Technology Entrepreneurship (ENT600) : Adidas Group / Nursyamimi Muhamad Fauzi
Bibliographic record
Abstract
The purpose of this study is to identify the problem of the product and improve it to be a better product. I got the opportunity to research a company which manufactured the same product that I want to develop, which is Adidas company. This company is currently based in Germany where Adolf Dassler is the founded the business in 1924 and successfully expanded to over 350 brand-stores branches in the country all around the world such as Germany, Australia, Canada, India, Korea, Mexico, Poland, Romania, South Africa, Sweden, Turkey etc. It was the largest sportwear manufacturer in Europe and the product are traditionally marked with three-striped trademark. The strength, weakness opportunities and threats of the company in real business world was analysed by using SWOT analysis. Thus, from the needs and demands from the existing consumers of this company was identified. There is a lot of problems regarding this issue. The main issue will be from this backpack is it lack the security as it does not secure the things inside the backpack. Moreover, the material that are used is less tough and easily absorb water. Furthermore, the colour of the product is limited and do not have many choices. The backpack also does not have more pockets as needed and the design is the same as the other brands. Then, the problems was analysed and the solution was fine to overcome and to fulfil their needs and making it as our innovation to be continued in new Product Development task. Various solution or idea was implemented to solve the issue. Every problems contain more than one ideas of improvement to make the product perfect and outstanding. Next, one of the ideas was chosen as the best solution. Firstly, the security problem of the backpack was solved by invented built-in biometric fingerprint sensor to the zipper. PVC coated polyester material was used because it is good waterproof material thus can protect the bag from being wet. Various choices of colour were added to make it look more attractive and enhance the buyers’ interest on that product. The backpack was provided with more pockets and features such as shoulder strap pockets, USB ports and LED light to make the product more reliable and productive so that it can fulfil consumers criteria for their ideal backpack.
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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 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".