Multi-Agent Modeling in Managing Six Sigma Projects

Transcription

1 Mult-Agent Modelng n Managng Sx Sgma Projects K. Y. Chau 1, S. B. Lu 1 and C. Y. Lam 2 1School of Economcs and Busness Admnstraton, Bejng Normal Unversty, Bejng, Chna 2Department of Industral and Systems Engneerng, The Hong Kong Polytechnc Unversty, Hung Hom, Kowloon, Hong Kong Correspondng author E-mal: gavnchau@yahoo.com Abstract: In ths paper, a mult-agent model s proposed for consderng the human resources factor n decson makng n relaton to the sx sgma project. The proposed mult-agent system s expected to ncrease the acccuracy of project prortzaton and to stablze the human resources servce level. A smulaton of the proposed multagent model s conducted. The results show that a mult-agent model whch takes nto consderaton human resources when makng decsons about project selecton and project team formaton s mportant n enablng effcent and effectve project management. The mult-agent modelng approach provdes an alternatve approach for mprovng communcaton and the autonomy of sx sgma projects n busness organzatons. Keywords: Modelng, Mult-agents, Smulaton, Sx Sgma 1. Introducton Sx sgma s a powerful phlosophy that ams at mprovng the qualty of an organzaton s performance, and focusses on dentfyng and quantfyng errors n busness processes. The Sx sgma ntatve s a famous qualty control phlosophy. Its am s to reduce defects n an organzaton s operatons usng a project-by-project approach. Sx sgma s one of the most popular qualty control approaches n many ndustres and has ganed huge success (Eckes, G., 2001). It has been suggested that a key success factor for sx sgma project mplementaton s the project selecton process (Meredth, R. & Mantel, S., 2006). Project selecton s the process of evaluatng ndvdual projects or groups of projects, selectng and schedulng some the most sutable canddates to mplement the project so that the objectves of the organzaton can acheve (Adams, C., et al., 2002; Kumar, U., et al., 2008). In most of the studes n the lterature, the decson makng n relaton to the project selecton consders manly the project tme scale, return on nvestment, total cost of ownershp, etc. Ths paper s an attempt to further mprove the project selecton and project team formaton processes, n the sx sgma projects, by consderng human resources factors. Ths wll ncrease the success of project management. A sx sgma project team generally conssts of two types of team players,.e. a black belt as the team leader, and a green belt as a team player. People are mportant to the sx sgma project selecton process. Ths s because n most of the cases, each black belt has ndvdual expertse n a specfc doman, and t s mpossble for them to partcpate n a sx sgma project that s not wthn ther doman. As a result, there wll be a lmted number of black belts avalable for most projects. On the other hand, due to the resource constrants n practcal stuatons, a company usually has a lmted number of avalable green belts. Therefore, the factors of team formaton and human resources should be consdered when decsons are beng made wth regard to project selecton. We therefore propose a mult-agent model for consderng human resources factors n the sx sgma project selecton process. Agent-based computaton s a new doman paradgm of nformaton and communcaton technology (Horlng, B., & Lesser V. R., 2004; Wess, G., 1999), and agents address autonomy and complexty that s able to adapt to changes and dsruptons, exhbt ntellgence and are dstrbuted n nature (Bussmann, S. & Schld, K., 2000; Cerrada, M., et al., 2007; Ferber, J., 1999). Wth the mult-agent model, human resources are nvolved n the decson makng n connecton wth the sx sgma project selecton process. The model has the authorty to prortze projects whle utlzng human resources nformaton. Ths mult-agent system s expected to ncrease project prortzaton accuracy and stablze the human resources servce level. The structure of ths paper s as follows: In secton 2, the proposed mult-agent model for the sx sgma project selecton wth ts four agents,.e. project agent, human resources agent, green belt/ black belt agent s defned and modeled. In secton 3, a smulaton of the proposed mult-agent model s descrbed, and an analyss of the results s presented. Fnally, a concluson wth wth suggestons for further development s gven n secton Development of the Mult-Agent Model In the proposed mult-agent model for the selecton of sx sgma project, four types of agent are developed,.e. 9 Internatonal Journal of Engneerng Busness Management, Vol. 1, No. 1 (2009), pp. 9-14

2 Internatonal Journal of Engneerng Busness Management, Vol. 1, No. 1 (2009) Fg. 1. Structure of the proposed mult-agent model and the nteracton flows of ts agents project agent, human resources agent, green belt agent, and black belt agent. The structure of the proposed multagent model and the nteracton flows of ts agents are llustrated n Fg Project Agent The project (PJ) agent acts as a project archtect that controls the process flow of a sx sgma project. The project agent s responsble for ntatng the project requrements and for provdng the relevant project nformaton. Each project agent s responsble for one sx sgma project only, so that the number of project agents s equals to the number of sx sgma projects n the system. After the project agent has fnshed preparng all the requred project nformaton, such as the project scale, the estmated return on nvestment, the number of green belt/ black belt agents requred, etc. the project agent then communcates wth the human resources agents n order to prortze the sx sgma project and to recrut the green belt/ black belt agents for the project. The project agent cannot start a project untl commtments are receved from the green belt/ black belt agents. Therefore, f the project s n a state of progress, t uses a certan number of green belt/ black belt agents that cannot work for other projects. However, when the project s fnshed, the project agent wll release all the green belt/ black belt agents, and the project agent wll termnate tself as a sgnal of project completon. In the mult-agent envronment, the project nformaton s generated by project agents and sent to human resources agents n an array form. The project nformaton ncludes: 1. The number of green belts requred 2. The number of black belts requred 3. Sx sgma Defne the stages 4. Sx sgma Measure the defned stages 5. Sx sgma Analyze the defned stages 6. Sx sgma Improve the defned stages 7. Sx sgma Control the defned stages 8. Allocate an ndex number to the agent responsble for the project 9. Allocate the suggested prorty of ths project 10. Allocate the requred skll code for the Black Belt 2.2. Human Resources Agent A human resources (HR) agent acts as a system manager n the proposed mult-agent model for the sx sgma project selecton. HR s also a communcaton hub for project agents and green belt/ black belt agents to exchange nformaton. As a system manager, a human resources agent has two job responsbltes,.e. project schedulng and the recrutng of green belt/ black belt agents. The human resources agent schedules the project accordng to the prorty rules, he/she decdes the earlest due date, frst come frst served, etc. When a new project request s ntated and s receved from project agents, human resources agents wll compare t wth the projects that are already scheduled, and then prortze t by puttng t n an approprate poston n the project schedule. The human resources agent wll also recrut the necessary green belt/ black belt agents needed for the project to start. Human resources agents wll then be able to make a decson regardng resources between the resources needed by the project and the number of green belt/ black belt agents avalable Green Belt and Black Belt Agents A Green belt (GB) agent s a basc team member n a sx sgma project. Green belt agents do not have specfc sklls and thus are sutable for takng part n any sx sgma project. All green belt agents are dentcal, and can serve n only one project at a tme. A Black belt (BB) agent s smlar to a green belt agent, however, a black belt agent has ts own specfc sklls, and these sklls are from a skll pool that contans all skll felds related to the operaton of the sx sgma project. Sklls of dfferent black belt agents may overlap, but for any partcular sx sgma project, there are a lmted number of sutable black belt agents avalable. Both green belt/ black belt agents are n ether a workng state or n an dle state. They never termnate themselves. In the recrutng process, human resources agents send a workng sgnal to dle green belt/ sutable black belt agents, and those green belt/ black belt agents who are avalable wll respond to t. However, the recrutment of black belt agents s more dffcult than the recrutment of green belt agents as most of the sx sgma projects requre agents who possess specfc knowledge and sklls Communcaton between Agents Communcaton takes place between agents, and all communcaton s acheved by sendng messages through broadcastng or one-to-one communcaton. Wth broadcast communcaton, all agents can receve the messages and the target agents can respond to t; whle for the one-to-one communcaton, only targeted agents are able to receve and respond to the message. A communcaton matrx between the agents n the proposed mult-agent model for sx sgma project selecton, s presented n Table Dynamc Executon States of Agents All agents are lsted by default as dle at the begnnng of the sx sgma project selecton process. All agents have ther own bult-n ntellgence and logc that motvate them to act and respond n the agent envronment so as to acheve system level automaton. 10

3 K. Y. Chau, S. B. Lu and C. Y. Lam: Mult-Agent Modelng n Managng Sx Sgma Projects PJ Agent HR Agent GB Agent BB Agent PJAgent HR Agent GB Agent BB Agent Project Confrm Project Detals Workng Sgnal Workng Sgnal Release Sgnal Work Invtaton Release Sgnal Work Invtaton Table 1. A matrx showng communcaton between agents Fg. 2. Project agent executon states A project agent can operate n several executon states. These are dynamc lnear executon states that nclude the state of project ntaton, project nformaton sharng, responds recevng, project runnng, agents releasng, and project termnaton. The graphc llustraton of the project agent executon states s shown n Fg. 2. The human resources agent has two job responsbltes, namely, project schedulng and the recrutng of green belt/ black belt agents. The job responsblty of project schedulng operates wth a smple dynamc lnear executon state whle the recrutng of agents uses a more complcated dynamc logcal executon state. The dynamc executon states for the two job responsbltes of human resources agents are shown n Fg. 3 and Fg. 4 respectvely. Fg. 3. Human resources agent executon states for project schedulng Fg. 4. Human resources agent executon states for agent recrutment Fg. 5. Green belt/ black belt agent executon states The green belt agents and the black belt agents have smlar dynamc executon states that nclude the state of work request recevng, workng, and work releasng. The graphc llustraton of the green belt/ black belt agent executon states s shown n Fg. 5. The four types of agents act accordng to ther respectve executon states; project agents have a one-to-one relatonshp wth the project that they have ntated. Project agents termnate themselves after a project s completed. The human resources agent s desgned to work contnuously so as to schedule projects and recrut green belt/ black belt agents. Green belt and black belt agents have smple states as ether workng or dle. The human resources agent, the green belt/ black belt agent do not termnate themselves as long as the system s n exstence Modelng the Mult-Agent Envronment The proposed mult-agent model s a decentralzed and ndvdual-centrc approach for the selecton of sx sgma projects. The mult-agent defnes the unversal propertes and provdes a platform for all the agents to execute ther tasks. The mult-agent envronment s consdered as a place where an organzaton s structure, rules, and plans are ntegrated. The organzaton s structure n the mult-agent envronment s defned as the organzaton s dentty and goals, together wth the agents nvolved and ther roles. Therefore, the organzaton s structure n the mult-agent envronment s generally defned as: ORG-Stru def <ORG_ID, ORG_GOAL +, AGENT +, AGENT_ROLES> (1) where ORG_ID s the dentfcaton of the organzaton; ORG_GOAL + s the group of organzatonal goals; AGENT + s the group of the agents nvolved,.e. project agent, human resources agent, green belt/ black belt agent; AGENT_ROLES s the role of each agent n the mult-agent envronment. For the group of organzatonal goals, a bnary functon R s used to represent the sequence of organzaton goals so as to acheve the organzaton s goal n a predefned sequence n the mult-agent envronment,.e. R def ORG_GOAL ORG_GOAL {SERIAL, BEFORE, PAR} (2) where SERIAL means the organzaton s goals should be acheved one by one n a lnear sequence; BEFORE means a specfc organzatonal goal should be acheved before other organzaton goals; PAR means the goals should be acheved smultaneously wth each other. Another functon Q s then used to represent the commtment of the organzatonal goal n the mult-agent envronment,.e. Q ( g ) 1 Commt g = (3) 0 Not Commt g 11

4 Internatonal Journal of Engneerng Busness Management, Vol. 1, No. 1 (2009) where g s the -th goal n the group of organzatonal goals. Apart from the organzatonal goals, organzatonal rules also apply n the mult-agent envronment. These rules refer to how the organzaton s formed, and one of the major rules n the mult-agent envronment s that the organzatonal goal should run smultaneously so that the same goal cannot be executed twce n the mult-agent envronment. The relatonshps between the organzatonal goal and the rules are formulated as: ( g ) 1 g GOAL RULE Q = (4) ( g ) 1 ORG _ GOAL " SERIAL" " BEFORE" " PAR" Q = (5) In addton, dfferent agents have ther own roles, and a one-to-many relatonshp exts between agents and roles, therefore, the parameters n the mult-agent envronment can be further defned as: ORG-Stru def <ORG_ID, ORG_GOAL +, AGENT + AGENT_ROLES> ORG_ID = SSPS; (Sx Sgma Project Selecton) ORG_GOAL = PSSP; (Prortze Sx Sgma Project) AGENT = HumanResourcesAgent; AGENT_ROLE = SYS_MAG; (System Manager) AGENT = ProjectAgent; AGENT_ROLE = PRJ_INT; (Project Intator and Manager) AGENT = BlackBeltAgent; AGENT_ROLE = SSP_MAG; (Project Leader) AGENT = GreenBeltAgent; AGENT_ROLE = TEAM_PLY; (Team Player) 3. Modelng and Smulaton of the Mult-Agent Model The proposed mult-agent model for sx sgma project selecton and ts four agents are modeled n a JAVA platform of a software package AnyLogc, n whch, the agents manage ther logc and actvtes accordng to ther state chart wth dfferent dynamc executon states, such that all agents autonomously and ndvdually nteract wth each other n the agent envronment. The herarchy of the proposed mult-agent model for sx sgma project selecton s llustrated n Fg Smulaton Package Anylogc AnyLogc (XJTek, 1992) s a software package desgn for smulaton tasks. It supports varous types of smulaton systems, such as System Dynamcs, Process-centrc, Agent Based approaches, etc. All these types of smulaton are acheved usng one modelng language and wthn one model development envronment. AnyLogc use Java as ts development language, whch s flexble enough for users to present the complexty and heterogenety of busness, economc and socal systems. The level of detals s also adjustable to meet dfferent smulaton requrements. AnyLogc supports the Objectorented model desgn paradgm, whch makes possble Fg. 6. The herarchy of the proposed mult-agent model for sx sgma project selecton the modular and ncremental constructon of large models. In Anylogc, the developed agents manage ther logc and actvtes accordng to ther ndvdual bult-n state chart. Each state chart s a JAVA based platform that drectly supports agent-based modelng Smulaton Results and Analyss In the proposed mult-agent model, the states of all agents as well as ther correspondng relatonshps are smulated. In order to llustrate the effect of the proposed model, the followng crtera are the most mportant ones: Can the proposed model start recrutng process when projects are n queue? Can the proposed model be suspended f green belt/ black belt agents are not avalable? Can the proposed model arrange all projects and able to complete the recrutment tasks for all projects? In the smulaton, four major types of statstcal data sets are captured n the proposed mult-agent model for the analyss,.e.: Dataset : <Number_of_Workng_GreenBeltAgent> Ths dataset captures the statstcal value of the number of green belt agents that are recruted by human resources agent and n workng state. Dataset : <Number_of_Workng_BlackBeltAgent> Ths dataset captures the statstcal value of the number of black belt agents that are recruted by human resources agent and n workng state. Dataset : <Number_of_Projects_Intated_by_ProjectAgent> Ths dataset captures the statstcal value of the number of project that ntated by project agent. Dataset : <Number_of_Projects_In_HumanResourcesAgent> Ths dataset captures the statstcal value of the number of projects n human resources agent,.e. the number of projects that actually can start wth the requred number of green belt agents and sutable black belt agent. The four types of dataset that captured n the smulaton are used to develop the tme plots of the smulaton 12

5 K. Y. Chau, S. B. Lu and C. Y. Lam: Mult-Agent Modelng n Managng Sx Sgma Projects results as shown n Fg. 7. Fg. 7(a), 7(b), and 7(c) show the smulaton results for the dataset of number of workng green belt agents, number of workng black belt agents, number of projects ntated by project agent, and the number of projects n the human resources agent. At the begnnng of the sx sgma project selecton smulaton, all agents are by default lsted as dle, and all agents have ther own bult-n ntellgence and logc. At tme perod 4, a sx sgma project s ntated by project agent, thus human resources agent starts the green belt/ black belt recrutng processes smultaneously, and as some of the agents are beng recruted for the project, then as shown n Fg. 7(c), the number of workng green belt/ black belt agents ncreases. Ths phenomenon shows that the proposed model s able to respond quckly to the newly ntated project as well as start the recrutng process. The maxmum avalable number of green belt and black belt agents are 15 and 30 respectvely. In the mult-agent envronment, snce the number of green belt/ black belt agents are lmted, so there wll be a certan perod of tme when the model s suspended, as the human resources agent cannot start the green belt/ black belt recrutng processes even though some projects have been ntated by project agents, and the projects are then watng n a queue untl some green belt and sutable black belt agents are avalable. Therefore the number of projects ntated by project agents s not equal to the number of projects n the human resources agent,.e. the tme perod 14, 17, 33, 36, 86, 89, 103, 108, 114, 136, etc. as can be seen by comparng Fg. 7(a) and Fg, 7(b). And at tme perod 325, the recrutment process declnes as most of the projects are fnshed, and the green belt/ black belt agents are released. Therefore, t can be nferred that the proposed model can run n an effcent way to balance the requrements of the project wth the avalable green belt/ black belt agents for the sx sgma project selecton. Fg. 7. Smulaton results for the mult-agent model for sx sgma project selecton 13

6 Internatonal Journal of Engneerng Busness Management, Vol. 1, No. 1 (2009) Fg. 7(c) shows a complete model smulaton result of all the agents nteractons. It shows that all agents can effectvely nteract wth each other n the mult-agent envronment. Although some projects are delayed (.e. the number of projects ntated by the project agents and the number of projects n the human resources agents are dfferent), the ntated projects can stll all be carred out and completed at the end of the model smulaton as shown n Fg. 7(c), and the human resources agents are able to complete the recrutment tasks for all sx sgma project selecton processes. Therefore, the human resources agent can effectvely partcpate and become successfully nvolved n the sx sgma project selecton process. 4. Concluson In ths paper, a mult-agent model s proposed for the selecton of sx sgma projects, In ths, human resources agents are nvolved n the selecton process, and thus enable an effcent and effectve sx sgma project selecton process. The satsfactory smulaton results n ths paper show that the mult-agent modelng approach provdes an alternatve way for selectng sx sgma project. The proposed mult-agent model can be further mproved n several ways, such as by enhancng the functons of predcton, auto-schedulng, and some mathematcal models from the lterature can also be adopted n ths model so as to make the model more practcal for managng sx sgma projects. 5. References Adams, C., Gupta, P. & Wlson, C. (2002). Sx Sgma Deployment, Elsever's Scence & Technology, ISBN: , USA Bussmann, S. & Schld, K. (2000). Self-Organzng Manufacturng Control: An Industral Applcaton of Agent Technology, Proceedngs of the ICMAS 2000, pp , ISBN , Boston, July 2000, Massachusetts, USA Cerrada, M., Cardllo, J., Agular, J. & Fanete, F. (2007). Agent-Based Desgn for Fault Management Systems n Industral Processes. Computers In Industry, Vol.58, pp , ISSN Eckes, G. (2001). The Sx Sgma Revoluton: How General Electrc and Others Turned Process nto Profts, John Wley, ISBN: X, New York Ferber, J. (1999). Mult-Agent Systems: An Introducton to Dstrbuted Artfcal Intellgence, Harlow: Addson- Wesley, ISBN: , USA Horlng, B & Lesser, V. R. (2004). A Survey of Mult- Agent Organzatonal Paradgms. The Knowledge Engneerng Revew, Vol.19, pp , ISSN Kumar, U., Nowck, D., Ramírez-Márquez, R. & Verma, D. (2008). On the Optmal Selecton of Process Alternatves n a Sx Sgma Implementaton, Internatonal Journal of Producton Economcs, Vol.111, February 2008, pp , ISSN: Meredth, R. & Mantel, S. (2006). Project Management: A Manageral Approach, Hoboken, ISBN: , N.J Wess, G. (1999). Mult-Agent Systems: A Modern Approach to Dstrbuted Artfcal Intellgence, MIT Press, ISBN: , USA XJ Technologes Company (1992), avalable from: 14