Big Data for Service and Manufacturing Supply Chain Management

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29 Νοε 2013 (πριν από 3 χρόνια και 4 μήνες)

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Big Data for Service and Manufacturing Supply Chain Management

George Q. Huang
1
,
Kwok Leung Tsui
2
, Ray Y. Zhong
1

1

HKU
-
ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems
Engineering
, The University of Hong Kong, Hong Kong

2
.
Department of Systems Engineering and Engineering
Management, Department of Mechanical
and Biomedical Engineering, City University of Hong Kong, Hong Kong

Big data refers to a data set collection which is so large and complex that
it is difficult to proce
ss
using current database management tools or traditional data processing approaches.
Big data is
initially driven from the service

supply chain management (SCM) such as healthcare, finance,
tourism,
telecommunication, information technology, etc
.
It is reported that, since 1980s, the per
-
capita capacity to store information has approximately doubled every
forty

months

(
Manyika,
Chui et al. 2011
)
.
Take the year of 2012 for example,
it was estimated that
2.5 Q bytes of data
were created every day
(IBM, 2013)
.

Manufac
turing
SCM

may
largely involve in a range of human activities
from high
-
tech products
such as space craft to daily
necessities

like toothbrush.
Manufacturing
is regarded as the

hard


parts of economy
using labors, machines, tools, and raw materials to produce finish
ed goods for
different purposes.
(
Hill and Hill 2009
;
Terziovski 2010
;
Eichengreen and Gupta 2013
)
.

According to
a

report from Mckinsey & Company, in 2010, manufacturing and service sector
stored about 2 exabytes of new data, which is more than any other sectors (
http://www.ge
-
ip.com/library/detail/13170
)
.

R
ecen
tly, Auto
-
ID technology (e.g. RFID, Barcode) has been widely used in supply chain. Such
automatic data collection
approach

brings new challenges which could be summarized from
horizontal and vertical
dimensions
. Horizontal dimensions indicate the dynamics of
big data,
which means the interaction and intertransverse feature of data among
manufacturing
, logistics,
and
retailing

phases. Vertical dimension describes the characteristics
of big data in supply chain
,
w
hich
are highlighted
as

5V

-

volume, velocity, variety, verification, and value.

This special issue of the International Journal of Production Economics is

devoted to publish
emerging technologies and significant insights related to big data in
service and
manufacturing

SCM
, aiming to upgrade and transfer these two sectors into a level that is more efficient and
smarter.

Typical
topics

include, but not limited to, the following dimensions:



Data collection techniques



Data quality management



Data processing models and methods



Data storage mechanisms



Data mining

and
Knowledge discovery



Data
-
driven decision support systems



Data
-
based applications



Case studies on big data



Data analytics



Bioinformatics, healthcare informatics



Data tools



Data model
ing



Data
-
based optimization



Data
-
based control and automation



Multi
-
dimensional data technology



I
ndustrial informatics and control

All submissions will be judged for their appropriateness to the journal’s remit and the novelty of
their
theoretical and practical research contributions. While quantitative research is preferred,
relevant qualitative research studies

as well as case studies

are also welcomed.

Manuscript Preparation and Submission

In preparing manuscripts, authors are require
d to follow the “Instructions to Authors” that are
presented at the back of any recent issue of the International Journal of Production Economics.
Authors should submit their papers via the EES http://www.ees.elsevier.com/ijpe and select
“Special Issue:
Bi
g Data for Service and Manufacturing Supply Chain Management
” when asked
to indicate the “Article Type” in the submission process. Submitted papers should not have been
previously published nor be currently under consideration for publication elsewhere.
Ma
nuscripts will be refereed according to the normal IJPE standards and procedures.

Publication Schedule

Manuscript submission:
31 December 2013

Reviewer reports:
30 April 2014

Revised paper submission:
31 July 2014

Final manuscript submissions to publisher:
30 October 2014

Special Issue Guest Editors

George Q. Huang
,

Professor

HKU
-
ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems
Engineering
, The University of Hong Kong.

Tel: 852
-
2859
2591
,
E
-
mail:
gqhuang@hku.hk

Kwok Leung Tsui, Chair Professor

Department of Systems Engineering and Engineering Management, Department of Mechanical
and Biomedical Engineering, City University of Hong Kong
, Tel: 852
-
34422177, E
-
mail:
kltsui@cityu.edu.hk

Ray Y. Zhong
,

PhD

HKU
-
ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems
Engineering, The University of Hong Kong.

Tel: 852
-
285925
79
,
E
-
mail:
zhongzry@gmail.com

References:

Eichengreen, B. and Gupta
,

P.
(2013). "The two waves of service
-
sector growth." Oxford
Economic Papers 65(1): 96
-
123.

Hill, T. and Hill
,

A.
(2009).
Manufacturing strategy: text and cases, Palgrave Macmillan.

IBM, (2013). “What is big data?


Bringing big data to the enterprise”, www. IBM.com.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C.,
and

Byers, A. H
. (2011).
"Big data: The

next frontier for innovation, competition, and productivity." McKinsey
Global Institute: 1
-
137.

Terziovski, M. (2010). "Innovation practice and its performance implications in small and
medium enterprises (SMEs) in the manufacturing sector: a resource

based view."
Strategic Management Journal 31(8): 892
-
902.