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Capacity Planning Using Leading Indicators at Agere Systems Project #

Capacity Planning Using Leading Indicators at Agere Systems Project #. Project Team Principal Investigator: S. David Wu, Ph.D. Co-Principal Investigator Rosemary T. Berger, Ph.D. Graduate Assistant Berrin Aytac Agere Liaisons Chris Armbruster, Herb Betz. Business Environment

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Capacity Planning Using Leading Indicators at Agere Systems Project #

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  1. Capacity Planning Using Leading Indicators at Agere Systems Project # Project Team Principal Investigator: S. David Wu, Ph.D. Co-Principal Investigator Rosemary T. Berger, Ph.D. Graduate Assistant Berrin Aytac Agere Liaisons Chris Armbruster, Herb Betz Business Environment • Volatile demand for products • unprecedented acceleration of technology innovations • rapid product introduction cycles • Need for innovative products and exceptional customer service • retain customer base • gain new revenue opportunities • Transformation of supply chain • from vertically integrated to horizontally integrated operations • integration of multiple contract manufacturers Overview of Agere Systems • Specializes in advanced integrated circuit products • wireless data • high density storage • networking solutions • Global manufacturing activity • wafer fab in Orlando, FL • assembly & test in Singapore, Thailand • strategic relationships with Taiwan Semiconductor Manufacturing Co. (TSMC) and Chartered Semiconductor Manufacturing (CSM), Ltd. • Fiscal 2004 revenues of $1.91B Research Questions • How volatile is demand? How well do time series forecasting methods handle the volatility? • Are there discernable patterns that can be derived from historical or current data? Can “leading indicators” be identified? • Can leading indicators be used to produce reliable demand forecasts? How can they be used within the capacity planning process? High-Tech Demand Volatility Time Series Forecasting • Generally not appropriate for high-tech products • innovations lead to short lifecycles • data available early in lifecycle typically insufficient for analysis • e.g., Mahajan et al. (1990), Sharma et al. (1993), Islam & Meade (1997) • Analysis of 560 products over 14-month period corroborates thesis • large values for trend-fitting error and forecast error • difficult to judge predictive strength from trend-fitting error Leading Indicator Analysis • Group products into clusters based on similarity measure • factors include statistical characteristics, lifecycle patterns, technology group, manufacturing resources, etc. • Identify one or more leading indicators whose demand pattern predicts overall demand of cluster • correlation of its demand pattern in relation to the group • time lag by which demand pattern leads the group Deviation (%) Between Average Order Board Quantity and Actual Shipment Quantity (2001-2002) for 560 Products Examples of Leading Indicators Leading Indicator Forecasts Implications to Operations • Leading Indicators provide time-lagged model that predicts demand pattern of broader group • Demand of broader group important to planning functions • Negotiating and securing appropriate capacity with foundry partners • Projecting revenue for financial forecasts • Future Directions • Inventory Forecasting • Demand Growth Projections Time Lag = 7 months, Correlation = 0.63 11-Month MAPE Forecast Error 20.1%

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