Adoption of lean manufaturing system with AIM of efficiency improvement within a late lean adopter company (a case study)
Bibliographic record
Abstract
“Lean manufacturing” (LM) and “Lean production” are the terms and concepts which have been introduced to industry world since 1990s. In these almost three decades many companies in various sectors have applied lean with aim of eliminating wastes and implementing continuous improvements in their organization. Although the capabilities and advantages of lean have been proven over the years, some companies are still hesitate to use this method. According to the theory of the “Diffusion of Innovation” (DOI), based on innovativeness factor, adopters to an innovation are divided into five categories: innovators, early adopters, early majority, late majority and laggards. Innovation can be considered as a new idea, procedure, practice, product and etc. Each of these categories has its own characteristics. The focus of this research is about adoption of LM in a laggard company. \n \nThe case of this study (company X) is a furniture manufacturer in Quebec with mainly push production system. They are currently encountering with long delivery times, lack of space in the shop floor and unsatisfied production rate. Similar companies with the same problems as company X have improved their total efficiency with applying lean concept but our case has not been turned into LM until now and they are not very interested in implementing this procedure. Innovation-decision process has five stages: knowledge, persuasion, decision, implementation and confirmation. In this study, it is intended to help the company to reach to the third stage to make decision about lean implementation. \n \nLean is a long and time taking journey and it will be longer in case of late adopters. The reason that they are reluctant in lean adoption is their ignorance about existing wastes and their impacts in the system. In such companies with traditional organizational structure, unawareness is the result of working in silos and having weak communications. In order to convince laggards to make decision about lean implementation, it is essential to break down the silos, improve interactions and deploy mutual synergy among them. Laggards will not accept an innovation until they completely become certain it is safe for them. In this research, we propose a road map to indicate the opportunity to increase total cycle efficiency from 29.4% to 78.3%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".