of labor,
equipment, inventory, and layout on the total operational
costs. [28] provides great detail on the cost structure,
whereas we focus on the interrelations between these
costs drivers. This paper aims to assist practitioners
in making warehouse design decisions, considering the
trade-offs between critical resources of the operation. We
provide a systematic method to assess the total operation
cost in terms of four cost drivers: labor, space, working
capital, and equipment; and a system dynamics model
to describe the interrelation between warehouse design
decisions and their impact on the total operational cost.
Figure 1 shows the calculation of the total cost presented
in the sections below. Here, we color the leaves of the
diagram depending on whether they are direct decisions
of the warehouse designer (green) or are better modeled
as external parameters (gray). All arrows in Figures 1–6
are positive in direction; that is, the higher the salary, the
higher the cost per worker, and so on.
Traditional warehouse location problems focus on
transportation costs [29, 30] or service level [31–33]. In
supply chain network designs, it is common that models
include a fixed cost of opening a distribution center in
a location [29, 34, 35] or consider just the cost of land
[22, 36], but the operational costs of varying the volume
among the distribution center are not accounted for.
Szeremeta-Spak [37] consider other criteria, such as
taxes, land availability, and organizational strategies. And
there are some location problems, mono-objective focus
on the industry and the product being distributed (such as
solid waste management [38]), regardless of costs.
Total operational costs are rarely a criteria in warehouse
location problems in the literature [39]. The main
contribution of this paper is to highlight the importance
of including operational costs in warehouse location
decisions in the context of logistics clusters. We
decompose the operational costs into two categories of
cost drivers. Cost drivers related to the geographic
location of the warehouse (wages, leasing costs, cost
of capital, and access to technology) and cost drivers
associated with the operational requirements (throughput
and storage capacity). Warehouse designers require an
overall perspective of warehouse costs to optimize the
processes within. This with the goal of highlighting
the great impact of the warehouse location on the
operational costs. Understanding the cost drivers and
89
yet fully articulated”, and “based on intuition, experience,
and judgment, make some initial design decisions about
the overall system architecture” [5]. Much of current
order-picking operations and warehousing practice relies
on rules-of-thumb [6–11].
Even if [2] and [12] provide more comprehensive
approaches to the warehouse design problem, there
are still many complex trade-offs that the designer needs
to face. When deciding the number of cross aisles in a
traditional warehouse layout, scientific methods focus on
minimizing the travel time of pickers [13–15], when, in
fact, there is a trade-off between labor costs and space
costs. The more cross aisles a warehouse has, the lower
the travel times, but also the lower the space utilization.
Non-traditional geometries for unit-load warehouses
use diagonal aisles to achieve 20% lower travel times
[16]; however, their storage density is lower, requiring
about 5% more footprint for the same number of storage
locations. Slotting strategies are primarily focused on
minimizing material handling costs [17]. Allocating
high-moving products to prime slots without consideration
of dimensional data can create wasted space within
slots, which in turn affects the space costs. Warehouse
operational costs are already affecting the industrial
real estate in Los Angeles, CA [18], where companies
moved their warehouse to urban periphery areas, because
“the gains from lower land prices and scale operation
outweigh the increase in transport costs.” Parallel, there
are many qualitative factors to consider when choosing
a warehouse location in dynamic environments. When
there are significant qualitative business drivers, experts
resort to multi-criteria decision-making methods [19, 20].
However, assessing the importance of the objectives
relative to each other is challenging [21], because the
decision factors are subjective, vague, and imprecise
[22, 23]. Alternatively, [24] discusses methods aimed at
maximizing decision-makers’ satisfaction using utility
functions from qualitative and quantitative factors.
Trade-offs are also common with other functional areas.
When procurement responsibilities are independent
of warehouse management, it is often the case that
purchasing decisions are made regardless of storage
capacity. Procurement is interested in reducing costs per
unit, buying in bulk without considering holding costs [25].
Another example comes from marketing strategies, where
service agreements with clients are changed without
consideration for their impacts on inventory levels and
throughput requirements of the warehouse [26]. We hope
these examples show the pitfalls of focusing on one cost
driver, while ignoring the impact on others.
All warehouses incur similar costs, but they are considered
differently from company to company [27]. Speh
[27] proposes a cost structure based on functions:
handling, storage, operations administration, and
general administrative expenses. However, to show
the interrelation of design decisions, we find it more
convenient to have a cost structure based on cost drivers,
because the sources of costs are easier to differentiate for
resources with multiple functions. Ackerman [28] provides
the most comprehensive framework of warehouse costs up
to date based on cost drivers. It brakes down operational
costs on costs of goods at rest and costs of goods in
motion. Then, the author establishes the effects of labor,
equipment, inventory, and layout on the total operational
costs. [28] provides great detail on the cost structure,
whereas we focus on the interrelations between these
costs drivers. This paper aims to assist practitioners
in making warehouse design decisions, considering the
trade-offs between critical resources of the operation. We
provide a systematic method to assess the total operation
cost in terms of four cost drivers: labor, space, working
capital, and equipment; and a system dynamics model
to describe the interrelation between warehouse design
decisions and their impact on the total operational cost.
Figure 1 shows the calculation of the total cost presented
in the sections below. Here, we color the leaves of the
diagram depending on whether they are direct decisions
of the warehouse designer (green) or are better modeled
as external parameters (gray). All arrows in Figures 1–6
are positive in direction; that is, the higher the salary, the
higher the cost per worker, and so on.
Traditional warehouse location problems focus on
transportation costs [29, 30] or service level [31–33]. In
supply chain network designs, it is common that models
include a fixed cost of opening a distribution center in
a location [29, 34, 35] or consider just the cost of land
[22, 36], but the operational costs of varying the volume
among the distribution center are not accounted for.
Szeremeta-Spak [37] consider other criteria, such as
taxes, land availability, and organizational strategies. And
there are some location problems, mono-objective focus
on the industry and the product being distributed (such as
solid waste management [38]), regardless of costs.
Total operational costs are rarely a criteria in warehouse
location problems in the literature [39]. The main
contribution of this paper is to highlight the importance
of including operational costs in warehouse location
decisions in the context of logistics clusters. We
decompose the operational costs into two categories of
cost drivers. Cost drivers related to the geographic
location of the warehouse (wages, leasing costs, cost
of capital, and access to technology) and cost drivers
associated with the operational requirements (throughput
and storage capacity). Warehouse designers require an
overall perspective of warehouse costs to optimize the
processes within. This with the goal of highlighting
the great impact of the warehouse location on the
operational costs. Understanding the cost drivers and
89
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Figure 1 Total operational cost
their interrelations helps practitioners to determine the
technologies, processes, and equipment that will bring
the highest efficiency. This is important for practitioners,
because the operational costs are, to a large extent,
determined at the design phase [12, 40, 41]. Furthermore,
this framework supports managers when interacting with
other areas of the organization, so they can show the
impact on warehousing costs of purchasing policies,
marketing strategies, supplier selection, etc.
2. Operational cost of warehouses
We follow the framework presented by [1], where the
operational costs are categorized according to their
drivers. In this paper, the annual operational cost of
warehouses is based on:
1. Labor: It accounts for the salaries and extra
benefits of workers. The main activities that could
require labor in a warehouse are receiving, putting
away, replenishment, picking, packing/sorting and
shipping. Nevertheless, these activities can also be
performed by automated equipment. There are also
activities that require labor, such as cycle counting
and returns processing.
2. Space: the cost of space comes twofold. The rental
cost and the operational cost - utilities, infrastructure
amortizations. The main areas of the warehouse are:
receiving, bulk area, picking area, and shipping. There
can also be support areas like a battery station for
forklifts, returns processing, and maintenance room.
3. Equipment: the amortization and operational cost
of equipment like conveyors, industrial trucks, and
racks.
4. Working Capital: The financial cost of the money
invested in inventory.
We do not consider administrative expenses throughout
this work, because they are constant. Also, all expenses
here will be given in an annual basis. Costs such as
utilities, accounting, and cleaning of the facility are
annualized for our study. Therefore, we refer to annual
operational costs as operational costs and will do the
same for all other expenses. Management can control
the use of these four resources through the number of
workers, the number of overtime hours, the area, and
90
Figure 1 Total operational cost
their interrelations helps practitioners to determine the
technologies, processes, and equipment that will bring
the highest efficiency. This is important for practitioners,
because the operational costs are, to a large extent,
determined at the design phase [12, 40, 41]. Furthermore,
this framework supports managers when interacting with
other areas of the organization, so they can show the
impact on warehousing costs of purchasing policies,
marketing strategies, supplier selection, etc.
2. Operational cost of warehouses
We follow the framework presented by [1], where the
operational costs are categorized according to their
drivers. In this paper, the annual operational cost of
warehouses is based on:
1. Labor: It accounts for the salaries and extra
benefits of workers. The main activities that could
require labor in a warehouse are receiving, putting
away, replenishment, picking, packing/sorting and
shipping. Nevertheless, these activities can also be
performed by automated equipment. There are also
activities that require labor, such as cycle counting
and returns processing.
2. Space: the cost of space comes twofold. The rental
cost and the operational cost - utilities, infrastructure
amortizations. The main areas of the warehouse are:
receiving, bulk area, picking area, and shipping. There
can also be support areas like a battery station for
forklifts, returns processing, and maintenance room.
3. Equipment: the amortization and operational cost
of equipment like conveyors, industrial trucks, and
racks.
4. Working Capital: The financial cost of the money
invested in inventory.
We do not consider administrative expenses throughout
this work, because they are constant. Also, all expenses
here will be given in an annual basis. Costs such as
utilities, accounting, and cleaning of the facility are
annualized for our study. Therefore, we refer to annual
operational costs as operational costs and will do the
same for all other expenses. Management can control
the use of these four resources through the number of
workers, the number of overtime hours, the area, and
90
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
the selection of the material handling equipment. In this
section, we analyze the factors that affect the use of these
four resources.
Equation 1 shows the labor cost will be given by the
salaries, bonuses and overtime of the workforce.
LaborCost = ∑
i
∑
p
(Wip(Sip + Bip) + Oip Hip) (1)
where i represents the activity that workers are assigned
to (receiving, putting away, replenishment, picking,
packing, sorting, and shipping), p the period of time, Wip
the number of workers assigned to activity i during period
p, Sip the salary of a worker assigned to activity i during
period p, Bip the bonus and other benefits of a worker
assigned to activity i during period p, Oip number of
overtime hours of a worker assigned to activity i during
period p, and Hip the cost of an overtime hour of a worker
assigned to activity i during period p.
In the labor cost, the salaries, bonuses, and cost of an
overtime hour are determined mostly by the environment
of the warehouse and its location- minimum wage, payroll
taxes, and regulations. On the other hand, the number of
workers (Wi) and the number of hours of overtime (Oip)
are decisions of the warehouse manager. It is typical that
warehouse managers have absolute control over labor
staffing.
Figure 2 presents the analysis of the most typical
activities - receiving, putting away, replenishment, picking,
packing/sorting, and shipping - that require labor in a
warehouse. The activities in dark red require labor hours.
In light gray, we present the process design decisions that
will define the labor requirements of these activities and
that can be directly controlled by the warehouse manager.
And, the factors in green are those that affect the labor
hour requirements, but are external to the process decision
of the warehouse operation.
The support activities vary depending on the material
handling equipment and level of IT support of the
warehouse. We are referring to activities such as cycle
counting, handling of returns, inbound inspections and the
maintenance of equipment and work areas.
The space cost is fixed, and it depends on the lease and
the operational cost. The lease cost is mostly settled by
the location of the warehouse but it can also be affected by
building characteristics such as ceiling height, docks, roof
structure, etc. Equation 2 calculates the space cost, where
Lease is the marginal cost per square foot for leasing
the space, BuildingOperational is the fixed operational
cost for using the space per square foot (utilities, taxes,
cleaning fees, etc), and Area is the area of the warehouse.
SpaceCost = (Lease + BuildingOperational)Area
(2)
Figure 3 shows the use of space for warehouse operations.
The areas that occupy warehouse space are presented in
dark red. The warehouse design decisions that affect these
areas are light gray. The factors in green are those that
affect the labor hour requirements, but that are not defined
in the design process of the warehouse.
Equation 3 calculates the equipment cost as the sum of the
depreciation Dk of each equipment k plus its operational
cost EOk (e.g., maintenance or charging batteries).
EquipmmentCost = ∑
k
(Dk + EOk) (3)
In the equipment cost (Figure 4), the prices of material
handling equipment - racks, conveyors, forklifts, cranes,
palletizers, etc - are determined by suppliers, but the
warehouse designer selects what equipment to use. The
selection of the material handling equipment will fully
determine the equipment in the warehouse because it is
a design decision in and of itself.
The working capital cost accounts for the opportunity cost
of holding inventory. Equation 4 calculates it as the average
working capital times - the opportunity cost interest rate
(r) as follows:
W orkingCapital =
∑
j
∑
p
Ijp Cj
P
r, (4)
where Ijp is the inventory level of product j in period p,
Cj is the cost of product j, and P is the number of periods
considered.
In the working capital cost, the cost of products is defined
by the manufacturing process and the cost of materials
purchased from the suppliers, both out of the purview of
the warehouse designer. On the other hand, the inventory
levels are, in most cases, under the umbrella of the
logistics division of the company, even if they can not be
decided solely by the warehouse manager. Figure 5 shows
how the working capital is defined by purchasing policies,
customer orders, and product cost.
Figure 6 shows the relationships between warehouse
design decisions (green), external parameters (light gray),
and resources (dark red) to determine the total operational
cost of a warehouse.
The customer orders trigger the process and, in general
terms, are out of the control of the warehouse designer,
as well as the SKU dimensional data, the cost of
resources, and the products’ care requirements (light
gray). Considering these parameters, the warehouse
designer provides labor Staffing, designs the process,
designs the material handling system, selects purchasing
91
the selection of the material handling equipment. In this
section, we analyze the factors that affect the use of these
four resources.
Equation 1 shows the labor cost will be given by the
salaries, bonuses and overtime of the workforce.
LaborCost = ∑
i
∑
p
(Wip(Sip + Bip) + Oip Hip) (1)
where i represents the activity that workers are assigned
to (receiving, putting away, replenishment, picking,
packing, sorting, and shipping), p the period of time, Wip
the number of workers assigned to activity i during period
p, Sip the salary of a worker assigned to activity i during
period p, Bip the bonus and other benefits of a worker
assigned to activity i during period p, Oip number of
overtime hours of a worker assigned to activity i during
period p, and Hip the cost of an overtime hour of a worker
assigned to activity i during period p.
In the labor cost, the salaries, bonuses, and cost of an
overtime hour are determined mostly by the environment
of the warehouse and its location- minimum wage, payroll
taxes, and regulations. On the other hand, the number of
workers (Wi) and the number of hours of overtime (Oip)
are decisions of the warehouse manager. It is typical that
warehouse managers have absolute control over labor
staffing.
Figure 2 presents the analysis of the most typical
activities - receiving, putting away, replenishment, picking,
packing/sorting, and shipping - that require labor in a
warehouse. The activities in dark red require labor hours.
In light gray, we present the process design decisions that
will define the labor requirements of these activities and
that can be directly controlled by the warehouse manager.
And, the factors in green are those that affect the labor
hour requirements, but are external to the process decision
of the warehouse operation.
The support activities vary depending on the material
handling equipment and level of IT support of the
warehouse. We are referring to activities such as cycle
counting, handling of returns, inbound inspections and the
maintenance of equipment and work areas.
The space cost is fixed, and it depends on the lease and
the operational cost. The lease cost is mostly settled by
the location of the warehouse but it can also be affected by
building characteristics such as ceiling height, docks, roof
structure, etc. Equation 2 calculates the space cost, where
Lease is the marginal cost per square foot for leasing
the space, BuildingOperational is the fixed operational
cost for using the space per square foot (utilities, taxes,
cleaning fees, etc), and Area is the area of the warehouse.
SpaceCost = (Lease + BuildingOperational)Area
(2)
Figure 3 shows the use of space for warehouse operations.
The areas that occupy warehouse space are presented in
dark red. The warehouse design decisions that affect these
areas are light gray. The factors in green are those that
affect the labor hour requirements, but that are not defined
in the design process of the warehouse.
Equation 3 calculates the equipment cost as the sum of the
depreciation Dk of each equipment k plus its operational
cost EOk (e.g., maintenance or charging batteries).
EquipmmentCost = ∑
k
(Dk + EOk) (3)
In the equipment cost (Figure 4), the prices of material
handling equipment - racks, conveyors, forklifts, cranes,
palletizers, etc - are determined by suppliers, but the
warehouse designer selects what equipment to use. The
selection of the material handling equipment will fully
determine the equipment in the warehouse because it is
a design decision in and of itself.
The working capital cost accounts for the opportunity cost
of holding inventory. Equation 4 calculates it as the average
working capital times - the opportunity cost interest rate
(r) as follows:
W orkingCapital =
∑
j
∑
p
Ijp Cj
P
r, (4)
where Ijp is the inventory level of product j in period p,
Cj is the cost of product j, and P is the number of periods
considered.
In the working capital cost, the cost of products is defined
by the manufacturing process and the cost of materials
purchased from the suppliers, both out of the purview of
the warehouse designer. On the other hand, the inventory
levels are, in most cases, under the umbrella of the
logistics division of the company, even if they can not be
decided solely by the warehouse manager. Figure 5 shows
how the working capital is defined by purchasing policies,
customer orders, and product cost.
Figure 6 shows the relationships between warehouse
design decisions (green), external parameters (light gray),
and resources (dark red) to determine the total operational
cost of a warehouse.
The customer orders trigger the process and, in general
terms, are out of the control of the warehouse designer,
as well as the SKU dimensional data, the cost of
resources, and the products’ care requirements (light
gray). Considering these parameters, the warehouse
designer provides labor Staffing, designs the process,
designs the material handling system, selects purchasing
91
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Figure 2 Labor
policies, and a warehouse layout. As Figure 6 shows, all
those decisions affect multiple cost factors and should be
considered jointly.
On the other hand, purchasing policies determine how the
warehouse places orders to its suppliers. It will affect the
inventory levels, and it will also define the arrival profile
of products to the receiving area (Figure 3). It is often
the case that purchasing departments are measured by
the prices they can negotiate when purchasing items,
and thus, acquire inventory without consideration of
warehouse capacity or inventory turns, which leads to
savings in working capital being paid by space and labor
costs, when you consider the overall picture.
The process design is the design of the flow of products
in the warehouse and the operation of each functional
area. The warehouse designer faces multiple trade-offs
in selecting the right balance between automation,
technology, and labor costs. Depending on the accessibility
to automation and wages, designers can choose from
labor intensive process to highly automated facilities and
anything in between.
Table 1 presents all the factors that affect the total
operational cost of a warehouse segregated in resources
- which administration is a direct responsibility of the
warehouse designer - and the cost of resources - out of
the control of warehouse designers.
92
Figure 2 Labor
policies, and a warehouse layout. As Figure 6 shows, all
those decisions affect multiple cost factors and should be
considered jointly.
On the other hand, purchasing policies determine how the
warehouse places orders to its suppliers. It will affect the
inventory levels, and it will also define the arrival profile
of products to the receiving area (Figure 3). It is often
the case that purchasing departments are measured by
the prices they can negotiate when purchasing items,
and thus, acquire inventory without consideration of
warehouse capacity or inventory turns, which leads to
savings in working capital being paid by space and labor
costs, when you consider the overall picture.
The process design is the design of the flow of products
in the warehouse and the operation of each functional
area. The warehouse designer faces multiple trade-offs
in selecting the right balance between automation,
technology, and labor costs. Depending on the accessibility
to automation and wages, designers can choose from
labor intensive process to highly automated facilities and
anything in between.
Table 1 presents all the factors that affect the total
operational cost of a warehouse segregated in resources
- which administration is a direct responsibility of the
warehouse designer - and the cost of resources - out of
the control of warehouse designers.
92
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Figure 3 Space Use
3. Operational costs and warehouse
location
In this section, we highlight how the cost drivers related
to the geographic location of the warehouse impact its
operational cost. In section 3.1, we illustrate the variability
of operational costs among logistic-intensive regions in
the USA. We provide a ranking of metropolitan areas from
low to high operational costs, mainly driven by their labor
costs and real estate costs. The difference in the top 25
metropolitan areas can be up to 50% of total operational
costs. Therefore, including operational costs in warehouse
location problems is important. In section 3.2, we provide
a case study where we use the operational cost as a driver
to justify the relocation of a distribution center.
3.1 Ranking logistics clusters in the USA
In this section, we use our framework of operational
cost of warehouses to rank logistics clusters as desirable
locations to operate distribution centers. For that purpose,
we build the cost analysis structure for a typical unit load
warehouse; then we find the cost parameters of each
logistics cluster. Finally, we rank logistics clusters by their
total operational cost, with the lower cost signifying a more
desirable location.
A typical unit load warehouse
We obtained the data on facility locations from one of the
largest commercial real estate companies in the Louisville
Greater Area [42]. From this database, we selected 243
unique facilities as current or potential warehouses for
distribution purposes. Figure 7 shows a histogram of their
footprint. The average warehouse area was 255,764 sq ft.
For our typical unit-load warehouse, we assume an area
of 250,000 sq ft. From the same database, we were able to
extract the building’s clear heights for 194 facilities (Figure
8). The average clear height of the buildings was 25.2′. For
our typical unit-load warehouse, we assume a clear height
of 25′.
We assume that pallets are stored in single-deep selective
racks with slots with a capacity for two pallets of standard
size (40′′ by 48′′). We assume rack bays have five tiers
93
Figure 3 Space Use
3. Operational costs and warehouse
location
In this section, we highlight how the cost drivers related
to the geographic location of the warehouse impact its
operational cost. In section 3.1, we illustrate the variability
of operational costs among logistic-intensive regions in
the USA. We provide a ranking of metropolitan areas from
low to high operational costs, mainly driven by their labor
costs and real estate costs. The difference in the top 25
metropolitan areas can be up to 50% of total operational
costs. Therefore, including operational costs in warehouse
location problems is important. In section 3.2, we provide
a case study where we use the operational cost as a driver
to justify the relocation of a distribution center.
3.1 Ranking logistics clusters in the USA
In this section, we use our framework of operational
cost of warehouses to rank logistics clusters as desirable
locations to operate distribution centers. For that purpose,
we build the cost analysis structure for a typical unit load
warehouse; then we find the cost parameters of each
logistics cluster. Finally, we rank logistics clusters by their
total operational cost, with the lower cost signifying a more
desirable location.
A typical unit load warehouse
We obtained the data on facility locations from one of the
largest commercial real estate companies in the Louisville
Greater Area [42]. From this database, we selected 243
unique facilities as current or potential warehouses for
distribution purposes. Figure 7 shows a histogram of their
footprint. The average warehouse area was 255,764 sq ft.
For our typical unit-load warehouse, we assume an area
of 250,000 sq ft. From the same database, we were able to
extract the building’s clear heights for 194 facilities (Figure
8). The average clear height of the buildings was 25.2′. For
our typical unit-load warehouse, we assume a clear height
of 25′.
We assume that pallets are stored in single-deep selective
racks with slots with a capacity for two pallets of standard
size (40′′ by 48′′). We assume rack bays have five tiers
93
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Figure 4 Equipment
Figure 5 Working capital
for a total of 10 storage locations. When two rack bays
are back to back to form a double rack bay column, there
should be a 6′′ flue space between them for fire protection
[43]. Considering the uprights’ posts and flue space, the
slot exterior dimensions are 100′′ and 51′′.
A typical layout includes receiving docks, the storage
area, shipping docks, and support areas. Each rectangle
in the storage area represents a rack bay of 100′′ by
51′′. For the storage area, we considered an aisle width
of 12′, which is the requirement for counterbalanced
forklifts. The docks occupy 11.1% of the footprint, and
the support areas 9.3%. The layout includes 2,033
rack bays and each rack bay has a capacity for 10 storage
locations, which results in 20,330 storage locations in total.
Now, the number of lift trucks depends on the nature of
the business. Pearless Research Group (PRG) conducts
an annual survey on behalf of Modern Materials Handling
(MMH) on lift truck fleets and maintenance. Figure 9 shows
the distribution of fleet sizes for the 2017 survey, which
included 144 facilities from the pool of subscribers of the
magazine. The average fleet size was 21 vehicles. For our
typical unit-load warehouse, we will assume a fleet size of
Table 1 Warehouse operational resources
Resources
Labor hours
Area
Equipment
Working Capital
Resources cost
Cost of an Overtime hour
Salaries
Bonuses
Rental cost
Building operational cost
Equipment prices
Equipment operation costs
Products cost
Opportunity cost interest rate
20 vehicles.
Outline of the cost analysis
Table 2 shows the outline of our cost analysis, where the
operational costs of the warehouse are the sum of the
space, rack, labor, and lift truck costs. In this way, we
need eight parameters to calculate the operational costs
of a unit-load warehouse: four cost parameters and four
characteristics of the warehouse. The characteristics of
the warehouse were given in Section 3.1.
In Table 3, we itemized the cost parameters. However,
there are several ways in which companies incur each of
the seven cost items depending on the ownership of the
assets, leasing terms, and contractual relationship with
employees. In the following, we detail each of the seven
cost items in Table 3 and how they are incurred in our
typical unit-load warehouse.
• Land costs: Companies can own or lease the space.
We assume that the company leases the space in a
triple net lease agreement, which is extensively used
in commercial real estate [44]. In triple net leases,
tenants are responsible for operating expenses on top
of the base rent. When we talk about land costs, we
refer only to the base rent. It is given in $/ sq ft and
depends on the location of the warehouse.
• Land operating expenses: In triple net leases,
operating expenses include the building maintenance,
insurance, and property taxes that must be covered
by the tenant. We estimated these costs as a fixed
percentage of the land costs.
• Racking costs: we assume that the racks are owned
by the company. Therefore, we consider their annual
cost as their depreciation. It is given directly in $ per
storage location.
94
Figure 4 Equipment
Figure 5 Working capital
for a total of 10 storage locations. When two rack bays
are back to back to form a double rack bay column, there
should be a 6′′ flue space between them for fire protection
[43]. Considering the uprights’ posts and flue space, the
slot exterior dimensions are 100′′ and 51′′.
A typical layout includes receiving docks, the storage
area, shipping docks, and support areas. Each rectangle
in the storage area represents a rack bay of 100′′ by
51′′. For the storage area, we considered an aisle width
of 12′, which is the requirement for counterbalanced
forklifts. The docks occupy 11.1% of the footprint, and
the support areas 9.3%. The layout includes 2,033
rack bays and each rack bay has a capacity for 10 storage
locations, which results in 20,330 storage locations in total.
Now, the number of lift trucks depends on the nature of
the business. Pearless Research Group (PRG) conducts
an annual survey on behalf of Modern Materials Handling
(MMH) on lift truck fleets and maintenance. Figure 9 shows
the distribution of fleet sizes for the 2017 survey, which
included 144 facilities from the pool of subscribers of the
magazine. The average fleet size was 21 vehicles. For our
typical unit-load warehouse, we will assume a fleet size of
Table 1 Warehouse operational resources
Resources
Labor hours
Area
Equipment
Working Capital
Resources cost
Cost of an Overtime hour
Salaries
Bonuses
Rental cost
Building operational cost
Equipment prices
Equipment operation costs
Products cost
Opportunity cost interest rate
20 vehicles.
Outline of the cost analysis
Table 2 shows the outline of our cost analysis, where the
operational costs of the warehouse are the sum of the
space, rack, labor, and lift truck costs. In this way, we
need eight parameters to calculate the operational costs
of a unit-load warehouse: four cost parameters and four
characteristics of the warehouse. The characteristics of
the warehouse were given in Section 3.1.
In Table 3, we itemized the cost parameters. However,
there are several ways in which companies incur each of
the seven cost items depending on the ownership of the
assets, leasing terms, and contractual relationship with
employees. In the following, we detail each of the seven
cost items in Table 3 and how they are incurred in our
typical unit-load warehouse.
• Land costs: Companies can own or lease the space.
We assume that the company leases the space in a
triple net lease agreement, which is extensively used
in commercial real estate [44]. In triple net leases,
tenants are responsible for operating expenses on top
of the base rent. When we talk about land costs, we
refer only to the base rent. It is given in $/ sq ft and
depends on the location of the warehouse.
• Land operating expenses: In triple net leases,
operating expenses include the building maintenance,
insurance, and property taxes that must be covered
by the tenant. We estimated these costs as a fixed
percentage of the land costs.
• Racking costs: we assume that the racks are owned
by the company. Therefore, we consider their annual
cost as their depreciation. It is given directly in $ per
storage location.
94
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Figure 6 Impact of warehouse decisions
Figure 7 Warehouse Footprint
• Operators’ salary: We assume that operators are
direct employees of the company and that all of them
have the same base salary. It is given in $ per
operator per year and depends on the location of the
warehouse.
• Operators’ salary overhead: Besides base salaries,
companies have to pay for health insurance benefits,
Social Securities taxes, state insurances, incentives,
and benefits for their employees. We estimate the
Figure 8 Buildings’ clear height
overhead as a fixed percentage of the operators’
salaries.
• Lift trucks costs: We assume that the company owns
electric lift trucks. Therefore, we consider their
annual cost as their depreciation. It is given directly
in $ per lift truck.
• Lift truck operating costs: Electric lift trucks require
mechanical maintenance and electrical maintenance
for their batteries and charging stations, electricity
95
Figure 6 Impact of warehouse decisions
Figure 7 Warehouse Footprint
• Operators’ salary: We assume that operators are
direct employees of the company and that all of them
have the same base salary. It is given in $ per
operator per year and depends on the location of the
warehouse.
• Operators’ salary overhead: Besides base salaries,
companies have to pay for health insurance benefits,
Social Securities taxes, state insurances, incentives,
and benefits for their employees. We estimate the
Figure 8 Buildings’ clear height
overhead as a fixed percentage of the operators’
salaries.
• Lift trucks costs: We assume that the company owns
electric lift trucks. Therefore, we consider their
annual cost as their depreciation. It is given directly
in $ per lift truck.
• Lift truck operating costs: Electric lift trucks require
mechanical maintenance and electrical maintenance
for their batteries and charging stations, electricity
95
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Table 2 Outline of the cost analysis of a unit-load warehouse
Component Cost per unit No. units
Space costs Cost per sq ft Area
Rack costs Cost per storage location No. storage locations
Labor costs Cost per lift truck operator No. Operators
Lift truck costs Cost per lift truck No. lift trucks
Figure 9 2017 survey on the size of lift truck fleets by PRG and
MMH.
Table 3 Cost parameters in a unit-load warehouse
Cost per sq ft
Land costs
Land operating expenses
Cost of racks per
storage location Racking costs
Cost per lift truck
operator
Operators salary
Salary overhead
Cost per lift truck
Lift truck costs
Lift truck maintenance
consumption, and supplies. It is given directly in $ per
lift truck.
Logistics-intensive regions
To identify logistics-intensive regions in the US, we resort
to the theory on logistics agglomerations laid out by [45].
A logistics cluster is a geographically concentrated set
of logistics-related activities, where members enjoy the
benefits of agglomeration economies: combined transport
capacity, sharing of resources, infrastructure, labor
availability and governmental incentives [46–48].
Here, we replicate the method laid out in [49] to identify
logistics clusters in the US using the County Business
Patterns Report of the United States Census Bureau
[50]. There are two major characteristics of logistics
clusters [49]: high employment levels and high number
of establishments in the logistics sector. In the County
Business Patterns, the employment level and number
of establishments data is segregated by economic
sector at a county level — county or county equivalent.
With this data, we filtered the list from 3,193 to 400
locations by establishing thresholds for a location to be
considered a logistics-intensive agglomeration, according
to [49]. Additionally, we considered that clusters are not
necessarily contained within one county; therefore, we
established a radius of 35 miles for each county and called
it a county region — making sure that county regions do
not overlap.
We use the commercial real estate marketplace [51]
for the 25 logistics clusters identified in Section 3.1 to
estimate the average cost per square foot of a leased
warehouse. We consider locations in the periphery of
the city in the country regions, given that warehouses
are generally located in those areas [52]. We looked for
warehouses with a minimum of 50,000 sq ft and with
industrial purposes and surveyed 289 facilities. Multiple
surveys and case studies [53–57] estimate that the leasing
96
Table 2 Outline of the cost analysis of a unit-load warehouse
Component Cost per unit No. units
Space costs Cost per sq ft Area
Rack costs Cost per storage location No. storage locations
Labor costs Cost per lift truck operator No. Operators
Lift truck costs Cost per lift truck No. lift trucks
Figure 9 2017 survey on the size of lift truck fleets by PRG and
MMH.
Table 3 Cost parameters in a unit-load warehouse
Cost per sq ft
Land costs
Land operating expenses
Cost of racks per
storage location Racking costs
Cost per lift truck
operator
Operators salary
Salary overhead
Cost per lift truck
Lift truck costs
Lift truck maintenance
consumption, and supplies. It is given directly in $ per
lift truck.
Logistics-intensive regions
To identify logistics-intensive regions in the US, we resort
to the theory on logistics agglomerations laid out by [45].
A logistics cluster is a geographically concentrated set
of logistics-related activities, where members enjoy the
benefits of agglomeration economies: combined transport
capacity, sharing of resources, infrastructure, labor
availability and governmental incentives [46–48].
Here, we replicate the method laid out in [49] to identify
logistics clusters in the US using the County Business
Patterns Report of the United States Census Bureau
[50]. There are two major characteristics of logistics
clusters [49]: high employment levels and high number
of establishments in the logistics sector. In the County
Business Patterns, the employment level and number
of establishments data is segregated by economic
sector at a county level — county or county equivalent.
With this data, we filtered the list from 3,193 to 400
locations by establishing thresholds for a location to be
considered a logistics-intensive agglomeration, according
to [49]. Additionally, we considered that clusters are not
necessarily contained within one county; therefore, we
established a radius of 35 miles for each county and called
it a county region — making sure that county regions do
not overlap.
We use the commercial real estate marketplace [51]
for the 25 logistics clusters identified in Section 3.1 to
estimate the average cost per square foot of a leased
warehouse. We consider locations in the periphery of
the city in the country regions, given that warehouses
are generally located in those areas [52]. We looked for
warehouses with a minimum of 50,000 sq ft and with
industrial purposes and surveyed 289 facilities. Multiple
surveys and case studies [53–57] estimate that the leasing
96
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
operating expenses represent between 25 and 50% of the
base leasing cost. Here, we assume them to be 40% for all
county regions.
The main components of selective racks are beams and
uprights. The first rack bay of a row will have two uprights
and a pair of beams for each tier; the other rack bays of
the row will have only one upright. We assume rows of
15 rack bays in average and each rack bay has 5 tiers. In
consequence, a rack bay has on average 16/15 uprights
and 10 beams.
The estimated price for a beam is $31.69 [58]. The
estimated price for an upright 240 ′′ high is $237.42 [58];
we assume that uprights 300 ′′ high cost $296.78. In this
way, a rack bay costs on average $633.38 and a storage
location $63.32. The U.S. Bureau of Economic Analysis
[59] considers the depreciation rate of material handling
equipment to be 0.1072, i.e., the equipment will be fully
depreciated in 9.32 years. Therefore, we estimate the
racking costs to be $6.79 per storage location per year.
We survey the average annual salary of lift truck operators
using the career website [60] for the 25 logistics-intensive
regions identified in Section 3.1. We take the largest city
in the corresponding county region as a reference and
look for the average annual salary of lift truck operators
in that city, which is provided directly by the website. The
overhead typically represents between 18 to 26% of the
worker’s salary [61]. Here, we considered a fixed overhead
of 20% for all regions.
Finally, Table 4 shows the top 25 county regions in the
continental US ordered by logistics employment, also
illustrated in Figure 10.
Now, the estimated price for an electric reach truck is
between $35,000 USD and $55,000 [62]; Consequently,
we assume a price of $45,000. Under IRS guidelines for
depreciation of assets, lift trucks are under the category
“Other Property Used for Transportation”, which means
that their depreciation rate is 0.2. Therefore, we estimate
the lift truck costs to be $9,000 per lift truck per year.
Manufacturers estimate that lift truck operating costs
range from $1 to $4 per hour, depending on the age of the
vehicle. Their general assumption is 1,500 hours per year,
which results in $1,500 to $6,000 per year. We assume a
lift truck operating cost of $2,000 per year.
With the estimated values for the cost parameters and
the characteristics of a typical unit-load warehouse,
we calculated the space costs and handling costs for a
typical unit-load as if it was located in each of the 25
logistics-intensive regions. Finally, Table 5 ranks the
logistics-intensive regions according to their operational
costs, the first being the one with the lowest costs.
3.2 Case Study
We used the insights presented in this paper in a
consulting project to redesign the supply chain of an
electronics company. The project involved the relocation
of the distribution center and the re-assignment of the
manufacturing of the products for the North American
market across 4 sites. The objective of the projects was to
decrease the logistics costs of the company, which include
inbound transportation, distribution center operation, and
outbound transportation. Given the robustness of the
facility location when only considering transpiration costs,
we use the operational costs as the focus of the decision
in the context of logistics clusters.
Before the project, the company had a manufacturing plant
in China for most of the volume, and small production sites
in Florida, Texas, and California for specific product lines.
The distribution for the North American market occurred
mostly from Massachusetts (85%). The rest of volume was
shipped to customers directly from the manufacturing
sites. We visited the distribution center in Massachusetts
and gathered data to analyze their operation. We gathered
process flows, dimensional data, layouts, operational
times, inventory snapshots, storage utilization, and labor
staffing.
Figure 11 shows the layout of the warehouse, and an
overview of the process is presented in Figure 12. It has
two docks which are used both for receiving and shipping.
Trucks park in the dock, and the receiver helps unload
products - which usually come in pallets - and receives
the documentation of the delivery. The receiver gives
the product to put away or quality control if the product
requires inspection. Put away workers unwrap the pallets
and put the cartons away, prioritizing primary locations
- bins and totes. They also have to dispose of extra
packaging and register the movement of products in their
information system.
The pickers pick multiple orders at the time on their carts.
This group of orders is called a cluster of orders. Picking
one cluster usually takes one picker about 20 minutes.
Once they complete a cluster, they deliver the outbound
cartons to a packer, each box with the items picked and the
paperwork associated with it. Packers take the cartons,
scan the items, print the shipping label, pack the items
again in the box, and put the cartons in the outbound
conveyor. When the outbound conveyor is full, packers put
the cartons in a staging area, waiting to be picked up by the
transportation carrier.
Then, we evaluated the distribution center in terms of
area cost and labor cost for a required throughput. The
warehouse has 17K ft2 and the annual cost per square feet
97
operating expenses represent between 25 and 50% of the
base leasing cost. Here, we assume them to be 40% for all
county regions.
The main components of selective racks are beams and
uprights. The first rack bay of a row will have two uprights
and a pair of beams for each tier; the other rack bays of
the row will have only one upright. We assume rows of
15 rack bays in average and each rack bay has 5 tiers. In
consequence, a rack bay has on average 16/15 uprights
and 10 beams.
The estimated price for a beam is $31.69 [58]. The
estimated price for an upright 240 ′′ high is $237.42 [58];
we assume that uprights 300 ′′ high cost $296.78. In this
way, a rack bay costs on average $633.38 and a storage
location $63.32. The U.S. Bureau of Economic Analysis
[59] considers the depreciation rate of material handling
equipment to be 0.1072, i.e., the equipment will be fully
depreciated in 9.32 years. Therefore, we estimate the
racking costs to be $6.79 per storage location per year.
We survey the average annual salary of lift truck operators
using the career website [60] for the 25 logistics-intensive
regions identified in Section 3.1. We take the largest city
in the corresponding county region as a reference and
look for the average annual salary of lift truck operators
in that city, which is provided directly by the website. The
overhead typically represents between 18 to 26% of the
worker’s salary [61]. Here, we considered a fixed overhead
of 20% for all regions.
Finally, Table 4 shows the top 25 county regions in the
continental US ordered by logistics employment, also
illustrated in Figure 10.
Now, the estimated price for an electric reach truck is
between $35,000 USD and $55,000 [62]; Consequently,
we assume a price of $45,000. Under IRS guidelines for
depreciation of assets, lift trucks are under the category
“Other Property Used for Transportation”, which means
that their depreciation rate is 0.2. Therefore, we estimate
the lift truck costs to be $9,000 per lift truck per year.
Manufacturers estimate that lift truck operating costs
range from $1 to $4 per hour, depending on the age of the
vehicle. Their general assumption is 1,500 hours per year,
which results in $1,500 to $6,000 per year. We assume a
lift truck operating cost of $2,000 per year.
With the estimated values for the cost parameters and
the characteristics of a typical unit-load warehouse,
we calculated the space costs and handling costs for a
typical unit-load as if it was located in each of the 25
logistics-intensive regions. Finally, Table 5 ranks the
logistics-intensive regions according to their operational
costs, the first being the one with the lowest costs.
3.2 Case Study
We used the insights presented in this paper in a
consulting project to redesign the supply chain of an
electronics company. The project involved the relocation
of the distribution center and the re-assignment of the
manufacturing of the products for the North American
market across 4 sites. The objective of the projects was to
decrease the logistics costs of the company, which include
inbound transportation, distribution center operation, and
outbound transportation. Given the robustness of the
facility location when only considering transpiration costs,
we use the operational costs as the focus of the decision
in the context of logistics clusters.
Before the project, the company had a manufacturing plant
in China for most of the volume, and small production sites
in Florida, Texas, and California for specific product lines.
The distribution for the North American market occurred
mostly from Massachusetts (85%). The rest of volume was
shipped to customers directly from the manufacturing
sites. We visited the distribution center in Massachusetts
and gathered data to analyze their operation. We gathered
process flows, dimensional data, layouts, operational
times, inventory snapshots, storage utilization, and labor
staffing.
Figure 11 shows the layout of the warehouse, and an
overview of the process is presented in Figure 12. It has
two docks which are used both for receiving and shipping.
Trucks park in the dock, and the receiver helps unload
products - which usually come in pallets - and receives
the documentation of the delivery. The receiver gives
the product to put away or quality control if the product
requires inspection. Put away workers unwrap the pallets
and put the cartons away, prioritizing primary locations
- bins and totes. They also have to dispose of extra
packaging and register the movement of products in their
information system.
The pickers pick multiple orders at the time on their carts.
This group of orders is called a cluster of orders. Picking
one cluster usually takes one picker about 20 minutes.
Once they complete a cluster, they deliver the outbound
cartons to a packer, each box with the items picked and the
paperwork associated with it. Packers take the cartons,
scan the items, print the shipping label, pack the items
again in the box, and put the cartons in the outbound
conveyor. When the outbound conveyor is full, packers put
the cartons in a staging area, waiting to be picked up by the
transportation carrier.
Then, we evaluated the distribution center in terms of
area cost and labor cost for a required throughput. The
warehouse has 17K ft2 and the annual cost per square feet
97
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Figure 10 Top 25 regions in the continental US with intensive logistics activities, 2013.
98
Figure 10 Top 25 regions in the continental US with intensive logistics activities, 2013.
98
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Figure 11 Warehouse Layout
99
Figure 11 Warehouse Layout
99
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Table 4 Top 25 regions in continental US with intensive logistics activities, 2013
County Region State Employment Establishments annual $ / sq.ft. lift truck Operator
Annual salary
Middlesex NJ 107,757 6,370 7.62 30,418
Los Angeles CA 104,917 5,514 8.35 31,116
Cook IL 92,511 7,087 6.93 31,484
Dallas TX 82,889 2,738 3.81 30,020
Harris TX 61,040 2,804 6.5 31,057
Fulton GA 60,789 2,573 4.19 28,365
San Bernardino CA 42,049 1,344 6.36 28,940
Shelby TN 40,104 1,105 3.16 27,769
Maricopa AZ 39,868 1,471 5.79 28,646
Marion IN 38,556 1,188 3.36 29,633
Miami-Dade FL 37,205 2,851 8.17 31,229
Alameda CA 37,084 1,955 8.53 38,086
Wayne MI 34,882 2,310 5.26 31,261
Franklin OH 34,881 1,015 4.07 29,127
King WA 34,865 1,711 6.44 34,263
Jefferson KY 30,871 769 3.84 29,781
Middlesex MA 29,359 1,669 9.56 34,301
Hennepin MN 26,762 1,659 6.45 32,385
Mecklenburg NC 26,196 1,233 5.42 27,343
Cumberland PA 25,434 693 3.87 31,218
Boone KY 25,217 1,015 4.2 28,600
Baltimore city MD 24,817 1,583 5.76 32,153
St. Louis MO 24,523 1,341 4.22 29,363
Johnson KS 23,175 987 4.42 31,736
Davidson TN 22,723 781 4.77 30,609
Figure 12 Warehouse process
is $14, therefore the annual area cost is $238K.
Figure 13 shows the headcount of the warehouse
segregated by area. The average salary for warehouse
associates in the area was $34K and with 22% of overhead,
the labor cost per associate was $41K. Therefore, the
annual labor cost was $753K. Finally, the total operation
cost of the warehouse was $991K (Table 6).
During the project, we identified several opportunities for
improvement in the operation, but our basic observation
was that the annual cost per square foot of $14 and the
annual wage of $30K were high and that there were better
locations for the distribution center.
For the purpose of this analysis, we will keep constant
the warehouse footprint and labor staffing. We also
considered that relocating the distribution center was
going to impact the transportation cost. Therefore, any
proposal would have to come with a balance of both
cost drivers. Finally, the alternative presented included
moving the manufacturing sites from Florida, Texas, and
California to Mexico and the distribution to be centralized
in Arizona. In this way, the distribution center in Arizona
receives products from China and Mexico, and serves most
of the North American market.
100
Table 4 Top 25 regions in continental US with intensive logistics activities, 2013
County Region State Employment Establishments annual $ / sq.ft. lift truck Operator
Annual salary
Middlesex NJ 107,757 6,370 7.62 30,418
Los Angeles CA 104,917 5,514 8.35 31,116
Cook IL 92,511 7,087 6.93 31,484
Dallas TX 82,889 2,738 3.81 30,020
Harris TX 61,040 2,804 6.5 31,057
Fulton GA 60,789 2,573 4.19 28,365
San Bernardino CA 42,049 1,344 6.36 28,940
Shelby TN 40,104 1,105 3.16 27,769
Maricopa AZ 39,868 1,471 5.79 28,646
Marion IN 38,556 1,188 3.36 29,633
Miami-Dade FL 37,205 2,851 8.17 31,229
Alameda CA 37,084 1,955 8.53 38,086
Wayne MI 34,882 2,310 5.26 31,261
Franklin OH 34,881 1,015 4.07 29,127
King WA 34,865 1,711 6.44 34,263
Jefferson KY 30,871 769 3.84 29,781
Middlesex MA 29,359 1,669 9.56 34,301
Hennepin MN 26,762 1,659 6.45 32,385
Mecklenburg NC 26,196 1,233 5.42 27,343
Cumberland PA 25,434 693 3.87 31,218
Boone KY 25,217 1,015 4.2 28,600
Baltimore city MD 24,817 1,583 5.76 32,153
St. Louis MO 24,523 1,341 4.22 29,363
Johnson KS 23,175 987 4.42 31,736
Davidson TN 22,723 781 4.77 30,609
Figure 12 Warehouse process
is $14, therefore the annual area cost is $238K.
Figure 13 shows the headcount of the warehouse
segregated by area. The average salary for warehouse
associates in the area was $34K and with 22% of overhead,
the labor cost per associate was $41K. Therefore, the
annual labor cost was $753K. Finally, the total operation
cost of the warehouse was $991K (Table 6).
During the project, we identified several opportunities for
improvement in the operation, but our basic observation
was that the annual cost per square foot of $14 and the
annual wage of $30K were high and that there were better
locations for the distribution center.
For the purpose of this analysis, we will keep constant
the warehouse footprint and labor staffing. We also
considered that relocating the distribution center was
going to impact the transportation cost. Therefore, any
proposal would have to come with a balance of both
cost drivers. Finally, the alternative presented included
moving the manufacturing sites from Florida, Texas, and
California to Mexico and the distribution to be centralized
in Arizona. In this way, the distribution center in Arizona
receives products from China and Mexico, and serves most
of the North American market.
100
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Table 5 Operational costs rank of logistics-intensive regions
Rank County Region State Largest city Operational costs
1 Shelby TN Memphis 2,130,497
2 Marion IN Indianapolis 2,245,233
3 Dallas TX Dallas 2,412,021
4 Jefferson KY Louisville 2,416,785
5 Cumberland PA Harrisburg 2,461,773
6 Franklin OH Columbus 2,481,589
7 Fulton GA Atlanta 2,505,301
8 Boone KY Cincinnati 2,514,441
9 St. Louis MO St. Louis 2,539,753
10 Johnson KS Kansas City 2,666,705
11 Davidson TN Nashville 2,762,157
12 Mecklenburg NC Charlotte 2,911,273
13 Wayne MI Detroit 2,949,305
14 Maricopa AZ Phoenix 3,072,045
15 Baltimore city MD Baltimore 3,145,713
16 San Bernardino CA Riverside 3,278,601
17 Harris TX Houston 3,378,409
18 Hennepin MN Minneapolis 3,392,781
19 King WA Seattle 3,434,353
20 Cook IL Chicago 3,539,157
21 Middlesex NJ New York City 3,755,073
22 Miami Dade FL Miami 3,967,037
23 Los Angeles CA Los Angeles 4,027,325
24 Alameda CA San Francisco 4,257,605
25 Middlesex MA Boston 4,527,265
Table 6 Warehouse operational cost in Massachusetts
Area (ft2) 17,000
Area Cost ($/year) $ 238,000
Labor head count 18
Labor Cost ($/year) $ 753,250
Total operational cost ($/year) $ 991,250
The annual cost per square foot was $6.6, therefore, the
annual area cost would be $112K. The average salary for
warehouse associates in the area was $28K, and with
22% of overhead, the labor cost per associate was $35K.
Therefore, the annual labor cost was $629K. Finally, the
total operation cost of the warehouse was $741K (see break
down in Table 7).
With the proposal, the operational cost would decrease
$250K, which was about 25%. However, the transportation
cost increased by 7%, and there were about $80K of
Table 7 Warehouse operational cost in Arizona
Area (ft2) 17,000
Area Cost ($/year) $ 112,200
Labor head count 18
Labor Cost ($/year) $ 629,066
Total operational cost ($/year) $ 741,266
investment to do the relocation, which diminished the
return on the investment, but it still was a financially
justifiable decision.
Management decided to implement the proposal for
the strength of the business case and other reasons
related to the consolidation of manufacturing sites. We
believe that the relocation of the distribution center from
Massachusetts to Arizona showcases of the importance of
balancing warehouse operational costs and transportation
costs when making location decisions.
101
Table 5 Operational costs rank of logistics-intensive regions
Rank County Region State Largest city Operational costs
1 Shelby TN Memphis 2,130,497
2 Marion IN Indianapolis 2,245,233
3 Dallas TX Dallas 2,412,021
4 Jefferson KY Louisville 2,416,785
5 Cumberland PA Harrisburg 2,461,773
6 Franklin OH Columbus 2,481,589
7 Fulton GA Atlanta 2,505,301
8 Boone KY Cincinnati 2,514,441
9 St. Louis MO St. Louis 2,539,753
10 Johnson KS Kansas City 2,666,705
11 Davidson TN Nashville 2,762,157
12 Mecklenburg NC Charlotte 2,911,273
13 Wayne MI Detroit 2,949,305
14 Maricopa AZ Phoenix 3,072,045
15 Baltimore city MD Baltimore 3,145,713
16 San Bernardino CA Riverside 3,278,601
17 Harris TX Houston 3,378,409
18 Hennepin MN Minneapolis 3,392,781
19 King WA Seattle 3,434,353
20 Cook IL Chicago 3,539,157
21 Middlesex NJ New York City 3,755,073
22 Miami Dade FL Miami 3,967,037
23 Los Angeles CA Los Angeles 4,027,325
24 Alameda CA San Francisco 4,257,605
25 Middlesex MA Boston 4,527,265
Table 6 Warehouse operational cost in Massachusetts
Area (ft2) 17,000
Area Cost ($/year) $ 238,000
Labor head count 18
Labor Cost ($/year) $ 753,250
Total operational cost ($/year) $ 991,250
The annual cost per square foot was $6.6, therefore, the
annual area cost would be $112K. The average salary for
warehouse associates in the area was $28K, and with
22% of overhead, the labor cost per associate was $35K.
Therefore, the annual labor cost was $629K. Finally, the
total operation cost of the warehouse was $741K (see break
down in Table 7).
With the proposal, the operational cost would decrease
$250K, which was about 25%. However, the transportation
cost increased by 7%, and there were about $80K of
Table 7 Warehouse operational cost in Arizona
Area (ft2) 17,000
Area Cost ($/year) $ 112,200
Labor head count 18
Labor Cost ($/year) $ 629,066
Total operational cost ($/year) $ 741,266
investment to do the relocation, which diminished the
return on the investment, but it still was a financially
justifiable decision.
Management decided to implement the proposal for
the strength of the business case and other reasons
related to the consolidation of manufacturing sites. We
believe that the relocation of the distribution center from
Massachusetts to Arizona showcases of the importance of
balancing warehouse operational costs and transportation
costs when making location decisions.
101
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
Figure 13 Labor Staffing
4. Conclusions
Most literature on warehouse location problems focus on
minimizing transportation costs. This paper advocates
the importance of considering total costs in location
problems, where the operational costs of the facility are
also a significant cost driver. We found that there are
location problems in practice, where factors such as labor
availability and leasing costs play a critical role in where
warehouses should be located.
The decision framework explained in this paper could be
used by a company that desires to distribute products
throughout the continental United States. This country
is a special case in worldwide distribution, due to its
sheer size and close-to-continuous nature in terms of
highway availability and space. However, the procedure
followed here could be easily applied to any other market
of interest: Large emerging countries such as the BRIC
(Brazil, Russia, India, and China), and integrated regional
economies such as the European Union come to mind.
Not all types of supply chains could directly apply the
framework presented in this work. In particular, different
types of products require different lead times and service
levels facing the end customer. For example, massive
consumption products such as paper cleaning products
(toilet paper, paper towels, paper napkins and so on) have
the characteristic that stores will not be willing to wait
several days to have their inventories re-stocked. These
types of supply chains will need an additional echelon
closer to big centers of demand, in order to rationalize
transportation costs and expedite delivery. For them, this
proposal requires adaptation to be of use. to consider the
infrastructure required to cover the last mile. However,
manufacturers and distributors of products that do not
have such expectations of availability and lead times could
apply this framework to their operations. Distributors
of electronics, auto parts, rare or hard-to-find items,
industrial products, and in general items over which there
is no expectation of immediate response (and that probably
do not have regular supply runs in order to take advantage
of vehicle routing) can use these analyses to configure
their supply chains in the most efficient (total-cost-wise)
manner.
The framework for logistics decisions presented in this
paper is useful for companies that have adopted an
integrated outlook towards the manufacturing vs. logistics
separation that used to be the norm. Even though it
has been known for many years that what counts for
the company is the total cost of logistics [63], it is still
common to observe companies that treat procurement,
manufacturing, and distribution as separate business
units. Adopting an operations management perspective
that integrates manufacturing, procurement, logistics
and distribution would make the implementation of this
framework that much easier and more productive, opening
the door to the re-consideration of production localization,
customer service, and lead-time demands and constraints.
5. Declaration of competing interest
We declare that we have no significant competing interests,
including financial or non-financial, professional, or
personal interests interfering with the full and objective
presentation of the work described in this manuscript.
6. Funding
This work was supported by Fulbright Colombia and the
Logistics and Distribution Institute in Louisville, KY, USA.
7. Author contributions
Luis F. Cardona and Leonardo Rivera developed the
Engineering Framework and wrote the paper together.
Luis F. Cardona was the manager of the project presented
in the case study.
8. Data availability statement
All the data associated with a paper is contained within.
102
Figure 13 Labor Staffing
4. Conclusions
Most literature on warehouse location problems focus on
minimizing transportation costs. This paper advocates
the importance of considering total costs in location
problems, where the operational costs of the facility are
also a significant cost driver. We found that there are
location problems in practice, where factors such as labor
availability and leasing costs play a critical role in where
warehouses should be located.
The decision framework explained in this paper could be
used by a company that desires to distribute products
throughout the continental United States. This country
is a special case in worldwide distribution, due to its
sheer size and close-to-continuous nature in terms of
highway availability and space. However, the procedure
followed here could be easily applied to any other market
of interest: Large emerging countries such as the BRIC
(Brazil, Russia, India, and China), and integrated regional
economies such as the European Union come to mind.
Not all types of supply chains could directly apply the
framework presented in this work. In particular, different
types of products require different lead times and service
levels facing the end customer. For example, massive
consumption products such as paper cleaning products
(toilet paper, paper towels, paper napkins and so on) have
the characteristic that stores will not be willing to wait
several days to have their inventories re-stocked. These
types of supply chains will need an additional echelon
closer to big centers of demand, in order to rationalize
transportation costs and expedite delivery. For them, this
proposal requires adaptation to be of use. to consider the
infrastructure required to cover the last mile. However,
manufacturers and distributors of products that do not
have such expectations of availability and lead times could
apply this framework to their operations. Distributors
of electronics, auto parts, rare or hard-to-find items,
industrial products, and in general items over which there
is no expectation of immediate response (and that probably
do not have regular supply runs in order to take advantage
of vehicle routing) can use these analyses to configure
their supply chains in the most efficient (total-cost-wise)
manner.
The framework for logistics decisions presented in this
paper is useful for companies that have adopted an
integrated outlook towards the manufacturing vs. logistics
separation that used to be the norm. Even though it
has been known for many years that what counts for
the company is the total cost of logistics [63], it is still
common to observe companies that treat procurement,
manufacturing, and distribution as separate business
units. Adopting an operations management perspective
that integrates manufacturing, procurement, logistics
and distribution would make the implementation of this
framework that much easier and more productive, opening
the door to the re-consideration of production localization,
customer service, and lead-time demands and constraints.
5. Declaration of competing interest
We declare that we have no significant competing interests,
including financial or non-financial, professional, or
personal interests interfering with the full and objective
presentation of the work described in this manuscript.
6. Funding
This work was supported by Fulbright Colombia and the
Logistics and Distribution Institute in Louisville, KY, USA.
7. Author contributions
Luis F. Cardona and Leonardo Rivera developed the
Engineering Framework and wrote the paper together.
Luis F. Cardona was the manager of the project presented
in the case study.
8. Data availability statement
All the data associated with a paper is contained within.
102
L. F. Cardona et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 111, pp. 88-104, 2024
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an activitybased perspective,” International Journal of Physical
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702–713, Jan 2001. [Online]. Available: https://doi.org/10.1108/
EUM0000000006286
104