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Lean CFP Driven is an new approach which takes into account not only the widely implemented Lean manufacturing, but combines the principles of Lean with the Operating Curve, an approach based on the theoretical approach of Queuing Theory developed in academia in the 1970s.^[1] The goal of Lean CFP Driven is to eliminate waste in order to achieve higher quality, increase productivity and at the same time understand the relationship between utilization, lead time and variability in order to maximize performance within the semiconductor industry.

CFP= Complex Flow Production

Lean CFP Driven – Lean Complex Flow Production Driven

1. Background Semiconductor industry

The semiconductor industry is one of the most productive and dynamic industries in the world. It faces a continuous and rapid advancement in technology which puts the companies under constant pressure to come up with superior and cheaper goods than those that were state-of-the-art only a few months ago.^[2] The market and development of the market is based on Moore’s Law.^[3] or More than Moore.^[4]

Customer demand in the semiconductor market evolves and changes at a swift pace which leads to the fact that a high level of flexibility is necessary to serve and meet the requirements of the customers.^[5] The semiconductor industry is furthermore very capital intensive based on the fact that the production equipment is highly complex, specialized and thus incredibly expensive.^[1] Challenges that the industry is facing are to continuously improve yield performance, achieve the highest possible return on the expensive equipment, speed and zero defects.^[1]

2. Lean CFP Driven and Traditional Lean

Lean CFP Driven moves in a new direction from the traditional Lean because of the additional focus on utilization, cycle time and variability. The different characteristics of the semiconductor industry, e.g. production structure and production related costs compared to other industries, forms the need to approach the Lean philosophy in a new way in order to meet these specific characteristics.

There are five key characteristics for the semiconductor industry:

- Long cycle time.^[6]

- No parallel process possible, high complexity^[7]

- Short product life cycle.^[3]

- Capital intensive production^[1]

- Drastic cost decrease over time^[3]

The complex production flow of a semiconductor fab is due to what is called a reentrance flow. A reentrant flow is a well-known attribute within a wafer fab and refers to the wafer visiting each tool not only once, but maybe 20 times during the course through the fab. To duplicate the expensive equipment and create a linear flow would make it even more challenging to get the highest possible return on the equipment and reach an optimized utilization of each tool, even though it results in a very complex production..^[8]

The reentrant flow does require a certain level of flexibility, which in terms of Lean, could be seen as muda (Waste). The necessary flexibility, also in order to meet fluctuations in customer demand, requires the companies to apply other tools to measure and forecast performance^[5] and this is what Lean CFP Driven provides to the Semiconductor Industry. Lean CFP Driven adds the Operating Curve to evaluate the factors utilization, cycle time and variability which cannot be done through implementation of Traditional Lean.

Typical tools within the Traditional Lean which are also included in the new approach of Lean CFP Driven are as follows:

- Poka-Yoke

- Visual Management

- Value Stream Mapping

- Kanban

- JIT

- 5S

- 5 Whys

What distinguishes Lean CFP Driven from the traditional approach of Lean in terms of tools is that the new approach applies the tool Operating Curve in addition to the tools listed above. The great advantage of adding the Operating Curve tool is to maximize performance by optimizing both utilization and speed at the same time for the complex industry of Semiconductors by reducing the variability via the 4-partner method.

The Operating Curve is a tool initially developed in academia in the1970s, based on the Queuing Theory, which uses the indicators Cycle Time and Utilization to benchmark and forecast a manufacturing line’s performance.^[1] The Operating Curve can be applied for different reasons, for example:

- Understanding the relationship between variability, cycle time and utilization^[5]

- Quantify trade-off between cycle time and utilization ^[1]

- Documenting a single factory’s performance over time ^[1]

- Calculate and Measure line performance ^[1]

File:OC formula.jpg

Operating Curve Formula

• FF Flow Factor: Flow Factor ranges from 0 - ∞

• Variability α: describes the non-uniformity of the production (low α indicates good performance) Alpha ranges from 0-1 and the lower the higher performance

• UUM: Utilization, Utilization ranges from 0-100 %

Flow Factor: FF= CT/RPT

• CT: the line’s actual cycle time

• RPT: the theoretical minimum amount of time that a lot would need to move from beginning to end (i.e. without queuing or process inefficiencies)

CT=WIP/GR

• WIP: Average Work in Process (including all products, also lots on hold)

• GR: Maximum number of units to be processed each day

UUM = GR/CapaPU

• Capacity Capa PU: capacity of the production unit

References

^ ^a ^b ^c ^d ^e ^f ^g ^h Aurand S. Steven, Miller J. Peter; The Operating Curve: A Method to Measure and benchmark manufacturing line productivity; IEEE, USA, 1997.
^ Investopedia; http://www.investopedia.com/features/industryhandbook/semiconductor.asp#axzz1vbOvyLAZ; 02.07.2012.
^ ^a ^b ^c Wikipedia Moore’s Law; http://en.wikipedia.org/wiki/Moore%27s_law; 28.06.2012.
^ ieee spectrum; http://spectrum.ieee.org/computing/hardware/moores-law-meets-its-match; 01.07.2012.
^ ^a ^b ^c Bauer H, Kouris I, Schlögl G, Sigrist T, Veira J, Wee D; Mastering variability in complex environments; McKinsey & Company, 2011 .
^ Weber C, Fayed A; Enabling Timely Revolutions in the Performance of Multi-Product Fabs, Portland State University, Austin Texas, USA, 2009.
^ Kohn R, Noack D, Mosinski M, Zhou Z, Rose O; Evaluation of modeling, simulation and optimization approaches for work flow management in semiconductor manufacturing; Institute of Applied Computer Science; Dresden Germany; 2009.
^ FabTime Cycle Time Management Newsletter – Volume 13, Number 3, 2012 by FabTime Inc.

The Order Lead Time (OLT) is the time that it takes from the day an order is place in the system (Order Date) to the date that the customer wants the material to be delivered (Wish Date); this measurement will help the company to understand the order behavior of the customers and help to design profitable models to fulfill customer needs.

The Order Lead Time

When talking about Order Lead Time it is important to differentiate the definitions that may exist around this concept. Although they look similar there are differences between them that help the industry to model the order behavior of their customers. The four definitions are :

The Actual Order Lead Time (OLT_Actual) ^[1]The order lead-time, refers to the time which elapses between the receipt of the customer's order (Order Entry Date) and the delivery of the goods.”^[2]
The Requested Order Lead Time (OLT_Requested) represents the time between the Order Entry Date and the customer requested delivery date; this measurement could help the company to understand the order behavior of the customers and help to design profitable models to fulfill customer needs.^[3]^[4]
The Quote Order Lead Time (OLT_Quote) is the agreed time between the Order Entry Date andthe supplier’s committed deliver date of goods as stipulated in a supply chain contract. ^[4]
The Confirmed Order Lead Time (OLT_Confirmed) represents the time between the Order Entry Date and the by the supplier confirmed delivery date of goods. ^[4]

OLT Formulas

OLT_Requested = Wish Date – Order Entry Date

The OLT_Requested will be determined by the difference between the date the customer wants the material in his facilities (wish date) and the date when they provided it’s order to the supplier.

OLT_Quote = Quote Date – Order Entry Date

The OLT_Quote will be determined by the difference between the date the customer agree to receive the material in their facilities (Quote date) and the date when the order is provided to the supplier.

OLT_Actual = Delivery Date – Order Entry Date

The OLT_Actual will be determined by the difference between the day the provider deliver the material (Delivery date) and the date when they enter the order in the system.

OLT_Confirmed = Confirmed Date – Order Entry Date

The OLT_Confirmed will be determined by the difference between the date the confirmed date by the provider to deliver the material in the customer facilities (Confirmed date) and the date when they provide the order to the supplier.

Average OLT based on Volume

The Average OLT based on Volume (OLT^V) is the addition of all the multiplications between the volume of product we deliver (quantity) and the OLT divided by the total quantity delivered in the period of time we are studying for that specific facility.
$OLTV={\frac {\sum _{j}{Quantity_{j}\cdot OLT_{j}}}{_{TotalQuantityDeliver}}}\,$
By doing this the company will be able to find a relation of volume weighted between the quantities of material required for an order and the time requested to accomplish it. The volume metric could be applied to the 4 types of OLT.
The figure obtained from this calculation will be the average time (e.g. in days) between order placing and the requested delivery date of a specific customer under consideration of the average quantities ordered during that particular time.

Potential Application Areas

The correct analysis of OLT will give the company:

Better understanding of the market behavior making it able to develop more profitable schemas that fit better with customer needs (Revenue Management).
Increases company ability to detect and correct any behavior that is not within terms agreed in the contract (by penalization or different contract schema).
The OLT measurement creates an opportunity area to improve the customer relations by increasing the level of communication with them.

^ Kumar, A. (1989). Component inventory costs in an assembly problem with uncertain supplier lead-times. IIE transactions, 21(2), 112-121.
^ Gunasekaran, A., Patel, C., & Tirtiroglu, E., 2001 "Performance measures and metrics in a supply chain environment." International journal of operations & production Management 21, no. 1/2: 71-87.
^ 2)Cousens, A., Szwejczewski, M., & Sweeney, M. 2009. “A process for managing manufacturing flexibility.” International Journal of Operations & Production Management, 29(4), pp.357-385.
^ ^a ^b ^c ^d Silva, L., 2013, “Supply Chain Contract Compliance Measurements” Master thesis (work in progress), Aalto University, Finland.

[The_Operating_Curve:_A_Method_to_Measure_and_benchmark_manufacturing_line_productivity-1] ^ ^a ^b ^c ^d ^e ^f ^g ^h Aurand S. Steven, Miller J. Peter; The Operating Curve: A Method to Measure and benchmark manufacturing line productivity; IEEE, USA, 1997.

[Investopedia-2] Investopedia; http://www.investopedia.com/features/industryhandbook/semiconductor.asp#axzz1vbOvyLAZ; 02.07.2012.

[Wikipedia_Moore’s_Law-3] Wikipedia Moore’s Law; http://en.wikipedia.org/wiki/Moore%27s_law; 28.06.2012.

[ieee_spectrum-4] spectrum; http://spectrum.ieee.org/computing/hardware/moores-law-meets-its-match; 01.07.2012.

[Mastering_variability_in_complex_environments-5] Bauer H, Kouris I, Schlögl G, Sigrist T, Veira J, Wee D; Mastering variability in complex environments; McKinsey & Company, 2011 .

[Enabling_Timely_Revolutions_in_the_Performance_of_Multi-Product_Fabs-6] Weber C, Fayed A; Enabling Timely Revolutions in the Performance of Multi-Product Fabs, Portland State University, Austin Texas, USA, 2009.

[Evaluation_of_modeling,_simulation_and_optimization_approaches_for_work_flow_management_in_semiconductor_manufacturing-7] Kohn R, Noack D, Mosinski M, Zhou Z, Rose O; Evaluation of modeling, simulation and optimization approaches for work flow management in semiconductor manufacturing; Institute of Applied Computer Science; Dresden Germany; 2009.

[FabTime_Cycle_Time_Management_Newsletter_–_Volume_13,_Number_3-8] FabTime Cycle Time Management Newsletter – Volume 13, Number 3, 2012 by FabTime Inc.

[9] Kumar, A. (1989). Component inventory costs in an assembly problem with uncertain supplier lead-times. IIE transactions, 21(2), 112-121.

[10] Gunasekaran, A., Patel, C., & Tirtiroglu, E., 2001 "Performance measures and metrics in a supply chain environment." International journal of operations & production Management 21, no. 1/2: 71-87.

[11] 2)Cousens, A., Szwejczewski, M., & Sweeney, M. 2009. “A process for managing manufacturing flexibility.” International Journal of Operations & Production Management, 29(4), pp.357-385.

[test-12] Silva, L., 2013, “Supply Chain Contract Compliance Measurements” Master thesis (work in progress), Aalto University, Finland.

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