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Explore the theory and algorithm behind organizational evolution (OrgEv) in supply chains, with a focus on team management, promotion frequency, and specialization in the workplace. Discover how implementing OrgEv can lead to healthier policies and better organizational outcomes.
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Discrete event/agent based/SD hybrids Supply chains Tangible interfaces Molecules of structure Model analysis Distance program in SD Organizational evolution Activities
Basic Theory Simulation environment Genetic algorithm + discrete event + SD-type model Genetic programming + discrete event + SD-type model Status
Team management Flavors of the month management Frequency of promotions Mergers and acquisitions Rank-and-fire policies New companies (punctuated equilibrium) Specialization Evolution of (internal) markets OrgEv: Applications
Agenda • SD vs. Org Ev • Theory and algorithm • Applications • Innovation • Flavor of the Month Management • Promotion frequency
You use SD to diagnose and treat poor policies (decision rules) You use OrgEv to create an internal environment that breads healthy policies SD vs. OrgEv
Agenda • SD vs. OrgEv • Theory and algorithm • Applications
Imitation and Bacteria Recombination Innovation and Mutation Algorithm Theory
Recombination Learner Teacher 2,146 1,849 2,849
Bacteria Recipient Pilus Donor Imitation
Innovation and Mutation ACGGCTTCG ACTGCTTCG
Learning by Recombination Before After Teacher (Donor) 11 1111 111 111 00 0000 Learner (Recipient) 11 0000
Corner office Badges Hierarchy Salary Pointing and Pushing Mechanisms
Agenda • SD vs. OrgEv • Theory and algorithm • Applications • Team promotion • Specialization *
Teams:Should we worry about team size or the number of teams?
1 Team in Population of 50 2 Teams in Population of 50 5 Teams in Population of 50 10 Teams in Population of 50 25 Teams in Population of 50 15 16 16 16 16 14 14 14 14 10 12 12 12 12 Individual Individual Individual Individual Individual 10 10 10 10 5 8 8 8 8 0 6 6 6 6 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 Generation Generation Generation Generation Generation Team Promotion 20 Runs 20 runs each
Final Policy 16 14 12 10 Final Policy 8 6 4 2 0 0 5 10 15 20 25 30 Final Policy # of Teams in Population of 50 16 14 12 10 Final Policy 8 6 4 2 0 0 5 10 15 20 25 Teamsize for 5 Teams in Population
Agenda • Introduction • Motivation • Theory and algorithm • Applications • Team promotion • Specialization
Practice group in medium-sized law firm Believe the law is becoming so complex that no one know it all Solution: specialization Initial approach identify areas of the law and have people “sign up” The Situation
Nix Zip Nil Zilch Nothing Naught Progress after Ten Years …
Required: Community of specialists (Min of 5 people per specialty?) Consequence: Firm is changing dimension of specialization to one that has only three categories Requirement and consequence
Allopatric Sympatric Kinds of Speciation
Frequency dependent selection Sexual selection Sympatric SpeciationProcesses