Big Data in Design and Manufacturing Engineering

Abstract

Big Data helps facilitate information visibility and process automation in design and manufacturing engineering. It also helps analyze trends through analytics and predict inventory, manufacturing output and equipment lifespan and cycles, etc. This paper introduces Big Data, its characteristics and a number of issues of Big Data in design and manufacturing engineering. These issues include design and manufacturing data, Big Data benefits and impacts and its applications and opportunities. Methods, technologies and some technology progress around Big Data are presented in this study. General challenges of Big Data and Big Data challenges in design and manufacturing engineering are also discussed.

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American Journal of Engineering and Applied Sciences
Original Research Paper
Big Data in Design and Manufacturing Engineering
1Lidong Wang and 2Cheryl Ann Alexander
1Department of Engineering Technology, Mississippi Valley State University, USA
2Technology and Healthcare Solutions, Inc., USA
Article history
Received: 27-03-2015
Revised: 25-04-2015
Accepted: 05-05-2015
Corresponding author:
Lidong Wang
Department of Engineering
Technology,
Mississippi Valley State
University, USA
E-mail: lwang22@students.tntech.edu
Abstract: Big Data helps facilitate information visibility and process
automation in design and manufacturing engineering. It also helps
analyze trends through analytics and predict inventory, manufacturing
output and equipment lifespan and cycles, etc. This paper introduces
Big Data, its characteristics and a number of issues of Big Data in
design and manufacturing engineering. These issues include design
and manufacturing data, Big Data benefits and impacts and its
applications and opportunities. Methods, technologies and some
technology progress around Big Data are presented in this study.
General challenges of Big Data and Big Data challenges in design and
manufacturing engineering are also discussed.
Keywords: Big Data, Design, Manufacturing, Sensors, Big Data Analytics,
Machine Learning, Data Mining, Map Reduce Algorithm, Hadoop
Introduction
Big Data and Characteristics
The McKinsey study defines Big Data as “datasets
whose size is beyond the ability of typical database
software tools to capture, store, manage and analyze.”
Big Data in many sectors ranges from a few dozen
Terabytes (TB: Approximately 1012 bytes) to multiple
Petabytes (PB: Approximately 1015 bytes) (Minelli et al.,
2013). It is too big, moves too fast, or does not fit the
strictures of conventional database architectures
(Dumbill, 2013). Characteristics (Russom, 2011;
Eaton et al., 2012; ORRT, 2012; Zikopoulos et al.,
2011; Demchenko et al., 2013) of Big Data can be
categorized into “6 Vs”. They are: Volume, Velocity,
Variety, Value, Variability and Veracity.
Volume
It means data size such as Terabytes (TB), Petabytes
(PB), Exabytes (EB: Approximately 1018 bytes),
Zettabytes (ZB: Approximately 1021 bytes) and
Yottabyte (YB: Approximately 1024 bytes).
Velocity
This relates to how frequently the data is generated. It
can be batch, near real time, real time, or streams.
Variety
It represents all types of data such as streamed
video, streamed audio and Radio Frequency
Identification (RFID) sensor readings. The data type
can be structured, unstructured, or semi-structured
(Bellini et al., 2013; IWT, 2014). Structured data has
fixed fields such as spreadsheets or relational
databases; unstructured data does not reside in fixed
fields-text from articles, email messages, untagged
audio or video data, etc.; and semi-structured data
does not reside in fixed fields, but it uses tags or other
markers to capture elements of the data such as
Extensible Markup Language (XML) and Hyper Text
Markup Language (HTML)-tagged text (Nedelcu,
2013). Data in variety or different formats makes data
integration difficulty or very expensive.
Value
It is defined by the added-value that the collected
data can bring. It refers to the value that the data adds
to creating knowledge. There is some valuable
information somewhere within the data. The valuable
information is golden data if it is extracted, although
most of the pieces of data individually may seem
valueless. Big Data consists of hidden gold (high-
valued data) mixed with dirty (noise, erroneous and

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raw) data. Big Data technologies can process massive
amounts of dirty data and extract the gold information
from it. Data value is related to data volume and data
variety. The economic value of different data varies
depending upon both the source and its end use
(Zaslavsky et al., 2012; Megahed and Jones-Farmer,
2013; Rajpathak and Narsingpurkar, 2013).
Variability
This refers to the fact that data can be changed at
times. It also means data unpredictability and how data
may change. Increasing variety and variability also
increases the attractiveness of data and the potentiality in
providing unexpected, hidden and valuable information
(Bellini et al., 2013).
Veracity
Veracity (Dove et al., 2012; Megahed and Jones-
Farmer, 2013; IWT, 2014) includes two aspects: Data
consistency (or certainty) and data trustworthiness. Data
can be in doubt: Uncertainty due to data inconsistency
and incompleteness, ambiguities, latency, deception and
model approximations. The following aspects help
ensure data veracity:
• Integrity of data and linked data (e.g., for complex
hierarchical data, distributed data)
• Data authenticity and (trusted) origin
• Identification of both data and source
• Computer and storage platform trustworthiness
• Availability and timeliness
• Accountability and reputation
Big Data is often dynamic, heterogeneous, inter-
related, noisy and untrustworthy. However, even
noisy Big Data could be more valuable than tiny
sample data because general statistics obtained from
frequent patterns and correlation analysis usually
overpowers individual fluctuations. Interconnected
Big Data forms large heterogeneous information
networks; therefore, information redundancy can be
explored to compensate for missing data, validate
trustworthy relationships, crosscheck conflicting
cases, disclose inherent clusters and uncover hidden
models and relationships (Agrawal et al., 2011).
Big Data means more information, but it also means
more false information. Its focus is on correlations, not
causality. It is about what, not why (Bottles et al., 2014).
In addition, the data we consider big today may not be
considered big tomorrow because of the advances in data
processing, storage and other system capabilities
(Zaslavsky et al., 2012).
Design and Manufacturing Engineering Data
Industrial operations and systems often produce a
continuous stream of sensor data, event data and
contextual data through sensors, smart machines and
instrumentation. In a factory, data sources possibly
include Computer-Aided Design (CAD) models,
Computer-Aided Manufacturing (CAM) models,
Computer-Aided Engineering (CAE) models, sensors,
instruments, Internet transactions and simulations. The
data is often large, fast-moving and complex. The data
is often in large variety, including documents, test data,
product failure data, CAD/CAM/CAE data,
unstructured CAD drawings and specifications and
product and process performance data, etc. The
increasing volume of data with different types needs to
be stored, managed and analyzed. Big Data
technologies, driven by innovative analytics, can
process large sets of heterogeneous data; and help
extract value and hidden knowledge from large and
diverse data streams (Noor, 2013; Rajpathak and
Narsingpurkar, 2013; Dayal et al., 2014).
Some examples of Big Data in manufacturing are
shown in Table 1. Structured data has the advantage of
being easily entered, stored, analyzed and queried.
Examples include manufacturing data stored in relational
databases, data from manufacturing execution systems
and data from enterprise systems. Unstructured data such
as log files and human-operator-generated shift reports
may be in a raw format that requires decoding before
data values can be extracted. Semi-structured data does
not conform to the models of relational databases or
other data tables, but contains tags or other markers to
separate semantic elements and demonstrate hierarchies
of field sand records (IC, 2014).
Table 1. Manufacturing data examples (IC, 2014)
Structured data Unstructured data Real-time, semi-structured data
Spreadsheets Operator shift reports RFID
Relational databases Machine logs, error logs XML
Enterprise data warehouse Texts, images, audio/video Machine builder standards like
Files stored in manufacturing PCs Manufacturing collaboration social platforms Sensors (vibration, pressure, valve and
acoustics), relays
Manufacturing historians (time series data
structures)

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Benefits and Impacts of Big Data in Design
and Manufacturing Engineering
Mining Big Data can offer benefits in design, such as
better detection of defects in design and design
improvement, saving design time and costs, fast
response to market, developing innovative products and
matching customers’ needs and gaining customer
satisfaction through extracting crucial customer
requirements from the customer-generated data to update
and refine existing designs (Wu et al., 2015).
The manufacturing sector generates a great deal of
text and numerical data in product development
processes. Big Data offers the following benefits in
manufacturing (Brown et al., 2011; McGuire et al.,
2012; Nedelcu, 2013; Noor, 2013; Wu et al., 2015):
Defect tracking and product quality:
• Perform predictive diagnostics for product/part failure
• Monitor product data quality
• Early detect quality problems
• Better detect product defects
• Provide real-time alerts based on analyzing
manufacturing data
• Reduces defects during manufacturing processes
by tracking every detail about every part that goes
into a product
• Boost quality
Improvements in supply planning:
• Unlock significant value and unearth valuable
insights by performing Big Data analytics and
making information transparent
• Better forecast products, production and
manufacturing output
• Better forecast sales volumes through semantic-
based Big Data analytics
• Improve relationship with suppliers and conduct
better contract negotiations according to collected
supplier performance data
• Improve decision-making and minimizes risks in
supply
Improved product manufacturing processes:
• Provide an infrastructure for transparency in
manufacturing
• Analyze sensor data from production lines, creating
self-regulating processes that cut waste, avoid
costly (and sometimes dangerous) human
interventions and ultimately lift output
• Better monitor and control manufacturing
processes by tracking every detail about every part
and procedure, better information visibility and Big
Data analytics for data in motion
• Perform predictive manufacturing and optimize
manufacturing processes
• Better simulate and test new manufacturing
processes
Driven efficiency across the extended enterprise:
• Increase the efficiency of the manufacturing
processes
• Increase energy efficiency
• Enable effective and consistent collaboration
through integrating datasets from multiple systems
and divisions
• Facilitate innovative design for manufacturing and
integration of CAD/CAE/CAM; reduce
unnecessary iterations in product development
cycles; and finally reduce production and
development costs
• Offer further opportunities to accelerate product
development; increase product innovation and
development of next-generation products
Improved service:
• Determine what manufacturing parameters most
influence customer satisfaction
• Develop new products and make products better
match customers’ needs through sentiment analysis
and recommendation systems for Big Data
• Enable mass-customization in manufacturing
• Better correlate manufacturing and business
performance information together
• Reduce warranty costs through warranty analysis
based on Big Data analytics
• Better perform remote intelligent services
Table 2 (MKGI, 2010) shows the degree of potential
benefits that Big Data could generate in the areas of
greatest benefits for manufacturing/operations.
Applications and Opportunities of Big Data
in Design and Manufacturing Engineering
Big Data in General Electric, General Motors and
the Automotive Industry
Some manufacturing firms, such as General
Electric, view Big Data from sensors in manufactured
products (e.g., locomotives, jet engines and gas
turbines in GE’s case) as key to effective and efficient
servicing strategies. In the same mode, automobile
manufacturers such as General Motors created self-

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driving cars based on the analysis of Big Data from
sensors and machine vision technologies (Davenport,
2013). Big Data has become the key asset for the whole
production and manufacturing cycle, as well as the
provision of services in the automotive and mobility
space. Big Data is actually at heart of how the extracted
sensor data and location data are combined to provide
services (Camilli and Duisberg, 2013).
Big Data in Semiconductor Manufacturing and
Integrated Circuits
Semiconductor companies have seen significant
opportunities for Big Data and analytics to optimize
semiconductor manufacturing (Hattori, 2013). For higher
product quality, semiconductor companies conduct
extensive tests and collect terabytes of data.
Semiconductor vendors mine Big Data for product
quality (Neison, 2014).
With the continuous shrink of integrated circuits in
feature sizes due to nanotechnologies, extracting
manufacturing information and mining valuable
intelligence from automatically collected Big Data in the
wafer fabrication facilities for real-time decisions and a
higher yield has become very important to support
intelligent manufacturing, enhance the service quality
and maintain competitive advantages for high-tech
companies in the global competition (Hsu et al., 2012).
Big Data at Work for a Missile Plant
A missile plant (Fig. 1), for instance, Raytheon Corp.
in Huntsville, Alabama, USA, monitors its assembly
operations down to the turn of ascrew. If a screw in a
missile fails to complete its full count of turns, Raytheon
will know about it immediately and be able to take
corrective steps. Raytheon’s monitoring technology is
often called “Manufacturing Execution Software
(MES),” and several manufacturers have used MES to
collect and analyze factory-floor data. The systems
enable the real-time control of multiple elements of the
production process (Noor, 2013).
Table 2. Greatest benefit areas for manufacturing/operations; (1: No benefits; 3: Moderate benefits; 5: Very high benefits)
Areas of greatest benefit for manufacturing/operations Degree of benefits
Product quality/defect tracking 3.37
Supply planning 3.34
Manufacturing process defect tracking 3.32
Supplier/supplier component/parts defect tracking 3.11
Collecting supplier performance data to inform contract negotiations 3.08
Forecasting manufacturing output 3.03
Increasing energy efficiency 2.97
Simulation and testing of new manufacturing processes 2.88
Enable mass-customization in manufacturing 2.75
Fig. 1. Big Data at a missile plant (Noor, 2013)

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Big Data in Cloud-based Design and
Manufacturing
Cloud-Based Design Manufacturing (CBDM) is a
service-oriented networked product development model.
Based on this model, service consumers can configure,
select and utilize customized product realization
resources and services. CBDM uses the Internet of
Things (IoT) (e.g., RFID), smart sensors and wireless
devices (e.g., smart phone) to collect real-time design-
and manufacturing-related data. IoT allows engineers to
have access to data such as equipment condition,
machine utilization and the percentage of defective
products from any location. Engineers can use Big Data
analytics for forecasting, automation and proactive
maintenance (Wu et al., 2015).
Technology Integration Based on Big Data for
More Value
Manufacturing sector generates data from a multitude
of sources such as instrumented production machinery
(process control). Most manufacturing companies have
Information Technology (IT) systems to manage the
product data generated via CAD, CAM, CAE and
Product Development Management (PDM) systems.
However, the large datasets generated by these systems
often remain trapped within their respective systems.
Manufacturers can create a significant opportunity to
create more value through effective and consistent
collaboration, the integration of datasets from multiple
systems and Big Data analytics for the integrated
datasets (Nedelcu, 2013).
Medical Device Design and Manufacturing
Simulation-based engineering methods involve
Finite Element Analysis (FEA), Finite Difference
Analysis (FDA), Computational Fluid Dynamics
(CFD) and/or multi-physics simulations to work
toward an optimal design. Advancements in medical
device design and manufacturing that is data-driven
and simulation-based have drawn upon and combined
emerging work in the areas of regulatory science and
Big Data. Big Data technologies enable a broader set
of device materials, anatomical configurations,
delivery methods and tissue interactions to be
evaluated. Computational methods and Big Data
technologies can play an important role in medical
device design and manufacturing (Erdman and Keefe,
2013).
Big Data and Additive Manufacturing
Understanding the use and implications of Big Data
and predictive analytics will be very important as
additive manufacturing (also called 3D printing) makes
traditional models of production, distribution and
demand obsolete in some product areas. Some
companies no longer need to invest in dedicated,
expensive milling machines or injection molding
equipment. A single 3D printer can produce a wide range
of parts-without expensive dies and jigs. People can even
modify designs to meet special, one-of-a-kind needs and
then use an affordable 3D printer to “manufacture” parts
(Waller and Fawcett, 2013b).
Production Process Monitoring, Maintenance,
Quality Assurance and Logistics for Manufacturers
Production sensors generate vast volume of data at
sub-second speeds. Big Data and advanced analytics
analyze this vast amount of data to monitor production
processes and identify when an event will affect
production quality and when maintenance is required,
before production quality is actually affected. Big
Data provides the ability to ensure Quality Assurance
(QA) tests are confirmations of high product quality
(Software AG, 2013).
Table 3 (Waller and Fawcett, 2013a) provides some
examples of potential applications of Big Data within
logistics for manufacturers.
Big Data in CAD/CAE/CAM and CAD Educational
Assessment
Big Data working with3D software in
CAD/CAE/CAM can greatly help companies, especially
companies in the aerospace industry (e.g., the areas of
aeronautics and astronautics) because these companies
have struggled to manage the constantly growing volume
of data. The datasets are large, complex and often fast-
moving. It means that these datasets are often in large
volume, velocity, variability and variety. A lot of these
datasets are unstructured, for example, CAD drawings
and CAD/CAE/CAM specifications. Big Data and
analytics are powerful in processing and managing these
kinds of heterogeneous information, improving data
veracity and creating more values.
Table 3. Examples of potential applications of Big Data in logistics for manufacturers
Forecasting Early response to extremely negative or positive customer sentiment
Inventory management Reduction in shrink, efficient consumer response, quick response and vendor managed inventory
Transportation management Improved notification of delivery time and availability; surveillance data for improved yard management
Human resources More effective monitoring of productivity; medical sensors for safety of labor in factories

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A computational method that is based on time series
analysis was proposed to assess engineering design
processes using a CAD tool. Educational data mining
and learning analytics were studied to assess student
performance in learning and designing in a project-based
setting. The time series process data can be as fine-
grained as the ‘atomic’ design steps (meaning that they
cannot be logically divided further). Data at this level of
granularity has all the four characteristics of Big Data
(IBM, 2012; Xie et al., 2014a):
• High volume: A large amount of process data in a
complex open-ended project
• High velocity: The data can be collected, processed
and visualized in real time to instantaneously
provide to students and teachers
• High variety: Many types of information provided
by a rich CAD system such as all the learner
actions and artifact properties
• High veracity: The data must be accurate and
comprehensive to ensure fair and trustworthy
assessments of student performance
CAD logs are instructionally sensitive and can serve
as an effective instrument for assessing complex
engineering design processes. High-volume and high-
variety software logs can be used to detect the effects of
what happens outside the computer on individual
students. CAD logs were used in performance
assessment because their fine-grained, temporal nature
can provide more comprehensive, more reliable and
more personalized process data for finding evidence of
deep learning related to design creativity and problem
solving. Deep learning generates a large amount of
datasets. Big Data can be used to analyze and visualize
these large datasets (Xie et al., 2014b).
‘Mobile Access to CAD’ is a growing with above
average importance and usage. ‘Cloud Based CAD’
currently has low average importance and below average
usage. ‘Big Data’ is not yet ‘big’ in CAD-very low
awareness (Turner, 2014).
Methods, Technologies and Technology
Progress around Big Data
Big Data analytics uses analysis algorithms running
on powerful supporting platforms to uncover potentials
concealed in Big Data, such as unknown correlations or
hidden patterns (Hu et al., 2014). Advanced modelling,
analysis, feedback and visualization are the techniques of
Big Data analytics. These techniques help manufacturing
companies eliminate waste and create value in the design
and production of the products (Papanagnou, 2014). In
addition, data mining, text mining, opinion mining,
social network analysis, cluster analysis are Big Data
analytics methods (Cho and Hwang, 2015).
Machine learning has been used in Big Data.
Massive Parallel-Processing (MPP), distributed file
systems and cloud computing, etc. are supporting
technologies of Big Data (Zaslavsky et al., 2012).
Besides general cloud infrastructure services (storage,
compute, infrastructure/Virtual Machine (VM)
management), the following services are also required
to support Big Data (Turk, 2012):
• Cluster services
• Hadoop related services and tools
• Specialist data analytics tools (logs, events, data
mining, etc.)
• Databases/Servers SQL, NoSQL
• MPP (Massively Parallel Processing) databases
• Registries, indexing/search, semantics, namespaces
• Security infrastructure (access control, policy
enforcement, confidentiality, trust, availability,
privacy)
Hadoop is an open source framework for writing and
running distributed applications that are capable of batch
processing large sets of data. Hadoop framework is
mainly known for Map Reduce and its distributed file
system. The Map Reduce algorithm consists of two basic
operations: map and reduce. It is a distributed data
processing model that runs on alarge cluster of
machines. Hadoop includes three parts: Hadoop
Distributed File System (HDFS), Hadoop Map Reduce
and Hadoop Common (Chardonnens, 2013).
To process and manage Big Data with parallel and
distributed data mining algorithms, a Cloud-Based
Design Manufacturing (CBDM) system should
employan open-source software/programming
framework that supports data-intensive distributed
applications (Ren et al., 2012). Map Reduce, a
parallel programming model, is one of widely used
programming models in cloud computing. It enables
CBDM systems to process large datasets. Hadoop is
one of the open source implementations of the Map
Reduce model. Hadoop divides computationally
extensive tasks into small fragments of work and each
work unit is processed on a computer node in a
Hadoop cluster (Dean and Ghemawat, 2008).
New methods for compact visualization of data
with ranging variety and veracity are constantly being
developed to present correlations across bases more
effectively. Visualization may be standalone or may
cross-filter with other views across feature bases.
Visualization may be measured in many ways,
including the bases spanned continuously, number of
points drawn, level of over plot and
precision/correctness (Schwartz et al., 2014).

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Challenges of Big Data
General Challenges of Big Data
Traditional Statistical Process Control (SPC) methods
only focus onnumeric datasets. However, most Big
Data applications are required to process non-numeric
data obtained from different databases. The modeling
of this kind of data is often based on disciplines that
are not in the areas of statisticians and quality
engineers. As for text data, models draw from
linguistic sciences, computer science and psychology.
The models maybe integrate data input in different
languages. The arrival rate of the data fluctuates
depending on factors that are often not understood before
analyzing it. This phenomenon is called trending/viral
for online content (Megahed and Jones-Farmer, 2013).
Big Data has challenges in capture, storage, search,
analysis and virtualization (Zaslavsky et al., 2012).The
specific information of some general challenges is
provided as follows:
• Challenges in Big Data management can be
categorized into two types: Engineering and
semantic. Engineering challenges lie in performing
data management activities such as query and
storage efficiently. Semantic challenges lie in
extracting the meaning of the information from
massive volumes of unstructured data, even dirty
data (Bizer et al., 2012)
• It is difficult to collect and integrate data with
scalability from distributed locations because of the
variety of disparate data sources and the sheer
volume (Hu et al., 2014)
• Data quality is a focal point. During the process
of data capture, sources of data are often
heterogeneous, geographically distributed and
unreliable, being susceptible to errors. Therefore,
a number of data preprocessing techniques, such
as data cleaning, data reduction, data integration
and data transformation, are often used to remove
noise and correct inconsistencies (Han and
Kamber, 2006)
• Big Data systems manage and store the gathered
heterogeneous and massive datasets, while
providing function and performance guarantee, in
terms of scalability, fast retrieval and privacy
protection (Hu et al., 2014). Privacy and
information security are concerns in Big Data
• Exponential growth of data volume is generated
from different instruments and/or collected from
sensors; it is necessary, but not easy to consolidate
e-Infrastructures as persistent platforms to ensure
continuity and cross-disciplinary collaboration
(Demchenko et al., 2012)
Big Data Challenges in Design and Manufacturing
Engineering
It is important and necessary to integrate
CAD/CAE/CAM and cyber-physical systems with Big
Data systems to make design and manufacturing more
competitive. How to fulfil the integration and how to
extract the right information from Big Data in design
and manufacturing for the right purpose at the right
time are major challenges.
Table 4. Big Data challenges for manufacturing; (1: Not at all a challenge; 3: Moderate challenge; 5: Very high challenge)
Areas of greatest challenges for manufacturing/production Degrees of challenges
Building high levels of trust between data scientists who present insights on Big Data and 3.31
functional managers
Determining what data to use for different business decisions 3.29
Being able to handle the large volume, velocity and variety of Big Data 3.25
Getting business units to share information across organizational silos 3.22
Finding the optimal way to organize Big Data activities in a company 3.20
Getting functional managers to make decisions based on Big Data, rather than on intuition 3.14
Putting the analysis of Big Data in a presentable form for making decisions 3.12
Getting top management in a company to approve investments in Big Data and is related investments 3.11
Determining what to do with the insights that are created from Big Data 3.09
Getting the IT function to recognize that Big Data requires new technologies and new skills 3.08
Finding and hiring data scientists who can manage large amounts of structured and 3.02
unstructured data and create insights
Determining which Big Data technologies to use 3.02
Keeping the data in Big Data initiatives secure from external parties 2.98
Understanding where in a company people should focus Big Data investments 2.98
Reskilling the IT function to be able to use new tools and technologies of Big Data 2.95
Other 2.80
Keeping the data in Big Data initiatives secure from internal parties 2.71

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Machine learning is an important method of Big
Data; however, it has challenges in implementation. One
challenge is the availability of the right data from
different operations and processes. Machine systems
such as Programmable Logic Controllers (PLC) and
Supervisory Control And Data Acquisition (SCADA)
often capture a lot of machine data, but this data may not
be relevant. PLC and SCADA do not store all the data
that is required for a Big Data predictive analytics
solution based on machine learning (Joseph et al., 2014).
Other major challenges of Big Data in design and
manufacturing include building high levels of trust
between data scientists and managers; confidence in
analyzing and managing data with large volume, velocity
and variety; deciding what methods and technologies
will be used; and maintaining the consistency of
managing and using Big Data, etc. (Nedelcu, 2013).
Table 4 (MKGI, 2010) shows some areas of greatest
challenge for manufacturing/production and degrees of
these challenges.
Conclusion and Future Research
Big Data is large in volume, velocity, variety, value,
variability and veracity. Big Data helps integrate various
types of datasets in design and manufacturing
engineering; uncover hidden correlation patterns through
analytics; improve design and production processes; and
create more values. Mining Big Data helps improve
design in quality, time, costs and mass-customization.
Big Data also offers greatest benefits for manufacturing
engineering such as detecting product defects, boosting
quality and improving supply planning, etc. It has had a
lot of applications or great opportunities in design and
manufacturing engineering. These applications or
opportunity areas include electricity, automotive, missile
plants, integrated circuits, semiconductor manufacturing,
additive manufacturing, medical device design and
manufacturing and cloud-based design and
manufacturing, etc. Generally, Big Data has challenges
such as data capture, date integration, data visualization,
extracting values from all of heterogeneous data and
privacy and information security, etc. Specifically in
design and manufacturing engineering, besides the above
challenges, other major challenges lie in: Trust between
data scientists and managers; confidence in analyzing big
data, choice of methods and technologies and
consistency of managing and using big data, etc. All the
aspects of these challenges can be future research. The
authors of the paper will focus on Big Data in
CAD/CAE/CAM of medical devices as further research.
Acknowledgment
This study was supported in part by Technology and
Healthcare Solutions, Inc. in Mississippi, USA. No
conflict of interest to disclose.
Funding Information
The authors have no support or funding to report.
Author’s Contributions
All authors equally contributed in this work.
Ethics
This article has not been published elsewhere. The
corresponding author confirms that both authors have
read and approved the manuscript and no ethical
issues involved.
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