Internet of Things for Enterprise Systems of Modern Manufacturing

Abstract

Design and operation of a manufacturing enterprise involve numerous types of decision-making at various levels and domains. A complex system has a large number of design variables and decision-making requires real-time data collected from machines, processes, and business environments. Enterprise systems (ESs) are used to support data acquisition, communication, and all decision-making activities. Therefore, information technology (IT) infrastructure for data acquisition and sharing affects the performance of an ES greatly. Our objective is to investigate the impact of emerging Internet of Things (IoT) on ESs in modern manufacturing. To achieve this objective, the evolution of manufacturing system paradigms is discussed to identify the requirements of decision support systems in dynamic and distributed environments; recent advances in IT are overviewed and associated with next-generation manufacturing paradigms; and the relation of IT infrastructure and ESs is explored to identify the technological gaps in adopting IoT as an IT infrastructure of ESs. The future research directions in this area are discussed.

Full text

Internet of Things for Enterprise Systems
of Modern Manufacturing
Zhuming Bi, Senior Member,IEEE, Li Da Xu, Senior Member,IEEE, and Chengen Wang, Senior Member,IEEE
Abstract—Design and operation of a manufacturing enterprise
involve numerous types of decision-making at various levels and
domains. A complex system has a large number of design variables
and decision-making requires real-time data collected from ma-
chines, processes, and business environments. Enterprise systems
(ESs) are used to support data acquisition, communication, and all
decision-making activities. Therefore, information technology (IT)
infrastructure for data acquisition and sharing affects the perfor-
mance of an ES greatly. Our objective is to investigate the impact of
emerging Internet of Things (IoT) on ESs in modern manufactur-
ing. To achieve this objective, the evolution of manufacturing
system paradigms is discussed to identify the requirements of
decision support systems in dynamic and distributed environments;
recent advances in IT are overviewed and associated with next-
generation manufacturing paradigms; and the relation of IT infra-
structure and ESs is explored to identify the technological gaps in
adopting IoT as an IT infrastructure of ESs. The future research
directions in this area are discussed.
Index Terms—Enterprise modeling, enterprise systems (ESs),
Internet of Things (IoT), literature review, manufacturing
enterprise, system paradigms.
I. INTRODUCTION
MANUFACTURING is woven into economy and society.
For example, manufacturing took 12% of gross domes-
tic product (GDP) and 11% of workforce in the Unites States in
2011 [27]. Moreover, the significance of manufacturing is far
beyond the scope these numbers represent. For example, the
manufacturing sector in the U.S. used to take 19% of GDP and
30% of workforce in the 1950’s [53]; however, this percentage
has been shrinking continually for several decades. More en-
terprises have relocated their facilities to developing countries
and it has shown that the manufacturing industry in the U.S. is
still in recession [24], [75]. Therefore, identifying new drivers to
boost manufacturing is crucial to regain the leading position in
manufacturing.
The advance of manufacturing technologies relates closely to
information technologies (ITs). Since design and operation of a
manufacturing system needs numerous types of decision-making
at all of its levels and domains of business activities, prompt and
effective decisions not only depend on reasoning techniques, but
also on the quality and quantity of data [26]. Every major shifting
of manufacturing paradigm has been supported by the advance-
ment of IT. For example, the widely adoption of computer
numerical control (CNC) and industrial robots made flexible
manufacturing systems (FMSs) feasible; the technologies for
computer-aided design (CAD), computer-aided manufacturing
(CAM), and computer-aided processing planning (CAPP) made
computer integrated manufacturing (CIM) practical. In develop-
ing their ESs, more and more enterprises rely on the professional
providers of IT software service to replace or advance their
conventional systems [56]. Therefore, it makes sense to examine
the evolution of the IT infrastructure and evaluate its impact on
the evolution of manufacturing paradigms, when a new IT
becomes influential.
We are motivated to investigate the impact of IoT on system
paradigms, when IoT can be applied in modern manufacturing
enterprises. To achieve this objective, both the evolutions of
manufacturing paradigms and IT are discussed. Their relations
are explored to identify the priorities of the subjects of research
and development. In Section II, the evolution of manufacturing
system paradigms is introduced and the focuses are enabling
technologies for ESs. In Section III, the IT development is
discussed; its impact on manufacturing technologies has been
discussed. In Section IV, the progress of emerging IoT is
introduced, and some potential issues related to its application
of ESs in modern manufacturing are explored. In Section V, the
reported work is summarized and future research activities of
adopting IoT in ESs of modern manufacturing are discussed.
II. EVOLUTION OF ESS
A manufacturing system is to produce value-added goods via
various manufacturing resources such as machines, tools, and
labors. Design and operation of a manufacturing system involves
numerous types of decision-making at all levels and domains of
manufacturing activities. System components and their relations
can be represented by enterprise architecture [7]–[9], which will
be discussed further in Section II-B. In any system or sub-system,
a decision-making process can be depicted as a series of design
activities:
1) defining the scope and boundary of a design problem and
its objective;
2) establishing relational models among inputs, and outputs,
and system parameters;
Manuscript received September 13, 2013; revised November 22, 2013;
accepted January 02, 2014. Date of publication January 16, 2014; date of
current version May 02, 2014. This work was supported in part by the
National Natural Science Foundation of China (NNSFC) under Grant
71132008, and in part by the U.S. National Science Foundation under Grant
SES-1318470 and Grant 1044845. Paper no. TII-13-0633. (Corresponding
author: Z. Bi.)
Z. Bi is with the Department of Engineering, Indiana University–Purdue
University Fort Wayne, Fort Wayne, IN 46805 USA (e-mail: biz@ipfw.edu).
L. D. Xu is with the Institute of Computing Technology, Chinese Academy of
Sciences, Beijing 100190, China; Shanghai Jiao Tong University, Shanghai
200240, China; University of Science and Technology of China, Hefei 230026,
China; and also with Old Dominion University, Norfolk, VA 23529 USA.
C. Wang is with the State Key Laboratory of Synthetical Automation for
Process Industries, Northeastern University, Shenyang 110819, China (e-mail:
wangc@mail.neu. edu.cn).
Color versions of one or more of the figures in this paper are available online at
http://ieeexplore.ieee.org.
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3) acquiring and managing data on current system states;
4) making decisions according to given design criteria.
An ES is to acquire and maintain data, and serve as a decision-
making system within an enterprise. Therefore, the characte ristics
of an ES can be examined from the perspective of decision-
making processes.
A. Description of Manufacturing Enterprise
The complexity of system operations relates directly to the
number and nature of inputs, outputs, and other system compo-
nents. Many researchers have discussed the evolution of
manufacturing technologies. The differences of scopes and
boundaries of a manufacturing enterprise are focused here. In
particular, inputs, outputs, and system parameters are examined.
Note that system parameters are used to represent system
components and relations; they can be classified into structural
parameters as the representations of system properties and
design variables as the factors changing with respect to time.
Inputs and outputs are typically the variables representing the
interactions of the system and its environment.
Fig. 1 has illustrated the evolution of manufacturing
systems, which is divided into the phases of craft systems,British
systems,American systems,lean production,FMSs/CIM [23],
and sustainable manufacturing [10]. With the evolution of a
manufacturing system, inputs, outputs, as well as system para-
meters can be changed with respect to time significantly. One can
find that design variables have being increased exponentially
with the evolution of manufacturing systems. A detailed discus-
sion on the similar subject has been covered by Galbraith [36].
Moreover, the adoption of the Internet has significantly influ-
enced the enterprises on how the profits are shared by participants
over their supply chains [35], [52], [55], [76]. The information
systems for the next-generation manufacturing systems must
accommodate the changes of the IT infrastructure as well as the
changes and uncertainties in the system environments.
B. Reference System Models
Reference system models describe the constitutional compo-
nents and their interactions within a system according to desir-
able system performances. An enterprise model represents basic
system elements, relationships, and the refinements up to the
necessary level of details. Conceptual models and architectures
of manufacturing enterprises have been extensively studied. A
few of widely used enterprise models were discussed in the
literature [15], [34], [63], [67], [74], [85], [91], and [94]. How-
ever, conventional enterprise models are static, which lack
of capability to accommodate changes. Some advanced con-
cepts, such as holonic manufacturing,agent-based intelligent
manufacturing,reconfigurable manufacturing, and agile manu-
facturing [7], [8], [10], have been proposed to be integrated with
enterprise models to increase system adaptability and flexibility.
Tao et al. [82] discussed a service-oriented manufacturing para-
digm for cloud manufacturing, where both of computational and
manufacturing resources are managed simultaneously. Xu [92]
discussed the architecture of information systems for supply chain
management. Recent progresses of enterprise modeling, ESs,
and the integration of distributed enterprise applications have
been discussed comprehensively [10], [45], [66], [93].
Fig. 2 has classified system models from the perspective of
system complexity. A craft system completes all activities at one
machine station and it can be viewed as a point model. Later on, a
transfer line organizes all manufacturing activities sequentially;
the corresponding system model is a line model. Manufacturing
systems are then expanded to multiple transfer lines and multiple
factories; therefore, the relational models can be described as
two-and three-dimensional models, respectively. With the ap-
pearances of enterprise alliances and virtual enterprises, system
components will cover related resources from enterprise partners
and virtual enterprises, which are beyond the boundary of one
manufacturing enterprise. It is another perspective to show the
increased complexity of manufacturing enterprises.
C. Data Acquisition and Management
Design variables represent for the states and the changes of
inputs and outputs with respect to time. Acquiring the values or
states of the variables is essential to support closed-loop decis ion-
making. Data acquisition is to collect data by sensors or other
measurement equipment. Although data acquisition covers man-
ual monitoring and recording methods such as inspecting a
product visually or measuring an object, data acquisition
generally refers to the use of electronic sensors and data collection
equipment [51]. Early manufacturing systems were open
Fig. 1. Evolution of inputs, outputs, and system components.
Fig. 2. Types of system models.
1538 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 2, MAY 2014

loop; with few sensors, humans played a central role in observing
the system’s behavior and making decisions accordingly.
Technologies for data acquisition and processing have been
rapidly evolved with the automated solutions. In Sections II-C1
and II-C2, data acquisition at the device level and the data
communication are discussed as follows, respectively.
1) Data Acquisition: Sensors or device for data acquisitions
are crucial to the success of new products [71]. Instrumentation
usually consists of some basic elements to collect, process, and
share data among objects. Hardware systems for data acquisition
instrumentation have been classified in Fig. 3. Instruments are
much diversified in terms of the functions, communication
modes, and other factors. With an increase in system compo-
nents in a system, how to collect, fuse, process, and use data
effectively, becomes very challenging.
2) Communication for Data Sharing: Data communication is
to exchange data among different devices via some transmission
medium such as wire cables and wireless networks. In a manu-
facturing enterprise, data are communicated and exchanged
among decision-making units locally or remotely. The perfor-
mance of data communication can be evaluated by delivery,
accuracy,timeliness, and jitter. Communication and networking
are changing the way an enterprise operates its business. Oper-
ating decisions have to be made quickly, and the decision support
system units require an instant access to sufficient and accurate
real-time data. The availability of data relies on reliable
communication and network [33].
Gungor and Lambert [41] gave a comprehensive survey on the
networks for data communication, and four phases described by
them are as follows.
1) The first phase was the nonstandardized communication
before 1985. Network architecture was hierarchical. It was
solo master or isolated substations and the communication
media were dial-up, RS232, and trunked radio. The com-
munication rate was less than 1200 bps.
2) The second phase was the standardized communication
during 1985–1995. Communication media were wired
lines and packet radio. The communication rate was in a
range of 9600–19 200 bps.
3) The third phase was the communication via the networks
that happened from 1995 to 2000; the Ethernet and spread
spectrum radio were adopted widely. The transmit rate
reached at megabit level.
4) The last phase started from 2000. The media such as
Internet Protocol (IP) radios and wireless Ethernet are
used. The transmission rate is over gigabit.
Communications can also be classified in terms of channel
types,channel interfaces,data rates,andapplications. For
examples, the types of channel interfaces include dc signaling
for telegraph, narrowband modems for sub-voice grade, 2/4 wire
modems for leased voice grade lines, 2-wire modems or acoustic
couplers for switched telephone network, digital station terminal
for digital service, Wideband moderns for wideband analog, Line
drivers and limited distance modems for private cable, and fiber
optic connector and fiber optics [40].
D. Critical Requirements to Adapt New Environments
A complete ES generally includes system components for data
acquisition,communication,data management, and decision-
making. ESs should be designed to meet the requirements of
modern manufacturing enterprises for these functionalities.
From the discussions in this section, we can summarize the
new requirements as follows, which form some critical chal-
lenges to ESs.
1) Complexity: Ever-increasing complexity is contributed by
two key factors: 1) Modern products become versatile
which need more parts and components to fulfill required
functions. Manufacturing resources have to be integrated
vertically to make these products. 2) Making complex
products involves numerous manufacturing activities.
Sometimes, it even becomes impractical to perform all
activities within an enterprise. In this case, enterprises have
to be allied the partner enterprises along the supply chain
horizontally. The hierarchy enterprise model with vertical
and horizontal integration of business departments in one
enterprise is ineffective to deal with such a level of
complexity.
2) Dynamics and uncertainties: Manufacturing environments
are dynamic. Traditional ESs divide the time domain into
sub-periods so that system parameters involved in one
decision-making task can be treated statically within an
individual period; the complexity of decision-making is
manageable. When an enterprise model becomes flat,
complex decisions have to be made within a shorter period.
It becomes extremely important to know the changes and
uncertainties in real-time; and share real-time data over
entire ES. Zeng and Skibniewski [96] conducted the risk
assessment in enterprise resource planning systems and the
fault tree approach was used to analyze uncertainties and
risks. Corrales et al. [22] studied the autonomous naviga-
tion of an indoor robotic system with a focus of
the information system in the unknown environment.
Chamdrashekhar and Bhasker [18] discussed the impact
of information uncertainty on automated negotiation
and developed new negotiation model to improve the
efficiency, simplicity, and stability.
3) Virtual entities: Manufacturing resources have to be inte-
grated to make a balance between the manufacturing
capabilities and system flexibilities, i.e., physical and
virtual resources can be considered simultaneously to
provide sufficient capabilities to make complex products.
The information systems of a host enterprise and its col-
laborating virtual enterprises must be integrated to opti mize
the collaboration globally. However, the ownership of data
from an individual participator might bring barriers to share
the data seamlessly for effective decision-making.
Fig. 3. Classification of data acquisition system [20].
BI et al.: IoT FOR ESs OF MODERN MANUFACTURING 1539

4) First-time correct: The fierce competition forces enter-
prises to minimize nonvalue added activities such as
buffering and repairing. To keep competitiveness, an
enterprise must organize its manufacturing activities to
avoid carrying excessive inventory and make product
correct at the first time. On the one hand, the production
line should be monitored to avoid the breakdown of
machines. If an error happens on one workstation, the
whole production line will be affected. On the other hand,
the product quality has to be strictly controlled; defected
parts, components or products increase the cost for end
products. Both of the efforts need reliable and real-time
data about products, processes, and resources. The states of
a manufacturing system should be monitored thoroughly
and real-time data about everything should be available to
make right decisions at different domains and levels.
E. Limitations of Existing Work
Existing manufacturing paradigms and control methodologies
are continually facing new challenges to meet these requirements
in a competitive environment. The following limitations are
observed in meeting these challenges.
Unbalance of software and hardware flexibility: The flexibil-
ity of a manufacturing system is required to deal with changes
and uncertainties [11]–[14]. System flexibility relies on both of
hardware and software systems and it can be maximized if the
flexibility of hardware and software systems is balanced. While
the flexibility of existing hardware systems seems to reach its
limits as far as the cost is concerned, the flexibility of software
systems is mostly unsatisfactory. Good examples are industrial
robots and FMSs. Hardware systems such as industrial robots are
able to take task variations; however, they have to be repro-
grammed for new tasks. For many advanced applications, it is
still a big challenge for the software system to acquire and
process sufficient data, and generate the program automatically
and promptly to changeable tasks.
Information islands: The problem of the isolation of informa-
tion sub-systems has been observed by many researchers [30],
[92]. The isolation not only happens to the top level of decision-
making, but also happens at device or sub-system levels where
raw data of machine status are collected. Within a large-scale
and complex system, information islands bring the delay in
information communication and sharing; for the cases where the
historical data are required by different components for decision-
making; the cost for storage and computing is increased and
additional care has to be taken to maintain the consistence of data.
Redundant resources: Computing resources are needed by any
decision-making units. Data usually need to be stored and
complex decisions are made locally. On the one hand, a number
of high-performance computers are required to support distrib-
uted decision-making; this increases the cost of an ES; the
computing capability is not fully utilized if it can only be accessed
locally. On the other hand, the distributed resources bring a
problem of synchronizing data, the requirements of multiple
decision-making units make it impractical to be equipped with
the highest computing power of an ES.
Encapsulation of system components: Components in an ES
are generally encapsulated and fully protected from its
manufacturing environment. This brings the obstacles for cus-
tomers to know manufacturing processes or learn more require-
ments about their products. It is particularly true when the system
sustainability becomes a critical factor to today’s enterprises. The
customers in supply chains play a mixed role with suppliers,
users, and even designers. Data collection, exchanges, and
sharing over the whole product life cycle become more and
more important.
III. EVOLUTION OF IT INFRASTRUCTURE
Primary functions of an ES are: 1) to acquire static and
dynamic data from objects; 2) to analyze data based on computer
models; and 3) to plan and control a system and optimize system
performances using the processed data. The implementation of a
manufacturing system paradigm relies heavily on available IT. In
this section, the IT infrastructure related to manufacturing is
discussed. IoT has been identified as a critical technology with its
great impact on the national economy [64]. Here, an overview
about the history, core components, and advantages of IoT is
given.
A. Overview of IoT
The Internet has changed the business and personal lives in
past years and continued doing so. IoT becomes a foundation for
connectingthings, sensors, actuators, and other smart technologies
[83]. IoT is an extension of the Internet [32]. IoT gives an
immediate access to information about physical objects and leads
to innovative services with high efficiency and productivity [6].
Arguable pioneers of coining the concept of IoT are Bill Gates,
Kevin Ashton, and Neil Gershenseld at Auto-ID Center of
Massachusetts Institute of Technology (MIT) [4], [72]. The first
IoT conference was held in Europe in 2006. Participators included
over 50 member companies such as Intel, SAP, Sun, and Google.
IoT has not been applied as people expected; however, it is
predicted as a combination of a number of advanced IT technolo-
gies, which will drastically change our societies in 5–15 years [17].
For the hardware and system development, several significant
milestones related to IoT are the Auto-ID Center at MIT in 1999,
the Internet Refrigerator at LG in 2000, the Ambient Orb by
David Rose in 2002, “smart earth”project by IBM in 2008, and
“experience China”project in 2009. Bandyopadhyay and Sen [6]
overviewed key technological drivers, potential applications,
and challenges in IoT. The identified central issues are interop-
erability, interconnected objects, and enabling the adaptation and
autonomous behaviors with trust, security, and privacy of users.
Zorzi et al. [98] discussed how existing “intranet”of things can
be evolved into an integrated and heterogeneous system.
B. Core Components and Enabling Technologies
The characteristics of IoT include: 1) the pervasive sensing of
objects; 2) the hardware and software integration; and 3) a large
number of nodes. Bui [14] discussed enabling technologies for
IoT including architecture frameworks, communications, stan-
dardizations, modeling techniques, communication protocols,
identification, objects platforms, and security and privacy. Atzori
et al. [5] surveyed key enabling technologies and discussed the
progress made on communication, identification, tracking, wired
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and wireless sensors,and distributed intelligence for smart objects.
Since two of the most important technologies to IoT are radio-
frequency indemnifications (RFIDs) and wireless sensor networks
(WSNs) [88], besidesubiquitous computing and cloud computing,
the studies on RFIDs and WSNs are discussed as follows.
1) Ubiquitous Computing: The Internet can be described as a
ubiquitous infrastructure. IoT is also known as ubiquitous
computing, ambient intelligence, and distributed electronics. In
an IoT, a virtual computer model can be seamlessly integrated
with physical networks of objects [83]. For example, Fang et al.
proposed an integrated approach based on the sensor network for
water resource management [31]. IoT will change the ways of
managing and operating production, distribution, transportation,
service and maintenance, recycling of their products.
Ubiquitous computing is enabled by smart devices. Smart
devices are capable of: 1) applying data mining and analysis,
modeling and simulation, fusion and computation, and scientific
analysis for decision-making; 2) integrating personal device,
organization, and other information systems for interacting, data
sharing, and real-time monitoring; and 3) using anytime, any-
thing, anywhere communication to sense, measure, capture, and
transfer data in planning and scheduling. For individual smart
devices, their performances have been improved greatly. They
become versatile, powerful, and intelligent to deal with changes
and complexity. For the networked system, simple devices
without superior computation capability can be integrated;
abundant information can be acquired for real-time decision-
making. In developing an IoT, objects must be capable of
interacting with each other, reacting autonomously to the
changes of residential environment, and responding people
appropriately [84].
2) RFIDs: RFID has been widely applied in modern
manufacturing, in particular, in supply chain management
[16], [54]. RFID is one of the cornerstones of IoT [28]. RFID
was initially developed to track and identify objects in retails and
logistics. In near future, single numbering scheme including
Internet Protocol version 6 (IPv6) will make it possible to
identify every single object. However, to achieve ambient
intelligence, major technological innovations are in demand.
These include governance, standardization, and interoperability
[46], [69], [86], [89], [95], and efficient and secure communi-
cation protocols. Furthermore, other major research challenges
are to enable device adaptation, autonomous behavior, intelli-
gence, robustness, and reliability.
3) Wireless Sensor Networks: IoT is a network of all physical
objects with identifiable IDs, which are based on the Internet or
other conventional communications. Early work on IoT was the
application of Auto-ID for the supply chain management. IoT in
near future will providea wider application by enablingindividuals
to acquire and take advantage of abundant data from objects [82].
For example, Li et al. discussed how networked body sensors
could assist in obtaining biomedical signals continuously [58]. The
integration of WSNs was discussed to provide cloud services to
enterprises [59]. WSNs are the most important infrastructure for
the implementation of IoT. Various hardware and software
systems are available to WSNs:
1) IPv6 makes it possible to connect unlimited number of
devices.
2) WiFi and Wimax provide high-speed and low cost
communication.
3) Zigbee, bluetooth, and RFID provide the communication
in low-speed and local communication.
4) A mobile platform offers communications for anytime,
anywhere, and anything.
The importance of WSNs to industrial control systems have
been discussed by Araujo et al. [3]. In the research field of WSNs,
most of ongoing work focuses on energy efficient routing,
aggregation, and data management algorithms; other challenges
include the large-scale deployment and semantic integration of
massive data [1], and security [38].
4) Cloud Computing: Cloud computing is a large-scale, low
cost processing unit, which is based on the IP connection for
calculation and storage. The characteristics of cloud computing
have been discussed in the literature [70], [73], [78], [79] and
summarized in Fig. 4. The characteristics such as on-demand
self-service are essential to support a computing cloud for an
enterprise in terms of cost reduction, system flexibility, profit,
and competiveness. Wang et al. provided cloud service
architecture for enterprises [87]. Besides, the benefits of cloud
computing to manufacturing system have been explored by
some researchers. Cheng et al. [19] investigated an information
system to schedule services based on energy saving in a cloud
manufacturing system.Tan et al. developed a grid-based operation
platform to support virtual enterprises [80]. Li et al. discussed
the modeling issues involved in the integration of a hybrid
cloud computing environment [56].
C. Applications
IoT is IT infrastructure, which is applied to measure, identify,
position, track, and monitor objects. IoT makes people’s lives
easier and automates our tasks [29]. IoT connects physical
objects as a network so that these objects can be interacted
effectively [55]. Domingo [25] provided an overview of IoT in
assisting peoples with disabilities. Jara et al. [45] proposed an
IoT-based solution for personalized health care systems. Many
new businesses have been developed via the application of IoT
[2]. For examples, Gama et al. [37] applied service-oriented
middleware to enhance system flexibility and dynamicity. Large
scale applications are emerging in many industries, such as
IBM, Cisco systems, and GE, from smart grid to real-time
Fig. 4. Characteristics of cloud computing.
BI et al.: IoT FOR ESs OF MODERN MANUFACTURING 1541

transportation management and optimization. Although IoT is at
its infant stage, 43% of large Chinese organizations started to
investigate private clouds and infrastructures, and this number
would be increased to 88% in two years [80].
D. Challenges
Currently, the IoT is all about information visibility, it is not
about autonomous decision-making. The combination of auto-
mated decision-making and IoT will offer a higher level of
system robustness, scalability, and agility. Traditionally, the IT
standards in enterprises are different from those for commercial
software tools. To adopt an IoT, IT infrastructure should be
service oriented so that different functions can be integrated over
heterogeneous platforms [77].
Jeffery [47] and Cooper and James [21] discussed the chal-
lenges of data management in IoT. Atzori et al. [5] argued that it
is challenging to enable the adaptation and automation for a high
degree of smartness, and assure the security and privacy. A new
infrastructure is in demand to meet these challenges. Moreover,
the Web is growing not arithmetically but exponentially [68]. To
deal with a large amount of data, Li et al. [56] proposed a
compressed sensing theory to eliminate redundant data in data
acquisition and transmission in the applications of WSN and IoT.
Standardization is another important issue for system integra-
tion [48], [49], [50]. Jakobs et al. [44] discussed how to adapt the
standards for IoT to meet customers’requirements. The works on
standardization have been classified and analyzed, major stan-
dards have been identified, and some typical application scenar-
ios have been developed. Alam et al. [2] discussed the security
issue of IoT-enabled services and interoperability in different
domains; as a result, the layered architecture of IoT has been
proposed. Welbourne et al. [90] developed a building-scale,
community-oriented infrastructure. To make IoT application
successful, software engineering must fulfill the requirements
of heterogeneity, dynamicity, and evolution. Heer et al. [43] used
the building automated control as an example to illustrate the
challenges in a secure IP-based information system.
IV. IOTFOR MODERN MANUFACTURING
In this section, the relations of new manufacturing paradigm
and IT infrastructure are considered. The requirements of an
enterprise model for a successful adaption of IoT are discussed.
A. IoT Infrastructure for Enterprises
The aforementioned discussion has shown that IoT is aligned
well with the architecture of a manufacturing enterprise. An
enterprise model consists of a set of modular components and
their interactions. Correspondingly, each system component in
an ES needs an information unit to make decisions on the
component’s behaviors based on the acquired data. Moreover,
data acquisition, communication, and decision-making are
essential functions for each module. Fig. 5 illustrates the relations
of the components in a manufacturing enterprise and the archi-
tecture of IoT. Based on the Axiomatic Theory, the IoT is able to
provide vital solutions to planning, scheduling, and controlling
of manufacturing systems at all levels.
B. Features of Next-Generation Enterprises
In this section, the features of next-generation enterprise are
discussed to evaluate if an IoT-based ES is capable of meeting
these challenging requirements.
1) Decentralized Decision-Making: Domains and levels of
manufacturing activities are increasing and becoming diver-
sified. Hierarchical architecture is used to the most efficient
enterprise architecture for system integration. However,
system complexity can be increased exponentially with the
system scale and dynamics. A centralized system may lead to
a significant time delay and inflexibility to respond changes
promptly. Therefore, distributed and decentralized architecture
would be effective means to deal with system complexity and
dynamics.
2) Flat and Dynamic Organization: Prompt responses to
uncertainties require distributed and decentralized enterprise
architecture. In such a way, acquired data can be directly used
for decision-making in real time. As far as the interactions among
system components are concerned, it forms the challenges to
distribute the information to associated components; in particular
under a centralized structure. The data are collected and sent
to the center database, and then it is sent to an object when
the system receives the request from this object. However, a
centralized model has its challenges in dealing with massive data
and the heterogeneity of environment.
a) Massive data: From the perspective of data management,
information systems for next-generation manufacturing enter-
prises are facing two situations: 1) the cost for decision-making
unit is likely increasing with system complexity and the need of
fast responsiveness and 2) it causes resources redundant to
maintain data locally and the wastes of time and resources for
communications when the data are shared by other decision-
making units.
b) Heterogeneous environment: Increased and diversified
manufacturing resources have increased the heterogeneous nature
of a manufacturing environment. The variety exists at the aspects
of personalized products, geographical distribution, cultures,
suppliers, regulations, optional operations, and standards.
c) Agility and adaptability for real-time changes: Manu-
facturing enterprises are functioned to meet customers’needs,
Fig. 5. IoT for modern manufacturing.
1542 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 2, MAY 2014

including functionalities, quantity, quantity, delivery time, and
changes. The enterprises must be capable of dealing with
changes at reasonable time and making products available as
early as possible to catch the market niche. Without such a
capability, the profit margin will be reduced significantly.
d) Reconfigurable capabilities: To increase system flexibil-
ity, the structures of hardware and software systems are not static
anymore. A system at a certain time can be decomposed into
sub-systems, and these sub-systems can be reconfigured as
manufacturing resources for other tasks. Extra system compo-
nents are required to support hardware and software system
configurations. System reconfigurability or modularization
decides the interoperability, which is extremely important in
the globalized market. The recent progress on reconfigurable
machines was discussed by Bi et al. [10].
C. Features of IoT for Manufacturing Applications
1) Integrated Networks of RFIDs and WSNs: One core
function of IoT is as the communication infrastructure for
data acquisition and sharing. A manufacturing system consists
of numerous sensors to acquire real-time data of actuators,
machine tools, fixtures, and conveyors; traditional wired com-
munications are confined as point-to-point or peer-to-peer, and
it is inflexible to make changes. RFID and WSN provide an
effective means to support the distribution and decentral-
ization of manufacturing resources [87].
2) Dynamics: The architecture of IoT is not static, which
allows the system components be reconfigured anytime when it
is needed. It facilitates the information integration across the
boundaries of enterprises. A host enterprise can incorporate with
virtual enterprises and establish dynamic relations for a specific
project. The organizations can be dismissed when the project is
completed and the enterprise can be ready for other projects. The
enterprise has its authority in controlling the reorganization of
virtual enterprise alley.
3) Cloud Computing: Operation of a modern enterprise
involves numerous decision-making activities, which requires
intensive information and high capability of computing.
Manufacturing enterprises used to require multiple computing
resources as servers as databases and decision-making units. This
causes the wastes of investment, unbalanced utilization of
manufacturing resources, low productivity, and ineffective
data exchanges among servers. Cloud computing provides a
vital solution to those problems. All data can be stored in private
or public cloud servers, and the complicated decision-making
can be supported by superior cloud computing.
4) Human and Things: Interactions happen between human
and human, human and thing, and thing and thing. Different
interactions used to have different mechanisms to support these
interactions. With the development of IoT, all of the interactions
can be performed under the same umbrella. In this way,
participators are able to focus on tasks instead of worrying
about interactions, which make the designs and operations of
manufacturing systems very productive. In human–machine
interaction, how to represent human behaviors in virtual
environment is critical, Tao et al. [81] discussed the recog-
nition of human behaviors in WSN.
5) Merging IoT in ESs: Changing trends of manufacturing
paradigms have been explored by many researchers [8]. In this
section, the needs of modern manufacturing and the features of
IoT are compared to see how modern manufacturing can benefit
greatly from the adoption of IoT infrastructure. It is found that
critical needs on the next-generation ESs are consistent to core
features the IoT can provide. The manufacturing systems can
benefit greatly by adopting IoT infrastructure at all aspects of
the information systems including data acquisition, communi-
cation, and decision-making at higher levels. The evolution of
ES has been discussed by many researchers. For example, Neal
[65] provided a roadmap of IT to describe the evolution of
information system from manual operation of the databases
based on service-orientated architecture. In addition, the impact
of IT on manufacturing systems has been discussed as well.
Information systems are the key to the success of manufacturing
systems; the advancement of information systems is revolu-
tionizing manufacturing systems. By comparing the features of
next-generation of ESs in Section II-B and those of IoT in
Section IV-C; it is found that the integration of IoT within
an ES can be expected to address the challenges of ESs
adequately.
1) Ubiquitous computing and grid computing can be applied
to network manufacturing resources. Everything can be
connected, so that the data can be acquired promptly and
readily shared by all decision-making units. This makes it
possible to integrate manufacturing resources at a very
broad scope, including the resources within an enterprise
and virtual resources from potential participators in a
supply chain.
2) Customers are empowered by the IT through electronic
commerce (e-commerce). Besides the privilege of com-
paring products from different vendors around the world,
IT allows customers to personalize product requirements,
place and change orders in real time based on their needs.
On the one hand, the satisfaction level of customers can be
enhanced greatly from the customers’perspective; on the
other hand, many new variables for uncertainties and
changes are involved in the management and operations
of an enterprise’s business.
3) A network-based environment fully supports the collabo-
ration of design, manufacturing, and assembly among
different partners. The system is targeted to an optimal
balance of flexibility and efficiency. On the one hand, the
manufacturing system is modularized; each module is
optimized at the module level for its specified function;
on the other hand, the selection of modules and assembling
topologies offer system flexibility to meet various func-
tions at the system level. Moreover, the topology of system
configuration can be optimized with the available global
information over the system. The phases of system design,
reconfiguration, and deployment are highly corresponded.
4) Information integration connects design database, data
acquisition, monitoring, and diagnosing together to assist
in making right products without iterations. Traditional
information systems are mainly at the macro level planning
and scheduling, and such systems will be integrated with
real-time control systems at the hardware level. Online data
BI et al.: IoT FOR ESs OF MODERN MANUFACTURING 1543

acquisition systems are not only used to serve for real-time
control for machines, but also provide feedbacks about the
changes and uncertainties to high-level system planning
and controlling. The plans and schedules can be adjusted to
accommodate changes and uncertainties promptly. A good
example is the changing trend of systems applications
products (SAP) software tools; SAP tools are used to focus
on the enterprise resource planning before 2005; they have
been integrated with manufacturing execution systems.
With the emerging of IoT, they would be integrated with
online process control eventually [61]. In contrast to hierar-
chical enterprise architectures, service-orientated architec-
tures become prevalent in industries to improve system
flexibility and seamless transition of reconfiguration [38].
V. SUMMARY AND PLANNED WORK
Current manufacturing environment has been extensively
discussed to identify key requirements of ESs of modern en-
terprises. It has concluded that the limitations of ESs are: 1) static
IT architecture incapable of dealing with all types of changes and
uncertainties; 2) unbalanced flexibility of hardware and software
systems; 3) rigid and confined boundaries of an enterprise with
the barriers for virtue collaboration; and 4) the lack of the
considerations on system sustainability. A comprehensive liter-
ature review is given on IoT infrastructure, and the opportunities
and challenges are explored when manufacturing enterprises
adopt IoT infrastructure in their ESs.
It is found that the emerging IoT infrastructure can support
information systems of next-generation manufacturing enter-
prises effectively. More specifically, anytime, anywhere, any-
thing data acquisition systems are more than appropriate to be
applied in collecting and sharing data among manufacturing
resources. Ubiquitous computing effectively supports mutual
interactions among humans and things seamlessly, and cloud
computing utilizes superpower computing resources to solve
complicated decision-making problems at any level and disci-
plines. IoT brings numerous great opportunities to advance
manufacturing enterprises in achieving better system perfor-
mances in globalized and distributed environments. However,
the application of IoT in ESs are at its infant stage, more
researches are in demand in the areas such as modularized and
semantic integration, standardization, and the development of
enabling technologies for safe, reliable, and effective communi-
cation and decision-making.
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Zhuming Bi (M’11–SM’12) received the Ph.D.
degrees from the Harbin Institute of Technology,
Harbin, China, and the University of Saskatchewan,
Saskatoon, SK, Canada, in 1994 and 2002,
respectively.
He is an Assistant Professor of Mechanical Engi-
neering at Indiana University–Purdue University Fort
Wayne, Fort Wayne, IN, USA. His current interests
include mechatronics, automatic robotic processing,
reconfigurable manufacturing, and assembling
systems.
Li Da Xu (M’86–SM’11) received the M.S. degree in
information science and engineering from the Univer-
sity of Science and Technology of China, Hefei,
China, in 1981, and the Ph.D. degree in systems
science and engineering from Portland State Univer-
sity, Portland, OR, USA, in 1986.
Dr. Xu serves as the Founding Chair of the Inter-
national Federation for Information Processing (IFIP)
Technical Committee on Information Systems (TC8)
Working Group on Enterprise Information Systems
(WG8.9) and the Founding Chair of the IEEE SMC
Society Technical Committee on Enterprise Information Systems.
Chengen Wang (M’13–SM’13) received the Ph.D.
degree from Nanjing University of Aeronautics and
Astronautics, Nanjing, China.
He is currently a Professor at the State Key Labo-
ratory of Synthetical Automation for Process Indus-
tries, Northeastern University, Shenyang, China,
since 1991. His research interests include product
assembly planning and simulation, product design
optimization, and finite element methods.
1546 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 2, MAY 2014