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Real-time data integration of an
internet-of-things-based smart
warehouse: a case study
Chelinka Rafiesta Sahara
Sampoerna University, Jakarta, Indonesia, and
Ammar Mohamed Aamer
Northeastern University, College of Professional Studies, Toronto, Canada
Abstract
Purpose –Creating a real-time data integration when developing an internet-of-things (IoT)-based warehouse is
still faced with challenges. It involves a diverse knowledge of novel technology and skills. This study aims to
identify the critical components of the real-time data integration processes in IoT-based warehousing. Then, design
and apply a data integration framework, adopting the IoT concept to enable real-time data transfer and sharing.
Design/methodology/approach –The study used a pilot experiment to verify the data integration
system configuration. Radio-frequency identification (RFID) technology was selected to support the
integration process in this study, as it is one of the most recognized products of IoT.
Findings –The experimentations’results proved that data integration plays a significant role in structuring
a combination of assorted data on the IoT-based warehouse from various locations in a real-time manner.
This study concluded that real-time data integration processes in IoT-based warehousing could be generated
into three significant components: configuration, databasing and transmission.
Research limitations/implications –While the framework in this research was carried out in one of the
developing counties, this study’sfindings could be used as a foundation for future research in a smart warehouse,
IoT and related topics. The study provides guidelines for practitioners to design a low-cost IoT-based smart
warehouse system to obtain more accurate and timely data to support the quick decision-making process.
Originality/value –The research at hand provides the groundwork for researchers to explore the
proposed theoretical framework and develop it further to increase inventory management efficiency of
warehouse operations. Besides, this study offers an economical alternate for an organization to implement the
integration software reasonably.
Keywords Cyber-physical systems, Industry 4.0, Internet-of-things, Real-time data,
Smart warehouse
Paper type Research paper
1. Introduction
Warehousing operations play a critical role in managing inventory levels, storing goods and
logistics activities. The warehouse’s function is at the heart point in the supply chain (Aamer,
2018a;Mostafa et al.,2019). The warehouse operations present a meaningful task in
Erratum: It has come to the attention of the publisher that the article, “Real-time data integration of
an internet-of-things based smart warehouse: a case study”by Ammar Mohamed Aamer and
Chelinka Rafiesta Sahara published in International Journal of Pervasive Computing and
Communications listed the authors in the incorrect order. The correct order of authors is Chelinka
Rafiesta Sahara and Ammar Mohamed Aamer. This error was introduced in the editorial process and
has now been corrected in the online version. The publisher sincerely apologises for this error and for
any inconvenience caused.
Smart
warehouse
Received 16 August2020
Revised 10 December2020
Accepted 21 January2021
International Journal of Pervasive
Computing and Communications
© Emerald Publishing Limited
1742-7371
DOI 10.1108/IJPCC-08-2020-0113
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1742-7371.htm
accommodating better-quality services and mitigating supply and demand (Sainathuni et al.,
2014). It is essential in warehouse operations to maintain costs at the minimum number and
improve customer satisfaction (Aamer, 2018c;Aamer, 2018b). Warehouse efficiency has become
the focal point of competency among organizations to develop (Jermsittiparsert et al., 2019).
Organizations have adopted advanced technology to form an adaptive and flexible
warehouse design for a competitive market that facilitates product visibility (Mourtzis et al.,
2019). Such a small operation of non-computerized managerial activity and motorized data
input is a piece of traditional complications that can also be overcome by adopting
appropriate technology. The internet-of-things (IoT) is theorized as one of the renowned
concepts of most promising technology in the fourth industrial revolution (Industry 4.0) that
is primarily used to enhance the control and improve performance in the supply chain such
as in the warehouse area (Mostafa et al.,2019). IoT accommodates the digital connectivity of
the physical and digital components to present real-time data storage and sharing,
positively impacting the customer’s satisfaction level (Rondero et al.,2019).
Real-time data storage and sharing provide a massive amount of data and location of goods
as continuous moving information (Vicuna et al., 2019). Thus, the virtue it gives to the product
enhances the movement accuracy and greater visibility of products (Mahroof, 2019).
Technology’s data-driven process reflects highly accurate and synchronized information for an
organization’s improvement and improved decision-making. The presence of synchronized
accurate information also contributes a significant role in eliminating the lead to a high
frequency of human errors, minimizing the handling process and reducing operating expenses
(Alyahya et al., 2016). Consequently, the real science behind successful warehouse management
can be inferred in obtaining, sharing and analyzing real-time information about the product’s
activities in the warehouse. It is worth noting that technology adoption in the warehouse has
also brought an adverse effect that hinders the assuring of performance refinement conduct.
For example, in e-commerce retailers’cases that used IoT in their operation, the problem
emerged from the time-critical picking orders that generated a massive amount of data from
various locations in monitoring needs (Boysen et al.,2019). There is a gap in the literature from
such a condition to explore technology advancement’spredefined requirements.
Real-time data integration is recognized as a significant part of IoT-based warehouse that
provides real-time monitoring. Real-time data integration helps acquire adequate information that
consists of raw data and well-ordered data (Ellis and Leek, 2018). Integration also powers the role
of IoT-based warehouse to assure an accurate and reliable information flow is promoted (Mitchell
and Kovach, 2016). The success of integration accumulates to better support and collaborative
decision-making by eliminating distances and connecting managers across functional. Further,
real-time connection maintains the monitoring of customer behavior feasible (Fawcett et al., 2007).
However, real-time data integration does not come with an affordable cost of implementation and
a simple adoption process (Li et al.,2019). Creating a real-time data integration as building an IoT-
based warehouse necessitates a high investment cost and, most importantly, a detailed mature
design (Sainathuni et al.,2014). It is still a challenge to integrate data in a real-time manner as it
involves a diverse knowledge of novel technology and skills to reach a small number of delays.
Besides, there is a paucity of knowledge sharing related to real-time data integration that
provides an applicable implementation for small-medium businesses.
Motivated by the rationale and reason mentioned above, this study’s main objective is to
design and apply a data integration framework, adopting the IoT concept in enabling real-time
data transfer and sharing. Radio-frequency identification (RFID) technology was selected to
support the integration process in this study. It is one of the most recognized IoT products that
reflect real-time behaviors to enable real-time visibility and tracking in a warehouse (Zhong et
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al., 2017). This study contributes to enriching the knowledge gap of the integration process of
real-time data in IoT-based warehouse to support the adoption of IoT-based warehouses.
Section 2 presents a theoretical background in which further insight about real-time data
integration for IoT-based warehouse in a systematic writing structure. Afterward, the
methodology of the study is explained in Section 3. Subsequently, the research result and
discussion consist of a fundamental concept, design framework, key factors and challenges
are discussed in Section 4. Section 5 summarizes the study, its significance, limitations of the
research and future research suggestions.
2. Literature review
2.1 Internet of things and integration
IoT infrastructure is based on the foundation of various technologies such as Ambient
Intelligence, Internet Protocol, Communication technologies: WiFi, Bluetooth, ZigBee,
Embedded devices: RFID or wireless sensor networks and applications (Reaidy et al.,2015).
The implementation of IoT infrastructure comprises four creation aspects as Things,
Gateways, Network infrastructure and Cloud infrastructure (Hamdy et al.,2018). Things
represent a technological apparatus to gather physical data in a real-time manner. Gateways
stand as the connection to bridge activity between Things and Network infrastructure, but
Network infrastructure facilitates data flow and track security to the Cloud. Cloud
infrastructure acts as a data processing process or analytical machine.
The IoT structure comprises three different aspects: perception, network and application
(Trappey et al.,2017). In terms of the perception aspect, the ability of IoT in sensing the object is
represented in such technological products with many variety and applicability such as 2D-
barcode, circuits, sensors, actuators, controllers, RFID tags, imaging, mobile communication and
terminals. The network supports the perceived ability to enable a share of information. Network
composes of features such as device modeling, security, WiFi, ethernet, gateway control, GSM,
UMTS, LTE, LTE-A, Hadoop, OS, USB, NFC, HTTP, FTP DDS, Bluetooth, Cloud and database.
While for its application, IoT encompasses many sectors such as tracking, health-care and
logistics. With the whole structure of IoT, the main objective of its adoption in a warehouse is to
introduce automation to the warehouse’s controlling process (Atieh et al., 2016).
The promising benefit of emerging IoT technology is the experience of real-time monitoring of
equipment, facilitates and goods (Chang et al.,2019). The mutual collaboration between IoT and
cloud computing promotes data accuracy at an advanced level (Tantalaki et al.,2019). The
enhancement of security and quicker handling is also the gain for organizations (Atieh et al.,
2016). In the warehouse operation, the reduction of cost has become the primary objective of
many enterprises (Leng et al., 2019). The warehouse’s business process’scomplexitysuchas
much work-in-process and variety of demand triggers more errors. With the presence of IoT,
mistakes could be reduced and, perhaps, eliminated through automation (Zhou et al.,2017). In
general, the high adoption of IoT generates higher perceived benefits and low cybersecurity risk
(Jalali et al.,2019). On the other hand, the IoT application challenges necessitated the need to
develop data acquisition methods and IoT technical capacity (Ding et al.,2020).
2.2 Internet of things-based warehouse
The compulsion of challenging activity tracking and identifying goods in a specificperiod
reflects a complex undertaking (Tejesh et al., 2018). The idea of goods tracking can be realized by
obtaining up-to-date information (Rahman, 2016). Industry 4.0 allows technology to transform the
practice in streamlining the supply chain to gain data (Lototsky, 2019). An IoT-based
warehousing system is a technological involvement product that connects the physical
warehouse to facilitate continuous interconnected real-time information from various locations
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warehouse
into a single database (Yerpude and Singhal, 2018). The adoption of technology into warehousing
enhances efficiency by providing transparency and accuracy (Mahroof, 2019). The IoT-based
warehouse also offers strong business knowledge consisting of a data depository of operations,
decisions and external processes (Khan et al.,2012).
The transformation from a conventional warehouse operation into a high degree automation
warehouse system is motivated by the consideration of efficient management lacking (Leng et al.,
2019). According to Trappey et al. (2017), IoT plays a vital role in enabling a smart logistics and
warehouse in the context of Industry 4.0. The utilization of IoT integrated technology is the brain
that facilitates traceability and decision support in warehouse and logistics activity. From the
managerial perspective, IoT supports an excellent improvement for a decentralized management
warehouse (Reaidy et al., 2015). The process of data management in an IoT-based warehouse
involves a historical data maintenance process, while the extracting process to transform data into
valuable knowledge consists of five activities presented in Figure 1. The IoT is adopted in the
warehouse management system to enable product information tracking (Tejesh and Neeraja,
2018).
2.3 Radio-frequency identification technology
RFID is a supporting technology that is considered a promising solution to address
inventory inaccuracy (Biswal et al.,2018). The automated industry trends were well-known
for many business applications such as Wal-Mart, Metro, Target and Tesco (Sydänheimo et
al.,2006;Hinkka et al.,2012). The use of RFID technology positively detects numerous
objects simultaneously at different distance levels in an instant way of communicating
(Rizzi, 2006). This advanced technological tool capable of enhancing information accuracy
and visibility through seamless monitoring. The usage of RFID in the warehouse
management system is commonly to track and trace goods exclusively for the inconsistency
of information due to changes and updates of the warehouse activities (Lee et al.,2017).
RFID allows storing a massive amount of data on the tags at a specific distance that requires
minor movement (Kamali, 2020;Trab et al., 2016). Data retrieval using RFID technology also
supports automatic identification and automated product identity extraction (Yaw Wong
and McFarlane, 2007). Therefore, human participation in the warehouse systems is slightly
altered from an actively involve worker to a human operator (Buntak et al.,2019).
The utilization of RFID technology in a warehouse is renowned for eliminating inefficiency
and ineffectiveness (Chen et al.,2013). RFID technology’s advantages simplify warehouse
management to provide better-quality control enablement, product visibility and delivery
reliability improvement (García et al.,2007). The integration of RFID technology with other
intelligence tolls such as sensors enables monitoring of goods in the warehouse, making operation
more flexible (Abad et al.,2007). RFID could also be used for improving storage efficiency and
process refinement by combining the technology with warehouse management tools, resulting in
a labor waste reduction (Xiao et al.,2017). Besides its tracking performance, RFID is used to
enhance information systems by coordinating warehouse operations such as planning, execution
and control for the quick decision-making process (Zhong et al.,2013).
Figure 1.
Data roles in shaping
valuable knowledge
Data cleaning Data
integraon Data reducon Data
transformaon
Data
discrezaon
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The RFID utilization returns lie in tracking in a simple, timely and accurate manner, providing more
visibility of object movements. In terms of capacity, RFID can store thousands of digits number and
track locations from a specific distance using frequencies via wireless communication networks, making
it possible to automatically read hundreds of tags per second (Wang et al., 2010). In general, it requires
significant investment in process redesign, technology infrastructure and operation transformation to
reap the benefits of RFID utilization (Lee et al., 2009).
2.4 Data integration
Data integration is defined as the process of connecting, managing and combining time-variant
data from various operations and locations (Kern et al.,2013;Shivtare and Shelar, 2015). This
process’s implementation is renowned for facilitating more efficient decision-making (Chau et al.,
2013). Therefore, data integration is the heart of the IoT-based warehousing enablement aspect
(Calvanese et al.,2001). Data integration provides sufficient assistance for technology as a
prerequisite in ensuring data reliability and minimizes errors (Rizzi, 2006). It also helps distribute
data to many enterprises to enable more efficiency (Fu et al., 2011). The integration process
necessitates a series of structuring actions from the specific textual format and numeric data into
homogenous format data (Huang et al.,2016;Rosenkranz et al.,2017;Shivtare and Shelar, 2015).
Moreover, various data acquisitions from multiple devices, software and systems generate more
complex heterogeneous unstructured data (Ren et al.,2019).
The integration of information and IoT technology allows improved organizational
decision-making (Huang et al.,2002). Moreover, the integration process gives the decision-
makers more accurate information at the right time for further analytical purposes (Pti
cek
et al., 2019). The integration process of data is also associated with the data warehousing
process. In data warehousing, it consists of a large scale of data sets that are non-relational
from each database (Bicevska and Oditis, 2016). The integration of data becomes as
essential as the data warehouse in extracting the vital data from a scattered form into
centralized, integrated storage (Santoso and Yulia, 2017). The data warehouse’s role plays a
significant contribution in storing data sources and destinations, leading to designs for data
extraction, transformation and loading (Rosenkranz et al.,2017). In running the task of
tracking and identifying products, RFID faced two enormous challenges in moving goods.
The study by Burmester et al. (2017) identified protection control of moving goods and
security at each stage of the chain regarding ownership transfer protocols as the main two
challenges.
2.5 Real-time data systems
A real-time system is perceived as a reaction to external events that a computer system
encounters within some stipulated deadline (Tongren, 1998). Real-time systems’primary
requirements include a stringent and low delay pattern and continuity of variation in time
relation between the data entities. Tantalaki et al. (2019) defined the real-time system as an
operation that behaves within the “real-world”time limit. The use of real-time data
positively contributes to the quality of decision-making. The result of performance
measurement based on its real-time value is required for allowing data visibility in
operations (Erkayaoglu and Dessureault, 2019). A real-time thing such as real-time location
or real-time data can provide the exact positioning of an object when combines with other
supporting technology (Halawa et al.,2019).
3. Methodology
The research plan presented in Figure 2 illustrates the detailed steps of the methodology
used to conduct the study at hand to address the research objectives comprehensively. The
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warehouse
research plan was initially generated from examining the research problem, exploratory
research of related literature review and the brainstorming of concepts. The research plan is
composed of seven different levels of activities to achieve the reporting and analysis step.
The first level is the preliminary activity comprising several tasks: qualitative data
collection, problem determination, objectives determination, exploratory research conduct,
best-practice selection and research approach determination. The second level involved the
Figure 2.
Research plan
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data collection process. Following the data collection process was the instrument selection
process. Subsequently, the integration design was developed. Afterward, a pilot study was
executed with the physical representation building of the case company warehouse. Finally,
the validity testing and integration steps were carried out. The following section presents
more details about each level of the research methodology shown in Figure 2.
3.1 Preliminary activity
Preliminary activity referred to a compilation of events before conducting the data collection
process and helped reach a proper research approach. The event took place in a warehouse
of one of the local companies. The case company is one of the foreign direct investments in
Indonesia. The general model of real-time data integration for the IoT-based warehouse in
this study is displayed in Figure 3.Figure 3 presents the data flow in the case warehouse,
represented by a solid line where the data transfer from one part of an information system to
another location.
There are four different locations in the selected case company warehouse and it has
distinct data input and output to be recorded and received. In warehouse 1 where product
slotting and sortation happened, the data represents the goods’properties such as the
number of daily goods stored from the production area, the number of goods based on their
product type and the time of goods entering the warehouse. In the labeling process area
where order picking took place, the goods were transferred from warehouse 1 to warehouse
2 based on the customer demand requirements. The detailed description included the
number of order goods according to the customer, the number of goods orders based on its
product type, the departure time of goods ordered and the time order entering the labeling
process. In warehouse 2 where it focused on zone picking, the specific detail of description to
Figure 3.
Real-time data
integration mapping
in the case warehouse
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ensure the correct number of orders labeled in the labeling process was checked in this area.
The last stage is to recheck the correctness of goods ordered by recalculating the ordered
product based on the pallet. The documenting method when the ordered product is shipped
based on the requested cargo happed in this area.
In the preliminary activity, the qualitative data collection was carried out first to identify
the case company’s main problem regarding the warehouse activity through in-depth
interviews and discussions with the employees and managers. The data from the
discussions and interviews were then used as resources for the problem determination
process. The problem was formulated at the case company as the inability to streamline the
warehouse’s performance because of manual data input, low visibility, back-and-forth
communication and the necessity of an improved decision-making process. This was in line
with this study’s objective to increase data visibility and decision-making process by
developing a data shared-point system and designing a data integration system to provide
real-time information. Further exploratory research was conducted to obtain a best-practice
solution. The RFID technology was selected as the best supporting tool to enable real-time
data to support the integration data process to enhance data visibility and decision-making.
3.2 Data collection
The data collection objective was to gather useful information regarding warehouse activity,
data flow, technology utilization and employees’movement. There were three approaches to
collecting data: in-depth interviews, informal discussions and comprehensive observations,
as suggested by Sileyew (2016). The in-depth interviews were conducted with the supply
chain director and management of the warehouse. The individual interviews from each
group provided the management perspectives on the current warehouse condition and a
thorough understanding of the existing warehouse situation and the business condition.
This further contributed to the refinement and enhancement of this study objective. Several
informal discussions and interviews with the shop floor workers took place at different
times in four separate areas, known as warehouse 1, labeling area, warehouse 2, docking
area and administration offices. Finally, the research team conducted in-depth observations
of employees’activities and goods flow process over two months to collect information. This
information was related to the employment roles in deploying warehouse activity data such
as customer demand, total demand, type of products, field setting, and many others.
3.3 Instrument selection
The instrument selection step involved the supporting tools selection step in managing the
obtained data and realizing the research objectives. The selected tools were generated as a
necessity resulted from data collection and an exploratory study conducted in the
preliminary activity section. This step mainly covered the activity of choosing RFID tag
types and RFID scanner types that best suited to the warehouse conditions such as
warehouse requirements, warehouse size, number of employees and employee involvement
in movable property.
3.4 Integration design
The foundation of integration design entails the data integration design framework, as
shown in Figure 4. The framework was generated based on the data flow and data exchange
in various case warehouse locations, which helped to realize the importance of the data
integration process. The integration process increases the usefulness of data and converts it
to information. The data would not add value if the data integration process was not
performed. This is because data would only be gathered from various places, neglecting the
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process of data mapping, cleansing and transformation. Most importantly, the data
integration process enables the unification of systems to produce consistent data to reduce
errors. Furthermore, integrated data promotes the efficiency of data analysis to generate
new knowledge and understanding.
3.5 Pilot testing
The pilot study aimed to look at the feasibility of a specific study design (Teijlingen and
Hundley, 2002;In, 2017). Pilot studies could be used as the “pre-testing”or “trying out”of a
research design or instrument (Baker, 1994). The primary objective of conducting a pilot
study is to point out any flaw if it exists in the execution of an instrument, model or system
(Srinivasan and Lohith, 2017). The practicality through piloting contributes to a greater
understanding of working intricacy within a previously designed study or ongoing study to
give appropriate options for selecting which instrument or model for a specific research
circumstance (Malmqvist et al.,2019). This study used a prototyping model to test and
verify the data integration system configuration. This study relied on the in-depth
investigation of processes and operation scrutiny in gathering the design requirements. An
iterative process of prototyping model carried out to reach high fidelity of the existing
system. The warehouse prototype represented the replicated case warehouse as a small-
scale model to perform the data integration process. The warehouse operations were
simulated according to the actual products’flow in the warehouses and the final goods’
shipment. The moving products’data was collected via the RFID tags attached to boxes
representing the case warehouse’s actual flow. Hence, the real-time data integration was
carried out with the movement of products between each warehouse location. The
Figure 4.
Data integration
design framework
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warehouse
realization of the integration process was established using several combinations of tools,
software and hardware to collect data from products as physical objects to a network
platform.
3.6 Validity testing
Validity testing aimed to assess the research quality reflected in the underlying construct in
fulfilling the requirements and deliverables. As suggested by Taherdoost (2018), content
validity is a highly recommended type of validity test with several technique options. Some
of the mentioned techniques included a literature review, performing expert panels, CV ratio
(CVR) or Q-sorting. Content validity commonly relies on the experts’judgment in the field
(Mohajan, 2017). In this study, a literature review was used as a tool to support validity
testing. A consecutive assessment based on semi-structured interviews with the experts was
also performed to refine the real-time data integration design.
3.7 Interpretation
Interpretation refers to the process of reporting the analysis from the constructed design and
research. The content of the analysis is discussed explicitly in Section 4. The interpretation
involved a means of drawing inferences and data presentation in the forms of a graph, figure
or table to illustrate, recap and evaluate thedesign of a real-time data integration process.
4. Results and discussion
In each area of the warehouse, data was generated and shared between different locations.
Data from each site were collected in one repository to enabling data sharing and converting
data into information and knowledge. The data was raised in a single location called the
database. The database is electronically storing the homogenous data from different places
and various time details based on data classification from many time ranges in a computer
system. Data stored in the database can be further displayed to enable data visibility and
goods movement in the warehouse. The integrated data stored in the database could also be
analyzed using data analytics tools such as Power BI, KNIME and Excel.
The overall picture of the requirements and process of real-time data integration is
displayed in Figure 5. The following section details the procedures of creating real-time data
integration and data flow from when the data is obtained until it is stored. Besides, this
section focuses on what tools and software were chosen to create a shared point system and
create a real-time data integration framework.
4.1 Data scanning
The automation of the data scanning process was accomplished by using RFID handheld
readers to scan many products simultaneously from a specific distance. RFID handheld
scanner was chosen because of its high mobility and lightweight. The RFID scanner was
required to speed up the process of data scanning and converting the data into electronic
format. The source of data came from scanning using a Bluebird RFID handheld scanner.
RFID scanner must be configurated with its application every time the scanning process
would take place. The employee in charge of each warehouse location would scan the
product to tag the arrival time. The scanner generated data in the form of a universally
unique identifier (UUID) number. Each UUID number belonged to one unique and specific
product. Apart from the scanning process, each product was labeled using RFID Tags
beforehand. The RFID scanner’s function was to detect and read data. In data collection, the
scanner was beneficial to read a massive amount of data in such a shorttime.
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4.2 Transitory data storing
The RFID handheld scanner had an RFID application. The RFID application was mainly
used to store the read data, but it could also be used to write code further. The application’s
stored data was in comma-separated values (CSV) file format, containing the products’
scanned UUID number. The scanner and application were linked via Bluetooth. The RFID
application had to be configurated with the scanner before using it for the first time by
simply clicking the “BT ON”button in the scanner’s application and connectivity button.
The reading result in the application consisted of the read UUID number, the frequency of
the same ID read for more than one time and the received signal strength indicator. The
closer a number to zero indicated a better signal. The Data stored in the application could be
accessed in the file manager located on the device used; the data stored in a different file
every time the scanning process was initiated.
4.3 Data to cloud synchronization
The data containing UUID numbers were sent to the Cloud directly from the RFID
application. The data was automatically synchronized to the Cloud without human
interference in the transfer process. The primary reason to place data in the Cloud was to
enable automatic synchronization of data from RFID application to the Cloud. Data would
automatically be updated every minute or every time new data was scanned. Besides,
storing data in the Cloud would facilitate an automatic data transfer to the visualization and
analysis tools carried out using Power BI. Hence, the data would be updated at any time to
provide real-time reports and analysis. In doing so, the synchronization created more
extensive accessibility of data. In each RFID device, a synchronization tool was installed to
facilitate linkage between the application and the internet to upload data to the Cloud. The
synchronization tool provided an offline or online synchronization of data. Figure 6 shows
the logic of using the synchronization device to accommodate real-time data transfer in this
study.
Figure 5.
Real-time data
integration tools and
software
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warehouse
Figure 7 displays the menu of the synchronization tools installed in each device. The
synchronization history recorded the synchronization process’s time and date at each start
and end of the process. The status menu displays the recent change of synchronization,
where it indicates the latest means of synchronization. In this study, an OneDrive was used
as the location to store the data in the Cloud. The synchronization tools’status folder
showed the synchronization’s duration, status and synchronization schedule.
The average duration for each synchronization process in this study is displayed in
Figure 8. The data synchronization duration presented in Figure 8 covered the operation of
multiple experiments date. The duration ranged between 0 and 10 s, which was the delay
time before the data was successfully uploaded in the Cloud. This result is considered
acceptable when compared to the actual duration result and human intervention factors.
However, a shorter delay with lower latency ispreferred.
Figure 6.
Real-time data
transfer to the cloud
Figure 7.
Synchronization tool
interface
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4.4 Data-to-database importing process
Importing data to a database system is a part of real-time data integration processes. The
data was retrieved from the Cloud and transferred to the database. The time it takes for an
operator to import one CSV file to the database is showed in Figure 9. The average duration
it took to import single data is 9 s and with a delay range of 0 s<delay 13 s. The average
duration was then assigned as timing tolerances in the process of real-time integration in
this study. Time tolerance represents a limited deviation from nominal timing, referring to
constant feedback delays and synchronization (Tongren, 1998).
When retrieving data from the Cloud, the data was the most recent uploaded. The data
was later imported into the database through the import menu on the Cloud. Afterward, the
data would be automatically sorted in the database based on the data categorization. The
type of imported data remained as a CSV file format that contained UUID numbers. By
introducing data to the database, there were several added values including keeping the
data file in a structured format, documenting the data as file history that could be
appropriately tracked and protected and adding more information that was not initially
specified in the data from the scanning process such as date, customer and product type.
4.5 Database structure
The database was accessed through the control panel menu in the Cloud, mainly used to
display the warehouse’s structured information. The process of creating a database was
carried out through a database relational management system using MySQL. The database
Figure 8.
Data synch duration
3-Mar 5-Mar 12-Mar 13-Mar 19-Mar 20-Mar 27-Mar
0
2
4
6
8
10
12
Date
Duraon (s)
Data Synch Duraon
Duraon AVG
Figure 9.
Data-to-database
importing duration
0:00:00
0:00:04
0:00:09
0:00:13
0:00:17
123456789101112131415
Time (seconds)
Repeon Number
Data-to-Database Imporng Duraon
Trial 1 Trial 2 Trial 3 Trial 4
Trial 5 Trial 6 Average
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was developed by fulfilling the database name, username and password under MySQL in
the hosting account’s database submenu. Afterward, the database was successfully created.
To access the database, a user simply clicks the database, under the current MySQL
database and user, then select phpMyAdmin. Other menu options such as deleting, repair,
backup, change password and permission were also available under the same line as
“phpMyAdmin.”By clicking phpMyAdmin, an interface, as in Figure 10, would be shown.
The “phpMyAdmin”is a designed software to manage the activity and administration of
MySQL Database over the Cloud. In this study, five tables were containing the necessary
information that appeared in the Cloud. The database structure is one component of the
proposed framework to enable real-time data integration in an IoT-based warehouse. The
first table (data_uuid) stored information of registered UUID placed on the moving product.
The input_data table contained the products’data that had been selected for order
fulfillment. Simultaneously, additional unique information was added according to order
fulfillment; the labeling table stored a new recorded time of product arrival. The scan_wh1
and scan_wh2 enabled verification of arrival product from the previous location as it
provided quantity information. The table’s name represented a product’s location; this was
initiated to easily recognize the product whereabout and record arrival time based on
location. The data in the table covered information such as time, quantity, demand and due
date.
4.6 Integration process workflow
According to this study results, Real-time data integration processes in smart warehousing
could be generated into three significant components: configuration, databasing and
transmission. Various data such as customer orders, product locations, products inbound
and outbound, shipment schedules, could be generated in the warehouse. The logic of the
data integration process in the case warehouse is presented in Figure 11. The integration
process was composed of five fundamental processes, from which the involvement of
components in the process could be detected. Different data formats were generated from
each method. This subsequently needed to be processed into an understandable data format.
Figure 10.
phpMyAdmin
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The configuration embraced the process of setting and arranging between single
or multiple technology tools such as hardware or software to permit a state of
integration within a specific range. Databasing is the process of storing data from
various locations and types into a single place and format. The heart of its function
lay in the structural approach of the data. Transmission represented the process of
data transfer between two or more different media, networks or electronic devices.
Understanding data transfer is a cornerstone to enable the data integration process.
The data was transmitted in the form of electronic signals that were later saved in the
designated format.
4.7 Data-shared point interface
Data-shared point interface (DPI) in this study refers to collecting internet pages that
structured data into specially formatted information. DPI was customized and
arranged to replicate the case warehouse operations in deploying information based
on warehouse locations. As for the motivation, the DPI architecture was structured
following the Flight Information Display System (FIDS) as requested by the case
warehouse management. DPI displayed warehouse information in electronic display
boards in a real-time manner to be visible for all warehouse operators who had no
access to their communication device at a specific moment. This interface provided
the visibility of interconnected data from various warehouse locations by providing
network-accessible details on the goods movement. This interface’s primary role was
to provide knowledge and information by transforming the unstructured data into
well-formatted data. This interface’s expected impactful outcome was to offer
accurate data visibility regarding the whereabouts and condition of the products,
further supporting the better operational decision-making process. The data-shared
point system played an essential role in enabling data sharing by providing a single
point of sharing multiples users could access. This interface was designed to have
complete visual information for specifically identified end-users. However, all
warehouse stakeholders could view the information on this interface if needed. The
information would be displayed in the center location of the warehouse. However,
access to the interface platform was restricted to each warehouse chief to import the
unstructured data. The data-shared point interface was composed of six main menus
with different contexts and activities, as depicted in Figure 12.
4.8 Key factors of real-time data integration for the internet of things-based warehouse
Based on the results and discussion presented above, one can conclude that some factors need to
be considered when designing data integration in a real-time manner, namely, adopting suitable
technology, selecting a data center and the capability of data distribution. The adoption of proper
Figure 11.
Integration process
workflow
Smart
warehouse
technology that supports real-time data synchronization would allow the user to receive data in a
short time frame such as seconds or milliseconds. The selection of technology should be adjusted
to fit the warehouse’s size, operation activities and the desired level of integration. For example,
the technology used in this study was RFID and XML (Extensible Markup Language). The RFID
could provide Real-time Locating System (RTLS’s) and automated the process of data scanning.
The XML is a programming language that facilitated multiples data sharing between the system.
The availability of a data center or database and its capacity must be adjusted to fitdata
generated from the warehouse operations. Having the right size to store data is essential because
it will affect the database performance if the capacity is not within the normal range. Finally,
processing and distributing data across multiple sources and points are crucial in enabling real-
time data integration for IoT-based Warehouses. Real-time data integration is a complex process
that requires the comprehension of various programming languages. This could be achieved by
having a diverse group of people from different backgrounds works on the integration
collaboratively.
Two studies related to real-time data integration for IoT-based warehouse were carried out in
recent years. Lee et al. (2017) conducted a study to manage a warehouse for a low-volume, high
product mix scenario. Another study by Wu et al. (2020) was carried out for a third-party
company warehouse to manage 3PL automotive parts. The important difference between these
studies was compared to our study in Table 1. The three studies are case study-based research
that used RFID technology to implement each proposed framework. The difference lies in
selecting supporting technology used to complement the implementation and the scope of the
study.
Figure 12.
Data-shared point
menu and its data
context
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Table 1.
Comparison with
other similar studies
Parameters This study Lee et al. (2017) Wu et al. (2020)
Study objectives To design and apply a
framework of data
integration, adopting the
IoT concept in enabling a
real-time data transfer and
sharing
To propose an Internet of
things (IoT)-based
warehouse management
system with an advanced
data analytical approach
using computational
intelligence techniques to
enable smart logistics
To provide a framework
system and workflow to
achieve higher operational
efficiency and eliminate
paper-based documentation
system
Scope Warehouse management
system for a double-labeling
product in a manufacturing
company
Warehouse management
system for a low-volume,
high product mix scenario
Warehouse management
system for a 3PL automotive
parts company
Methodological
standpoint
Case study Case study Case study
Background study Inability to obtain, share
and analyze real-time
information of warehouse
operations and high cost of
real-time data integration
software and solution
There is an increase of
complexity and variety of
customer orders and
inefficient and inaccurate
order picking process
The warehousing operations
still relying on paper for
guiding and recording and
information is not
distributed evenly within
different divisions or
companies
Adopted
technology
RFID technology (tag and
scanner), Cloud system,
Web technology
RFID technology, wireless
sensors, mobile apps
IoT enabled cyber-physical
system (RFID), wireless
communication technology
(Bluetooth) and Cloud
system
Result/practical
implementation
Provide groundwork for
exploring theoretical
framework, guidelines for
developing more accurate
and timely data for enabling
smart warehouse and
framework for the more
economical application of
IoT-based warehouse
adoption
Increase data visibility in
performing seamless
operations and decision-
making processes in the
warehouse
A framework of proposed
IoT-based warehouse
management system
Fuzzy logic technique was
used to control the picking
process
Improve the efficiency of
the receiving process and
order picking
Enhance the order fulfill
performance (order fill rate
and order accuracy)
Provide configuration of the
proposed system
Provide workflow of the
proposed system
Enable paperless operations
Provide challenges from
managerial, technical and
workforces point of view
Limitation study Human intervention was not
fully eliminated
The routing and storage
policies were not discussed
in detail
Missing details of correct
goods’code to maintain a
user-friendly interface
Future research Eliminate human
intervention and perform a
longitudinal study to
improve the study’s
findings and increase
sampled products
Application of fuzzy logic
for batch the zone,
sequential zone and wave
picking approaches
involving artificial
intelligence and smart
robotics will further
improve warehouse
efficiency
To furtherly integrate more
wireless transmitting
technology to improve the
reliability of the proposed
system
Smart
warehouse
5. Conclusions
The complexity of customer behavior and the digitalization of operations have driven the
need for warehousing transformation. The complexity of back and forth communication,
high latency of product rate and insufficient control of goods could be addressed with real-
time data integration. Real-time data integration increases data visibility in performing
seamless operations and decision-making processes. This study presented a framework for
designing and applying real-time data integration processes in an IoT-based warehouse
using RFID technology. The study found that data integration plays a significant role in
structuring assorted data in the IoT-based warehouse.
Furthermore, having real-time visibility of integrated data enables quick response and
decision-making processes to overcome operational issues such as delays, poor tracking and
communication. In this study, the data went through several methods before it became
valuable information. Variation was apparent in the process of synchronization of data from
the RFID application to the Cloud. The divergence between the upper and lower limits was
between 0 and 7 s. The length of duration was affected by the size of the data and the
internet connection. The average time for transferring data to the database was 9 s, with a
delay range of 0 s<delay 13 s. In this study, the design of the shared-point interface was
also presented and discussed. This interface enabled the visibility of interconnected data
from various case warehouse locations by providing network-accessible information on the
goods movement. This interface’s primary role was to provide knowledge and information
by transforming the unstructured data into well-formatted data. The initial step toward real-
time integrated data was identifying and reengineering the warehouse processes, followed
by designing a proper infrastructure of real-time data integration. The study concluded that
real-time data integration processes in IoT-based warehousing could be generated into three
significant components: configuration, databasing and transmission. Real-time data
integration could be executed for as long as there is a data input process and the data flow is
maintained between interface and software.
Theoretical and practical implications
The design and application of real-time data integration processes and the creation of data-
shared point systems in this study add to the body of knowledge of smart warehousing,
Industry 4.0 and the digitalization of supply chain management. The research at hand
provides the groundwork for researchers to explore the proposed theoretical framework and
develop it further to increase inventory management efficiency of warehouse operations.
From a practical perspective, the study provides guidelines for practitioners to design a
low-cost IoT-based smart warehouse system to obtain more accurate and timely data to
support the quick decision-making process. The proposed framework of the IoT-based
warehouse in this study should help practitioners in adopting a more feasible and
economical application in acquiring the data integration software.
Limitations and future research
The study could be enhanced by performing a longitudinal study to improve the study’s
findings and increase sampled products. While the status of real-time data integration was
ensured in this study to maintain the products’visibility and shared-point system creation,
there is still a human intervention in one of the processes of real-time data integration.
Further development is required to eliminate human intervention, even if it is minimal.
The current proposed framework involved six different processes in deploying the
information. The method covered reading, storing and processing, synchronizing and importing.
To reach a better level in allowing the data integration process in a real-time manner and most
IJPCC
importantly, to eliminate more delays, further research should be conducted. First, develop
customized mobile applications that support tools between the RFID tags reading result and
database. The customized mobile applications would allow the process of data transfer without
having to have human interference. With that in mind, the synchronization process between
Cloud and database could be facilitated and replace RFID applications. Second, further research
could be conducted to reduce the human intervention rate by using synchronization platforms
such as Zapier or Skyvia. Using such synchronization platforms, the data transfer could be
executed automatically from the Cloud to the database.
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Corresponding author
Ammar Aamer can be contacted at: a.aamer@northeastern.edu
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