Review of a journal in Big Data

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TOPIC : Review of the journal
“ BIG DATA IN DESIGN AND MANUFACTURING
ENGINEERING ”
SUBMITTED BY:
HEMANTH KRISHNAN R
S7MA
ROLL NO: 44
NSSCE

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BIG DATA IN DESIGN AND MANUFACTURING
ENGINEERING
REVIEW
STRUCTURE OF THE RESEARCH PAPER
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.
 What is Big Data?
 Extremely large data sets that may be analysed computationally to reveal patterns,
trends, and associations, especially relating to human behaviour and interactions.
 Datasets whose size is beyond the ability of typical database software tools to capture,
store, manage and analyze.
 Ranges from a few dozen Terabytes (TB≈1012
bytes) to multiple Petabytes (PB≈1015
bytes).
 Characteristics of Big Data
BIG DATA
6Vs
volume
velocity
variety
value
variability
veracity

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 It is an optimum value-adding approach by squeezing a huge data volume for more
enhanced data flow velocity into a lesser time, and also stepping up the data variety
into more enhanced date veracity in a lesser time.
 Benefits and Impacts of Big Data in Design and Manufacturing
Engineering
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

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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
 Applications and Opportunities of Big Data in Design and Manufacturing
Engineering
 Big Data in Automotive Industry
 Big Data in Semiconductor Manufacturing and Integrated Circuits
 Big Data at Work for a Missile Plant
 Big Data in Cloud-based Design and Manufacturing
 Technology Integration Based on Big Data for More Value
 Medical Device Design and Manufacturing
 Big Data and Additive Manufacturing
 Production Process Monitoring, Maintenance, Quality Assurance and Logistics for
Manufacturers
 Big Data in CAD/CAE/CAM and CAD Educational Assessment

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 Table 1 Operations and Supply Chain Management as a data/information
driven business
Sl
No
Concept (Use case) Competitive
Advantage Factors Attributes
1
Operational efficiency
 Using data to
predict crime
flashpoints.
 Operational
shift planning
in retail stores
or
manufacturing
industries
 Near real-time
authentic crime
prevention
information and
transparency.
 Appropriate staffing
for efficient output
by improving
process quality for
good performance.
2
Customer experience
 Social
influence and
analysis for
customer
retention
 Avoiding “out
of stock”
conditions for
customer
satisfaction
 Customer loyalty
 Precise customer
segmentations for
optimum approach
 Interactive and
integrated customer
services
 Economies of scale
and/or “push/pull”
bullwhip effect
3
New product
development /
introduction NPD/I (New
business models)
 New product
development
and
introduction
(NPD/I)
 Request/demand for
new product lines &
business models.
 New revenue
creation and
expansion of
existing product
lines.
 Methods, Technologies and Technology Progress around Big Data
The following services are also required to support Big Data :
 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)

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 Challenges of Big Data
 Difficulty in data management activities such as query and storage efficiently.
 Challenges in extracting the meaning of the information from massive volumes of
unstructured data.
 Difficulty in collecting and integrating data with scalability from distributed locations
because of the variety of disparate data sources and the sheer volume
 Data are susceptible to errors and noises.
 Ensuring privacy and information security.
 Conclusion
Big data frame work
Big data analytics is the process of examining large data sets containing a variety of data
types i.e., big data to uncover hidden patterns, unknown correlations, market trends, customer
preferences and other useful business information. The analytical findings can lead to more
effective marketing, new revenue opportunities, better customer service, improved
operational efficiency, competitive advantages over rival organizations and other business
benefits.

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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. 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. Big Data
means more information, but it also means more false information. Its focus is on
correlations, not causality.
Data is no longer simply numbers in a database. Text, audio and video files can also provide
valuable insight; the right tools can even recognize specific patterns based on predefined
criteria. Big data can contribute a lot to the production and operations management of any
industry.
It is estimated that by 2020 there could be four times more digital data than all the grains of
sand on earth. The world is one big data problem. Without big data analytics companies are
blind and deaf, wandering out onto the web like deer on a freeway.
Data is the new science. Big data holds the answers. Are you asking the right questions ?