MODELING AND INTEGRATION OF PLANNING, SCHEDULING, AND EQUIPMENT CONFIGURATION IN SEMICONDUCTOR MANUFACTURING. PART I. REVIEW OF SUCESSES AND OPPORTUNITIES

Authors

  • Kenneth Fordyce Arkieva Supply Chain Management Solutions
  • R. John Milne Clarkson University School of Business
  • Chi-Tai Wang National Central University
  • Horst Zisgen IBM Software Group

DOI:

https://doi.org/10.23055/ijietap.2015.22.5.1766

Keywords:

demand supply network, system responsiveness, tool capacity planning, waiting time, hierarchical production control, systems integration, semiconductor manufacturing

Abstract

Managing the supply chain of a semiconductor based package goods enterprise—including planning, scheduling, and equipment
configurations—is a complicated undertaking, particularly in a manner that is responsive to changes throughout the demand
supply network. Typically, management responds to the complexity and scope by partitioning responsibility that narrows the focus of most of the groups in an organization—though the myriad of decisions are tightly integrated. Improving system
responsiveness is best addressed by an advanced industrial engineering (AIE) team that is typically the only group with the
ability to see the forest and the trees. These teams integrate information and decision technology (analytics) into an application which improves some aspect of planning, scheduling, and equipment configuration. This paper illustrates the need for AIE teams to serve as agents of change, touches on three success stories, highlights the sporadic progress and incubation process in applying analytics to support responsiveness where forward progress by early adopters is often followed with stagnation or reversal as subsequent adopters require a natural incubation period. This paper and its companion paper (Part II. Fab Capability Assessment) identify modeling challenges and opportunities within these critical components of responsiveness: semiconductor fabrication facility/factory capability assessment, moderate length process time windows, moving beyond opportunistic scheduling, and plan repairs to modify unacceptable results. Although aspects of this paper have the feel of a review paper, this paper is different in
nature—a view from the trenches which draws from the collective clinical experience of a team of agents of change within the IBM Microelectronics Division (MD) from 1978 to 2012. During much of this period MD was a fortune 100 size firm by itself with a diverse set of products and manufacturing facilities around the world. During this time frame, the team developed and institutionalized applications to support responsiveness within IBM and by IBM clients, while staying aware of what others are doing within the literature and industry. The paper provides insights from the trenches to shed light on the past but more importantly to identify opportunities for improvement and the critical role of advanced industrial engineers as agents of change to meet these challenges.

Author Biographies

Kenneth Fordyce, Arkieva Supply Chain Management Solutions

Director Analytics Solutions at Arkieva Suppy Chain Management Solutions.

Adjunct Professor at Lubin School of Business

IBM retired - Senior Computational Decision Scientist

R. John Milne, Clarkson University School of Business

Neil '64 and Karen Bonke Assistant Professor in Engineering Management at Clarkson University School of Business

Chi-Tai Wang, National Central University

Associate Professor at National Central University

Horst Zisgen, IBM Software Group

Manager in the IBM Software Group

Published

2015-10-26

How to Cite

Fordyce, K., Milne, R. J., Wang, C.-T., & Zisgen, H. (2015). MODELING AND INTEGRATION OF PLANNING, SCHEDULING, AND EQUIPMENT CONFIGURATION IN SEMICONDUCTOR MANUFACTURING. PART I. REVIEW OF SUCESSES AND OPPORTUNITIES. International Journal of Industrial Engineering: Theory, Applications and Practice, 22(5). https://doi.org/10.23055/ijietap.2015.22.5.1766

Issue

Section

Special Issue: International Symposium on Semiconductor Manufacturing Intelligence 2014 (ISMI2014)