Supply Chain Planning Blog

Why Lot Release is So Important in Semiconductor Manufacturing

Posted by Cyrus Hadavi on Thu, Jun 08, 2017

If I arrive on-time or early at the airport but there is a problem with the airplane and a long delay in the scheduled takeoff time, does it get me to my destination on time? Clearly the answer is No! Is it better for me to stay in the comfort of my home and go when I know the plane is ready to take off? Or even better, find another flight to my destination so that I get there on time. By going to the airport at the “wrong time” and waiting I am only increasing “WIP” or waiting time and I am also contributing to airport congestion which adds to the traffic and boarding of other flights possibly causing others to miss their flights.  Such problems can be avoided by an intelligent planner or, release strategy, that can figure out exactly what the right time is for me to leave home given the traffic situation, the speed of cars, the parking time and time it takes to go through security. This kind of predictive planning is ideal for releasing lots in a semiconductor manufacturing line where depending on the mix of products, availability of resources and masks as well as WIP, it can decide which lots should be released and which ones should be held back so that we meet the following three objectives optimally:

  • Cycle time
  • Equipment utilization
  • Delivery performance

The following diagram shows the relationship between these three parameters and how they change with WIP increase. In a high mix environment, increase in WIP does not necessarily imply additional wait times or delay in delivery because of multiple routes and balance of allocation of jobs by the system. The grey shaded area represents optimal region of operation where the desired objectives can be achieved.

graph.png

In environments where there is a high mix of products, such as foundries, we can increase the number of lots released without increasing their waiting time by ensuring that they are balanced across different bottleneck equipment such as lithography equipment. Given the complexity of such environments where each process has 400-600 steps using hundreds of equipment requiring anything from 10 minutes to 10 hours with highly sensitive set up times (implanters) or batching requirements (ovens), one has to intelligently look ahead and look behind to ensure proper balance of lots re-entering the process and or entering the process with different priorities.

Unfortunately, sequencing engines with simplistic rules have been given too much attention in order to solve such a complex problem. Through years of R&D, we have concluded that unless a proper release strategy is deployed, sequencing would not be of much value. It is a reactive engine not a preventive one. But more importantly, in the presence of an adequate release strategy, sequencing can be a liability in the sense that it would try to resolve issues locally not being aware of the potential issues it might be causing 50 steps later! Can you imagine being at the gate, and the airline personnel try to sequence your entry into the plane when the plane is not even at the gate or being fixed!

One other myth is the use of simulation tools to plan fabs! Simulation tools look nice and show movement. It is like a video game, we all enjoy watching it. However, they DO NOT PROVIDE a strategy. They only show you where the problem might lie ahead without telling you how to avoid it. How could they? They do not look ahead; by definition simulation is one sequence at a time!

As in our opening example, a good release strategy is aware of the right mix of products in the fab as well as the work load of each equipment, now and future, and is constantly trying to balance what needs to go next such that the bottlenecks, as they are changing, will be fully utilized and at the same time keeping in mind which lots need to be ready and when for on-time delivery. In fact, our research shows that in the presence of a good release strategy, a simple FIFO is the best sequence for the resources. In the context of our airport example, if you left your home at the right time, as you approach your gate, without much waiting, you will show your boarding pass and get into your seat for takeoff.  No need to be sequenced!

Topics: Supply Chain, Supply Chain Planning, Supply Chain Performance Management, Manufacturing Software, Manufacturing Planning, Inventory Optimization, Semiconductor, Factory Planning, Fabrication planning

Inventory Optimization is like Baseball's Moneyball

Posted by kameron hadavi on Wed, Oct 10, 2012

iStock Baseball Money XSmall resized 600

How do the Oakland A’s achieve results like this at a fraction of cost of a team like the Yankee’s?

2002 New York Yankees: Team Salary $126 million; 103 Wins 59 Losses; Division Winner
2002 Oakland Athletics: Team Salary $ 40 million;   103 Wins, 59 Losses; Division Winner
2012 New York Yankees: Team Salary $198 million; 94 Wins 68 Losses; Division Winner
2012 Oakland Athletics: Team Salary $ 55 million;   93 Wins, 69 Losses; Division Winner

You have most likely seen or heard of the story behind the movie “Moneyball”.  In 2002 the Oakland Athletics had a very limited budget to “carry” their team roster through the season, and they still had to compete with topnotch teams in their league.  Some of the teams they had to compete with, like the Yankees, spent up to four times (4x) as much as they did on their “inventory” of ball players (i.e. “products” in baseball).   The A’s turned away from traditional thinking on how to allocate their budget to field a team, which meant relying on the gut feel of managers and buying the highest priced players.  Instead, they started to rely on “Sabermetrics”, the use of statistical analysis to determine the most cost-efficient baseball players based on measure of in-game activity/history.  Hence, based on mathematical models, the A’s figured out how to best optimize the team at every position on the field.  The result was that Oakland won 103 games in 2002, made it to the playoffs, and tied with the Yankees for most wins that season. Again, Yankees spent more than three times (3x) of what Oakland paid for its team, in the same year.

 
Coming back to the manufacturing world, in the same manner that Sabermetrics can help optimize the baseball players on a team, Multi Echelon Inventory Optimization (MEIO) can optimize your inventory that is deployed throughout your supply chain, in order to achieve target customer service levels, and maximize profit.  There are obvious parallels in taking the Moneyball philosophy to the optimization of inventories.  Instead of the General Manager in baseball using statistics to determine the best players to have on a baseball team, the Supply Chain Manager can use statistics and mathematical models in a MEIO system in order come up with the highest profitable scenarios.  By examining these scenarios, the Supply Chain Manager can decide how to right-size the inventory levels at different locations, and achieve targeted customer service levels, at the highest margins.


Of course, instead of baseball metrics (e.g. RBI’s, on base%, ERA, salary), there are statistical supply chain metrics (e.g. Demand variability, supply variability, BOM, Inventory value, etc.) that can be used to objectively calculate the value of each unit of inventory that you plan to place at a given “position” in your supply chain (e.g. Raw Materials, WIP, Finished Goods, etc.).  This would make it possible to optimize inventory deployment for meeting certain customer service objectives, and squeeze the most profit out of your supply chain, while not exceeding the budget allocated for working capital. 

 
The Oakland A’s are back in the playoffs again this year, with a budget that is one-third of the Bronx Bombers.  Not surprisingly, the use of statistics (i.e. the right system) is helping them get the most out of their small budget.  


Adexa has the equivalent of Moneyball’s Sabermetrics for your Supply Chain, it’s called the Inventory Optimizer to ensure each dollar of inventory is spent in the best possible way.

 

About the Author:  Bill Green is the Vice President of Solutions at Adexa, for more information about him please visit William Green profile link.    

Topics: Multi Echelon Inventory Optimization, Supply Chain Planning, Inventory Planning, MEIO, Inventory Management System, Manufacturing Software, Inventory Optimization, Inventory