Using Human Resource Management to Drive Business Strategy: The Case of BAE Systems
Bibliographic record
Abstract
BAE Systems is the 3rd largest defense company in the world, the largest outside of the US, and in 2016, they reached sales of £25 bn. Their vision is to be the premier international defense aerospace and security company in the world. This vision is vague and ‘premier’ can be interpreted in quite a few ways. They have mission and strategic objective statements that qualify how they are going to reach their vision. In this paper, the authors interpret what BAE Systems means when they state that they want to be the ‘premier’ defense company. The authors do this by examining the evidence supporting a strategy that aligns with financial dominance (focus on sales), product quality dominance (focus on R&D), or global dominance (focus on spread of operations). The authors achieve this by looking at the movement of people within the company, as well as qualifying those movements with financial expenditure. What the authors found is that from 2012 to 2016, BAE Systems increased their employee numbers in the UK and Saudi Arabia, but decreased in the US and Australia, and their employee numbers in other markets remained consistent (Australian Government, 2016). The changes of where revenues come from, alongside with the movement in employees, suggests that the company is centralizing R&D and production in the UK but is maintaining a consistent global sales effort. Within the UK, they have consistently recruited university graduates, and have surged the number of new apprenticeships, which supports the hypothesis that they are hiking production within the UK. In the US, they are one of the top recruiters of military veterans, which likely leads to better government relationships. Based on the global market trends, competitors’ composition, and how BAE Systems is constructed, the authors reason that BAE Systems’ top competitors are in a position of much higher financial risk. If the US decreases its relative military dominance over other countries, this could be beneficial for a company like BAE Systems, which has 59% of employees and 79% of its total sales located outside of the UK. In essence, BAE Systems is employing effective human resource strategies which align with their R&D and sales objectives. They are effectively recruiting new, young, and skilled STEM graduates to support their R&D initiatives, as well as apprentices to ramp up their domestic production. The recruitment of young persons demonstrates effective succession planning on behalf of the company. The distribution of employees globally means that the company is in a position of reduced financial risk, which might put them in an advantageous position in the future.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".