Changeover Efficiency IMprovement Using Systems Engineering and Lean Manufacturing
Bibliographic record
Abstract
Niagara Bottling LLC (Abbreviated as Niagara hence forth) is the largest private label beverage manufacturing company in all North America as of 2017. With as many as 38 manufacturing plants globally of which 34 are presently functional in the US, Niagara is a highly valued privately-owned company in large scale manufacturing. The plant under consideration here is the one located on Concours Street (Abbreviated as Con hence forth) in Ontario in Southern California. This plant has 4 production lines named as C1, C2, C3 & C4. C1 is used to manufacture only 8 oz type of bottles. C3&C4 are dedicated to 0.5L Kirkland bottles for Costco as they are the biggest customer by volume for Niagara. C2 has the capability of producing multiple designs of bottles, in different packs and of different type of water. Therefore, C2 is heavily subjected to changeovers and was not originally designed to manage the exorbitant number of changeovers. This is mainly due to the lack of design upgrades on the line vs the steep growing demand of different types of bottles the line can produce. \n \nThe aim therefore becomes to reduce the downtime caused due to changeovers on the production line by applying various methods such as SMED and by reducing any wastes (labor or material) related to the changeover. The objective has been set to introduce the concept of Industry 4.0 in the plant which already has advanced automation mapping as a philosophy that will lead to automatic information collection and key performance analysis using existing automation infrastructure. \nThese implementations, ideas and major concepts could later on be implemented in other production lines in the factory both locally in the plant, but also in other plants globally. The approach to be used is by creating job aids, cross training maintenance technicians, effective utilization of resources, time management, trainings for thorough realization of responsibilities, spaghetti diagrams, pareto charts and time studies. \n \nThe courses from the MSSE program applied for this particular project are SE 5110 Advanced Engineering Economics, SE 5130 System Engineering Life Cycle Design & Management, SE 5180 Human Systems Interaction, SE 5190 Operations Research in Systems Analysis, SE 5200 System Simulation for Managers, SE 5150 System Sustainability and SE 5160 Facility Planning Systems
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".