Lead Time Estimation Method for Complex Product Development Process

Jun, Hong-Bae; Park, Jin‐Young; Suh, Hae Sun

doi:10.1177/1063293x06073302

Cited by 17 publications

(7 citation statements)

References 33 publications

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“…Li et al [14]; Tootle et al [15] LT is estimated based on regression concept linearly or nonlinearly with specified attributes Heuristic Jun et al [16]; Chiang [17] LT is estimated by developing a heuristic based on network transformation and network reduction methods capabilities of the methods in different modeling environments. Furthermore, in this study, within a flexible neurofuzzy-regression model, three influential input parameters -failure, repair, and process times -for predicting lead time are used.…”

Section: Regressionmentioning

confidence: 99%

A Neuro-Fuzzy-Regression Algorithm for Improved Prediction of Manufacturing Lead Time with Machine Breakdowns

Asadzadeh

Azadeh

Ziaeifar

2011

Concurrent Engineering

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Manufacturing lead time estimation is an important task in production system with machine breakdown and maintenance. This study presents a flexible algorithm for estimation and forecasting lead time based on artificial neural network (ANN), fuzzy regression (FR), and conventional regression (CR). First, an ANN is illustrated based on supervised multi-layer perceptron network for the lead time forecasting. The selected ANN model is then compared with fuzzy and conventional regression models with respect to Mean Absolute Percentage Error, hence the name neuro-fuzzy regression algorithm. To show the applicability and superiority of the flexible neuro-fuzzy regression, the proposed algorithm is used to estimate the weekly lead times of an actual assembly shop (producer of heavy electric motor). This is the first study that introduces a flexible neuro-fuzzy algorithm for improved estimation of lead time in manufacturing systems with machine breakdown and maintenance. In addition to accuracy, simplicity and short execution time of lead time estimation are desirable features of the presented flexible algorithm.

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Section: Regressionmentioning

confidence: 99%

A Neuro-Fuzzy-Regression Algorithm for Improved Prediction of Manufacturing Lead Time with Machine Breakdowns

Asadzadeh

Azadeh

Ziaeifar

2011

Concurrent Engineering

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“…These phenomena emphasize the need for effective process controls as well as reasonable time and cost estimations for each activity. Jun et al [4] provided a heuristic algorithm to estimate the lead time of a PD process by using series-parallel network transformation. Tormos and Lova [5] extended the concepts of activity slack and defined a new activity criticality index to classify the activities in the resource-constrained project scheduling and control context.…”

Section: Literature Reviewmentioning

confidence: 99%

The Minimum-cost PD Process Planning and Control Methodology with the Consideration of Resource/Time Constraints and Skill Levels

Chiang¹

2009

Concurrent Engineering

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This research applies the benchmarking-based concept to propose a minimum-cost product development (PD) process planning and control methodology to consider resource/time constraints and skill levels. The methodology can aid PD project managers to more objectively perceive the relationship among activity times, activity costs, as well as quantities of different skill-level manpower and facilities. Hence, the minimum-cost PD process management approach proposed in this paper can assist project managers to optimize R&D resource allocation of a PD process and estimate activity times and activity costs. In addition, this paper provides the minimum-cost PD process planning and control mechanisms to dynamically modify the PD project process.

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“…One objective of the PERT model is to assist a project manager to identify bottlenecks and overruns in the project before they happen so that corrective actions can be taken before it is too late to effect any change ( Van Slyke, 1963). According to (Jun et al, 2006), the PERT technique "evaluates the lead time of a project under the assumption that each activity has a stochastic duration time." Similar to the CPM model, the PERT technique estimates the project completion time based on the expected duration of the activities on the critical path.…”

Section: Introductionmentioning

confidence: 99%

“…The authors proposed a new scheduling framework called "PERT 21" using Parkinson distribution with the lognormal core. The study by (Jun et al, 2006) also argued that the PERT model cannot handle complex patterns among activities. Similarly, the study by (Park and Cutkosky, 1999) argued that the PERT model was designed for linear processes and thus, it cannot represent circuits.…”

Section: Introductionmentioning

confidence: 99%

Schedule Risk Analysis using a Proposed Modified Variance and Mean of the Original Program Evaluation and Review Technique Model

Sackey

Kim

2019

KSCE Journal of Civil Engineering

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The Program Evaluation and Review Technique (PERT) model uses parameters such as the specified project completion time, mean, and variance to estimate the probability of project completion time. However, this model uses a weighted average and unweighted value in the variance, which is based on six sigma of the mean. Despite many proposed modifications to improve the traditional PERT model, the hidden error in the calculation of the variance and mean of the PERT approach has not been adequately addressed. This error leads to underestimation of the schedule risk. Considering the impact of variance and mean on the probability of project completion times, this study contributes to the improvement of the accuracy of schedule risk estimation by proposing a modified variance and mean of the original PERT model. The original PERT model was first used to estimate the project completion time. However, using the proposed modified model to estimate the completion time, a 95% confidence interval assumption and the corresponding distribution within ±2 standard deviation of the mean and standard or Z values were employed to model the new mean and variance equations. To prove the validity of the proposed modified variance and mean assumptions, we performed a schedule risk analysis through simulation using Oracle Crystal Ball for comparison. The results showed that the proposed PERT model had a better mean error rate of 2.46% as compared to 3.31% of the original PERT model.

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Lead Time Estimation Method for Complex Product Development Process

Cited by 17 publications

References 33 publications

A Neuro-Fuzzy-Regression Algorithm for Improved Prediction of Manufacturing Lead Time with Machine Breakdowns

A Neuro-Fuzzy-Regression Algorithm for Improved Prediction of Manufacturing Lead Time with Machine Breakdowns

The Minimum-cost PD Process Planning and Control Methodology with the Consideration of Resource/Time Constraints and Skill Levels

Schedule Risk Analysis using a Proposed Modified Variance and Mean of the Original Program Evaluation and Review Technique Model

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