Our main article this month is about a new performance metric we are proposing. After discussing what attributes we believe should be found in metrics for daily production meetings, we propose Quality Moves. Quality Moves measure, on a shift basis, the best performance that can be achieved given the fab’s WIP profile and resource availability.
In this month’s subscriber discussion forum we have many responses to last month’s main article about the impact of staffing (particularly operator delays) on cycle time. Most of the respondents agreed that operator delays do have an impact on fab cycle times, at least some of the time. We also have new topics raised by subscribers related to performing tool qualification on the bottleneck and estimating the impact of hand-carry lots on other lots.
We are interested in estimating the impact of staffing on cycle time. In this article, rather than tackle this issue in detail, we focused on one particular aspect - forced idle time on tools due to operator delays. To look at this visually, we built a very simple simulation model to study the issue. We found that even in models with only 3 tools, and light operator loading (50% busy), operator delays may increase cycle time significantly.
This month we also have subscriber discussion on capacity planning using simulation, as well as using fab-level metrics for understanding variability. We also present the results from last month’s survey question about the number of certifications per operator that people have in their factories.
When a batch tool (e.g. diffusion furnace) is available and there are one or more lots ready to be processed, the operator must decide whether to start the batch immediately, or wait for more lots. When a full batch of some recipe is available, the decision to start that batch is fairly easy. However, when less than a full batch of lots is available, the decision becomes more complex. On average, it is usually better for cycle time to start the batch immediately than to wait to form a full batch. However, despite this general rule, there are sometimes specific cases where it makes more sense to wait for the next lot before starting the batch. In this article, we propose a simple rule for deciding when to wait for the next lot, and when to just start the batch.
In this month’s subscriber discussion forum we have continuing discussion on recipe management, batch size decision rules, and operator cross-training.
In this month’s main article, we have chosen to briefly review the topics described in the FabTime newsletter issues to date (both the main articles and the subscriber discussion topics). The primary reason for this is that we have many new subscribers, who may not be aware of the topics already covered. Even for long-time subscribers, job descriptions and market conditions change regularly. A topic that wasn’t of interest to you when it first came out may be more relevant now.
In this month’s Recommendations and Resources section, we review the many resources available on FabTime’s website (papers, tutorials, book reviews, software demos, etc).
In this month’s main article, we propose three distinct cycle time management styles, and describe how each can be used to improve cycle time. We have named these three styles: The Traffic Cop; The Shepherd; and The Relay Coach. These are management styles we have observed in real fabs, although the names and descriptions are our own. Each style is suited to a particular cycle time focus. Traffic Cops control starts and WIP flow for production lots. Shepherds prevent engineering lots from disappearing onto shelves and hiding in corners. Relay Coaches ensure that critical hot lots are handed smoothly from one operation to the next. Graphical examples, using charts from FabTime’s software, can be found on our website, at www.FabTime.com/ctmstyles.php
Discussion topics in this issue include: responses on wafer starts methodologies, treating scrap in product costing, and ramp planning; a reference to a conference presentation about operator modeling; a question about how much is too much in reference to operator cross-training; a question about how people handle recipe management; and a request for benchmarks for gallium arsenside fab cycle times.
Our main article this month is about quantifying the bottom-line benefits of cycle time improvement. We discussed one particular benefit in a previous newsletter issue. In this new article, we provide a more comprehensive framework for linking cycle time management to financial returns. An Excel spreadsheet tool for what-if analysis is provided on FabTime’s website (here). There’s both money to be saved and additional revenue to be earned through cycle time improvement. Under the assumptions in our default example, the total annual benefit of cycle time improvement could be more than half a million dollars.
Discussion topics in this issue include: a request for information on wafer start methodologies; a request for research on staffing models; a request for literature on ramp models; a question about how companies treat cost of scrap; and a question about calculating mean time between assists.
This month’s main article, Cycle Time and the Core Conflict, is a guest article, written by Dan Siems, of Philips. Dan was recently appointed World Wide Wafer Fab Cycle Time Manager for Philips Semiconductors. This article represents Dan’s thoughts on a core conflict that often exists in managing wafer fabs - trying to get lots out quickly, but having to frequently stop the lots for quality checks. Dan proposes the elements that he believes must exist to weaken this conflict, and maintain good cycle times over the long term.
This month in the subscriber discussion forum we have several responses to last month’s main topic of equipment dedication. Other topics discussed in this issue include lot size change, foundry performance data, and the interaction of AMHS control and dispatching.
We talked back in Issue 1.8 about the fact that single path tools tend to drive up cycle times. The question is, how much does tool dedication inflate cycle times? The are sometimes important reasons to have dedicated tools. What’s needed is a way to explore trade-offs. In this article, we present an approximation for queue time as a function of number of machines in a tool group. This approximation clearly shows that queue time decreases as the number of tools in the group increases (for the same total traffic intensity of the tool group).
Discussion topics in this issue include: a question about segregating downtime and idle time into "good" and "bad" for PEE calculations; a request for opinions on how to model single wafer lots; a question about the details of generating characteristic curves; a request for foundry performance data benchmarks; and several detailed responses to the Volume 3, Number 2 hot lot article.
The article is drawn from a presentation that Frank Chance made at Arizona State in January. We present a formula for estimating the average cycle time of lots through a tool that processes lots with different priorities (regular lots and hot lots). We provide a numerical example that shows how the cycle time of the regular lots increases as the percentage of hot lots is increased, and discuss implications for managing hot lots in a wafer fab. An example can be seen here.
Discussion topics in this issue include: a response to the question about performance measures regarding human resource to activity relationships; a request for cycle time reduction case studies; and an observation on production equipment efficiency (PEE) as a measure of tool variability.
In a wafer fab, cycle time tends to increase with increasing equipment loading (with some exceptions for batch tools). In large part to combat high cycle times, fabs typically plan for some amount of idle time on most tool groups. OEE, in its traditional definition, is contradictory to such planned idle time, since all standby time (including planned idle time) drives down OEE values. This puts fab personnel in a tight spot when they are pushed to simultaneously increase OEE values and decrease cycle times. Production Equipment Efficiency (PEE) is a related metric that calculates equipment productivity only during the time that product is available at the tool. Improving PEE, therefore, is not in conflict with reducing cycle times. PEE only penalizes tools for standby time during which lots are waiting (e.g. time when WIP is present, but there is no operator to load the tool). For bottlenecks, there will likely be very little time during which no WIP is waiting. Therefore, for bottlenecks, PEE and OEE will yield similar values. For non-constraint tools, however, PEE values will usually be higher than OEE values. The important thing is that increasing PEE values will not conflict with reducing cycle times. For fabs trying to improve or maintain cycle times, using PEE instead of OEE may be more effective, at least for non-constraint tools.
Discussion topics in this issue include: a request for information on measuring shift performance; a question about performance measures regarding human resource to activity relationships; and a question about model accuracy relative to actual performance.
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