1. Virtual Laboratory for Simulation and Gaming A Virtual Lab Project Presented by Prof. B. Mahanty Department of Industrial Engineering and Management, IIT Kharagpur

2. 2 Objectives To develop simulation-based experiments on various aspects of industrial engineering and management. To demonstrate discrete-event and continuous simulation of real-life systems otherwise not accessible to students. Broad Areas supply chain management, e-business, and decision sciences. Target Student Group Senior UG & PG Students of Industrial Engg. Students of MBA 2

3. 3 Nature of the Experiments Web based virtual business and industrial environment Concepts of gaming employed to offer interactive sessions Every participant making decisions for the virtual business environment Outcomes of the decisions obtained by continuous and/or discrete-event simulation Graphics and animation outputs fed back to the participants for making further decisions Online quiz for self evaluation 3

4. 4 List of experiments 1) Dynamics of quality fluctuations of an electronic product in a buyer’s market 2) Simulation of auction markets 3) An illustration of Monte-Carlo Simulation in project investments ------------------------------------------------------------------------ 4) Decision-making in Production-Distribution Systems using simulation 5) Simulation of organizational procurement 6) Modelling inventory decisions through simulation 4 Sept 2010 review March 2011 review

5. 5 List of experiments (continued) 7) Supply chain coordination through contracts 8) Demonstration of bullwhip effect in multi- echelon supply chain 9) A simulation model to illustrate limits to growth system archetype 10) Modelling supply chain risks using simulation. 5 Dec 2011 review

6. 6 Answer to reviewer’s comment set 1 Efforts will be made to generate publishable information from the virtual laboratory. The design of the website will utilize the power of the visuals. Efforts will be made to provide remote controllability. The project has no relation with internet gaming. All the fields mentioned are within the broad areas of Industrial Engg. and Management and all the experiments are simulation-based. No real-life data from plants are required.

7. 7 Answer to reviewer’s comment set 2 Proprietary software DELMIA QUEST for simulation is avoided. It has been found the SIMULA software provides a facility for stand-alone applications. Hence, SIMULA will be used instead. The simulation experiments are useful for UG/PG students of Industrial Engineering and also for MBA students. Auction markets and Buyer behaviour are part of supply chain management which is an important component of Industrial Engineering.

8. 8 Answer to reviewer’s comment set 3 It is indeed agreed that the title of the experiment should not include the phrase "Game" or "Games". “Gaming” means that simulation experiments will provide the students facilities to interact in a manner that their decisions will influence the outcome of the experiments in a direct manner. Use of special software is limited only to the extent that they provide stand-alone applications so that no special simulation software need be procured by engineering colleges.

9. 9 Project Status Recruitment of Project Personnel ▫ Only one person is recruited on 89 days basis from 11 th November 2010 ▫ Appointment was given to one person for one year – however, the candidate did not join. Hardware/Software purchase status Money has been released only recently. Quotations are being asked for the hardware/software. Work done so far ▫ Framework for few experiments have been developed by the students in the class project mode.

10. 10 Projects for first phase review 1)Dynamics of quality fluctuations of an electronic product in a buyer’s market 2)Simulation of auction markets 3) An illustration of Monte-Carlo Simulation in project investments

11. 11 Other projects currently under development Decision-making in Production-Distribution Systems using simulation Modelling inventory decisions through simulation Supply chain coordination through contracts Dynamic pricing strategies in electronic markets Modelling supply chain risks using simulation.

12. 12 Objective of the Experiments To demonstrate the concepts related to simulation To introduce some real life systems otherwise not accessible to students.

13. 13 Outline of the experiments Introduction to the real life situation under consideration Definition of learning objectives Simulation flow chart/Logic in structured English Instruction for conducting the experiment Experiment Analysis of the results Quiz to test the understanding of the student about the system under consideration Reference to further studies 13

14. 14 Simulation of Auction Markets Auction is a market mechanism for discovering price under specific demand and supply conditions. ▫ Traditional examples: Selling of Scraps/Obsoletes, Arts, Properties ▫ More recent examples: Selling of radio spectrum, Buying and selling over the Internet, Allocation of ISP bandwidth, Organizational procurement through reverse auctions Classification of auction ▫ Resources (single/multiple items) ▫ Market structure (single buyer-multiple seller or vice- versa) ▫ Bidding rule (ascending/descending/sealed bid price) ▫ Payment rule (first price/second price)

15. 15 The auction environment created for the experiment Forward auction ▫ Auction for selling an item Single object ▫ Auction to sell a single object First price ▫ Highest bidder gets the item with the price he bids Ascending bid and Competitive ▫ Price increases with each new bid coming in at least with some minimum increment.

16. 16 Learning Objectives From problem perspective, to study ▫ The effect of changes in size of the bidder pool  The theory says that more the number - the higher the price. ▫ The effect of reserve price -the minimum price at which the item will be sold (from seller’s perspective)  The theory says that higher the reserve price – higher item price up to certain level after which it suddenly becomes zero. The problem is to choose the right reserve price

17. 17 Learning Objectives ▫ Effect of auction duration  Duration does not affect the final price  Sharp increase in bid price towards the end  Large number of bids arrive at the end ▫ Effect of minimum bid increment ▫ The winner’s curse (from buyer perspective)  The act of buying the item in a price which is much more than its true price. This happens due to the competition.

18. 18 Learning Objectives From simulation perspective, to study ▫ The use of a simulation clock ▫ Use of theoretical distribution for input data generation

19. 19 Simulation Flowchart 19 Simulation Clock Use of theoretical distributions for input data

20. 20 Dynamics of quality fluctuations of an electronic product in a buyer’s market This simulation experiment is developed as a learning tool with the help of a System Dynamics based Game. Explanatory power of system dynamics is demonstrated in this game Management scientists stress on the development of learning laboratories and computer ‑ based case studies Building of learning organizations is a key to corporate success.

21. 21 Quality dynamics The system dynamics model considers: inventory of products customer order backlog production and hiring decisions training of newly hired personnel quality of products on which depend the customer orders, and customer complaints for a company producing electronic products, where the quality of the product fluctuates due to a step change in the customer order function.

22. 22 Basic Data Objective:To inculcate systems thinking by creating a computer ‑ based microworld where decisions and policies can be experimented and evaluated. Target Audience: Engineeting students, Management Students. Managers. Playing Time: One to two hours. Debriefing Time: One hour.

23. 23 Basic Data Number of Players: One team comprising of two players: a production manager and a personnel manager. A number of teams can play at one time. Materials Required: Briefing Package compiled by the game director. Equipment/Setup: A personal computer and the QGAME software for each team. Layout:The teams should sit at some distance from one another.

24. 24 The System Dynamics Model

25. 25 Quality Dynamics Each team makes the production ordering decision, and the people hiring decision. A team continues with a set of decisions throughout a given plan ‑ period of 3 months. The dynamic nature of quality fluctuations stems from:  delays in production processes  training and hiring of people  non ‑ linear relations among work rate, quality and customer orders, and  multiple feedback loops in the system

26. 26 Decision Variables A participating team makes two decisions for a plan ‑ period of three months from the second plan ‑ period onwards: (a) Quarterly Production Ordering Decision and (b) Quarterly Personnel Hiring Decision. The first plan results are provided to the participants as initial feedback material.

27. 27 Measuring Performance At the end of the game, a team should achieve a high average value of the game. To achieve a high average value of the game, the team should make appropriate decisions to do the following throughout the simulation: maintain good quality of the product (about 1.0 or higher) maintain reasonably less inventory and order backlog (less than 4000 units) have adequate number of people (about 150).