Extra Credit Paper on Electric Car industry

 

1. Prepare a detailed SWOT* analysis for Tesla within the U.S. market (currently). Elements of your SWOT should come from and be supported by the application of the relevant frameworks. Supporting frameworks should be provided in an appendix at the end of the paper.
2. Create a detailed strategic plan for Tesla based on your SWOT analysis.
Here is a Fast Company article to get you started: https://www.fastcompany.com/90770757/tesla-has-been-down-a-rough-road-this-year-whats-next-for-elon-musks-cash-cow?partner=rss&utm_source=rss&utm_medium=feed&utm_campaign=rss+fastcompany&utm_content=rss
• The paper should be 1 page (single spaced, 12pt font, 1 inch margins) plus your appendix.
• The paper is worth up to 15 points and will be graded based on the following scale:
o 0 points = not completed or insufficient level of analysis
o 5 points = A few good elements but not enough detail in the SWOT and/or Plan.
o 10 points = Good SWOT but the plan is not detailed enough or well supported by the SWOT (misaligned).
o 15 points = Good SWOT that is well supported and is aligned with your proposed strategic plan.

 

 

 

Sample Solution

regards to the osmosis of pieces into lumps. Mill operator recognizes pieces and lumps of data, the differentiation being that a piece is comprised of various pieces of data. It is fascinating to take note of that while there is a limited ability to recall lumps of data, how much pieces in every one of those lumps can change broadly (Miller, 1956). Anyway it’s anything but a straightforward instance of having the memorable option huge pieces right away, somewhat that as each piece turns out to be more natural, it very well may be acclimatized into a lump, which is then recollected itself. Recoding is the interaction by which individual pieces are ‘recoded’ and allocated to lumps. Consequently the ends that can be drawn from Miller’s unique work is that, while there is an acknowledged breaking point to the quantity of pieces of data that can be put away in prompt (present moment) memory, how much data inside every one of those lumps can be very high, without unfavorably influencing the review of similar number

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