An Analysis of Revenue and Product Planning for Automatic Test Equipment Manufacturers By Paul D. DeCosta B.S. Electrical Engineering, Worcester Polytechnic Institute, 1990 M.S. Biomedical Engineering, Rutgers University, 1992 Submitted to the Sloan School of Management and the Department of Electrical Engineering and Computer Science In partial fulfillment of the requirement for the degrees of Master of Science in Management and Master of Science in Electrical Engineering and Computer Science At the Massachusetts Institute of Technology May 1998 © 1998 Massachusetts Institute of Technology, All rights reserved Signature of Author Sloan School of Management Depaqment of Electricp.L Engineering and Computer Science Certified by Associate Professor Duan Boning, Thesis Advisor Department of Electrical Engineering and Computer Science Certified by Professorawrence WeiThesis Advisor .aiiSchoolb f Manaement Accepted by Arthur C. Smith, Chairman EECS Department Committee on Graduates FTECHNOLOGY ECOLOGYeffry 4ef A. Barks, Associate I 9. 1qqR Sloan Master's and Bachelor's Programs An Analysis of Revenue and Product Planning for Automatic Test Equipment Manufacturers By Paul D. Deposta Submitted to the Sloan School of Management and the Department of Electrical Engineering and Computer Science on May 8. 1998 in Partial Fulfillment of the Requirement for the Degrees of Master of Science in Management and Master of Science in Electrical Engineering and Computer Science Abstract Revenue and product planning for automatic test equipment (ATE) manufacturers has been difficult due to the cyclical nature of the semiconductor industry. Any insight into this area can provide a significant competitive advantage through tighter controls on cost and improved customer service. This work reviews possible improvements in revenue planning and introduces tools to help better understand the market. A model for developing forecasts of ATE demand based on planned semiconductor fab capacity increases is proposed. This is meant to address some of the shortcomings of the current approach, which is based on semiconductor revenue forecasts. The accuracy of the new model's predictions is compared against the existing process's output. Although the data used to support the model has not been in existence for long, early results show some improvement. Both the new and old techniques are then used to assess the impact of possible technology changes that would greatly affect the ATE market. This helps to demonstrate the strengths and weaknesses of each approach. A financial model is also presented which provides a quantitative assessment of tradeoffs in selecting capacity levels. The competition and volatility in the semiconductor manufacturing equipment industry drive the results of this analysis. Sensitivity analysis shows the impact of improvements to forecasting accuracy and flexibility. Finally, more general recommendations and observations are abstracted from each of the models and applications presented. These primarily focus on the need to incorporate forecast error into revenue and product planning. Thesis Advisors: Associate Professor Duane Boning, Electrical Engineering and Computer Science Professor Lawrence Wein. Sloan School of Management Acknowledgements This work was supported partially by funds made available by the National Science Foundation Award #EEC-915344 (Technology Reinvestment Project #1520). I would like to gratefully acknowledge the support and resources made available to me through the MIT Leaders for Manufacturing program, a partnership between MIT and major U.S. manufacturing companies. I would like to thank my supervisors at Teradyne, John Casey and Jeff Hotchkiss, for their insightful ideas and continuing support. Also, I extend my gratitude to all of the product, sales, and engineering managers in the semiconductor test group who were willing to share their knowledge. Several other people at Teradyne stand out as important collaborators. Dana Smith, Rich MacDonald. Jim Matthews. Ric Conradt, Andy Blanchard, Joan Bums, Chip Thayer, and Margaret Bulens are a few of many supporters who contributed to my work. John Sheridan and Jim Tolley. who provided customer information, were also invaluable. I would also like to thank my thesis advisors, Professors Lawrence Wein and Duane Boning, who provided feedback which was critical to my success. This thesis is dedicated to my wife. Lea, for her constant love and support. Bibliographic Note on the Author Paul DeCosta graduated from Worcester Polytechnic Institute, Worcester, MA in June, 1990 with a Bachelor of Science degree in Electrical Engineering, and then from Rutgers University, New Brunswick. NJ in June, 1992 with a Masters of Science degree in Biomedical Engineering. Upon graduation, he worked in the Medical Imaging Systems division of Polaroid Corporation as a senior engineer in hardware manufacturing. He had both technical and managerial responsibilities. Paul joined the Leaders for Manufacturing Program at MIT in 1996, and completed his research internship at Teradyne, Incorporated in Boston. MA. He will be joining Hewlett Packard's Medical Products Group upon the completion of his MIT degrees. Table of Contents 1. Introduction ............................................................................................................... 9 1.1. Problem Statement .... ........................................ 9 1.2. Thesis O bjective .................................................................................................... 10 1.3. R esearch D escription ....................................................................................... 11 1.4. Thesis Structure .............................................................................................. 11 2. Top-Down Forecasting ............................................................ 13 2.1. M odel D escription ......................................................................................... 13 2.2. Evaluation of the Model....................................................... 14 2.3. Assumptions in to Top-Down Forecasting ..................................... ........ 21 3. Bottoms-Up Forecasting Model .......................................................................... 23 3.1. M odel D escription ......................................................................................... 23 3.2. ATE / Fab Capacity Relationships......................................... 25 3.3. Evaluation of the Model.........................................................29 3.4. Assumptions in Bottoms-Up Forecasting ...................................... ......... 33 4. Capacity Planning Model ............................................................ .................... 35 4.1. M odel Form ulation ......................................................................................... 36 4.2. Selection of Constants ................................................................. 40 4 .3 . M odel U se ............................................................................................................. 42 4.4. R esults Sum m ary ........................................................................................... 44 5. Modeling the Impact of Technology on the ATE Market ................................ 47 5.1. Review and Outlook for Design for Test.................................... ........... 47 5.2. Projections Using Top-Down Model ........................................ ............ 49 5.3. Projections Using Bottoms-Up Model..................................... ............. 52 5.4. R esults Sum m ary ........................................................................................... 55 6. Conclusion ................................................. 57 6.1. Forecasting in Semiconductor Related Markets ...................................... 57 6.2. Operating with Forecast Error ..................................... ..... ......... 58 6.3. Impact of Technology ........................................................................................... 58 Appendix A: Regression Line Plots ............................................................................... 61 Appendix B: User Interfaces for Models ........................................ ........................... 67 References ....................................... .......... 71 List of Figures Figure 1.1 Revenue Growth: Semiconductor and Semiconductor Equipment Markets..... 9 Figure 2.1 BIAS for the IC Revenue Forecasts (1987-1996) .................................... 16 Figure 2.2 MSD 2n for the IC Revenue Forecasts (1987-1996)..................................... 17 Figure 2.3 BIAS for Top-Down Forecasting Model (1987-1996)................................ 19 Figure 2.4 MSD'n for Top-Down Forecasting Model................................... ...............20 Figure 3.2 BIAS for Bottoms-Up Forecast Model (Oct 94 - Oct 96) ...........................31 Figure 3.3 MSD in for Bottoms-Up Forecasting Model (Oct. 94 - Oct. 96) ................ 31 Figure 4.1 Demand Distribution and Selected Planned Capacity Level..................... 38 Figure 4.3 Demand A Distribution and Selected Planned Capacity .............................. 39 Figure 4.4 Regression Line Fit: Total Cost Per Revenue Dollar .................................. 41 List of Tables Table 2.1 BIAS and MSD a2 for Semiconductor Revenue Forecasts ..........................15 Table 2.2 Parameters from Buy-Rate Regressions .......................................................18