Rethinking the Trading Agent Competition: Lessons Learned from a Decade of Enhancing Autonomous Supply Chain Management

Transcription

1 Rethinking the Trading Agent Competition: Lessons Learned from a Decade of Enhancing Autonomous Supply Chain Management Vedran Podobnik University of Zagreb, Croatia Faculty of Electrical Engineering and Computing ERIM Research Seminar Erasmus University Rotterdam School of Management March 6, 2013, Rotterdam, Netherlands

2 Outline Brief overview of my research A decade of TAC Supply Chain Management TAC SCM game An analysis of TAC SCM competitions Towards TAC SCM of 41

3 Brief overview of my research 3 of 41

4 University of Zagreb Faculty of Electrical Engineering and Computing (2006- ) Master of Electrical Engineering (2006) thesis title: "Software Agents in Electronic Markets PhD in Computer Science (2010) thesis title: Multi-Agent System for Telecommunication Service Provisioning based on User Profiles 4 of 41

5 University of Cambridge Judge Business School ( ) postdoc specialization in Technology Policy developing skills needed to meet challenges of integrating technology, management and policy courses in Policy, Strategy, Globalisation, Innovation, Marketing, Finance and Accounting consultancy projects academically-biased MBA 5 of 41

6 My research interests (1) Information systems Information systems Electronic markets Software agents Social networks 6 of 41

7 My research interests (2) Trading Agents Trading Agent Competition Supply Chain Management Power TAC Ad Auctions Market Design (CAT) Electronic markets Software agents Social networks 7 of 41

8 My research interests (3) Social Technologies Social Technologies Electronic markets Social Recommender Corporate Social Networking Social Media Marketing Social Networking in Telecommunications Software agents Social networks 8 of 41

9 My research interests (4) Information systems in the 21st century information systems in the 21st century networked (from 2000) social-aware (from 2010) challenges of information systems in the 21st century how to filter big data? how to value data? economic choice (rational choice theory?) decision-support systems adaptive proactive interoperable 9 of 41

10 A decade of TAC Supply Chain Management TAC SCM game An analysis of TAC SCM competitions Towards TAC SCM of 41

11 TAC SCM game (1) Background the game has been designed jointly by a team of researchers from the e-supply Chain Management Lab at Carnegie Mellon University (CMU) and the Swedish Institute of Computer Science (SICS) TAC SCM was designed to capture many of the challenges involved in supporting dynamic supply chain practices, while keeping the rules of the game simple enough to entice a large number of competitors to submit entries. 11 of 41

12 TAC SCM game (2) Purpose global competition in real-world markets creates pressure to: reduce costs & increase profitability increase responsiveness / reduce lead times deliver on time & plan accurately purpose of the TAC SCM game is to explore how to maximize profit in the stochastic environment of volatile market conditions it is important to develop an agent capable of reacting quickly to changes taking place during the game adaptability (reactivity) it is critical to implement predictive mechanisms which enable an agent s proactive behavior proactivness 12 of 41

13 TAC SCM environment (1) Stakeholders B2B e-market B2C e-market SUPPLIERS PC MANUFACTURER concurrent competition in multiple markets with interdependencies and uncertainties one game in real time: 15 sec/day x 220 days 13 of 41

14 TAC SCM environment (2) Suppliers 8 suppliers CPU Memory Motherboard Disk drive Each supplier needs to perform three tasks every day: 1. Manage production capacity to satisfy outstanding orders. 2. Create offers to agents based on projected future capacity. 3. Ship components to agents. 14 of 41

15 TAC SCM environment (3) PC manufacturer 6 competing agents During the course of a day, each agent must decide: TAC SCM AGENT Each competitor in the game enters an agent that is responsible for following tasks: 1. Negotiate supply contracts. 2. Bid for customer orders. 3. Manage daily assembly activities & ship completed orders to customers. These three tasks are performed autonomously by the agent each day. Human intervention is not allowed during the course of a game. 1. Which customer RFQs to bid on, if any, by returning offers to customers. 2. Which components to (attempt to) procure, by issuing RFQs to suppliers. 3. Which supplier offers to accept, if any, by issuing orders to suppliers. 4. How to allocate available component inventory and factory capacity to production of PCs, by sending a daily production schedule to the factory. 5. Which assembled PCs to ship to which customers, to satisfy outstanding customer orders, by sending a daily delivery schedule to the factory. 15 of 41

16 TAC SCM environment (4) Customers 100s of customers 16 PC types classified into three market segments. Customer demand is expressed independently in each segment. PCs Each customer in a game is responsible for requesting PCs to be delivered by a certain due date. Customer demand is expressed as requests for quotes (RFQs), specifying: q A PC type, q Quantity, q Due date, q Reserve price, q Penalty amount. 16 of 41

17 TAC SCM environment (5) Interactions (1) 17 of 41

18 TAC SCM environment (6) Interactions (2) 18 of 41

19 TAC SCM environment (7) Specification revisions 2003 (initial specification) , 2008 the price function has been modified to better reflect demand storage costs have been introduced customer demand has been segmented into multiple markets the supplier reserves some portion of long-term capacity for future orders and requires down payments when orders are placed the supply lottery" in which agents were randomly chosen for consideration by suppliers is eliminated agent reputation is now a significant factor in pricing and allocation ability to learn the identities of competing agents a slight modification in the handling of agent-supplier reputation no technical changes, just clarifications 19 of 41

20 TAC SCM participants (1) Number of participants through years 20 of 41

21 TAC SCM participants (2) Hall of Fame 2003 RedAgent (McGill University, Canada) 2004 FreeAgent 2005 TacTex (University of Texas at Austin, USA) 2006 TacTex (University of Texas at Austin, USA) TAC SCM Winners 2007 PhantAgent (University of Bucharest, Romania) 2008 DeepMaize (University of Michigan, USA) 2009 DeepMaize (University of Michigan, USA) 2010 TacTex (University of Texas at Austin, USA) 2011 TacTex (University of Texas at Austin, USA) 2012 DeepMaize (University of Michigan, USA) 21 of 41

22 TAC SCM participants (3) Research agendas & agent design approaches Collins, John, Ketter, W. & Gini, Maria (2008). Architectures for agents in TAC SCM. In AAAI Spring Symposium on Architectures for Intelligent Theory-Based Agents 22 of 41

23 A decade of TAC SCM competitions: an analysis (1) B2B market: component prices game rules changing fixed game rules 23 of 41

24 A decade of TAC SCM competitions: an analysis (2) B2B market: component type prices (fixed game rules) CPU Relative price (% of nominal) vs Component types 15% Memory 24 of 41

25 A decade of TAC SCM competitions: an analysis (3) B2B market: component quantities game rules changing fixed game rules 25 of 41

26 A decade of TAC SCM competitions: an analysis (4) B2B market: component type quantities (fixed game rules) CPU Quantity vs Component types 7% Memory 26 of 41

27 A decade of TAC SCM competitions: an analysis (5) B2C market: unmet demand through a game 27 of 41

28 A decade of TAC SCM competitions: an analysis (6) B2C market: average unmet demand 28 of 41

29 A decade of TAC SCM competitions: an analysis (7) B2C market: PC prices game rules changing fixed game rules 29 of 41

30 A decade of TAC SCM competitions: an analysis (8) B2C market: PC range prices (fixed game rules) Relative price (% of nominal) vs PC range 11% 30 of 41

31 A decade of TAC SCM competitions: an analysis (9) B2C market: transacted PC quantity game rules changing fixed game rules 31 of 41

32 A decade of TAC SCM competitions: an analysis (10) B2C market: transacted PC range quantity (fixed game rules) High range: 5 PC types Mid range: 6 PC types Low range: 5 PC types Quantity vs PC ranges 70% 32 of 41

33 A decade of TAC SCM competitions: an analysis (10) Conclusions unmet demand minimized through years B2B e-market B2C e-market SUPPLIERS average prices dropping through years, while quantities remain stable PC MANUFACTURER differences both in prices and quantities of specific PC types exist through years, but the average trend does not suggest any significant changes in prices 33 of 41

34 TAC SCM & business strategy frameworks (1) Porter s Five Forces: TAC SCM 1.0? fixed (n=6) TAC SCM 1.0? 34 of 41

35 TAC SCM & business strategy frameworks (2) Porter s Five Forces: TAC SCM 2.0 number of competitors changes during the game monopoly duopoly oligopoly TAC SCM 2.0 PC types enter/leave the market 35 of 41

36 TAC SCM & business strategy frameworks (3) Porter s Five Forces: networked markets transformation supply chain -> supply network e.g. logistics broker for managing transport 36 of 41

37 TAC SCM & business strategy frameworks (4) Porter s Five Forces: what about prosumers in TAC SCM 2.0 end-users can sell back PCs and/or components information trading reverse logistics 37 of 41

38 TAC SCM 1.0 environment Software agent only simulation platform B2B e-market B2C e-market SUPPLIERS PC MANUFACTURER TAC SCM of 41

39 TAC SCM 2.0 environment Mixed simulation platform of software agents and humans B2B e-market B2C e-market SUPPLIERS PC MANUFACTURER TAC SCM 2.0 decision automation vs decision support 39 of 41

40 TAC SCM 2.0 TAC SCM 1.0 TAC SCM 2.0 platform Utilization scenarios TAC SCM competition (research & industry) enhancement of (semi-)automated supply chain management policy making (governments) open simulations are great tool for testing policies for complex systems financial markets collapse smart-grid (Power TAC) learning about market dynamics (higher education) strategy game for business school students great for popularization and proliferation of TAC SCM Industry Academia (research & higher education) Government 40 of 41

41 Vedran Podobnik, Ph.D. Contact info Personal homepage: TAC SCM official homepage: 41 of 41