Bibliographic record
Abstract
Lean thinking has become a part of North American manufacturing and service sectors. This is occurring in an atmosphere of growing customer demands, heavier reliance upon technology and new environmental challenges. The move to create more agile and responsive organizations has become a global race to perfection and of survival. An ever-expanding list of industries and areas of the economy are moving to become lean. Canadian engineers of many disciplines are being called upon to rise to this challenge, yet most do not receive anything more than a brief introduction to lean through their undergraduate studies. What is lean thinking? Lean is simply eliminating all waste within any process. Waste is anything your customer does not wish to pay for. There are 8 different forms of waste. They are: overproduction, inventory, waiting, transportation of materials, motion, inefficient processes, rework and not using your people’s abilities to the fullest. Leaning an organization is through reducing the lead time between the customer’s request and fulfilling that request. Value added steps are retained and non-value added steps are removed to reduce cost and time requirements. The paradigm has changed for design and consulting engineers. For example, system and machine designs that do not continue to reduce or eliminate the costs associated with not only operation and maintenance but now also setups will undermine the success of those engineers and their firms. This is because lean focuses on the waste of setups within processes. Additional tools such as design for manufacturing (DFM), design for assembly (DFA), design for operations (DFO) and quality functional deployment (QFD) are critical skills in the new paradigm. Developing an organizational culture where individual leadership is widespread, spontaneous and visibly supported is a fundamental lean skill. Entire firms are trained in problem solving skills. All of these efforts, through a consistent method, are to create an enterprise-wide continuous improvement culture. Lean knowledge and the ability to pass it on to others are the fundamental skills that Canadian engineers need to be successful. Some Canadian engineers may fall further behind in this race to become lean. The gap will continue to widen between an ever-growing need for lean education and Canada’s ability to fill it. Canadian universities must respond to this need in an effort to create well-rounded engineers and to sustain the Canadian economy and standard of living.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.088 | 0.025 |
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".