Opportunity Analysis for Enterprise Collaboration between Network of SMEs

Transcription

1 Presenter: M. Naeem Opportunity Analysis for Enterprise Collaboration between Network of SMEs Supervisor: Abdelaziz Bouras, Yacine Ouzrout, Néjib Moalla Laboratoire Décision et Information pour les Systèmes de Production (DISP),Université Lumière Lyon 2, France 27-May

2 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

3 Background Context of Research Network of SMEs Diversified Data Emergence of Big data technologies Open data modeling 3

4 Opportunity 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 SME (Plastic Manufacturer) DP ERP BA SME (Metal Manufacturer) DP ERP BA Martin Hilbert, Priscila Lopez, The world s technological capacity to store, communicate, and compute information, Science 332 (6025) (2011)

5 Background Challenge and Opportunities Big Data Opportunities: above 50% of 560 enterprises think Big Data will help them in increasing operational efficiency, etc. Philip Chen, C. L., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275,

6 Bottom Up Approach 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 Provide ontologies Output (Collaborative Added Value) Service Orchestrator Asset Enabler New Data Saving configuration Service Orchestrator Asset Enabler New Data DP + DMS DP + DMS Federated enterprises data repositories to create new collaboration capabilities. Digital preservation system Acquisition Enterprises legacy systems 6

7 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

8 Background Expected Results Repository of assets published as services. Assessment model for new collaboration opportunities. Service matchmaker for collaborative business process composition. 8

9 Literature Review Enterprise Collaboration Ontology Engineering Framework/Architect ure Year Methodology Comments SnoBase 2006 Ontology KAON 2004 Ontology SymOntoX 2003 Ontology Large organizations are producing complex data, focus on acquisition and other aspects of valorizations of collaborations were missing 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

10 Literature Review Enterprise Collaboration Bigdata 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

11 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

12 Back-End Front-End Towards Solution Enterprise Collaboration Framework Consortium of SMEs 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 SME-2 Added Value Output Repository Assets Asset as Service (AaS).. (AaS) (AaS) (AaS) Collaborative Model Added Value Assessment Model Service Orchestrator Input New Opportunities Ontological Modeling Business Process Inf. Tech. Dig. Res. Business Process Inf. Tech. Dig. Res. Big Data Technologies Acquisition Organize Analyze Decide Data Anonymizer Data Anonymizer Data Anonymizer SCM SRM ERP PLM CRM Document Management System (Un-structured docs) Digital Preservation Platform 12

13 Back- End Front- End Phase of Enterprise Collaboration: Big Data Perspective Proposed Architecture for Enterprise collaboration Consortium of SMEs 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 Added Value Output Repositor y Assets Collaborative Model Added Value Assessme nt Model Input New Opportunities Analysis in the phase of Acquisition (Case Studies) SME-1 Business Process Inf. Tech. Dig. Res. SME-2 Business Process Inf. Tech. Dig. Res. Asset as Service (AaS) (AaS) (AaS)..(AaS) Acquisitio n Big Data Technologies Organiz e Service Orchestrator Ontological Modeling Analyze Decid e Data Anonymizer Data Anonymizer Data Anonymizer ER P PL M Document Management System (Un-structured docs) SCM CP M SR N Digital Preservation Platform Big Data Technologies Acquisition Organization Analysis Decide 13

14 Results Pig / Hive Results Data Mining Results (MapReduce) Big Data (Deep Learning) 14

15 Results Hive / Pig Results 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? 15

16 Results Hive / Pig Results 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) 16

17 MAP Reduce Functional Layer Grouping Business Assets Business Ontology = small data Data Sources Big Data Storage Results Intermediate key-value pairs k k k v v v. k v Group by Key Big Data Processing Aggregation Summarize Filter / Transform Visualization Key-value groups k v k v v k v v v reduce reduce Output key-value pairs Sort Shuffle k v k v k v Document Management System (SCM) PLM CRM SRM ERP 17

18 Functional Layer Contribution 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 AIC. Aikac Information Criteria BIC. Bayes Information Criteria MDL. Minimum Description Length 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 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 18

19 Enterprise Collaboration Functional Flow Customers ( u) t rating event ( c, p, r) t Train Model model with validity time interval f ( c, p) r Get unrated items Predict rating recommend f ( ) t, t lr ( ) s e f x, *r, i xj cr xi c x c j sim( xi, x j) xx i. j, x, x Select top k 2 2 c c i c c j r r feedback ( cpr,, ) t feedback Customers Collaborative Recommendation Model Why Big Data.no cold start Dim Date ensi of ons Order Co m pa ny Customer Grading Product Detail Company Detail P r o d u Ide c ntifi t er P ri c e Produ ct Name Identified by Customer Value Massive Detail Quantity Ordered Category of Company Revenue in offpeak Coefficient for Price Calculation Identified by P r i c Versioning e Detail Revenue Detail Product History Business Assets Order Detail Order Detail Supplier Detail Client Quota Detail Business Object Information Asset Data Element Business Rule Capability Symbol Legend 19

20 Results Data Mining Results Famille Format Mode Charge ABS Coulé Chargé CHAUDRO Extrudé Bronze Granule CONSO Coulé Polyester COULEE PU Pressé Chargé Pressé Bronze last 2 Couleur quantity production Blanc Bleu Rouge 5789 Jun Famille Format Mode Charge Tube GRAPHITAGE Pressé Anti UV GRAVAGE Stabilisé INJECT APR Rectifié INJECT CLI Régénéré INJECTION Grainé Anti Rayure JONC Régénéré Grainé Couleur Rouge Incolore Beige Fumé Bronze quantity last production Nov

21 Results Data Mining Results Famille Format Mode Charge Couleur quantity last production Plaque CONSO GRANULE COULEE PU DECOUPE PETG FABRIQUES PETG NEGOCE Grainé Médical Moulé Poreux OIL Antistatique Diffusant HI Prismatique Lubrifiant NEGOCE OIL GRANULE Moulé Antistatique COULEE PU Expansé Diffusant DECOUPE Moulé HI Beige Fumé Bronze Gris Gris Bleu Ivoire Jaune Incolore 9476 May

22 Polyamide Results Data Mining Results Famille Format Mode Charge INJECT CLI INJECTION JONC Chargé Bronze MAINT LOC Chargé Carbone MATIERE Chargé Calcium MONTAGE Extrudé Lubrifiant NEGOCE Pressé Antistatique PA Rectifié Diffusant Granule PC Grainé Additif Jonc PE Moulé Anti UV PEEK Lisse AXPET PETG Plaxe Confetti PETIT EQUI FROST PF Polyester PLAQUE Prismatique PONCTUELS TUBE USINAGE Famille Format Mode Charge PONCTUELS Expansé Confetti TUBE Lisse Poreux USINAGE Plaxe Prismatique TUBE USINAGE Lisse Poreux Prismatique Couleur quantity last production Blanc Bleu Transparent Rouge Incolore Fumé Bronze Gris Bleu Ivoire Jaune Orange Vert 3 Couleur quantity Blanc Bleu Naturel Transparent Noir Rouge NON DEFINI Beige Fumé Bronze Gris Bleu Ivoire Jaune Vert Aluminium Sep Last production 2967 Nov

23 Polyoxym Results Data Mining Results Famille Format Mode Charge Couleur quantity Last production DIVERS Grainé Diffusant Blanc FABRIQUES Médical HI BUR & INFO Moulé Additif Noir 1758 Dec JONC Expansé Moulé HI 23

24 Results Data Mining Results Quantity-Ordered Base Price Type of Customer Abandoned Cart Price Discount Recommendation less than more than A <10% 5%-6% B <10% 5%-6% C <7% 1%-3% A <7% 6%-8% B <7% 6%-8% C <5% 3%-5% A <5% 9%-12% B <6% 8%-12% C <4% 6%-9% 24

25 Results Data Mining Results Quantity- Ordered Base Price Nomenclature Gamme Interne Outillage Transport Devis lie Gamme soustraitance technique Globale Discount Recommendation less than more than to 3 >12% <7% >15% > 70% >3 5%-6% 3 to 7 >10% <7% >14% > 65% >3 5%-6% 8 to 10 <4 >9% <5% >11% > 55% >40 >2 1%-3% 2 to 3 >12% <7% >16% > 65% 6%-8% 4 to 8 >10% <7% >14% > 60% 6%-8% <8 and 9 to 13 >3 >9% <6% >12% > 58% >50 >5 3%-5% 1 to 3 >12% <7% >18% > 67% >2.4 9%-12% 4 to 9 >10% <7% >15% > 65% >2 8%-12% <8 and 10 to 14 >3 >9% <5% >12% > 60% >70 >1.5 6%-9% 25

26 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

27 Enterprise Collaboration Big Data Capability Results 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 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 27

28 contains contains determined by contains contains Ontological Modelling Relationship among Information Assets, Data Elements, and Business Objects Thing Order demanded by demands are Customer Product Recommendation Detail Quotation Detail determined by determined by N.R.P Base Price Product History Color Creation Hours Famille R.P uses uses rating event Train Model Predict rating determined by Order Date Category Format Mode Business Object Abandoned Cart Amount Charge Information Asset Coefficient of Price Discount Recommended sion Last-prod Data Element Data Properties 28

29 Asset As Service (SWRL) APR(? x) produce. product(? x,?y) (mode(?y,?m) selection. range(divers,fabriques,bur info)) (charge(?y,?c) range(diffusant, HI?Additif)) Production Capability dim ension((?y,?d) d1(?d,?d1) range((?d1,?r) (?r,70 164)) qty((?y,?q) (?q,1800))) production. capability($ x,$ y) conditions(($ y,$m) ($ y,$c) ($ y,$d) ($ y,$q)) Timing Capability APR(? x) produce. product (? x,?y) production. hours(?y,?z) min(?z,57) max(?z,78) average(?z,70) granule ($y) APR(? x) produce. product (? x,?y) production. hours(?y,?z) min(?z,19) max(?z,23) average(?z,20) tube($y) APR(? x) produce. product (? x,?y) production. hours(?y,?z) min(?z,87) max(?z,101) average(?z,90) plaque ($y) APR(? x) produce. product (? x,?y) production. hours(?y,?z) min(?z,68) max(?z,79) average(?z,73) jonc($y) APR(? x) produce. product (? x,?y) production. hours(?y,?z) min(?z,65) max(?z,74) average(?z,70) polyamide ($y) Discount Recommendation (previous purchase history) APR(? x) produce. product(? x,?y) ( base. price(?y,?z) range(300,400)) (quantity.ordered(?y,?q) range(300,400)) abandoned. cartprice((?y,?a) min(?a,10)) customer. type(?c,a) discount($c,range(5,6)) 29

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