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, horst.zisgen@de.ibm.com

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.”

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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 for sales and operation planning (S&OP) (Fordyce et al., 2012) 2.3. FAB tool planning—extending tool (equipment) or capacity planning to complexities such as deployment, time slices, and operating curve (Fromm, 1992; Hopp and Spearman, 2011; Fordyce et al., 2010) 2.4. Picking losers and winners: deciding which lots to expedite and near term estimates of when lots will exit 3. Challenges for FCA and dispatch scheduling, in particular, the hidden menace of yield, process, or "Q" time windows (Burda et al., 2005, 2007, and 2009; Tu et al., 2010; Wu et al., 2014). This refers to a sequence of manufacturing operations a lot must complete within a limited period of time or else the quality of the product will degrade. As a simple analogy, think about replacing a large window of a house with a rain storm approaching and no tarp to cover the hole left by the removal of the window. Once the window has been removed, there is a series of steps that must be completed in sequence to secure the new window and the time to complete this task is variable since there is only an estimate of when the storm will arrive. Additionally, there is competition for some of the tools required to install the new window (skilled labor, framing tools, specialty saws, etc.) for other projects with critical delivery dates, but not facing the storm issue. To add to the complexity (a) more than one window is being replaced at the same time and (b) there are tarps, but there is uncertainty about how effectively they work especially with regards to the duration of the storm. 4. The forgotten world of repairing a plan. The execution of a model to create a plan is the start of the planning process, not the end (Fordyce and Singh 2012). The planner (whether focused on the demand, factory, or enterprise) highlights unacceptable results (for example, demand not being met on time, tool utilization too high, insufficient demand, etc.) and then does 2

Table 1: Kempf-Sullivan decision grid for demand supply network (DSN) for planning, scheduling, dispatch, & equipment configuration activity areas by decision tiers Enterprise wide global view – central Enterprise subunits (manufacturing, distribution, planning retail) factory planning Once or twice a year for 2 to 5 year Capacity analysis typically at the tool family level horizon at an aggregate level with and overall manpower to support forecasted demand, Tier 1: forecasted demand focused on business creation of production flow and capacity information Strategic scenarios. Net result: strategic direction for central plan, determining new production established and financial commitments processes to introduce and estimated learning curve made. Weekly/bi-weekly/monthly Capacity (tools and manpower) analysis to gauge • create demand statement impact of changing product mix, identify challenges, (current orders, forecasts) review and modify deployment decisions, configure • capture capacity, WIP, BOM, equipment and manufacturing engineering business policy requirements, and create capacity constraint Tier 2: • central planning engine to information for central planning and WIP status. Tactical match assets with demand Monitor tool level performance and take appropriate • estimate supply line linked to actions. Establish rules and metrics to set global lot demand, early warning, importance; example: how many priority classes, production requirements, algorithm to set lot importance within a priority chase situations class, limits on number of expedites Reduced focus/what-if Provide information to central plan and daily • what-if commit on large factory adjustments orders • establish target outs, due dates on lots • what-if on major asset change • maintenance priorities • status of key WIP and actions • short term changes in deployment to take if needed • review key lot status and change priority Tier 3: • cross factory signals (up or down) based on progress (either Operational manually or dynamically) (daily) • one time changes in lot importance guidance • establish manufacturing lot versus development lot preference • revised projected outs for enterprise planning Change in priorities, updated supply As needed updates to guidance to support projections based on updated WIP or response decisions capacity status; change in customer • regular updates to lot status based its Tier 3.5: reserved supply progress, entering a time process window, Sub-daily status of short term manufacturing targets, guidance WIP position and tool status • regular updates to tool status based on manufacturing engineering requirements, tool events, etc. Available to promise or automated Dispatch scheduling & tool response order commit process, cross factory • assign sequence of lots to a tool Tier 4: signals • change status of a lot (for example, place a Response lot on or off hold) • monitor signals from tools and respond as needed

analysis to repair (improve) the plan. This world of plan repair often has limited analytical support and is a major opportunity for AIE teams and research teams that develop and build core tools in analytics and resource allocation (mixed integer programming, genetic algorithms, simulated annealing, tabu search, etc.). The review paper by Aytug et al. (2005) provides an excellent overview of reactive scheduling and the importance of explaining solutions in that setting.

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5. Moving dispatch scheduling beyond opportunistic scavenging (Kempf and Fox, 1985; Kempf, 1989, 1991, and 1994). The bulk of the logic used to assign lots to tools (equipment) in a wafer FAB are focused metrics local to the tool set (specific to the tool set and the lots waiting at the tool set or about to arrive) and some generic FAB metrics loosely coupled with the performance of the entire network (for example, overall cycle time and on time delivery). There is limited work on looking across an extended section of the manufacturing flow and the detailed impact on meeting exit demand for the firm. 6. Last, the dominant challenge has been and remains that planning and scheduling have to be partitioned into components and the integration of the components is at best loosely coupled. The partitioning varies across firms and across time. The decision grid in Table 1 is one example of partitioning. Figure 1 is an example of a more traditional partition. Typically the only group with the skills and mission to look across the entire system is an advanced industrial engineering team. This need cuts across all industries. Rangarajan and Snapp (2015) observed that the largest supply chain analytics challenge was the lack of a unified view of the supply chain domains. Vargas-Villami et al. (2003), Kempf (2004 and 2008) and Patel et al. (2009) explore this issue.

Figure 1: Representation of classical planning and scheduling hierarchy.

Opportunities and challenges are intentionally described in this paper and its companion paper (Part II. FAB Capability Assessment, Fordyce et al., 2016) via spreadsheet based examples (historically data tables and ledgers) for the following reasons: this is the dominant tool of thought (Iverson 1979) and prototype environment of the client and therefore agents of change must have native fluency in this environment; the examples (subset of actual discussions with planners) illustrate proven methods to successfully work with clients, tables are the dominant form of the client’s second favorite environment, SQL; and common ad hoc programming environments such as APL, MATLAB, Python and R(S) are array orientated. We are not saying robust solutions can be developed with spreadsheets (whose limits were recognized early in the 1980s, Sandberg, 1984) or that modern mathematical notation is unimportant. As Iverson (1979) noted, all notations have limits and traditional mathematical notation can disguise critical complexities such as objective function costs and time buckets. Although this paper and its companion paper (Fordyce et al., 2016) focus on the FAB production facilities within the SBPG DSN, many of these same challenges apply to post FAB production facilities within the SBPG network and these facilities are now the focus of attention to improve planning and scheduling to improve responsiveness. In this regard, the work by Uzsoy et al. (1992 and 1994) remains timeless. A key question is why are successful applications of analytics to support responsiveness in SBPG sporadic and not sustained? One can find parallels with the American frontier at the eastern boundary of the Great Plains from 1820 to 1875 (Gwynne, 2011) — forward progress is sporadic—bursts forward are followed by reversals, forts built in isolation, and lessons lost. Perhaps the best example is the revolver: used with success, abandoned, and then rediscovered. As with most technologies, there is an incubation period during which the process of absorption follows an erratic path. After a long time, the revolver 4 became best practice replacing muzzle loading rifles. Our hope is this paper will support the process of increasing the rate of effective and sustained absorption of analytics. The starting point is the emergence of opportunities, but without a skilled AIE team, often opportunities are lost without even being noticed—until the organization faces some challenge and finds it current set of analytics insufficient.

2. INDUSTRY APPLICATION—AIE TEAMS AS AGENTS OF CHANGE

For AIE teams to be successful agents of change, they need a cohesive mix of skills from those technical to understanding the application area to organizational dynamics. These components of success, individually and as a group, have been covered extensively in the literature and presentations from trade press to academic journals. The information provided usually takes one of three forms: 1. High level set of guidelines 2. Case study or summary paper, for example, the Franz Edelman Award and Wagner Prize special issues of Interfaces 3. Set of loosely coupled observations, for example, “For ordinary lives the goal is security, i.e. stability - by definition the limiting of change - Stasis. For them, the term disruptive technologies may sound more like Orwellian Newspeak rather than anything rational, let alone desirable” (Ferro, 2013). All three forms are valuable to educate and train agents of change. However, these do not convey the sense of uncertainty and nervousness at the start of a project or that the road to success will have many twists and turns that require improvising and seizing opportunities as they emerge (Crowder 1997). Most importantly, when progress looks unlikely (hopeless), success may come to those that are patient and persistent—the operative word is “may”—sometimes one needs to learn from a failed project and move on to the next project. In the first three subsections below, we briefly touch on three projects the authors have a detailed familiarity with that eventually become successful. We focus on the challenge of transitioning from the status quo at the start of each project and how initial progress was made. All three projects were an Edelman Award finalist, a Wagner Prize finalist, or both. Two received IBM Outstanding Technical Achievement awards and one the Artificial Intelligence Association innovative application award. In addition to these three applications, we point the reader to material on the IBM Microelectronics implementation of FPO at its 300mm Wafer FAB in East Fishkill, NY—one of the first applications to use mixed integer and constraint programming to schedule and dispatch FAB tool sets (Dietrich et al., 2014; Bixby, 2009; Bixby et al., 2008; Bixby et al., 2006) enabling the FAB to move beyond real-time rule based logic that IBM had been an early pioneer in during the early 1980s. Subsection 2.4 briefly covers the work by the most successful IE agent of change of the modern era and the appendix provides observations and insights for agents of change accumulated by the authors from mentors.

2.1. Logistics Management System (LMS) LMS was one of the first real-time reporting and dispatch systems for FABS initially developed in the early 1980s by Gary Sullivan’s AIE team—initially for IBM’s FAB in Burlington, VT and later used in a number of FABS inside and outside of IBM. In fact, part of LMS remains in use today. The technical details and the business contribution of LMS are covered in Fordyce and Sullivan (1990 and 1994). As with most innovations, work occurred simultaneously in multiple places. The early work in dispatch scheduling is covered in section 5. Today the features and functions in LMS are standard practice—from the concept of dashboards to relational data base structures to a centralized source of current status updated in real time to the use of rules (more generally procedural logic) to dispatch scheduling. In fact, a young AIE might say, “How else would you do it? Did not companies always do it this way?” In fact, when Sullivan proposed the concept of real-time information and the use of rules for dispatch decisions, the initial reaction of many was, “Why do we need this?” or, “It cannot be done.” The status at the time had been paper reports generated overnight and phone calls to the factory floor. There was strong, but quiet, opposition, ranging from, “it is impossible to capture the data needed in real-time” to “paper kanbans and tool dedication will work fine.” The opposition arose from the best of intentions—they believed their approach was better or the proposed approach would never work and was a waste of resources better spent on more immediate needs. Sullivan’s AIE team made initial progress because of the following: 1. One prior success and one prior failure. In the early 1970s, Sullivan and his team of agents of change built and successfully deployed a system for real-time enrollment and training (RET) for IBM customer engineers who repaired IBM machines in the field. The RET team received the prestigious IBM Outstanding Technical Achievement Award. The failure was RMS (resource management system)—an application to dispatch customer engineers. RMS was a success on many technical fronts, but gained no momentum. As Ferro (2013) observed, “Following a brilliant unveiling, many have been the well- crafted plan / adopted policy, that languished; subsequently overtaken by daily demands and events - to inexplicably fade into failed obscurity.” After the RMS failure, the team moved on. However, the experience and technical accomplishments from RMS and RET carried forward to LMS.

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2. The ability to secure funding focused on new development that could not be absorbed by day to day needs. In this case, IBM dedicated funds from Corporate for advanced manufacturing projects. 3. The ability to capture the required data dynamically and in relevant time. 4. Physical proximity—the AIE team was physically located where the daily management of the FAB was happening. 5. A subset of the FAB management team that was supportive. 6. A team of dedicated AIEs—some experienced and some new—where AIE is broadly defined to include skills in the business (domain knowledge), programming, modeling, and a systems approach that enabled the team to move effortlessly between the big picture and details as needed. 7. Two technologies that were needed emerged:  the original personal computer network software  a material requirements planning (MRP) system that provided a need date for each lot The LMS team found a path through a situation that looked barren and hopeless in 1982-1983 to 1986 when the FAB could not remember life before LMS and IBM actively promoted LMS (Feigenbaum 1988) as an example of expert systems (now called analytics). The core approaches worked out by this team and others working in this area in the early 1980s (for example, Kempf and Fox, 1985) have become the mainstay of dispatch scheduling for FABS where the Applied Materials Real Time Dispatcher (RTD) software is often used presently. The path from LMS and the early work of others to standard practice was sporadic (and frustrating) with a long incubation period. Of note, the early innovators were not the firm that successfully brought to market a commercial product. IBM made an attempt to market the product commercially and abandoned it (while continuing to use LMS at its Vermont fab through the present time).

2.2. IBM Operational Framework for Supply Chain Planning (OPS) The IBM Operational Framework for Supply Chain Planning (referred to as OPS) was and is a suite of integrated applications (Figure 2) to support enterprise wide supply chain management for the IBM Microelectronics division. (IBM is in the process of selling its semiconductor FABS to Global Foundries.) The internal name of OPS is PROFIT (Planning Resources Optimally for IBM Technology). The components of this suite are demand management which enables a firm to create an agreed upon demand statement (part, date, quantity, type and priority); repository for how to produce and ship products (bills of materials, transport, capacity consumption rates, cycle or lead times, etc.); current asset position (capacity available, inventory, work in progress (WIP); available to promise; and best can do central planning engine (CPE) to match assets with demand to create a projected supply linked to demand and synchronize the activities of the demand supply network (DSN), and planners workbench. From an operations research perspective, the most significant technical achievements occurred in the CPE (Denton et al., 2006 and Degbotse et al., 2013). Fordyce et al. (2011) has the best overall description of OPS. The business contribution of OPS is covered in Lyon et al. (2000), Denton et al., (2006); and Fordyce (1998 and 2001). The work done by IBM in collaboration with Arkieva on demand management captured a critical paradigm shift that successful demand management was about a single integrated and flexible view of the key data sources (orders, forecast history, shipping, etc.), capturing sales estimates, collaboration, and developing insight—where statistical forecasting methods was just one component (Fordyce and Sullivan, 2016); this approach has carried forward to become best practice. Today the features and functions in OPS are standard best practice, but not standard common practice. We define standard best practice as using the best available methods of analytics to capture as much complexity as possible to improve responsiveness without increasing planner confusion and successful implementation in multiple organizations. Common practice refers to the set of methods used by the majority of firms with some success. Specific to OPS, we refer to the ability to capture and work with an end to end view of the DSN, a robust infrastructure, the level of the complexity of the DSN captured in the central planning model, demand management, available to promise, and support for planners. For example, one common practice for the central planning model is rough cut capacity planning (RCCP) in contrast to the OPS ability to model with lot and order level granularity. We do not make a value judgment; it may be that RCCP works fine for some firms. In late 1994, the concept of single unified central planning process supported by a model (engine) that could regenerate a plan overnight with lot level and daily granularity was beyond the imagination of many. To put this in perspective, in early 1995, IBM set a target for this planning organization to complete a new plan within 21 days—a target considered overly aggressive by the planners. This was the task Peter Lyon’s AIE team faced in January 1995 when the initial OPS team was formed. The status at the time was a loosely coupled planning process across different locations supported by locally developed tools with limited function—such as EZSIZER (this so-called “Easy Sizing” tool was developed in APL2 running on VM in the 1980s that enabled a planner to create a projected supply of parts based on date effective yields and cycle times and capacity limits), lots of emails, and lengthy discussions with key decisions posted on white boards. Opposition (again well meaning) was everywhere. Many said the project was impossible because of challenges ranging from generating a single data repository to solving linear programming problems this large. Initial progress was made because of: 1. Creating a team of seasoned veterans and new AIEs (again, broadly defined as with LMS)—such interdisciplinary teams are a recurring theme for successful agents of change. 6

2. The ability to secure funding focused on new development that could not be absorbed by day to day needs. 3. The ability to capture the required data dynamically and in relevant time. 4. Physical proximity—the AIE team was physically located near the planners. 5. A subset of the management team that was supportive. 6. Technologies that were needed emerged, for example, faster LP solvers and a consolidated planning data base to feed the LP model.

Figure 2: Components in IBM Operational Framework for Supply Chain Planning (OPS).

The OPS team went from looking at a situation that was unchartered territory and vaguely defined in 1995 to being part of the day to day planning fabric at IBM by 1999 and then marketed by IBM and used in organizations outside of IBM. OPS met the goal of reliably regenerating a complete plan each night in four hours—capturing the current data, executing the central planning model, and creating the reports to support analysis by the planners in the morning. The technical advances made by the IBM team in the algorithms to match assets with demand within the CPE (Degbotse et al., 2013) included: an advanced heuristic best can do (HBCD) with two pass logic, optimization for binning, and commit and request date (Milne and Wang, 2014); extensions to the core LP material balance equations (called SCOPE) to capture such nuances as dynamic time buckets and more robust time bucket logic, variations in demand class, honoring contractual obligations (Milne et al., 2015), smarter skills in setting costs in the objective function to meet priorities and avoid things the finance organization does not like, and runtime performance; the ability to partition the problem along two dimensions, apply HBCD or SCOPE as needed, and then create the image of a single solution, completely data driven; ability to use the same CPE structure for various planning needs (MRP type business needs, capacity planning needs, best can do for ATP, tactical decisions), and a full soft pegging. Despite the technical achievements and business success; many firms still follow the traditional path laid out in introductory textbooks and consultant power point sides: master production scheduling (MPS), RCCP, MRP, and much planner work. Additionally, there has been little interest in academic circles in the technical advances in the CPE—from partitioning and heuristics to time buckets to a complex objective function that requires great skill to get to get the

7 cost coefficients correct for the model to be of any use. For now, OPS is an example of sporadic interest; perhaps time will demonstrate its concepts are in an incubation period.

2.3. Enterprise Production planning and Optimization System (EPOS) EPOS was and is a critical application to support capacity and lead time planning for the IBM 300mm FAB in East Fishkill, NY that was originally deployed successfully for the IBM Storage Technology (hard drive) division (Zisgen, 1999; Hanschke et al., 2000; Dohse et al., 2003). It is an outstanding example of how the combined efforts of an AIE team and research team can extend and integrate two sophisticated areas of analytics (queuing networks and optimization techniques) developed over a period of time to substantially improve responsiveness and effectiveness without confusion. The queuing network component of EPOS enables the FAB to capture the flow of wafers and the relationship between cycle time and utilization (operating curve) without the burden of discrete event simulation. One critical extension to queuing networks was the ability to model batch service and batch arrivals and multi-chamber process equipment. The optimization component enables EPOS to dynamically allocate jobs to the right queues when there are route choices. Details on the business contribution of EPOS and some technical information are covered in Brown et al. (2010) and Zisgen et al. (2008). Technical information on EPOS with respect to queuing networks can be found in Hanschke and Zisgen (2005), Hanschke (2006) (describes a different approach to model single queues with batches), Zisgen et al. (2008), Zisgen (2009) (diffusion approximation for batch queues), and Hanschke and Zisgen 2011 (queuing networks with batch service). Related work and important core information can be found in Bitran and Tirupati (1989a and 1989b), Chen et al. (1988), Connors et al. (1996), Hanschke and Speck (1995), and Whitt (1983 and 1993). The use of optimization to select alternative deployments in static situations is discussed in the companion paper (Fordyce et al., 2016). An implemented related work prior to EPOS is discussed by Bermon and Hood, 1999. The suggested use of optimization within IBM for deployment dates back to the late 1980s and the release of the Optimization Subroutine Library (OSL) when IBM reentered the scientific and engineering computing market. Today the features and functions in EPOS are still advanced practice and far from common practice. At the start of the EPOS project in East Fishkill circa 2003, they were in unchartered territory. At the time, practice at IBM’s new three billion dollar FAB was static tool planning models done with IEs sequestered for a week doing spreadsheets and an occasional simulation. EPOS not only generated more intelligent solutions, but it optimized and automated the planning process from data collection to presentation of results to support post-plan investigation by IEs. The agents of change were able to see beyond the previous practice of spreadsheets and manual data inputs to a one stop solution that was more intelligent and faster. Again, opposition appeared and for the usual reasons: not possible, the improvement in plan results would not justify the effort, the need to focus on near term challenges, etc. Initial progress was made with EPOS because of the following: 1. The team was able to draw upon prior work IBM had invested in as a starting point. 2. Creating a team of season veterans and new AIEs. 3. Ability to secure funding focused on new development that could not be absorbed or redirected on day to day needs. 4. The ability to capture the required data dynamically and in relevant time. 5. Physical proximity—the AIE team was physically located near the planners. 6. A subset of the senior management team that was supportive. 7. Technologies that were needed emerged—from the ability to capture planning data from the manufacturing execution system to improvements in optimization to new methods in queuing networks. The 300mm FAB went from hours with a spreadsheet to a dynamic planning environment and anchored a best in class FAB capability assessment process that support IEs from planning (EPOS) to detailed understanding of tool performance (Martin, 1999). EPOS is another example of sporadic progress and required incubation. Interest in the academic community is limited, despite pushing the frontier of two critical components of the analytics toolkit (queuing networks and dynamically generated optimization) and being a Wagner Prize finalist. Interest by other FABS (IBM did and may still market EPOS as a service) was simultaneously high and hesitant. High interest relates to the recognized need to incorporate the flow of wafers and best use of tools while understanding the capabilities of the FAB to make commitments to the business, alter the deployment of the tools, and acquire additional equipment. Hesitant touches on many areas including: the need for more detailed data on flows than simpler approaches and a lack of data infrastructure at the FAB, unfamiliarity with queuing networks, existing methods in place that are simple and do not create confusion, other projects with a near term higher priority, and the cyclical nature of FAB demand. For now there is sporadic progress; in the future it may be this is simply the incubation period as was the case with LMS.

2.4 Challenges and Frontiers of Decision Making in Large Companies—Cleaning up after the Laureates (Kempf 2015) We point the reader to a forthcoming 2015 paper with this section’s title by Dr. Karl Kempf—Intel and INFORMS fellow and the most successful IE agent of change in the modern era (Shirodkar and Kempf, 2006; Wu et al., 2010;Weiland et al., 2012; Kempf et al., 2013; Sampath and Kempf, 2015). As noted by Kempf (2015), “Over the past 200 years our view of human 8 rationality has moved from perfect (Adam Smith) to bounded (Herbert Simon, Nobel Laureate 1978) to biased (Daniel Kahneman, Nobel Laureate 2002). Unfortunately the laureates left two practical questions unanswered: a) how bad is our decision making and b) how do we make better decisions? This talk addresses these two questions from an Intel perspective. I will give a brief overview of decision support tools at Intel over the past 20 years (addressing boundedness) the benefits from which can be used to set a lower bound on how bad we are. Then I will describe our recent projects to improve decision making (addressing biases) including applications of wisdom of crowds and decision markets.” In 2009, Intel Chairman Craig Barrett (Horner 2009), said, “For the past two decades, Intel's decision technology group has worked behind the scenes to provide sound recommendations for designing factories, improving manufacturing, making accurate sales forecasts and prioritizing the features that should be introduced during new product development. They have literally saved Intel billions of dollars.”

3. FAB CAPABILITY ANAYSIS

The global challenge that torments FAB management, keeps planners working late, and creates frustration with occasional glory for modeling professionals is FAB/Factory Capability Assessment (FCA). This challenge takes the form of two questions. 1) Given a set of conditions, what is the outcome and impact? and 2) Given desired outcomes, what conditions (if any) will generate these outcomes? Outcomes are commonly expressed as the quantity of good wafers (from WIP and new starts) that will exit the FAB by part number and by exit date (or time period). Cycle time is a secondary outcome measure. Impact is the workload placed on FAB assets (tools, manpower, consumables, etc.). Conditions are any aspects of the FAB production and finance environment that can be changed (at least in theory) such as: the starts profile, adding capacity, different tool deployments, limits on chemicals to control costs, expedite guidelines, cost accounting, dispatch scheduling logic, manufacturing engineering requirements, changing production process(s), and altering tooling characteristics. The corollary challenge is a model that summarizes—without adding to planner confusion—FAB capabilities to support the interplay between organizations in the demand supply network. With FCA, the primary attention is on the end products and services of the FAB. Under the FCA umbrella, there are a wide range of questions and diverse methods for modeling and analysis. Relevant work includes: Sematech, 1994; Bermon et al., 2003 and 2005; Bixby, 2009; Chien et al., 2011; Chuang et al. 2014; Dobson and Karmarkar, 2011; Ewen et al. 2014; Geng and Jiang, 2009; Horn and Podgorski, 1998; Hanschke and Speck, 1994; Kempf et al., 1991; Kempf, 1994 and 2004; Kempf and Shirodkar, 20006; Lan et al., 2014; Lin et al., 2000; Monch et al., 2011; Monch et al., 2013; Monch and Ramachar, 2014; Morrison et al., 2005 and 2006; Morrison and Kim, 2014; Rodriquez et al., 2012; Woolsey, 1979; Wu et al., 2014; Wu and Hung, 2008; Milne and Zisgen, 2007; Fordyce and Milne, 2012; Fordyce et al., 2012). Many FCA questions have been raised. Which lots will exit near-term? Which tool sets are short of capacity to meet targets for output rates and cycle time (Bermon and Hood, 1999; Zisgen et al., 2008; and Klemmt et al., 2012)? What is impacting tool set performance (Martin, 1999; Konopka, 1995)? What average cycle time can be expected? What if lot priorities change? What if demand changes? Near term, where will a WIP bubble occur? What output can be expected from cluster tools (Morrison and Martin, 2007; Morrison and Kim, 2014)? How best to deploy tools with a changing demand pattern (Chien et al., 2012)? What is the impact of the preventive maintenance schedule on the operating curve (Brown et al., 2010)? And so forth. Methods include simple deterministic models with spreadsheets, but regular use predates spreadsheets by years (Fordyce and Sullivan, 1983 and 2016), heuristics (such as cascade), historical allocations, optimization (Bermon and Hood, 1999; Bermon et al., 2003 and 2005; Habla and Monch, 2008; Monch et al., 2013, Klemmt et al. 2012; Brown, et al., 2010), queuing equations/operating curve and parameter estimation (Horn and Podgorski, 1998; Schelasin, 2011 and 2013; Bitran and Tirupati, 1989; Morrison and Martin, 2006; Morrison and Lee, 2014; Fordyce et al., 2010), queuing networks (Chen et al., 1988; Fromm, 1992; Hanschke and Speck, 1994; Brown et al., 2010; Zisgen et al., 2008; Zisgen, 2009), discrete event simulation (Gowling et al., 2013, Bagchi et al., 2008; Dayoff and Atherton, 1984), column generation and dynamic optimization, clearing functions (Asmundsson et al., 2009; Missbauer et al., 2011; Kacar et al., 2013), fuzzy demand (Busaba etl. 2011), hybrid with scheduling (Morais, M. et al. 2013), and twin FABS (Ying-Mei 2014). Further details on FCA are discussed at length in the companion paper (Fordyce et al., 2016).

4. THE HIDDEN MENACE—MODERATE LENGTH PROCESS TIME WINDOWS

At certain points along the manufacturing route for a wafer, once the wafer begins a certain manufacturing process (step), it must complete a specified number of downstream manufacturing processes within a certain period of time to avoid a likely yield loss. Such losses are typically generated by some type of contamination from exposure. These types of time windows occur in other aspects of life (e.g. roof work, new window, plumbing that turns off the water, lane closures on highways, treating some medical conditions). For FABS, these process time windows have become increasingly numerous and a menace. The number of steps between the start and end of danger is typically 3 to 8 with a time window of 8 to 24 hours. The tool sets that execute these manufacturing steps and the activities that must finish these steps within a fixed amount of time are referred to as a zone of 9 control (ZOC). An example ZOC is shown in Figure 3 where the manufacturing activities (operations) for the ZOC lots are MA- ZC 1 to MA-ZC n, each operation is supported by a tool set, and the ZOC lots must complete MA-ZC n within a certain amount of time or risk yield loss. Dispatch scheduling the ZOC involves two key issues: when should a ZOC lot enter the ZOC and the assignment of lots to tools within the ZOC. Factors that influence the decision on lots entering the ZOC include: the anticipated available capacity at each of the ZOC tool sets, the number of ZOC lots within the ZOC and their location, the comparison between the target time to complete the manufacturing operations in the ZOC and the total raw process time (RPT) for the operations in the ZOC, the relationship between the risk of yield loss and the length of time the wafer is in the ZOC (typically this is a probabilistic relationship, not deterministic), and the global importance of each ZOC lot. The situation is further complicated by non ZOC lots. These lots require processing by manufacturing activities (MA-NZi) that are outside the ZOC, but that are performed on tool sets within the ZOC as illustrated in Figure 3. The decision to release the ZOC lots into the ZOC and the lot tool assignment must consider the needs of the non ZOC lots. For example, if too many ZOC lots are released into the ZOC, then zero non ZOC lots can be processed even if they are more important than the ZOC lots for meeting FAB commitments. It is likely that the ZOC and non ZOC lots have different deployment preferences for the tool sets. Reentrant flow increases the complexity. It is likely that each lot of a specific part number will pass through multiple process time windows during its journey as the metal layers are deposited and other parts will have their own process time windows.

Figure 3: Zone of control (ZOC) for process time window management.

The two dispatch decisions—releasing lots into the ZOC and assigning lots to tools within the ZOC—must reflect the following elements: 1. For the ZOC lots within the ZOC, assign them to tools in a timely manner to finish before the time limit expires. 2. Logic that controls the release of the ZOC lots into the ZOC should determine there is a high probability the lot will exit the ZOC on time. 3. Logic that controls the release of ZOC lots into the ZOC must preserve enough capacity to handle the non ZOC lots. 4. Balance the work load across the ZOC and non ZOC passes for each tool set within the ZOC based on priorities. Dispatch scheduling of moderate length process time windows continues to grow in importance. There is work being done in the trenches at different levels of sophistication, but there only appears to be limited information in the literature (for example, 10

Wu 2014; Klemmt and Monch, 2012; Tu et al., 2010; Levy et al., 2010; Burda et al., 2005, 2007 and 2009; Robinson, 1998). It is a worthy area for a tutorial paper on the core challenge, possible solution approaches, detailed literature review, and current practice; research collaboration between universities and industry; and a focus topic within conferences such as the International Symposium on Semiconductor Intelligence (ISMI) and Modeling and Analysis of Semiconductor Manufacturing (MASM)— perhaps in conjunction with Sematech. Perhaps the more pressing question is how to incorporate moderate length process time windows into the FAB Capability Assessment models described in the companion paper (Fordyce et al, 2016). None of the existing methods the authors are familiar with account for the capacity impact. Executives have asked for it.

5. REPAIRING THE PLAN—THE BEST SOLUTION DISGUISES BEST REPAIR

The completion of a model to create a plan is the start of the planning process, not the end (Fordyce and Singh 2012). The planner (whether focused on the demand, factory, or enterprise) highlights unacceptable results (for example, demand not being met on time, tool utilization too high, insufficient demand, etc.) and then conducts analysis to repair the plan. This involves improving the asset position to better satisfy demand or shifting the demand position. A similar situation occurs in some cases for short term factory scheduling which is why software to support scheduling often combines algorithms with interactive visuals (Epp et al., 1989 and Maxwell and Singh, 2010 which draws on Conway, Maxwell, and Miller, 1967). The review paper by Aytug et al. (2005) provides an excellent overview of reactive scheduling and the importance of explaining solutions in that setting. A related area is constraint directed search (Fox, 1987 and 1989). Work in iterative repair (Zweben et al., 1993 and 1994), which became the foundation of the Red Pepper planning engine, and similar work (see Aytug et al., 2005 for a list of work in this area) had some success, but it is important to note the substantial difference between iterative repair within matching or scheduling algorithms and that directed by the planner or scheduler The first step in plan repair is the understanding how the demand is being satisfied. This is a challenge for MIP based solutions where the question “Why did the model create that answer?” is a regular occurrence. The answer: “It all depends on the objective function coefficients and constraints rarely satisfy the client” (Fordyce 2015). In some cases, this challenge precludes the use of MIP based methods. A robust generic explain feature for MIP would be well received and improve planner effectiveness without confusion. Initial progress in this direction has been made by Greenberg (1994, 1993a, 1993b, 1993c, 1993d) and Degbotse et al., 2006. Customizing explain functionality to specific applications has resulted in practical success. For example, the success of Fab PowerOps (FPO) at IBM (Bixby et al., 2006) benefited from an explain feature (Burda et al., 2008) as did an enterprise plan navigation (EPN) system for analyzing a supply chain central planning model or engine (CPE, Fordyce et al., 2011) involving large scale optimization Analog Devices Inc. EPN is an example of an excellent application that one could only learn about in the trenches. Interestingly, a critical success factor for the same CPE (part of OPS defined in section 2.2) deployed at IBM Microelectronics was a suite of demand and supply pegging applications (Hedge et al., 2004, Orzell et al., 2009, and Degbotse et al., 2013) that represent an aspect of explain function. Interestingly two different planning teams using the same basic central planning engine preferred different variations of explain, but explain function was critical to both. Note that explaining and repairing are not identical which is made clear in the next paragraph. The following example shows how a “simple” repair action can quickly become complex and the optimal solution can disguise the optimal repair plan. In Table 2, the two demands for part P111 are D001 with a due date of 4/4/2016, priority of 2, and quantity of 80, and D002 with a due date of 4/5/2016 priority of 1, and quantity of 100. The lower the priority value assigned to the demand, the more important the demand. Consequently, in this example, D002 (with priority 1) is more important to satisfy on time than D001 (with priority 2). The firm’s business policy is a demand with a lower priority value is always more important than a demand with a higher priority value. Sometimes this is referred to as pre-emptive priorities. Table 3 has the current anticipated supply (often called projected WIP) of part P111 from the factory. Supply S00A will be available to meet demand on 4/4/2016 with quantity of 100 and S00B is projected to be available on 4/6/2016 with quantity of 100. This information (demand, projected supply, and business policy on demand priorities) is input to the central planning engine (CPE) to best match assets with demand. Given this information the best match is illustrated in Table 4, where the CPE assigns S00A to D002 and S00B to D001. The on time delivery (OTD) score card is demand D001 is 2 days late and D002 is 1

Table 2: Demand for part P111

Demand ID Date needed Priority Qty

D001 04/04/16 2 80 D002 04/05/16 1 100

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Table 3: Supply for part P111 Supply ID Date available Priority Qty

S00A 04/04/16 N/A 100

S00B 04/06/16 N/A 100

Table 4: Best-can-do solution given business policy on demand priorities to meet demands for part P111 information on demand supply assigned to meet demand on time status ID of Days Date supply supply Qty committed pegged Date early short (-) to Dmd to meet supply Supply or late or Dmd ID customer Priority qty dmd available qty (-) excess D001 04/04/16 2 80 S00B 04/06/16 100 -2 20

D002 04/05/16 1 100 S00A 04/04/16 100 1 0

Table 5: Report highlighting important demand and the supply supporting this demand based on the optimal solution information on demand supply assigned to meet demand on time status ID of Days Date supply supply Qty committed pegged Date early short (-) to Dmd to meet supply Supply or late or Dmd ID customer Priority qty dmd available qty (-) excess

D001 04/04/16 2 80 S00B 04/06/16 100 -2 20

Table 6: Updated supply for part P111 after initial repair action Original New Expedite days (= Supply date date new supply date - ID available Qty available original supply date) S00A 04/04/16 100 04/04/16 0

S00B 04/06/16 100 04/04/16 -2

day early. If we switch the assignment of supply with demand (so that S00A is assigned to D001 and S00B is assigned to D002), this solution is not optimal because since D002 would have an OTD score of -1 (1 day late) and D002 has a pre-emptive priority over D001. Assuming the planning model has the ability to soft peg what supply is supporting what demand at lot and order level, the planner will be provided information similar to what is found in Table 4. Typically a planner will have the important demands not being met on time highlighted in a report (Table 5) and then attempt to identify a repair action either by improving the asset position or reducing or shifting the demand position. After reviewing the information in Table 5, the planner decides the solution is to ask manufacturing management if it can expedite the completion of S00B by two days so it completes on 4/4/2016 instead of 4/6/2016. If manufacturing management agrees, the updated supply projection is shown in Table 6 and a summary of the new match (with the improved asset position) between demand and supply is shown in Table 7. This information is typically sent to manufacturing and used to update the projected supply used for the CPE run the next day. Normally, the planner would not review the Table 7 report. In Table 6, there is a column called Expedite days—the number of days a job must finish earlier than indicated by normal cycle time. This has a total of -2, negative meaning manufacturing has to move some lots faster. However this is not the optimal repair action, where optimal is defined as minimizing the workload on manufacturing which in this case means minimizing the total expedite days. The optimal repair action swaps the assignment of supply with demand which then reduces the number of expedite days to -1. Table 8 has the new supply picture with the optimal repair solution. Table 9 summarizes how each demand will be met with the optimal repair plan. S00A is now assigned to support D001 and S00B is assigned to support D002. If we think of this from the perspective of the flow of lots in the manufacturing line, the original repair action is creating a leap frogging condition and the optimal repair action does not. Leap frogging occurs when a 12 wafer that starts later passes a wafer of the same part that started earlier. Table 10 illustrates this concept. In this table, the column Percent complete refers to the fraction of manufacturing that has been completed to date for each supply. The larger the percent complete, the closer the supply is to completion. In the original repair action, the lot that is 90% finished is slowed down (in terms of need date) from 4/4/2016 to 4/5/2016. The lot that is 80% complete is sped up from 4/6/2016 to 4/4/2016. Hence a leapfrog situation is created. The optimal repair action eliminates the leapfrog situation. However, optimality comes with a risk. If manufacturing is unsuccessful in having supply S00B finish by 4/5/2016 instead of 4/6/2016 and S00A is sent to the customer for demand D001 on 4/4/2016, then a suboptimal (and dangerous to the director of supply chain) situation has occurred. The purpose of this example is to give the reader a feel for the complexity and the critical importance of repair to the planning process and directors of planning—one of whom referred this scenario as the illusion of optimization. It is beyond the scope of this paper to provide a thorough review of this topic. It appears to be a major opportunity for analytics and to the best knowledge of the authors, little sustained work has been done.

Table 7: Summary of OTD adjusted for original repair action to have all demand met on time information on demand supply assigned to meet demand on time status

Days Expedite supply days (= ID of early or new supply late (-) supply Date pegged Original New based on Qty date - committed to date date new short (- original Dmd to Dmd meet supply Supply supply supply ) or supply ID customer Priority qty dmd available qty available date excess date) D001 04/04/16 2 80 S00B 04/06/16 100 04/04/16 0 20 -2 D002 04/05/16 1 100 S00A 04/04/16 100 04/04/16 1 0 0

Table 8: Updated supply for part P111 after optimal repair action Original New Expedite days (= date date new supply date - Supply ID available Quantity available original supply date) S00A 04/04/16 100 04/04/16 0 S00B 04/06/16 100 04/05/16 -1

Table 9: Summary of OTD adjusted for optimal repair action to have all demand met on time information on demand supply assigned to meet demand on time status

Days Expedite supply days (= ID of early or new supply late (-) supply Date pegged Original New based on Qty date - committed to date date new short (- original Dmd to Dmd meet supply Supply supply supply ) or supply ID customer Priority qty dmd available qty available date excess date) D001 04/04/16 2 80 S00A 04/04/16 100 04/04/16 0 20 0 D002 04/05/16 1 100 S00B 04/06/16 100 04/05/16 0 0 -1

Table 10: Two repair actions from manufacturing need date view Revised need date Supply Percent Original after original repair Revised need date ID Quantity complete need date action after optimal repair S00A 100 90% 04/04/16 04/05/16 04/04/15 S00B 100 80% 04/06/16 04/04/16 04/05/15

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6. DISPATCH SCHEDULING BEYOND OPPORTUNISTIC SCAVENGING

Thirty-plus years after the initial launch of the dispatch scheduling application called LMS (Fordyce and Sullivan, 1990 and 1994; Fordyce et al., 1992) in the early 1980s at the IBM FABS and the work by Kempf and Fox (1985), even the most sophisticated use of optimization in dispatch scheduling relies on opportunistic scavenging and the basic framework from the 1980s with updates (see Chapter 6 of Fordyce 2010). Briefly, the basic structure consists of advocates or guidance and a judge. There are advocates for each of the primary factors that need to be considered in the dispatch decision. For example, the affinity of a particular lot to a tool (can the lot run on the tool, it is preferred for the tool, does a tool need one of this lot type to stay qualified); the local importance of the lot to the efficiency of the tool set (does it complete a batch or train); the global lot importance (priority and estimate of whether the lot is behind or ahead of schedule for on time delivery), and downstream needs—to briefly name a few. The judge selects the lot to place on the tool next in pure dispatch. In schedule dispatch, Judge Part 1 creates the tentative sequence of assignments and Judge Part 2 selects the next lot. A few FABs have begun using optimization and constraint programming methods to replace rules (select, sort, if-then-else) within the judge function (Bixby et al., 2006 and 2008; Bixby, 2009). Tabu search methods have shown promise (Geiger et al 1997). Much, but not all, of the academic research has been focused on controlling starts or a few smarter rules. Older examples include Glassey, 1996; Glassey and Weng, 1999; Glassey and Lozinsky, 1988; Glassey and Resende, 1988; and Wein 1988). Recent examples include Chuang, 2014; Lan et al., 2014; Monch and Ramachar, 2014; Wu and Hung, 2014. Most recent work follows the pattern of identifying one to three rules focused on overall FAB metrics (overall on time delivery, cycle time, or throughput) and tests using one or two standard test data sets that only capture some aspects of the variability in the FAB. The limitation of test beds is outside the scope of this paper and is almost always outside the control of the researcher. The reader is referred to Monch et al. 2013; Monch et al. 2011; and Pfund et al., 2006 for a complete summary of current state of the art and challenges of dispatch scheduling. In contrast to most research, the practice of dispatch scheduling typically has the following characteristics:  An effective schedule dispatch application has one component for each advocate (for example, global lot importance, batching or trains, and manufacturing engineering preference) and a judge which balances competing priorities to make the dispatch decision (see chapter 6 of Fordyce 2010 for a full description).  There is never a single dominant simple lot importance rule, except perhaps in the cases of very high volume FABs that make just a handful of parts in a build to stock mode. The IBM 300mm FAB in East Fishkill initially tried when the FAB was first opened to limit dispatching to a few simple rules; a few years later this FAB was deploying FPO—and for good reason and with great success.  The decision to put in or not put in (or change the emphasis of) certain schedule dispatch logic is based on the direct evaluation of the dispatch decisions made in “white board” design sessions and watching the logic execute in real-time.  Some “before” and “after” metrics are reviewed, but that is often post-decision justification and is not a controlled experiment.  The bulk of the effort in developing and implementing logic for dispatch scheduling is focused on capturing tool characteristics and developing logic to improve the productivity of the toolset. At times this work has to reflect the dispatch logic internal to the tool set (provided by the vendor who built the tool and outside of anyone else’s control).  This effort is ongoing as tool characteristics change. For example, logic was developed to adjust for the tool characteristic that it increases the temperature on its plate quickly, but cooling is a very slow process. After about a year, the tool was modified by the vendor such that cooling was accomplished much faster.  The ability to control starts into the manufacturing line is limited and less important when on time delivery to meet near term demand is the dominant measure of success as opposed to cycle time or throughput. For example, assume demand for part AAA is 100 wafers on day 40 with an expected cycle time of 37 days. To minimize the cycle time for these 100 wafers, one would wait to start (release) the wafers on day 3. If instead they are released now (on day 1) and they take 39 days to finish, they would still finish on time. That would be fine in terms of on time delivery. The FAB might decide to release two days early (on day 1) to keep some of the tools supporting the initial steps of manufacturing busy and then have the flexibility to run more slowly (incurring more wait time) at key tool sets near the end of the manufacturing process.  It is only recently that Brooks Automation (now Applied Materials) has finally linked RTD to its Autosim simulator so dispatch rules do not have to be recoded to be tested (the original requests for this were made 15 years ago). Even with this improvement, there are still substantial barriers to a controlled experiment. Whether rules or optimization methods are used for scheduled dispatch, the paradigm has remained the same—doing the best with current WIP at a tool set and occasionally some estimate of near term arrivals or where departures will go next. In fact, part of the success of FPO was its capability to look upstream and identify likely arrivals. There is limited work (research or applied—one exception being work on shifting bottleneck, Monch et al., 2011 and 2013) that coordinates dispatch decisions (more generally manufacturing action decisions) across tools and across time. To give this some perspective, it is like making your next chess move without concern for future moves by yourself or your opponent (not exactly a strategy Deep Blue 14 employed—Weber, 1997) or making a move in backgammon without at least assessing risk. Additionally, the metrics are local, for examples, maximize throughput, keep WIP at a certain level, and limit the amount of time a lot waits. Peter Lyon (Fordyce and Sullivan, 2016) was once asked, given his 40+ years of experience as an IBM semiconductor manufacturing high level manager which includes a dozen-years stint as the manager of strategic systems, what was the best FAB metric to influence the assignment of a lot to a tool; his answer was, “whatever best positions the entire demand supply network to meet the needs of its customers; all else is parochial.” Suppose there are 20 lots waiting at tool set AAA with 3 machines, all of them identical in their deployment. One simple look-ahead strategy is to identify the next toolset for each lot after completing tool set AAA. For example, assume lot 101 goes to tool set BBB and this toolset has many lots already waiting to be processed. It would be logical for toolset AAA not to yet process lot 101, since it will just wait at toolset BBB. However, lot 101 may be more important than the other lots waiting at toolset BBB or maybe lot 101 would complete a batch or train. In such cases, it may be advantageous to process lot 101 soon after all. The limitations in the general skill set to make decisions across tools and across times should further account for the hidden menace—process time windows described in section 4.

7. CONCLUSION

In the middle 1980s, Ed Feigenbaum (Stanford University Professor of Computer Science, Scientific Director Heuristic Programming Project, and later Turing Award winner in 1994), at the request of IBM Group Director (Advanced Systems) Dr Herb Schoor, visited the IBM Vermont (USA) FAB to see how effectively IBM was using “expert systems,” an amorphous term that generated as much interest in the middle 1980s as analytics in 2015 and quickly included a wide array of methods (for perspective, modern graphical user interfaces, relational data bases, and spreadsheets were just emerging—the first version of Microsoft Excel for Windows was released in November 1987). During this visit, Feigenbaum spent considerable time with Gary Sullivan, senior engineer & manager of Advanced Industrial Engineering (later Edelman Laureate and AAAI Innovative Application award winner; also recipient of three IBM Outstanding Technical Achievement awards for his ground breaking work in what is now called analytics). Feigenbaum was in the process of writing the book, “The Rise of the Expert Company” (Feigenbaum, 1988). He chose to write about the IBM FAB and one quote is, “Dispatch Scheduling is a new kind of entity— community intelligence. Born from the collective wisdom of various disciplines, experiences, and points of view, which dynamically disseminates the new intelligence around the same community that engendered it, solving problems that are ‘too tough’ for us humans to figure out.” In private correspondence (Fordyce and Sullivan, 2016), Feigenbaum noted the “immense undertaking.” The focus of his observation was a linked set of projects being done by AIE teams serving as agents of change. The team focused on advancing dispatch scheduling via the Logistics Management System (LMS) and aspects of tool planning and was led by Gary Sullivan. A team focused on advancing production planning was led by Peter Lyon. In the middle 1990s, the two teams would combine under Lyon as Strategic Systems and would build and deploy a suite of application for supply chain management referred to as OPS (Degbotse et al., 2013; Denton et al., 2006; Lyon et al., 2001; Fordyce, 1998; Fordyce, 2001; Fordyce et al., 2011). Dr. Elizabeth Williamson, originally hired into IBM as a member of Sullivan’s AIE team, would become the director of manufacturing for the IBM East Fishkill 300mm FAB in the middle 2000s and oversee the development and deployment of EPOS (Brown et al., 2010) and FPO (Bixby et al., 2006, Bixby et al. 2008, and Bixby 2009) with occasional collaboration from Lyon’s team. The FPO effort was led by Dr. Robert Bixby (ILOG, pre-IBM acquisition) and Rich Burda (IBM) becoming one of the first industrial strength uses of optimization methods in real-time dispatch (something the LMS team could only dream of in 1983). The collected efforts of agents of change across two decades provided IBM Microelectronics some of the best planning and scheduling for its time. Analytics opportunities for addressing the myriad complexities of SBPG demand supply network to improve responsiveness continue to appear as boundless now as they were in 1980s. This is true for three reasons: advances in analytic methods and computation power create new opportunities; propagation of best practices to common practice; and opportunities generated when earlier progress had been reversed (part of the incubation process). Modeling opportunities identified in this paper include the consideration of process time windows, the need for decision support systems with better explain and repair functionality, and moving dispatch scheduling beyond opportunistic scavenging. Further opportunities are identified in the companion paper (Fordyce et al., 2016). The greatest technical challenge with these opportunities involves the integration of the models throughout the business processes for planning, scheduling, and equipment configuration decision-making. The complexity and large scale of these problems requires judicious choices in modeling and the development of algorithms to solve these integrated problems efficiently without adding to planner confusion. Three essential components for forward progress are: AIE agents of change teams, management commitment, and risk. Without the willingness to take risks, responsiveness will stagnate—imagine if FABs dispatched with paper and nightly reports as they once did or if it took 21 days to create a central plan. AIE teams willing to assume this risk have made tremendous progress as agents of change in increasing the responsiveness of demand supply networks. However, risk must be balanced with politics, the art of the possible. As outlined in this paper, opportunities remain for AIE teams to make even greater progress in 15 the future. All agents of change must remember: for all technologies from technical to social there is an incubation period during which the process of absorption follows an erratic path, which in the moment, makes progress looked sporadic and can generate a feeling of barrenness and hopelessness. AIE teams who persist in the face of temporary setbacks will reap the fruits of the opportunities outlined in this paper, its companion paper (Fordyce et al., 2016), and otherwise.

8. REFERENCES 1. Asmundsson, J., Rardin, R.L., Turkseven, C.H. and Uzsoy, R. (2009). Production Planning with Resources Subject to Congestion. Naval Research Logistics, 56(2):142-157. 2. Aytug, H., Lawley, M., McKay, K. Mohan, S. and Uzsoy, R. (2005). Executing production schedules in the face of uncertainties: A review and some future directions. European Journal of Operational Research, 161:86–110. 3. Bagchi, S., Chen-Ritzo, C-H., Shikalgar, S.T. and Toner, M. (2008). A Full-Factory Simulator as a Daily Decision-Support Tool for 300mm Wafer Fabrication Productivity. MASM 2008. Proceedings of the 2008 Winter Simulation Conference, 2021-2029. 4. Bermon, S. Barahona, F., Hood, S. and Gunluk, O. (2005). Robust Capacity Planning in Semiconductor. Manufacturing. Naval Research Logistics, 52:459–468. 5. Bermon, S., Barahona, F. and Hood S. (2003). Capacity Planning Under Demand Uncertainty for Semiconductor Manufacturing. IEEE Transactions on Semiconductor Manufacturing, 16(2):273–280. 6. Bermon, S. and Hood, S. (1999). Capacity optimization planning system (CAPS). Interfaces, 29(5):31–50. 7. Bitran, G.R. and Tirupati, D. (1989a). Tradeoff Curves, Targeting and Balancing in Manufacturing Networks. Operations Research. 37(4):547-555. 8. Bitran, G.R. and D. Tirupati. (1989b). Approximations for product departures from a single-server station with batch processing in multi-product queues. Management Science, 35(7):851-878. 9. Bixby, R. (2009). From Planning to Operations: The Ever‐Shrinking Optimization Time Horizon. Workshop on Combinatorial Optimization at Work, Conference, Sep 21 - Oct 9, 2009 at ZIB, Berlin, Germany, co-at- work.zib.de/berlin2009/downloads/2009-09-25/2009-09-25-1600-BB-Real_Time_Applications.pdf. 10. Bixby, R., Burda, R. and Fordyce, K. (2008). Technology that upsets the social order—a paradigm shift in assigning lots to tools in a wafer fabricator—the transition from rules to optimization. MASM 2012. Proceedings of the Winter Simulation Conference, 2277-2285. 11. Bixby, R., Burda, R. and Miller, D. (2006). Short-interval detailed production scheduling in 300mm semiconductor manufacturing using mixed integer and constraint programming. Advanced Semiconductor Manufacturing Conference, 148- 154. 12. Brown, S.M., Meents, I., Wheeler, B. and Zisgen, H. (2010). Queuing Model Improves IBM’s Semiconductor Capacity Lead-Time Management. Interfaces, 40(5):397-407. 13. Buchholz, J. (2005). On the Spot series. Nick Donofrio interview with IBM w3’s John Buchholz, 8 Sept. 2005. 14. Burda, R., Degbogse, A., Denton, B.T., Fordyce, K. and Milne. R.J. (2009). Method of Release and Product Flow Management for a Manufacturing Facility. U.S. Patent 7,477,958. 15. Burda, R., Dickstein, E. and Dziedzic, A (2008). System for dispatching semiconductors lots. US Patent 7,426,420. 16. Burda, R., Degbogse, A., Denton, B.T., Fordyce, K. and Milne. R.J. (2007). Method, system, and computer program product for controlling the flow of material in a manufacturing facility using an extended zone of control, U.S. Patent 7,305,276. 17. Burda, R., Degbogse, A., Denton, B.T., Fordyce, K. and Milne. R.J. (2005). Applying Extended Zone of Control Methods to the Emerging Challenges of Moderate Length Process Time that Balances the Needs of Time Sensitive and Non-time Sensitive Lots Competing for Shared Resources,” IBM internal report. 18. Busaba, P., Ohsato, A. and Yenradee, P. (2011). Aggregate Production Planning with Fuzzy Demand and Variable System Capacity Based On TOC Measures. International Journal of Industrial Engineering. 18(5):219-231. 19. Cecere, L. (2015). Orbit Charts. http://www.supplychainshaman.com/tag/orbit-charts/ accessed May 2, 2015. 20. Chen, H., Harrison, M., Mandelbaum, Ackere, A., and Wein, L. (1988). Empirical Evaluation of a Queuing Network Model for Semiconductor Wafer Fabrication. Operations Research, 36(2):202-215. 21. Chiamsiri, S. and Leonard, M. (1981). A diffusion approximation for bulk queues. Management Science. 27(1):1188–99. 22. Chien, C.-F., Wu, C.H. and Chiang, Y.S. (2012). Coordinated capacity migration and expansion planning for semiconductor manufacturing under demand uncertainties. Int. J. Prod Economics, 135:860–869. 23. Chien, C.-F., Dauzere-Peres, S., Ehm, H., Fowler, J.W., Jiang, Z., Krishnaswamy, S., Monch, L. and Uzsoy, R. (2011). Modeling and Analysis of Semiconductor Manufacturing in Shrinking World: Challenges and Successes. European Journal of Industrial Enginering, 5(3):254-271. 24. Chuang, S. et al (2014). A Novel Dynamic Policy for Shorting the Waiting Time of Big Jobs. Proceedings of the 2014 International Symposium on Semiconductor Manufacturing Intelligence (ISMI2014).

16

25. Conners, D., Feigin, G. and Yao, D. (1996). A queueing network model for semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing 9 (3):412–427. 26. Conway, R. Maxwell, W. and Miller, L. Theory of Scheduling. (1967, 2012). Courier Corporation. 27. Crowder, H. (1997). Seven Helpful Hints for OR/MS Consultants. ORMS Today, 24(1). 28. Court, D. (2015). Getting big impact from big data. McKinsey Quarterly, January 2015, 1-8, mckinsey.com. 29. Davenport, T. (2006). Competing on Analytics. Harvard Business Review, January 2006, 1–9. 30. Dayoff. J. and Atherton, R. (1984). Simulation of VLSI Manufacturing areas. VLSI Design, 84-92. 31. Degbotse, A., Denton, B., Fordyce, K., Milne, R.J., Orzell, R. and Wang, C.-T. (2013). IBM blends heuristics and optimization to plan its semiconductor supply chain. Interfaces, 43(2):130-141. 32. Degbotse, A., Denton, B., Milne, R.J., Rice, R. and Waite, J. (2006). Method for isolating improvement opportunities from linear program output. U.S. Patent Application Publication US 2006/0235814 A1, publication date: Oct. 19, 2006. 33. Denton, B., Forrest, J., and Milne, R.J. (2006). IBM solves a mixed-integer program to optimize its supply chain. Interfaces, 36(5):386-399. 34. Dietrich, B., Plachy, E. and Norton, M. (2014). Analytics across the Enterprise. IBM Press. pp. 108-111. 35. Dobson, G. and Karmarkar, U. (2011). Production Planning Under Uncertainty with Workload-Dependent Lead Times: Lagrangean Bounds and Heuristics. Chapter 1 in Planning Production and Inventories in the Extended Enterprise: A State of the Art Handbook, Volume 1, edited by Kempf, Keskinocak, and Uzsoy. 36. Dohse, L., Hanschke, T. Meents, I. and Zisgen, H. (2003). Integration der Simulation in die Programmplanung einer globalen Supply Chain mittels EPOS, Operations Research Proceedings 2003, Springer. 37. Epp, H., Kalin, M. and Miller, D. (1989). An interactive adaptive real-time scheduler for steel making. Proceedings of the Third International Conference: Expert Systems and the Leading Edge in Production and Operations Management. 495-503. 38. Ewen, H. et al (2014). Reduced Modeling Approach for Semiconductor Supply Chains. Proceedings of the 2014 International Symposium on Semiconductor Manufacturing Intelligence (ISMI2014). 39. Feigenbaum, E. (1988). The Rise of the Expert Company, Macmillan, NY. 40. Ferro, A. (2013). Some Observations for Agents of Change. www.qbdecisions.com. 41. Fordyce, K. and Sullivan, G. (2016). Memoirs from Two Retired Jedi Knights. www.qbdecisions.com, to be published. 42. Fordyce, K., Milne, R.J., Wang, C-T. and Zisgen, H. (2016). Modeling and Integration of Planning, Scheduling, and Equipment Configuration: Part II. Fab Capability Assessment. to appear in International Journal of Industrial Engineering: Theory, Applications, and Practice. 43. Fordyce, K. (2015). Private correspondence with Dr. Robert Tenga, senior optimization consultant Arkieva. 44. Fordyce, K., Milne, R.J. and Singh, H. (2012). Illusion of FAB Capacity in Central Planning the Ongoing Challenge - Tool Deployment and the Operating Curve. MASM 2012, Proceedings of the Winter Simulation Conference. 45. Fordyce, K. and Milne, R.J. (2012). Ongoing Challenge for a Responsive Demand Supply Network: Final Frontier Controlling the Factory. Chapter 3 in Decision Policies for Production Networks. edited Armbruster, H. and Kempf, K. Springer. 31-69. 46. Fordyce, K. and Singh, H. (2012). Optimizing” the Planning Process – not Just the Initial Solution - Central Plan Navigation and Repair – Critical Function for Responsiveness. Arkieva Inc. Delaware (USA). www.qbdecisions.com/doctor-kens- notes/doctors-kens-notes-from-the-trenches-sponsored-by-arkieva2 47. Fordyce, K. (2011). The Semiconductor Supply Chain Enterprise Wide Planning Challenges. www.arkieva.com/learn- more/books) or www.qbdecisions.com Arkieva Inc. Delaware, USA, editor Harpal Singh. 48. Fordyce, K., Wang, C-T, Chang, C-H, Degbotse, A, Denton, B., Lyon, P., Milne, R.J., Orzell, R., Rice, R. and Waite, J. (2011). The Ongoing Challenge: Creating an Enterprise-wide Detailed Supply Chain Plan for Semiconductor and Package Operations. Chapter 14 in Planning Production and Inventories in Extended Enterprise, Springer, NY, 313-387. 49. Fordyce, Milne and Singh (2010). Fundamentals of the Operating Curve for Planners. Arkieva Inc. Delaware, USA 50. Fordyce, K. (2001). New Supply Chain Management Applications Provide Better Customer Service: Serious Gets Exciting. MicroNews, a publication of the IBM Microelectronics Division, 6(3):10-12. 51. Fordyce, K. (1998). Matching Assets with Demand Engines for PROFIT and Supply Chain Management. MicroNews, a publication of the IBM Microelectronics Division. 4(3):4-8. 52. Fordyce, K. and Sullivan, G. (1995). Dynamically Generated Rapid Response Capacity Planning Model for Semiconductor Fabrication. Impact of Emerging Technologies on Computer Science and Operations Research, edited S. Nash et al, Kluwer. 53. Fordyce, K. and Sullivan, G. (1994). Logistics Management System (LMS): Integrating Decision Technologies for Dispatch Scheduling in Semiconductor Manufacturing. In M.B. Morgan, Ed., Intelligent Scheduling, Morgan Kaufman Publishers, San Francisco, 473-516. 54. Fordyce, K., Dunki-Jacobs, R., Sell, G.R. and Sullivan, G. (1992). Logistics Management System: an Advanced Decision Support System for the Fourth Decision Tier Dispatch or Short-Interval Scheduling. Production and Operations Management, 1(1):70-86

17

55. Fordyce, K., Morreale, M., McGrew, J. and Sullivan, G. (1992).Knowledge Based Systems: APL Techniques., in Encyclopedia of Microcomputers, Volume 9, edited by A. Kent and J. Williams, Marcel Dekker, New York, pp. 345-378. 56. Fordyce, K. and Sullivan, G. (1990). IBM Burlington’s Logistic Management System. Interfaces, 20(1):43-64. 57. Fordyce, K., Jantzen, J., and Sullivan, G. (1989). Representing Knowledge with Functions and Boolean Arrays. IBM Journal of Research and Development, 33(6):627 646. 58. Fordyce, K. and Sullivan, G. (1983). A Self Programming Decision Support System. IBM Manufacturing Technology Digest. 59. Fox, B.R. (1989). Mixed Initiative Scheduling. Proceedings of the Artificial Intelligence in Manufacturing Symposium. Association for the Advancement of Artificial Intelligence, Stanford University, March 1989. 60. Fox, M. (1987). Constraint-Directed Search: A Case Study of Job-Shop Scheduling. Morgan Kaufman Publishers, Los Altos, California. 61. Fox, M. (1983), “Constraint Directed Search: A Case Study of Job Shop Scheduling,” Technical Report, CMU-R1-TR-83- 22, Robotics Institute, Carnegie-Mellon University, Pittsburg, PA. 62. Fromm, H. (1992), “Some Remarks on Cycle Time, Variability, Zero Inventories, and Costs in Microelectronic Manufacturing Lines,” IBM TR 28.167. 63. Galbraith, J. (1973). Designing Complex Organizations, Addison-Wesley, Boston, MA. 64. Geiger, C.D., Kempf, K.G. and Uzsoy, R. (1997). A tabu search approach to scheduling an automated wet etch station. Journal of Manufacturing Systems, 16(2):102-116. 65. Geng, N. and Z. Jiang. (2009). A review on strategic capacity planning for the semiconductor manufacturing industry. International Journal of Production Research 47(13):3639–3655. 66. Glassey, C. (1996). Linear Control Rules for Production Control of Semiconductor Fabs. Transactions on Semiconductor Manufacturing. 9(4):536-549. 67. Glassey, C. and Weng, W. (1991). Dynamic Batching Heuristic for Simultaneous Processing. Transactions on Semiconductor Manufacturing 4(2):77-82. 68. Glassey, C. and Lozinsky, C. (1988). Bottleneck Starvation Indicators for Shop Floor Control. Transactions on Semiconductor Manufacturing 1(4):147-153. 69. Glassey, C. and Resende, M. (1988). Closed Loop job release control for VLSI circuit manufacturing. IEEE Transactions on Semiconductor Manufacturing. 1(1):36-46. 70. Goldman, S. (2004). Great Scientific Ideas that Changed the World. Great Courses, The Teaching Company, LLC. 71. Gowling, B., Peterson, J., O’Donnell, T., Kidambi M. and Muller, D. (2013). An integrated approach to real-time dispatching rules analysis at Seagate Technology. MASM 2013, proceedings Winter Simulation Conference, 3766-3776 72. Graves, R., Knonopka, J. and Milne, R.J. (1995). Literature Review of Material Flow Control Mechanisms. Production Planning and Control, 6(5):395-403 73. Greenberg, H.J. (1994). How to Analyze Results of Linear Programs-Part 4: Forcing Substructures. Interfaces, 24(1):121-30. 74. Greenberg, H.J. (1993d). A Computer-Assisted Analysis System for Mathematical Programming Models and Solutions: A User’s Guide for ANALYSE, Kluwer, Boston. 75. Greenberg, H.J. (1993c). How to Analyze Results of Linear Programs-Part 3: Infeasibility diagnosis. Interfaces, 23(6):56-67. 76. Greenberg, H.J. (1993b). How to Analyze Results of Linear Programs-Part 2: Price Interpretation. Interfaces, 23(5):97-114. 77. Greenberg, H.J. (1993a). How to Analyze the Results of Linear Programs-Part 1: Preliminaries. Interfaces, 23(4):56-67. 78. Gwynne, S. (2011). Empire of the Summer Moon. Scribner, NY. 79. Habla, C. and Monch, L. (2008). Solving volume and capacity planning problems in semiconductor manufacturing: A computational study. MASM 2008, Proceedings of the Winter Simulation Conference, 2260-2266. 80. Hanschke, T. and Zisgen H. (2011). Queueing networks with batch service, European Journal of Industrial Engineering. 3(5):313-326. X (b,b) 81. Hanschke, T. (2006). Approximations for the mean queue length of the GI =G /c queue. Operations Research Letters 34(2):205–213. 82. Hanschke, T., Kramer, M., Meents, I. and Zisgen, H. (2000). EPOS - Ein System zurunternehmensweiten Produktionsplanung und Optimierung. Fortschrittsberichte Simulation, 14. Symposium Simulationstechnik, Hamburg. 83. Hanschke, T., and H. Zisgen. (2005). Queueing networks with batch service. IBM Internal Technical Report. 84. Hanschke, T. and Speck K. (1995). AMS-an APL Based Modeling Tool for Production Planning. Operations Research Proceedings 1994, edited U. Derigs et al, Springer-Verlag, 318-323. 85. Hegde, S., Milne, R.J., Orzell, R., Pati, M. and Patil, S. (2004). Decomposition system and method for solving a large-scale semiconductor production planning problem. U.S. Patent 6,701,201. 86. Hopp, W. and Spearman M. (2011). Factory Physics 3rd Edition. McGraw-Hill. 87. Horn, G. and Podgorski, W. (1998). A focus on cycle time-vs.-tool utilization "paradox" with material. Advanced Semiconductor Manufacturing Conference and Workshop Proceedings, 405 – 412. 18

88. Horner, P. (2009). Intel Wins INFORMS Prize, OR/MS Today, 36(3). www.lionhrtpub.com/orms/orms-6-09/fredelman- sb2.html. 89. Iverson, K. (1979). Notation as a Tool of Thought. 1979 ACM Turing Award Lecture. 90. Kacar, N.B., Monch, L. and Uzsoy, R. (2013). Planning Wafer Starts using Nonlinear Clearing Functions: A Large Scale Experiment. IEEE Transactions on Semiconductor Manufacturing, 26(4):602-612. 91. Jesse, R., Sell, R., and Sullivan, G. (1992). Daily Output Planning: Integrating Operations Research, Artificial Intelligence, & Real time Decision Support. Expert System Applications, 5:245-256. 92. King, I. (2012). Karl Kempf, Intel's Money-Saving Mathematician. Business Week May 12, 2012. 93. Kempf, K.G. (2015). Challenges and Frontiers of Decision Making in Large Companies - Cleaning up after the Laureates. Invited presentation to appear in the Proceeding of the International Congress on Industrial and Applied Mathematics (ICIAM), Beijing, Aug 2015. 94. Kempf, K.G., Erhun, F., Hertzler, E.F., Rosenberg, T.R. and Peng, C. (2013). Optimizing Capital Investment Decisions at Intel Corporation. Interfaces, 43(1):62-78. 95. Kempf, K.G. (2008). Complexity and the Enterprise: The Illusion of Control. in Managing Complexity: Insights, Concepts, Applications Understanding Complex Systems, Springer, Berlin, 57-87. 96. Kempf, K. and Shirodkar, S. (2006). Supply Chain Collaboration thru Shared Capacity Models. Interfaces. 36(5):420-432. 97. Kempf, K. (2004). Control-Oriented Approaches to Supply Chain Management in Semiconductor Manufacturing. Proceedings of the 2004 American Control Conference, Boston, MA, 4563–4576. 98. Kempf, K. (1994), Intelligent Scheduling Semiconductor Wafer Fabrication. Chapter 18 in Intelligent Scheduling, edited by Mark Fox and Monte Zweben, Morgan Kaufman Publishers, San Francisco, CA, 517-544. 99. Kempf, K., et al. (1991). Issues in Design of AI Based Schedulers. AI Magazine, 11(5):37-45. 100. Kempf, K. (1989). Manufacturing Scheduling: Intelligently Combining Existing Methods. Proceedings AAAI in Manufacturing Symposium Series, AI in Manufacturing, Stanford University, March 1989, 51-55. 101. Kempf, K. and Fox, B. (1985). Complexity, Uncertainty, and Opportunistic Scheduling, Proceedings of the IEEE Second Conference on Artificial Intelligence Applications: The Engineering of Knowledge Based Systems, Miami, Florida. 102. Klemmt, A., Laure, W. and Romauch, M. (2012). Product mix optimization for a semiconductor fab: modeling approaches and decomposition techniques. MASM 2012. Proceedings of the Winter Simulation Conference. 103. Klemmt, A. and Monch, L. (2012). Scheduling jobs with time constraints between consecutive process steps in semiconductor MFG. MASM 2012, Proceedings of the Winter Simulation Conference. 104. Konopka, J.M. (1995). Capacity Utilization Bottleneck Efficiency System—CUBES. IEEE Transactions on Components, Packaging, and Manufacturing Technology—Part A 18(3):484-491. 105. Lan, Y., Chuang, S., Wu, J. and Chien, C. (2014). A New Priority to Computer Experimental System. Proceedings of the 2014 International Symposium on Semiconductor Manufacturing Intelligence (ISMI2014). 106. Levy J., Burda, R. and Stahlecker, T. (2010). Method for Determining Amount of Product Released into a Time Sensitive Operation. 2010 Winter Simulation Conference. 107. Lin, G., Ettl, M., et al. (2000). Extended Enterprise Supply Chain Management at IBM Personal Systems Group and Other Divisions. Interfaces, 30(1):7–25. 108. Little, J. (1992). Tautologies, models and theories: can we find ‘laws’ of manufacturing? IIE Transactions, 24(3): 7-13. 109. Little, J. (1970). Models and Managers: The Concept of a Decision Calculus. Management Science, 16(8): 466-485. 110. Lohr, S. (2014). The Google Formula for Success. New York Times, September 28, 2014. 111. Lohr, S. (2013). Program Seeks to Nurture ‘Data Science Culture’ at Universities. New York Times. Nov. 12, 2013. 112. Lyon, P., Milne, R.J., Orzell, R. and Rice, R. (2001). Matching Assets with Demand in Supply Chain Management at IBM Microelectronics. Interfaces, 31(1):108-124. 113. Maxwell W. and Singh, H. (2010). Combining algorithms and interactive visuals for scheduling. www.arkieva.com. 114. Martin, D. (1999). Capacity and cycle time-throughput understanding system (CAC-TUS). Advanced Semiconductor Manufacturing Conference and Workshop Proceedings, 127-131. 115. Milne, R.J., Wang, C-T., Denton, B.T. and Fordyce, K. (2015). Incorporating Contractual Arrangements in Production Planning. Computers & Operations Research, 53:353-363. 116. Milne, R.J. and Wang, C-T. (2014). Enhancing mathematical programming models to account for demand priorities increasing as a function of delivery date. Journal of Industrial and Production Engineering, 31(1):51-63. 117. Milne, R.J., Wang, C-T., Yen, C-K A. and Fordyce, K. (2012). Optimized material requirements planning for semiconductor manufacturing. Journal of the Operational Research Society, 63(11):1566-1577. 118. Milne, R.J. and Zisgen, H. (2007). Linking EPOS and CPE. IBM internal report and presentation. 119. Monch, L. and Ramacher, R. (2014). Parameterizing Dispatching Rules for Dynamic Complex Job Shops Using Local and Global Information. Proceedings of the 2014 International Symposium on Semiconductor Manufacturing Intelligence (ISMI2014).

19

120. Monch, L., Fowler, J. and Mason, S.J. (2013). Production Planning and Control for Semiconductor Wafer Fabrication Facilities: Modeling, Analysis, and Systems, Springer, New York. 121. Monch, L. and Ponsignon, T. (2012). Using Iterative Simulation To Incorporate Load-Dependent Lead Times In Master Planning Heuristics, MASM 2012, proceedings of Winter Simulation Conference. 122. Monch, L., Fowler, J. Dauzere-Peres, S., Mason, S.J. and Rose, O. (2011). A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations. Journal of Scheduling, 14(6):583-599. 123. Morais, M., Filho, M.G. and Boiko, T.J.P. (2013). Hybrid Flow Shop Scheduling Problems Involving Setup Considerations: A Literature Review and Analysis. International Journal of Industrial Engineering: Theory, Applications, and Practice. 20(11-12):613-629. 124. Morrison, J. and Kim, W. (2014). The throughput rate of serial production lines with deterministic process times and random setups: Markovian models and applications to semiconductor manufacturing. Computers & Operations Research (in press) 125. Morrison, J. and Lee, M. (2014). Planning of Preventive Maintenance Activities: Incorporating Imperfect Maintenance into a G/G/m Queueing Model with Multiple Maintenance Cycles. Proceedings of the 2014 International Symposium on Semiconductor Manufacturing Intelligence (ISMI2014). 126. Morrison, J. and Martin, D. (2007). Performance evaluation photolithography cluster tools. OR Spectrum, 29(3):375-389. 127. Morrison, J. and Martin, D. (2006). Cycle Time Approximations for the G/G/m Queue Subject to Server Failures and Cycle Time Offsets with Applications. ASMC 2006 Proceedings, pp. 322. 128. Morrison, J., Dews, E. and LaFreniere J. (2006). Fluctuation smoothing production control at IBM’s 200mm wafer fabricator: Extensions, application and the multi-flow production index (MFPx). Proceedings of the 2006 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, Boston, MA. 129. Morrison, J., Campbell, B., Dews, E. and LaFreniere. (2005). Implementation of a fluctuation smoothing production control policy in IBM’s 200mm wafer fab. Proceedings of the 44th IEEE Conference on Decision Control, 7732-7737. 130. Orzell, R., Patil, S. and Wang, C-T. (2009). Method for identifying product assets in a supply chain used to satisfy multiple customer demands. U.S. Patent 7,606,743. 131. Patel, M.H., Wei, W., Dessouky, Y., Hao, Z. and Pasakdee, R. (2009). Modeling and Solving an Integrated Supply Chain System. International Journal of Industrial Engineering 16:(1):13-22. 132. Pfund, M., Mason, S. and Fowler, J. (2006). Dispatching and scheduling in semiconductor manufacturing: state of the art and survey of needs. Handbook of production scheduling. Springer 213–241. 133. Press, G. (2013). Short History of Data Science. Fortune. May 28, 2013. 134. Rangarajan, S. and Snapp, J. (2015). Supply Chain Analytics: Pharma’s Next Big Bet. January 9, 2015. PharmaExec.com. 135. Rodriguez-Verjan, G. and Dauzere-Peres, S. (2012). A Mathematical Model for Estimating Defect Inspection Capacity with a Dynamic Control Strategy. MASM 2012, Proceedings of the 2012 Winter Simulation Conference. 136. Robinson, J. (1998). Capacity Planning in a Semiconductor Wafer Fabrication Facility with Time Constraints between Process Steps. PhD Thesis, University of Massachusetts Amherst. 137. Sampath, S. and Kemmpf, K. (2015). Project Portfolio Planning at Intel Corporation. Interfaces, forthcoming. 138. Sandberg, E. 1984. Avoiding the Pitfalls or Spreadsheets. NY Times March 10, 1985. 139. Schelasin, R. (2013). Estimating Wafer Cycle Time Using Improved G/G/M Queue. MASM 2013. Proceedings of Winter Simulation Conference. 140. Schelasin, R. (2011). Using Static Capacity Modeling and Queuing Theory Equations to Predict Factory Cycle Time Performance In Semiconductor Manufacturing. MASM 2011, proceedings of the Winter Simulation Conference. 141. SEMATECH (1994). Measurement and Improvement of Manufacturing Capacity (MIMAC). Technology Transfer 94062424A-XFR. 142. Shanthikumar, G., S. Ding and Zhang, M. (2007). Queueing Theory for Semiconductor Manufacturing Systems: A Survey and Open Problems. IEEE Trans-actions on Automation Science and Engineering. 4(4):321-335. 143. Shirodkar, S. and Kempf, K. (2006). Supply Chain Collaboration through Shared Capacity Models. Interfaces, 36(5):420- 432. 144. Simon, H. (1957). Administrative Behavior. Macmillan, New York. 145. Singh, H. (2007). Next-generation Supply Chain Optimization- It's not how well you plan, but how well you replan that can drive your success. Supply & Demand-Chain Executive Magazine. June/July 2007. 146. Smadi, H.J. and Kamrani, A.K. (2011). Product Quality-Based Methodology for Machine Failure Analysis and Prediction. . International Journal of Industrial Engineering, 18(11):568-581. 147. Tu, Y., Hsin-Nan C. and Liu, T, (2010). Shop-Floor Control for Batch Operations with Time Constraints in Wafer Fabrication. International Journal of Industrial Engineering, 17(2):142-155. 148. Uzsoy, R., Lee, C. and Martin-Vega, L.A. (1994). A Review of Production Planning and Scheduling Modules in the Semiconductor Industry, Part 2: Shop Floor Control. IIE Transactions, Scheduling Logistics, 26(5), 44 – 55.

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149. Uzsoy, R., Lee, C. and Martin-Vega, L.A. (1992). Review of Production Planning and Scheduling Modules in the Semiconductor Industry, Part 1: System Characteristics, Performance Evaluation, and Production Planning. IIE Transactions, Scheduling Logistics, 24(4):47 – 60. 150. Vargas-Villami, F.D., Rivera, D.E. and Kempf, K.G. (2003). A hierarchical approach to production control of reentrant semiconductor manufacturing lines. IEEE Transactions on Control Systems Technology, 11(4):578-587. 151. Weber, B (1997). IBM Chess Machine Beats Humanity's Champ. NY Times, May 12,1997. 152. Wein, L. (1988). Scheduling Semiconductor Wafer Fabrication. IEEE Transactions on Semiconductor Manufacturing, (1)3:115-130. 153. Whitt, W. 1993. Approximations for the GI/G/m queue. Production and Operations Management, 2(2):114-161. 154. Whitt, W. 1983. The queueing network analyzer. The Bell System Technical Journal 62:2779-2815. 155. Wieland, B., Mastrantonio, P., Willems, S.P. and Kempf, K.G. (2012). Optimizing Inventory Levels within Intel’s Supply Demand Operations. Interfaces, 42(6):517-527. 156. Woolsey, G. (1979). Ten Ways to Go Down with Your MRP. Interfaces, 9(8):77–80. 157. Woolsey, G. (1974). The Fifth Column: Homage to W.A.C. Bennette and the Canadian Operations Research Society or “Entia no Sunt Multiplicanda Praeter necessitatem.” Interfaces, 4(4):43-46. 158. Wu, S.D., Kempf, K.G., Atan, M.O., Ayac, B., Shirodkar, S.A. and Mishra, A. (2010). Improving New-Product Forecasting at Intel Corporation. Interfaces, 40(5):385-396. 159. Wu, C. et al (2014). Dynamic Production Control in Serial Production Systems with Queue Time Constraint Considerations. Proceedings of the 2014 International Symposium on Semiconductor Manufacturing Intelligence (ISMI2014). 160. Wu. M. and Hung, T, (2008). A Hit Rate Based Dispatching Rule for Semiconductor Manufacturing. International Journal of Industrial Engineering 15(1):73-82. 161. Ying-Mei, T. (2014). Model to Determine the Capacity Support for Twin Fabs of Wafer Fabrication. International Journal of Industrial Engineering: Theory, Applications, and Practice, 21(2). 162. Zisgen, H. (2009). A diffusion approximation for batch queues of type GX/G(b,b)/1. Performance Evaluation. 66:9-10. 163. Zisgen, H., Meents, I., Wheeler, B.R. and Hanschke. (2008). A Queuing Network Based System to Model Capacity and Cycle Time for Semiconductor Fabrication. MASM, Proceedings of the Winter Simulation Conference, 2067-2074. 164. Zisgen, H. (1999). Warteschlangennetzwerke mit Gruppenbedienung. Ph.D. thesis, Clausthal University of Technology, Clausthal-Zellerfeld, Germany. 165. Zweben, M., Davis, E., Daun, B. and Deale, M.J. (1993). Scheduling and Rescheduling with Iterative Repair. IEEE Transactions on Systems, Man, and Cybernetics. 23(6):1588-1596. 166. Zweben, M., Daun, B., Davis, E., Deale, M. (1994). Scheduling and rescheduling with iterative repair. In: Zweben, M., Fox, M. (Eds.), Intelligent Scheduling. Morgan Kaufman, San Francisco.

Appendix: Observations and Insights for Agents of Change It is important to view organizations as proceeding through an ongoing sequence of loosely coupled decisions where current and future assets are matched with current and future demand across the demand-supply network at different levels of granularity (Table 1) ranging from a placing a lot on a tool to an aggregate capacity plan across five years. Within this grid a myriad of decisions are made that impact organizational performance. The goal is to make smart decisions; however, each decision is a result of a complex interaction between process (culture), data, and the models (math) used. The net result is an ongoing challenge for the “math” guys to (a) find the right models and (b) navigate the political terrain—to be successful, IEs must be prepared to be agents of change. Being an agent of change is first complicated by substantially different views of optimization that exist within an organization. IE professionals view optimization as a mathematical representation of a set of relationships, a metric to define optimal, and a search method. For the IE professional, optimization is focused on the methods to create a resource allocation decision at a point in time that best meets criteria such as most profit, least cost, and a variety of more complex measures. However, other members of the planning and scheduling community (executives, managers, planners, manufacturing, etc.) often have a different view of optimization. In practical terms, optimization may reference either the decision making process itself or the output from a sequence of decisions over time. Developers of successful applications understand and meet the need to optimize the process and decision sequence while evolving the firm to the use of more sophisticated methods within the optimized process. Second is varying standards of expertise in modeling and statistics. In some organizations, having taken one class in statistics, a basic knowledge of Little’s Law, and skills in Excel modeling, pivot tables, and graphics constitutes advanced level skills. Expert level is achieved when one can make whisker plots and execute a T test. The ability to write a basic depth first search with backtracking program to solve SUDUKO or understanding the difference between confidence intervals and prediction intervals lies outside the known universe of some organizations. In other organizations, expertise requires mastering the ever-growing set of heuristics to support MIP, full competency with constraint programming, and some pattern matching. 21

One must assess the local view of expertise and proceed appropriately. Both types of organizations have their limitations in creating positive change. Third is gauging the “seriousness of management” for modeling. Harpal Singh (2009) defines two types of organizations in terms of whether they view supply chain management as a necessary evil or central to the business. Those who view supply chain management as a necessary evil may be most interested in opportunities to: automate decision processes, simplify decision making by creating functional islands that are highly automated, plan for certainty, restrict judgment, provide minimal customer service, and reduce costs. Those organizations who view supply chain as central to the business will want to: create competitive differentiators, exploit intrinsic strengths, integrate decision making to create a flexible and responsive supply chain, develop a robust framework for making decisions, and plan for uncertainty. Fourth, as noted by Goldman (2004), “The great 20th century revelation is complex systems can be generated by the relationships among simple components.” This applies to almost all aspects of planning, scheduling, and dispatch and statistical analysis in organizations from hospitals to semiconductor wafer fabricators. Fifth, Spreadsheets. The use of spreadsheets is pervasive in modeling and data analysis because the tool is familiar, readily available, and initially easy to use. While a spreadsheet may be a good place to start, the following limitations are common place (Singh 2009): spreadsheets do not often have the latest data because the data updates from corporate systems are not automated or systematized, and sharing spreadsheets can be awkward. In an environment where the data is changing, often a number of copies of the same schedule exists, each slightly different. Formats change and considerable manual effort is required to resynchronize the spreadsheets when spreadsheets are passed from one person to another. Basic software methods such as testing and documentation of code are all but impossible; many models start with simplified assumptions about the relationships of components in the system. As reality (complexity) is added, many such spreadsheets collapse under their own weight. Spreadsheets lack advanced tools that dynamically adjust to changing situations. Sixth - Fundamentals of decision support for decision makers. Although the available computer and software technology in 2015 is dramatically advanced compared to the late 1970s and early 1980s, the core foundations of decision support remain the same. All decision support systems, from passive data stores to highly proactive systems that participate in the decision making process, follow the hierarchical growth pattern. Data is increasingly extended into more compact and useful information. Models are extended to capture additional complexity or work arounds are created. At its ultimate, the system is able to carry the momentum of the data and information forward by adding structure to the data to formulate alternatives and to predict the logical outcomes of decisions. What frequently happens in crisis-driven decisions is reliance by the decision maker on his or her experience with similar situations in the past. An experience base allows the decision maker to rapidly sort relevant input from non-relevant to quickly arrive at a choice and take action on that basis. The experience base is difficult and costly to build. And it is not easily transferable from one mind to another. Seventh, despite advances in algorithms and computing power, there are limitations in the ability of our tool kits to handle large problems in detail and to handle uncertainty. Eighth, on a regular basis a new term will appear on the scene that will quickly mean many things to many people, but it will be the umbrella term to cover modeling and analysis. Agents of change should embrace popular buzzwords to advance their causes. Therefore, the successful practitioner is a part-time lobbyist. All agents of change must carry two insights from our political science brethren:  "There is no more delicate matter to take in hand, nor more dangerous to conduct, nor more doubtful of success, than to step up as a leader in the introduction of changes. For he who innovates will have for his enemies all those who are well off under the existing order of things, and only lukewarm support in those who might be better off under the new.“ Niccolo Machiavelli  Politics is the art of the possible.

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