Advanced Materials Research Vols. 712-715 (2013) pp 3191-3194 © (2013) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMR.712-715.3191
A framework for implementing social manufacturing system based on customized community space configuration and organization Kai Ding1,a, Pingyu Jiang1,b and Xi Zhang1,c 1
State Key Laboratory for manufacturing systems engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China a
b
c
[email protected],
[email protected],
[email protected]
Keywords: social manufacturing, outsourcing service, community space, resource configuration, outsourcing service matching
Abstract: As the production mode evolves, social manufacturing has emerged for enterprises to outsource non-core production tasks, make full use of the distributed socialized manufacturing resources and get value-added services. A social manufacturing system was proposed to configure customized community space (CCS) of an enterprise, organize CCS into manufacturing community and search for best partners for the enterprise. The framework of the proposed system is given. Correspondingly, three key enabling technologies are discussed to support the implementation of the system, that is, socialized manufacturing resources (SMRs) configuration, social manufacturing community (SMC) organization and collaboration and manufacturing outsourcing service (MOS) searching and matching. Finally, the conclusions are drawn and the future work of research in this area is given. Introduction With the rapid development of manufacturing technology and the continuous evolution of production mode, manufacturing industry has gained momentum in recent decades. Contrary to the “everything-in-one” production mode [1] (i.e. all the design, manufacturing, assembly, sale and after-sale service tasks are finished by one enterprise), a new production mode called social manufacturing gradually arises recently whose aim is to fully utilize distributed socialized manufacturing resources (SMRs). Enterprises begin focusing on their core production tasks and seeking to outsource many of their non-core production tasks to their supporting companies scattered all around the world in order to sharply cut down the investment of equipment, labor, funding, etc, achieve more value-added services and improve quick response to the dynamic market [2,3]. Hence, the manufacturing sector becomes smaller and smaller, and a dumbbell-shaped enterprise model is formed. A sound example is that the company merely undertakes the design and after-sale service of a product like the iPhone mode of Apple Corporation, whose production tasks are totally outsourced to Foxconn Corporation and other supplying companies. Under the circumstance of social manufacturing, customized community spaces (CCSs) of enterprises, where corresponding SMRs including both hardware and software are well organized and dynamic production capability of resources is evaluated, should be configured firstly. According to different respective of organization rules, the CCSs can be embedded into different social manufacturing communities (SMCs), in which enterprises can interact perfectly under the support of many social network platforms. When an outsourcing task comes, a set of candidate partners from manufacturing community are formed and matched rapidly. After the matching is done and an outsourcing order is reached, the following commercial activities would be performed in the SMC. By combining social manufacturing and the concept of CCS and SMC, a social manufacturing system based on customized community space organization and configuration (CCS-SM) is proposed. In the rest of this paper, an implementing framework of the system is put forward and some key enabling technologies are discussed. Finally, several conclusions and the future work are given. All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP, www.ttp.net. (ID: 117.32.153.146-16/04/13,12:13:40)
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Framework of CCS-SM To understand the scope of CCS-SM, the framework is depicted in Fig. 1. It can be seen that there are three important modules in the framework, that is, CCS configuration, social manufacturing community (SMC) organization and manufacturing outsourcing service (MOS) discovery and matching. These modules form the generic logic to implement social manufacturing. 2 Social Manufacturing community (SMC) organization local space 2
local space 1
local space 1
local space 1 local space 2 global space
global space
local space i
global space
… local space 2 local space i CCS of core-enterprise CCS of supplying-enterprises
local space i
1 Customized community space (CCS) configuration
resources ontology
history credit
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credit-based resources evaluation industry credit
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production competence
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multi-perspective classification
technique credit
resources formalization
machining ability
objects
Enterprises’ resources description and production capability modelling static manufacturing dynamic production resource description capability modelling
Manufacturing outsourcing service (MOS) discovery and matching outsourcing resources demands partitioning information features
dynamic information management Commercial activity management
3
process
Industrial web Apps configuration Social manufacturing resource configuration
CCS of versatile manufacturers CCSs of different manufacturers based on process category
CCSs of different manufacturers based on industry chain
service discovery outsourcing demands searching & matching production capability bargain & make a deal Generate an outsourcing relationship
Fig.1. The framework of CCS-SM CCS configuration. As the core component of manufacturing community, CCS provides an independent space for enterprises to configure their manufacturing resources, release their manufacturing capabilities and personalize industrial web applications they actually required. Besides, enterprises can also the acquire supply/demand information from their partners, and manage their own commercial activities as well. To support the implementation of CCS, technologies such as manufacturing resources description and dynamic production modelling should be discussed. SMC organization. Geographically dispersed CCS could be organized into different kinds of SMCs according to different rules, such as core-enterprises based community, industry chain based community, process type based community, etc. SMC is a bridge for enterprises to communicate efficiently and timely with their business partners by using many social network platforms. In the SMC, enterprises could release their outsourcing demands, production capabilities and commercial activities, which are available for all the enterprises in the SMC. MOS discovery and matching. After CCS configuration, manufacturing resources in the SMC are properly classified using granular computing based multi-perspective classification. Simultaneously, resources of MOS provider are evaluated according to their credit to support searching and matching between production capabilities (from MOS providers) and outsourcing demands (from MOS demanders), which is resolved by a hybrid hierarchical algorithm. An outsourcing relationship between the demander and provider is generated in SMC after game theory based bargaining and transaction.
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Key enabling technologies In this section, three key enabling technologies including manufacturing resources (SMRs) configuration, social manufacturing community (SMC) organization and collaboration, manufacturing outsourcing service (MOS) discovery and matching are discussed in detail. SMRs configuration. To make the manufacturing outsourcing service easy to discovery and match under the complex internet environment, social manufacturing resources are expected to be defined and expressed with a unified and complete method. The configuration includes static manufacturing resource description and dynamic production capability modelling. Generally, manufacturing resources should be described from two levels - the equipment level and the workshop/enterprise level. In concrete terms, it mainly includes process, quality, assembly, inventory and logistics, etc. Each resource can be formally described as SMR= {ID, BasicInfo, CapaInfo, UnfnInfo, DynInfo} where the five elements represent its unique ID, basic parameters, capability attributes, non-functional features and dynamic information respectively. On the basis of the resource formalization, the domain ontology can be built which definitely describes the concept model of resources from the sematic and knowledge level. As the critical part of the SMRs configuration, dynamic production capability modelling plays an important role in MOS matching. It is composed of machining ability and production competence. The machining capability explains what a resource can machine, which can be estimated from its function, performance and performance quality, while the production competence reveals the production availability and efficiency of the resource by synthetically analyzing the equipment Gantt chart, production plan and machining ability. SMC organization and collaboration. SMC is a rule-based logically organized set of enterprises where have their own CCS, and can provide rapid searching and matching, service assessment, commercial activities sharing, information interaction for MOS demanders and providers. SMC consists of one global space (GS), several local spaces (LSs) and customized community spaces (CCSs) as the Fig. 1 shows. The difference between GS and LSs is that GS is the space where all the enterprises share their public business dynamics and communicate with each other through public social network platform, but LSs are the spaces where enterprises exchange order information with their on-going partners efficiently. By using set theory and relational algebra, the SMC can be defined as G= (V, E, W) where V= (v1, v2, v3,…, vn) represents CCS of enterprises, E= (e1, e2, e3,…, em) indicates the collaboration relationships among enterprises of both GS and LSs. The credit-based collaboration probabilities is defined as W= (w1, w2, w3,…, wp) showing the weight of enterprises’ collaboration relationships. Based on different rules of organization and classification granular, SMC is sorted as follows: Core enterprise based social manufacturing communities (ce-SMC), Industry chain based social manufacturing communities (ic-SMC), Process type based social manufacturing communities (pt-SMC) and so on. Similar to the industrial park, ce-SMC is a traditional community where several supplying companies provide different production capabilities for the core enterprise. However, the difference is that supplying companies are logically aggregated and geographically distributed. Contrary to the virtual enterprise mode and enterprise alliance, ic-SMC is a kind of supplying-company-centered community which focuses on the profit of supplying companies and changes the corporation mode especially for small and medium-sized enterprises. Pt-SMC is another supplying-company-centered community where companies provide similar manufacturing method for outsourcing demanders. MOS searching and matching. In order to generate proper matching results, SMRs of CCS in the SMC are classified from multi-perspective (i.e. process type, process feature, machining object, etc.) by using granular computing. Meanwhile, a key attribute ‘credit’ is defined to evaluate reliability of the SMRs. Through assigning different weights to the technique credit, industry credit and history credit, a synthetical value of credit is given. Based on the classification and credit-based evaluation, a hybrid three-level matching algorithm is proposed to match the MOS demands and SMR capability. Fig. 2 shows the logic flow of the proposed algorithm.
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start
First-level
Initialize outsourcing demands
Set credit value interval
Get service providers set in SMC
Calculate the credit value of candidate set Third-level
Service type satisfied ?
N
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Set semantic similarity level Calculate the semantic similarity of manufacturing capacity Second-level Calculate the semantic similarity
of production capacity Y
Candidate set is null ? N
Sort candidate set credit value interval satisfied?
N
Y
Game theory based bargain Reach a deal ?
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Y
Generate an order, information interaction End
Fig.2. The logic flow of the hybrid three-level matching algorithm When a MOS demand comes, firstly, a set of service providers with satisfied SMRs in the SMC is achieved and the first-level matching is built based on the outsourcing service type. Secondly, a semantic similarity based matching of manufacturing capacity and production capacity is given to reach a better candidate set. The third-level is credit-based matching. All the providers in the candidate set would be ordered by their credits. After that, a game theory based bargain would be carried out between the demander and provider. Through the three-level matching, the demander would select the most proper provider, then a deal is reached and an outsourcing order is generated. Conclusions and future work In this paper, a new advanced manufacturing mode called social manufacturing based on the customized community space organization and configuration (CCS-SM) is proposed. The framework of the proposed CCS-SM system is established which shows a generic flow to implement the system. To make sure the CCS-SM runs smoothly, some key enabling technologies are discussed in detail. By configuring enterprises community space, virtually embedded into SMC, an extensive enterprise social management network forms, which would help find better partners and exchanging outsourcing order information with its partners. Future work will deal with expanding the content of customized community space configuration and studying the realization method of state-tracking of MOS order formed in our system. Acknowledgements This work was financially supported by both the National 12-5-Plan S&T Supporting Project (2012BAH08F06) and National Natural Science Foundation of China (51275396). References [1] Wei Cao and Pingyu Jiang: Applied Mechanics and Materials Vol. 220-223 (2012), p. 61-64 [2] J. Momme: Computers in Industry, Vol. 49 (2002), p. 59-75 [3] A. Heshmati: Journal of Economic Surveys, Vol. 17(2003): p. 79-112 [4] K.L. Choy and W.B. Lee: Supply Chain Management: an International Journal, Vol. 8 (2003), p. 140-154
