Diminishing Returns on Knowledge in Operations Management...ETM Slide # 1 PICMET ’08 Charles M. Weber & Asser Fayed INFORMS 2008 Date: Oct. 12-15, 2008 Abstract • A empirically

Charles M. Weber & Asser FayedPICMET ’08INFORMS

2008INFORMS

2008

Diminishing Returns on Knowledge in

Operations ManagementCharles Weber and Asser Fayed

INFORMS – October 12-15, 2008Washington, DC, USA

ETMETM

Slide # 1


2008 Date: Oct. 12-15, 2008

Abstract• A empirically grounded model of the operating

curve a semiconductor fabrication facility, which is sufficiently accurate to make capitalization decisions, has been developed. The model is used to simulate the performance of a hypothetical semiconductor fabrication facility that operates under very realistic conditions. Results of the simulation show that the value of additional technological knowledge can be negative. Learning more of a good thing is not always a good idea!

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2008 Date: Oct. 12-15, 2008

Outline

• About technological knowledge• Optimizing the operating curve

– Prior approaches– Output maximization– Value-driven approach.

• Insights pertaining to the value of knowledge

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2008 Date: Oct. 12-15, 2008

The Traditional Learning Curve(adapted from Bohn, 1994, p. 66)

ProductionImproved Cost andQuality

Somethinghappens

• Statistical linkage between input and output• See, for example, Wright (1936); Searle and Gody (1945); Alchian

(1963); Rapping (1965); Hayes and Clark (1985); Dutton and Thomas (1984); Argote and Epple (1990).

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2008

Inside the Learning Curve(adapted from Bohn, 1994, p. 66)

• Explicit recognition of knowledge.– See, for example, Adler and Clark (1991);

Jaikumar and Bohn (1992).

– Definition of Technological Knowledge (Bohn, 1994): • understanding the effects of input variables (x) on the output (Y).• Y = f(x1, ….., xn); uncharacterized function!

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2008 Date: Oct. 12-15, 2008

Volume Learning and Production Knowledge

• Mishina (1999) – Studied Boeing’s B-17 heavy bomber production

during World War II, – which underwent a 60-fold increase in output over

a period of less than four years. – Found that investments pertaining to a scale-up

triggered learning activity. – Increases in production volume drives learning – Factory encounters new experiences, new

phenomena and new challenges, – which Mishina concludes drive learning.

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2008 Date: Oct. 12-15, 2008

The Value of Technological Knowledge

• Has not been characterized• The value of the same set of skills …

– May vary under different circumstances – Such as different production volume– Or different positions on the operating curve

• Research Question: Is learning more of a good thing always a good idea?

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2008 Date: Oct. 12-15, 2008

The Operating Curve • A manifestation of Little’s Law• Tradeoff between throughput and cycle time

(Aurand and Miller, 1997). – Decreasing cycle time

• requires reduction of lot starts; • insufficient product output?

– Loading a factory to capacity• Increases cycle time and WIP excess inventory, • Greater vulnerability to production problems • Decreased flexibility.

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2008 Date: Oct. 12-15, 2008

Profitability and the Operating Curvein Semiconductor Manufacturing

• Profitability in semiconductor manufacturing, to a large degree, depends upon how a fab(fabrication facility) manages its operating curve.

• Every fab responds to a particular economic environment and is subject to a particular set of constraints (Fayed and Dunnigan, 2007).

• Every fab has to optimize it own operating curve.

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2008 Date: Oct. 12-15, 2008

Prior Approaches to Optimizing the Operating Curve

• Learning by experience (Potti & Whitaker, 2003) • Simulation of demand (Cakanyildrim and

Roundy, 2002)• Simulation of capacity (Nazzal, et al., 2006)• Queuing theory (Leachman, 2004) • To date, no general solution to the operating

curve problem has been found.

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2008 Date: Oct. 12-15, 2008

Characterizing the Operating Curve(Fayed & Weber, 2008)

• Accurate measurement of – setup time, qualification time and process time for

each recipe, – availability, uptime and throughput of every piece of

equipment. • All these variables are integrated into a queuing model

(Hopp & Spearman, 2000)• that represents all processes, flows, operations,

equipment, recipes and qualifications in the fab. • The output of this model is the expected operating curve

of the fab.

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2008 Date: Oct. 12-15, 2008

Validation of Model (Fayed & Weber, 2008) • Simulate current conditions

(OC-1) • Validate OC-1 at two actual

operating points of the fab(OC-1 actual).

• The predicted and actual values agreed within 2%.

• Cycle time rises modestly until the fab is at 90% capacity and rises dramatically as the fab load approaches capacity.

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2008 Date: Oct. 12-15, 2008

Capital Purchase Decisions --Capacity Planning

• Expansions to the fab were planned by predicting operating curves under particular sets of conditions (OC-2 and OC-3).

• OC-2 was validated at two data points after expansion (OC-2 actual).

• Predicted values and actual values for cycle time differed by less than 3%.

• Further expansion has been simulated (OC-3).

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2008 Date: Oct. 12-15, 2008

Optimizing the Operating Curve• We define the operational effectiveness ratio

(OER) as throughput divided by cycle time.• OER is the general performance metric for the fab. • We run our model in a manner that shows OER as

a function of throughput and cycle time. • We validate the model by comparing predicted

conditions to actual conditions observed in the fab. • If necessary, we adjust the model to predict actual

data more accurately.

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2008 Date: Oct. 12-15, 2008

Key Finding-1 (Fayed & Weber, 2008)• OER is nearly optimal

for a wide range of throughput regardless of the level of capital investment.

• This suggests stable fab operations are possible if the fab’soperating curve is well characterized.

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2008 Date: Oct. 12-15, 2008

Key Finding-2 (Fayed & Weber, 2008)• OER is near optimal

levels for a very narrow range of cycle time regardless of the size of the fab’s equipment set.

• Not surprisingly, optimal cycle time decreases as the equipment base increases.

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2008 Date: Oct. 12-15, 2008

Implications• A relatively wide range of throughput will yield

a particular, nearly optimal cycle time. • However, if a fab’s throughput exceeds its

near-optimal range, • then the operational effectiveness and fab

productivity “fall off the cliff.”• The value of volume learning becomes

highly negative!

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2008 Date: Oct. 12-15, 2008

An Optimal Level of Knowledge• Optimal cycle time for every fab needs to be

targeted. A model such as the one presented in this paper will be required to determine the optimal cycle

• and the throughput range that will yield a nearly optimal cycle time.

• The optimal level of volume production knowledge has to

be discovered by trial and erroror be simulated in advance.

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2008 Date: Oct. 12-15, 2008

Limitations of Model • Useful heuristic that assists with capacity

planning and output maximization, but ... • Suggests no approach towards

maximizing profit. • A value-driven approach is required.• Incorporates revenue, cost, profit and

time.

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2008 Date: Oct. 12-15, 2008

Operating a Modern Fab• New products get introduced into the fab on a regular basis, • Product unit price can decay over time. • Planning for profitability is likely to include attempts to

optimize the product mix that the fab realizes. • The fab’s management would like to run products with a

high unit price. • However, the fab capacity is limited, and overloading the

fab will increase cycle time. • The commitment to realizing older, less profitable products

may have to be abandoned in favor of running newer, more profitable ones.

• How do you manage in this environment?

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2008 Date: Oct. 12-15, 2008

Simulation of Realistic Modern Fab(Fayed & Weber, 2008)

• Capacity = 1000 wafer starts per day• 190 surviving dice per 200-mm wafer • Highly nonlinear operating curve• Decaying unit price -- P(t) = $100*10-t/(365 days)

– (order of magnitude per year)• At t=0 the fab starts 100 wafers per day of a

new product. • Output metrics – OER, Cumulative Revenue

and Profit

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2008 Date: Oct. 12-15, 2008

The Value-Driven Operating Curve

• Success is a function of the operating curve.– Maximum OER at 810 wafers

per day.– Maximum cumulative revenue

at 900 wafers per day. – (Life of product=365 days.)– All variables drop off the cliff

near maximum capacity.

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2008 Date: Oct. 12-15, 2008

Cumulative Profit as a Function of TimeFab respectively runs at 72%, 81%, 90%, 96% and 99%

of its maximum capacity of 1000 wafers per day.

• The point of maximum cumulative profit – is the ideal time to stop

processing the new product (exit time).

– It is a function of fabthroughput.

• A fab that is nearly fully loaded can never make a profit.

99%

81%

72%

96%

90%

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2008 Date: Oct. 12-15, 2008

Optimizing the Operating Curve• Optimal exit time varies

near-linearly with fabthroughput.

• OER is at a maximum at 810 wafers per day.

• Cumulative profit is at a maximum at 900 wafers per day.

• Using OER as metric results in $3M of unrealized gain.

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2008 Date: Oct. 12-15, 2008

Implications for Operations • Only a minority of fabs are capable of making

accurate data-driven decisions that maximize profit. (Weber, 2006)

• Most fabs are still driven by maximizing output. • The direct connection between the operating

curve and profitability has not yet been made. • Millions of US $ in gain are not realized as a

consequence.

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2008 Date: Oct. 12-15, 2008

Summary and Conclusion• This paper has presented a model of a fab

operating curve that is sufficiently accurate to make capitalization decisions.

• The model has been used to simulate the performance of a hypothetical fab that operates under very realistic conditions.

• Simulation shows that significant additional revenue can be generated by shifting from today’s output-driven approach to the operating curve to one that is value-driven.

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2008 Date: Oct. 12-15, 2008

Implications Pertaining to Technological Knowledge

• The value of volume learning is not always positive.

• Near maximum capacity revenue and profit drop dramatically.

• Simulation determines the optimal level of volume production knowledge.

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2008 Date: Oct. 12-15, 2008

List of References (1)• Adler, P. S. and Clark, K. B., 1991. Behind the learning curve: A sketch of

the learning process. Management Science 37(3), 267-281.• Alchian, A. 1963. Reliability of progress curves in airframe production.

Econometrica 31, 679-693. • Argote, L. and Epple, D. 1990. Learning curves in manufacturing. Science

247, 920-924.• Aurand , S. S. and Miller, Peter J. 1997. The Operating Curve: A Method to

Measure and Benchmark Manufacturing Line Productivity. IEEE/SEMI-ASMC, Boston, MA, Sept. 10-12, 1997, 391 – 397.

• Bohn, R. E. 1994. Measuring and managing technological knowledge. Sloan Management Review 36(1), 61-73.

• Çakanyildirim, M. and Roundy, R. O. 2002. SeDFAM: semiconductor demand forecast accuracy model. IIE Transactions 34(5), 449 – 465.

• Dutton, J. M. and Thomas, A. 1984. Treating progress functions as a managerial opportunity. Academy of Management Review 9(2), 235-247.

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2008 Date: Oct. 12-15, 2008

List of References (2)• Fayed, A. and Dunnigan, B. 2007. Characterizing the Operating Curve –

How Can Semiconductor Fabs Grade Themselves? ISSM, Silicon Valley, October 2007, Pages 289-292.

• Fayed, A. and Weber C. M. 2008. Optimizing the Operating Curve: How can every fab maximize its performance? IEEE/SEMI-ASMC, Boston, MA, May 6, 2008.

• Jaikumar, R. and Bohn, R. 1992. A dynamic approach to operations management: An alternative approach to static optimization. International Journal of Production Economics 27, 265-282.

• Hayes, R. H. and Clark, K. B. 1985. Exploring the source of productivity: Differences at the factory level, in: Clark, K. B., Hayes, R. H. and Lorentz, C. (eds.), The Uneasy Alliance: Managing the Productivity-Technology Dilemma. Boston, MA, Harvard Business School Press, 151-188.

• Hopp, W. and Spearman, M. 2000. Factory Physics, McGraw-Hill/Irwin; 2nd edition.

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2008 Date: Oct. 12-15, 2008

List of References (3)• Leachman, R. C. 2004 . The economics of speed. presented at Portland

State University’s ETM Seminar, Portland, OR, Nov. 6, 2004. • Leachman, R. C. and Ding, S. 2007. Integration of speed economics into

decision-making for manufacturing management. International Journal of Production Economics, 39-55.

• Mishina, K. 1999. Learning by new experiences: Revisiting the Flying Fortress learning curve. In Lamoreaux et al. (eds.) Learning by doing in markets, firms and countries. A National Bureau of Economic Research Conference Report. The University of Chicago Press, Chicago, IL.

• Nazzal, D., Mollaghasemi, M. and Anderson, D. 2006. A simulation-based evaluation of the cost of cycle time reduction in Agere Systems wafer fabrication facility -- a case study. International Journal of Production Economics, 300-313.

• Potti, K. and Whitaker, M. 2003. “Cycle Time Reduction at a Major Texas Instruments Wafer Fab. IEEE/SEMI-ASMC, 3/31 – 4/1, 2003, 106 – 110.

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2008 Date: Oct. 12-15, 2008

List of References (4)• Rapping, L. 1965. Learning and the World War II production functions.

Review of Economics and Statistics 48, 98-112. • Reinhardt, U. E. 1973. Break-Even Analysis for Lockheed’s Tri Star: An

Application of Financial Theory,” The Journal of Finance 28(4), 821-838.• Searle, A. D. and Gody, C. S. 1945. Productivity increases in selected

wartime shipbuilding processes. Monthly Labor Review 60, 1132-1147. • Weber, C. 2004. Yield Learning and the Sources of Profitability in

Semiconductor Manufacturing and Process Development. IEEE Transactions on Semiconductor Manufacturing 17(4), 590-596.

• Weber, C. M. “Do learning organizations have strokes of genius?”Proceedings of PICMET ‘06, Istanbul, Turkey, July 8-13, 2006.

• Wright, T. P. 1936. Factors affecting the cost of airplanes. Journal of Aeronautical Science 3, 122-128.

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Diminishing Returns on Knowledge in Operations ManagementAbstractOutlineThe Traditional Learning Curve(adapted from Bohn, 1994, p. 66)Inside the Learning Curve(adapted from Bohn, 1994, p. 66)Volume Learning and Production KnowledgeThe Value of Technological KnowledgeThe Operating CurveProfitability and the Operating Curvein Semiconductor ManufacturingPrior Approaches to Optimizing the Operating CurveCharacterizing the Operating Curve(Fayed & Weber, 2008)Validation of Model (Fayed & Weber, 2008)Capital Purchase Decisions --Capacity PlanningOptimizing the Operating CurveKey Finding-1 (Fayed & Weber, 2008)Key Finding-2 (Fayed & Weber, 2008)ImplicationsAn Optimal Level of KnowledgeLimitations of ModelOperating a Modern FabSimulation of Realistic Modern Fab(Fayed & Weber, 2008)The Value-Driven Operating CurveCumulative Profit as a Function of TimeFab respectively runs at 72%, 81%, 90%, 96% and 99% of its maximum capacity of 1000 wOptimizing the Operating CurveImplications for OperationsSummary and ConclusionImplications Pertaining to Technological KnowledgeList of References (1)List of References (2)List of References (3)List of References (4)

Documents

Diminishing Returns on Knowledge in Operations Management...ETM Slide # 1 PICMET ’08 Charles M. Weber & Asser Fayed INFORMS 2008 Date: Oct. 12-15, 2008 Abstract • A empirically