Modeling and Integration of Planning, Scheduling, and Equipment Configuration in Semiconductor Manufacturing: Part I
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International Journal of Industrial Engineering, volume(issue), pages, year . Modeling and Integration of Planning, Scheduling, and Equipment Configuration in Semiconductor Manufacturing: Part I. Review of Successes and Opportunities Ken Fordyce1, R. John Milne2, Chi-Tai Wang3, Horst Zisgen4 1Arkieva Supply Chain Solutions & Lubin School of Business, Wilmington, DE, U.S., [email protected] 2Clarkson University School of Business, Potsdam, NY, U.S., [email protected] 3National Central University, Jhongli City, Taiwan, [email protected] 4IBM Software Group & Clausthal University of Technology, Mainz, Germany, [email protected] 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. Keywords: demand supply network, system responsiveness, tool capacity planning, hierarchical production control, systems integration, dispatch scheduling, process time windows, semiconductor manufacturing 1. INTRODUCTION Little (1992) observes: “Manufacturing systems are characterized by large, interactive complexes of people and equipment in specific spatial and organizational structures. Because we often know the sub units already, the special challenge and opportunity is to understand interactions and system effects. There are certainly patterns and regularity here. It seems likely that researchers will find useful empirical models of many phenomena in these systems. Such models may not often have the cleanliness and precision of Newton's laws, but they can generate important knowledge for designers and managers to use in problem solving.” Nick Donofrio (Lyon et al., 2001), then IBM Senior Vice President, Technology & Manufacturing (now retired) notes in his Franz Edelman Finalist Award video, “The ability to simultaneously respond to customers’ needs and emerging business opportunities in an intelligent, orderly manner is a survival requirement for today’s marketplace. Our customers continue to tell us that the quality of our responsiveness is as important to them as the quality of our products.” Herbert Simon (1957) observes, “As humans, we have ‘bounded rationality’ and break complex systems into small manageable pieces.” To adjust for bounded rationality and interactions between system elements, Galbraith (1973) suggests the use of “slack” (for example excess inventory) to reduce information load in managing interconnected operations. Galbraith (1973) refers to this cost as slack; in the absence of information and decision support, organizations rely on slack (for example excess inventory). Nick Donofrio (Buchholz, 2005) contends “access to computational capability [will enable us] to model things that would never have been believed before.” 1 The challenge for any extended organization and including those producing semiconductor based packaged goods (see Fordyce, 2011, for background information on most aspects of planning and scheduling in semiconductor based packaged goods (SBPG) and Monch et al., 2013 for extensive technical coverage) is to integrate information and decision technology (analytics) into an effective “decision calculus” (Little, 1970) to extend the boundaries of rationality and improve the responsiveness (reduce slack) of the entire demand supply network (DSN) (Fordyce, 2011; Fordyce et al., 2011). Success comes to those organizations with the best ability to “replan” (Singh, 2007). The challenge comes in two components: initial success and sustained success. Sustained success has been receiving attention recently in executive forums (Cecere, 2015). These challenges are best addressed by small advanced industrial engineering (AIE) teams with a skill set that ranges from programming algorithms and the data science of extracting insights from flawed data (Lohr, 2013; Press, 2013) to deciding on the right combination of methods or creating new ones to understanding the nuances of nudging an organization out of its current comfort zone to its next (more advanced) comfort zone, that is, to function as agents of change. AIE teams are not only the logical choice, but they have a 40 year track record of success (as touched on in the next section). In today’s Google parlance (Lohr, 2014), the AIE teams are “smart creatives.” Although we strongly differ with Mr. Schmidt and Mr. Rosenberg’s statement that these smart creatives “are a new kind of animal,” we agree “they are the key to achieving success.” Based on our experience, it is important to note that agents of change can and do quickly disappear if organizational leadership loses focus on the need for innovation in its core decision technology. This happens easily—despite the mantra of “competing on analytics” (Davenport, 2006)—especially since the impact is often delayed as the organization survives on past efforts with manual workarounds and/or economic circumstances temporarily cover the limitations in responsiveness. Regardless of delay, the challenge remains. The key is fitting a new decision technology (analytics) into the current scheme such that they successfully upset the current social order to become integrated into a new social order—that is, the applications move from a dream to something the people in an organization cannot imagine life without. This must be done without creating confusion for the planners and management team. As noted by Woolsey (1974), “A manager would rather live with a problem he cannot solve than accept a solution he cannot understand.” A successful agent of change must learn to spot a confused look on a client’s face—though it might show for only a moment—to explain away the confusion or open a conversation. Key users need to understand the basic logic generating the results (though they may not comprehend all technical details and from time to time will ask for explanations of solution results). Over time, they grow to appreciate the model’s ability to tackle complexity and develop confidence. This reduces confusion. Across the decision hierarchy of planning, scheduling, dispatch, and equipment configuration (Kempf-Sullivan Decision Grid in Table 1, Fordyce and Milne, 2012, and Fordyce, 2011) where might AIE teams serving as agents of change be effective in improving responsiveness? The opportunities are too numerous to cover in any single paper. This paper will address the following topics: 1. Illustration of the value of dedicated AIE teams as agents of change by briefly reviewing three specific applications with which the authors are knowledgeable. We note the difficulties and dangers of being agents of changes, the sporadic use of analytics, the challenge of not regressing (which does happen), a few successful industrial engineer (IE) agent of change of the modern era, and some guidelines for agents of change. 2. Emerging opportunities for agents of change within a challenge that torments management: FAB (semiconductor fabrication facility or factory) Capability Assessment (FCA) – estimating (committing) what the factory can accomplish under various conditions or what needs to be done to meet specified targets. Topics covered include: 2.1. FAB demand—dynamic adjustment of demand on the FAB in aggregate form or need dates on individual lots 2.2. Public face of FAB capacity—moving beyond nested wafer starts