Integrating lean production strategies, virtual reality technique and building information modeling method for mass customization in cabinet manufacturing
Bibliographic record
Abstract
Purpose In response to increasing demand for a fully customized and individualized home environment, mass customization (MC) has been suggested as an effective strategy to fulfill the customer’s customization needs while keeping production cost-effectiveness. However, in current practice, the implementation of the MC in the industrialized housing industry has not achieved an ideal level. Little effort was devoted to customer value generation and achieving lean production in a multi-disciplinary MC environment. In this concern, a highly efficient and flexible production information system is expected to capture accurately the customer’s demand and efficiently perform work planning for encouraging customer involvement and mass efficiency production. Design/methodology/approach To gain an insight into the development of the MC production information system for the housing industry and to depict the interaction among system modules, this study used a design science research methodology for a case study of customized cabinet production information system development. Findings A prototype of the production information system was proposed in this paper, supported by three information technologies to facilitate the MC implementation in the millwork manufacturer. A focus group discussion method was carried out for evaluating the system feasibility and the subsequent survey analysis on the virtual reality (VR) interface experiment. The evaluation process results showed that the VR interface is an effective medium for design information communication and encourages customer involvement. Most participants believed that the proposed production information system could generally benefit the MC implementation and improve production efficiency. Originality/value This study integrated lean production principles along with building information modelling, VR and discrete-event simulation in the production information system to assist the manufacturer in effectively handling variant product information and enabling quicker reactions in response to diverse customer requirements in housing industries. The coordination among system modules and the managed information flow could be a valuable reference for future MC production system development in housing industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".