Coordinating product and process variety for mass customized order fulfilment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Traditional mass production offers a limited variety of products, in which order processing mostly concentrates on the acquisition of customer needs, and support for the sales configuration at most. With mass customization, the fulfilment of customized orders involves frequent design changes and recurrent process variations due to product differentiation. It is imperative to manage the complexity and large variety associated with customer orders, product design and production planning. This paper adopts a holistic view of order fulfilment encompassing sales, design and production. The principle of collaborative product and process variety management is presented. The paper develops a coordination mechanism of variant handling based on the specification of variety handlers and their states. A product-process variety grid is introduced to unify product data and routing information. Also proposed is a coding-based system for effective variety management. A Web-based collaborative platform is outlined to support distributed order fulfilment among multiple functional departments. Keywords: Mass customizationOrder fulfillmentVariety managementVariant handlingCodingWeb-based application Acknowledgments Jianxin (Roger) Jiao is Assistant Professor of Systems and Engineering Management, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. He received his PhD in Industrial Engineering from Hong Kong University of Science and Technology. He holds a Bachelor degree in Mechanical Engineering from Tianjin University of Science and Technology in China, and a Master degree in Mechanical Engineering from Tianjin University, China. His research interests include mass customization, design theory and methodology, reconfigurable manufacturing systems, engineering logistics, and intelligent systems. Lianfeng Zhang is a PhD candidate at the School of Mechanical and Aerospace Engineering at Nanyang Technological University, Singapore. She received her Bachelor of Engineering Degree from the School of Management at Tianjin University, China. Before her PhD study, she worked as an industrial engineer in ST Microelectronics in Shenzhen, China. Her current research is focused on product-process platform development for mass customization, Petri net application for process configuration modelling, and process configuration platform evaluation. Shaligram Pokharel is an Assistant Professor of Systems and Engineering Management Division, School of Mechanical and Aerospace Engineering, the Nanyang Technological University, Singapore. He received his BE in Mechanical Engineering from the University of Kashmir, India, and MASc and PhD, both in Systems Design Engineering, from the University of Waterloo, Canada. His main research interests are in systems design, logistics and supply chain management, product and process platform development and energy planning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it