Quantile regression metamodeling

Toward improved responsiveness in the high-tech electronics manufacturing industry

Demet Batur, Jennifer Bekki, Xi Chen

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input-output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment "on demand" to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.

    Original languageEnglish (US)
    JournalEuropean Journal of Operational Research
    DOIs
    StateAccepted/In press - 2017

    Fingerprint

    Quantile Regression
    Metamodeling
    Manufacturing Industries
    Simulation Model
    Electronic equipment
    Electronics
    Industry
    Simulation
    Throughput
    Semiconductor Manufacturing
    Customer Satisfaction
    Setup Times
    Spreadsheet
    Efficient Estimation
    Customer satisfaction
    Spreadsheets
    Metamodel
    Quantile
    Unloading
    Execution Time

    Keywords

    • Lead-time quotation
    • Manufacturing
    • Predictive analytics
    • Simulation metamodeling

    ASJC Scopus subject areas

    • Modeling and Simulation
    • Management Science and Operations Research
    • Information Systems and Management

    Cite this

    @article{c4a6df23c2b14a35a7e6248026284dc6,
    title = "Quantile regression metamodeling: Toward improved responsiveness in the high-tech electronics manufacturing industry",
    abstract = "Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input-output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment {"}on demand{"} to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.",
    keywords = "Lead-time quotation, Manufacturing, Predictive analytics, Simulation metamodeling",
    author = "Demet Batur and Jennifer Bekki and Xi Chen",
    year = "2017",
    doi = "10.1016/j.ejor.2017.06.020",
    language = "English (US)",
    journal = "European Journal of Operational Research",
    issn = "0377-2217",
    publisher = "Elsevier",

    }

    TY - JOUR

    T1 - Quantile regression metamodeling

    T2 - Toward improved responsiveness in the high-tech electronics manufacturing industry

    AU - Batur, Demet

    AU - Bekki, Jennifer

    AU - Chen, Xi

    PY - 2017

    Y1 - 2017

    N2 - Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input-output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment "on demand" to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.

    AB - Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input-output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment "on demand" to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.

    KW - Lead-time quotation

    KW - Manufacturing

    KW - Predictive analytics

    KW - Simulation metamodeling

    UR - http://www.scopus.com/inward/record.url?scp=85021755497&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85021755497&partnerID=8YFLogxK

    U2 - 10.1016/j.ejor.2017.06.020

    DO - 10.1016/j.ejor.2017.06.020

    M3 - Article

    JO - European Journal of Operational Research

    JF - European Journal of Operational Research

    SN - 0377-2217

    ER -