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Opportunity Analysis for Enterprise Collaboration between Network of SMEs
Presenter: M. Naeem Supervisor: Abdelaziz Bouras, Yacine Ouzrout, Néjib Moalla Laboratoire Décision et Information pour les Systèmes de Production (DISP),Université Lumière Lyon 2, France
27-May-2015 1
Agenda
Background
Context of Research
Challenge & Opportunities
Objective
Research Problem
Expected Results
Related Work
Proposed Framework
Results
Pig/Hive Results
Enterprise Collaboration Functional Flow
Enterprise Collaboration Big Data Capability Results
Ontological Modeling Results
Asset AS Service (SWRL)
2
Background
Context of Research
Network of SMEs
Diversified Data
Emergence of Big data technologies
Open data modeling
3
Background Challenge
• The diversity of data sources and the ontology modeling perspective
• The analysis of data repositories to create enterprise assets (services) for collaboration
• The composition of collaborative business processes from identified services
Martin Hilbert, Priscila Lopez, The world’s technological capacity to store, communicate, and compute information, Science 332 (6025) (2011) 60–65.
SME (Plastic Manufacturer)
DP ERP BA
SME (Metal Manufacturer)
DP ERP BA
Op
po
rtu
nit
y
4
Background
Challenge and Opportunities
Philip Chen, C. L., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
Big Data Opportunities: above 50% of 560 enterprises think Big Data will help them in increasing operational efficiency, etc.
5
Background
Objectives
Integrate systems to capitalize and reuse enterprise capabilities and experiences when making decision.
Support concurrent/collaborative partners consortium in the definition of added value collaboration schema
Federated enterprises data repositories to create new collaboration capabilities.
6
DP + DMS
New Data
Asset Enabler
Service Orchestrator
Output (Collaborative Added Value)
Pro
vide o
nto
logies
Saving co
nfigu
ration
DP + DMS
New Data
Asset Enabler
Service Orchestrator
Bo
tto
m U
p A
pp
roac
h
Acquisition
Enterprises’ legacy systems
Digital preservation
system
Background
Research Problem
How high degree of data integration in corporate data sources can be associated with perceived benefits of Added value during Inter-Enterprise collaboration?
How to define data and information assets in an enterprise
Find out the unique characteristics associated with this data
How to accelerate the creation of new business collaboration
7
Background
Expected Results
Repository of assets published as services.
Assessment model for new collaboration opportunities.
Service matchmaker for collaborative business process composition.
8
Literature Review Enterprise Collaboration
Ontology Engineering
Framework/Architecture
Year Methodology Comments
SnoBase 2006 Ontology
Large organizations are producing complex data, focus on acquisition and other aspects of valorizations of collaborations were missing
KAON 2004 Ontology
SymOntoX 2003 Ontology
pOWL 2005 Ontology
KACP 2008 Ontology Limited to only enterprise security access
Yuh-Jen et, al., 2009 ontology Covers PLM but ignores numerous complexities related to unstructured data
Daniel et al., 2010 ontology
ARIS 1998 Rstatic ontology Generalization not possible
CRE 2012 Fuzzy Logic Limited to risk analysis
NEGOSIS 2014 Ontology Limited to analysis phase only
9
Chelmis et. al., (2013) Studied the exploitation of big data technologies for working collaboration with focus on interesting questions:
users' communication behavioral patterns
dynamics and characteristics, statistical properties and complex correlations between social and topical structures.
However limited to a single enterprise and did not address impact of big data for product improvement
10
Literature Review
Enterprise Collaboration Bigdata
Bigdata bring new opportunities for:
Business analytic techniques and strategies. (Özcan et al., 2014)
Resources, capabilities, and skills needed to maximize business analytics impact. ( Shvachko et al., 2010 )
Challenge of globalized standard for inter-enterprise collaboration (Lin et al., 2007).
11
Literature Review
Enterprise Collaboration Bigdata
Towards Solution
Enterprise Collaboration Framework
Consortium of SMEs
Big Data Technologies
Data Anonymizer
Fro
nt-
End
B
ack-
End
Make best use of collaboration capabilities in order to answer to new business requirements: • Co production of new product • Find best supplier of a specific raw material • Find a sub-contractor • Join capacity building • ....
SME-1
Inf. Tech.
Business Process
Dig. Res.
SME-2
Inf. Tech.
Business Process
Dig. Res.
Collaborative Model Added Value
Asset as Service (AaS) Service Orchestrator
Data Anonymizer
Data Anonymizer
Ontological Modeling
Digital Preservation Platform
SCM SRM ERP PLM CRM
Acquisition Organize Analyze Decide
Document Management System
(Un-structured docs)
Output
Added Value
Repository Assets
Assessment Model
Input
New Opportunities
(AaS) (AaS) (AaS) …..
12
Big Data Technologies
Phase of Enterprise Collaboration:
Big Data Perspective
Proposed Architecture for Enterprise collaboration
Analysis in the phase of Acquisition (Case Studies)
Acquisition Organization Analysis Decide
Consortium of SMEs
Big Data Technologies
Data Anonymizer
Fro
nt-
End
B
ack-
End
Make best use of collaboration capabilities in order to answer to new business requirements: • Co production of new product • Find best supplier of a specific raw material • Find a sub-contractor • Join capacity building
SME-1
Inf. Tech.
Business Process
Dig. Res.
SME-2
Inf. Tech.
Business Process
Dig. Res.
Collaborative Model Added Value
Asset as Service (AaS) Service
Orchestrator
Data Anonymizer
Data Anonymizer
Ontological Modeling
Digital Preservation
Platform
SCM ERP
PLM
CPM
SRN
Acquisition
Organize
Analyze Decid
e
Document Management
System (Un-structured
docs)
Output
Added Value
Repository Assets
Assessment Model
Input
New Opportunities
(AaS) (AaS) (AaS) …..
13
Results
Pig / Hive Results
Data Mining Results (MapReduce)
Big Data (Deep Learning)
14
Query-1. Three types of clients. How to review it, given three
parameters ?
Query-2. Three types of clients. How to review it provided four
parameters ?
Query-3. Which specific business-deals pays us more ?
Results
Hive / Pig Results
15
Query-4. List of customers with orders abandoned greater than
specific threshold ?
Query-5. Churn out analysis (leaving out customers). ?
Query-6. Identification of valuable customers who left away. ?(those
who paid n% more than customers who stayed)
Results
Hive / Pig Results
16
Functional Layer
SRM ERP PLM CRM
Document Management System (SCM)
17
Business Assets
Business Ontology = small data
Big Data Processing
Re
du
ce
MA
P
Gro
up
ing
Sort
Shuffle Filter /
Transform
Aggregation
Summarize
v k
k
k
k
Intermediate
key-value pairs
v
v
v ……. k v
…
k v
k v v
v v k
k
k …
v
v
v
reduce
reduce
Group by Key
Output
key-value pairs Key-value groups
Da
ta S
ou
rce
s B
ig D
ata
Sto
rag
e
Visualization Results
Functional Layer Contribution
18
1.1 For each feature in dataset Run Map without Reduce Run Sort/Shuffle Output mean and SD in individual file 1.21 Run Map without Reduce
Calculate MDL Run Sort 1.22 Run Map without Reduce
Calculate BIC Run Sort 1.23 Run Map without Reduce
Calculate AIC Run Sort
•Classification for continuos variables •Simple Naive Bayes is parallel in nature. No need for memory resident problem •Tradeoff . Poor Performance because of underfitting •Better solution is Graphical Bayesian Network
1.41 Run Map MDL-BestScore (HDFS) Run Reduce 1.42 Run Map BIC-BestScore (HDFS) Run Reduce 1.43 Run Map AIC-BestScore (HDFS) Run Reduce 2.1 Run Map and Reduce Output Optimized Model
AIC. Aikac Information Criteria BIC. Bayes Information Criteria MDL. Minimum Description Length
rating event
model with validity time interval Train Model
Get unrated items Predict rating recommend
( , , )tc p r
( , )f c p r
( ) t , ts ef
( , , )tc p r
Customers
( )l R f
Select top k
( )tu
Customers
feedback feedback
2 2
, *r ,( , )
. , ,
c i c ji j ci j
i ji jc cc c
r x xx xsim x x
x x x xr r
Dimensions
Category of Company
Price
Product
Quantity Ordered
Date of
Order
Company
Product
Name Identified by
Identifi
er
Order Detail
Product History
Product Detail
Order Detail
Supplier Detail
Versioning Detail
Coefficient for Price Calculation
Client Quota Detail
Company Detail
Revenue Detail
Customer Grading
Revenue in off-peak
Customer Value
Massive Detail
Price
Identified by
Business Object Data Element Business Rule Capability
Symbol Legend
Information Asset
Business Assets
Enterprise Collaboration Functional Flow
Collaborative Recommendation Model
19
Why Big Data….no cold start
Famille Format Mode Charge dimen
1 dimen
2
Couleur quantity last
production
Granule
ABS Coulé Chargé Bronze
0.5 1.5
4 7 8
Blanc Bleu
Rouge 5789 Jun - 2014
CHAUDRO Extrudé CONSO Coulé Polyester
Chargé Bronze
COULEE PU Pressé Pressé
Famille Format Mode Charge dimen
1 dimen
2 dimen
3 Couleur quantity
last production
Tube
GRAPHITAGE Pressé Anti UV
8 - 12 35
21 - 60
120 165 200 250
Rouge Incolore
Beige Fumé Bronze
99904 Nov - 2013
GRAVAGE Stabilisé
Anti Rayure
INJECT APR INJECT CLI INJECTION
Rectifié Régénéré
Grainé
JONC Régénéré
Grainé
20
Results
Data Mining Results
Famille Format Mode Charge dimen
1 dimen
2 dimen
3 Couleur quantity last
production
Plaque
CONSO GRANULE
COULEE PU DECOUPE Grainé
Médical Moulé
Poreux OIL
Antistatique Diffusant
HI
12.7 14 16 45 70 80
110 140 180 300
55 - 70
260 300 310 325 330
Beige Fumé Bronze
Gris Gris Bleu
Ivoire Jaune
Incolore
9476 May - 2014
PETG Prismatique
FABRIQUES PETG
NEGOCE Lubrifiant
NEGOCE OIL
GRANULE Moulé Expansé
Antistatique
COULEE PU Diffusant
DECOUPE Moulé HI
21
Results
Data Mining Results
Famille Format Mode Charge dimen
1 dimen
2 dimen
3 Couleur quantity last production
Granule Jonc
INJECT CLI INJECTION
JONC MAINT LOC
MATIERE MONTAGE
NEGOCE PA PC PE
PEEK PETG
PETIT EQUI PF
PLAQUE PONCTUELS
TUBE USINAGE
Extrudé Pressé
Rectifié Grainé Moulé Lisse Plaxe
Chargé Bronze Chargé Carbone Chargé Calcium
Lubrifiant Antistatique
Diffusant Additif Anti UV AXPET
Confetti FROST
Polyester Prismatique
0-950 4-1200 120-25000
Blanc Bleu
Transparent Rouge
Incolore Fumé Bronze
Gris Bleu Ivoire Jaune
Orange Vert
75976 Sep - 2014
Famille Format Mode Charge dimen
1 dimen
2 dimen
3 Couleur quantity Last
production
Po
lyam
ide
PONCTUELS TUBE
USINAGE
Expansé Lisse Plaxe
Confetti Poreux
Prismatique 45 50 70 80 90
100 110 300
120-410
1230 1240 1250 1350
Blanc Bleu
Naturel Transparent
Noir Rouge
NON DEFINI Beige
Fumé Bronze Gris Bleu
Ivoire Jaune Vert
Aluminium
2967 Nov - 2013 TUBE
USINAGE Lisse
Poreux Prismatique
22
Results
Data Mining Results
Famille Format Mode Charge dimen
1
dimen
2
dimen 3
Couleur quantity Last
production
Poly
oxy
m
DIVERS Grainé Diffusant
70-164 - 550-1000
Blanc
1758 Dec - 2013
FABRIQUES Médical HI
Noir
BUR & INFO Moulé Additif
JONC Expansé Moulé
HI
23
Results
Data Mining Results
Quantity-Ordered Base Price Type of
Customer Abandoned Cart Price
Discount Recommendation
less than 100 300-400 A <10% 5%-6% B <10% 5%-6% C <7% 1%-3%
100-300 301-500 A <7% 6%-8% B <7% 6%-8% C <5% 3%-5%
more than 301 501-3000 A <5% 9%-12% B <6% 8%-12% C <4% 6%-9%
24
Results
Data Mining Results
Quantity-Ordered
Base Price Nomenclature Gamme Interne
Outillage Transport Devis lie Gamme sous-
traitance technique Globale
Discount Recommendation
less than 100 300-400
1 to 3
<4
>12% <7% >15% > 70%
>40
>3 5%-6%
3 to 7 >10% <7% >14% > 65% >3 5%-6%
8 to 10 >9% <5% >11% > 55% >2 1%-3%
100-300 301-500
2 to 3
<8 and >3
>12% <7% >16% > 65%
>50 >5
6%-8%
4 to 8 >10% <7% >14% > 60% 6%-8%
9 to 13 >9% <6% >12% > 58% 3%-5%
more than 301
501-3000
1 to 3
<8 and >3
>12% <7% >18% > 67%
>70
>2.4 9%-12%
4 to 9 >10% <7% >15% > 65% >2 8%-12%
10 to 14 >9% <5% >12% > 60% >1.5 6%-9%
25
Results
Data Mining Results
Famille Production Hours
Minimum Maximum Average
Granule 57 hours 78 hours 70 hours
Tube 19 hours 23 hours 20 hours
Plaque 87 hours 101 hours 90 hours
Granule 123 hours 189 hours 169 hours
Jonc 68 hours 79 hours 73 hours
Polyamide 65 hours 74 hours 70 hours
26
Results
Data Mining Results
27
Companies Items (Mode) x/10
NEC75 BUR & INFO (2) COULEE PU (6) MARCHES (9) METALISA (9) PONCTUELS (9)
LABEL74 DIVERS (9) FABRIQUES (7) INJECT APR (10) MAINT LOC (6) OUTILLAGE (3) PONCTUELS (1)
CEZUS44 CHAUDRO (1) GRAVAGE (7) PETIT EQUI (1) PONCTUELS (6)
HEULIE79 FABRIQUES (1) MAINT LOC (3) PONCTUELS (6)
GLYNWE34 DIVERS (2) INJECT APR (5) MAINT LOC (2) MARCHES (6)
AER69 FABRIQUES (7) GRAVAGE (4) METALISA (3) OUTILLAGE (4)
RHODIA93 CHAUDRO (4) MARCHES (3) PONCTUELS (8)
DINEL76 FABRIQUES (7) OUTILLAGE (3)
NEC75 LABEL74 CEZUS44 HEULIE79 GLYNWE34 AER69 RHODIA93 DINEL76
NEC75 1,0 11,8 11,4 9,3 11,4 10,0 22,0 0,0
LABEL74 11,8 1,0 4,5 12,6 21,9 14,8 5,8 10,5
CEZUS44 11,4 4,5 1,0 9,8 0,0 12,0 19,0 0,0
HEULIE79 9,3 12,6 9,8 1,0 6,1 10,7 17,1 8,0
GLYNWE34 11,4 21,9 0,0 6,1 1,0 0,0 9,0 0,0
AER69 10,0 14,8 12,0 10,7 0,0 1,0 0,0 15,7
RHODIA93 22,0 5,8 19,0 17,1 9,0 0,0 1,0 0,0
DINEL76 0,0 10,5 0,0 8,0 0,0 15,7 0,0 1,0
Enterprise Collaboration Big Data Capability Results
NEC75 LABEL74 CEZUS44 HEULIE79 GLYNWE34 AER69 RHODIA93 DINEL76
NEC75 1,0 11,8 11,4 9,3 11,4 10,0 22,0 0,0
LABEL74 11,8 1,0 4,5 12,6 21,9 14,8 5,8 10,5
CEZUS44 11,4 4,5 1,0 9,8 0,0 12,0 19,0 0,0
HEULIE79 9,3 12,6 9,8 1,0 6,1 10,7 17,1 8,0
GLYNWE34 11,4 21,9 0,0 6,1 1,0 0,0 9,0 0,0
AER69 10,0 14,8 12,0 10,7 0,0 1,0 0,0 15,7
RHODIA93 22,0 5,8 19,0 17,1 9,0 0,0 1,0 0,0
DINEL76 0,0 10,5 0,0 8,0 0,0 15,7 0,0 1,0
NEC75 CHAUDRO
LABEL74 MARCHES
CEZUS44 MARCHES
HEULIE79 CHAUDRO MARCHES
GLYNWE34 FABRIQUES OUTILLAGE PONCTUELS
AER69
RHODIA93 METALISA BUR & INFO COULEE PU
DINEL76 GRAVAGE METALISA
Thing
Customer Product Recommendation Order
R.P N.R.P
Category
Detail
Quotation Detail
Product History
Order Date
Coefficient of Price
Creation Hours
Famille
Format
Mode
Charge
Color
dimension
Last-prod
Abandoned Cart Amount
Discount Recommended
rating event
Train Model
Predict rating
conta
ins
conta
ins
conta
ins
conta
ins
Base Price
demands
demanded by
dete
rmin
ed b
y
dete
rmin
ed b
y
uses
uses
Ontological Modelling Relationship among Information Assets,
Data Elements, and Business Objects
28
dete
rmin
ed b
y determined by
Business Object Information Asset Data Element Data Properties
are
Asset As Service (SWRL)
APR(? ) . (? ,?y) (mode(?y,?m) . (divers, fabriques,bur info))
(charge(?y,?c) (diffusant,HI?Additif))
dim ((?y,?d) 1(?d,?d1) ((?d1,?r) (?r,70 164)) ((?y,?q)
x produce product x selection range
range
ension d range qty
(?q,1800)))
. ($ x,$ y) (($ y,$m) ($ y,$c) ($ y,$d) ($ y,$q))production capability conditions
Production Capability
APR(? ) . (? ,?y) . (?y,?z) min(?z,57) max(?z,78) average(?z,70) ($y)x produce product x production hours granule
APR(? ) . (? ,?y) . (?y,?z) min(?z,19) max(?z,23) average(?z,20) ($y)x produce product x production hours tube
APR(? ) . (? ,?y) . (?y,?z) min(?z,87) max(?z,101) average(?z,90) ($y)x produce product x production hours plaque
APR(? ) . (? ,?y) . (?y,?z) min(?z,68) max(?z,79) average(?z,73) ($y)x produce product x production hours jonc
APR(? ) . (? ,?y) . (?y,?z) min(?z,65) max(?z,74) average(?z,70) ($y)x produce product x production hours polyamide
Timing Capability
APR(? ) . (? ,?y) ( . (?y,?z) (300,400))
(quantity.ordered(?y,?q) (300,400)) . ((?y,?a) min(?a,10))
. (?c,a) ($c, range(5,6))
x produce product x base price range
range abandoned cartprice
customer type discount
Discount Recommendation (previous purchase history)
29
30
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