Orientation and standards

Example 1 Risk register for survey flights

Assessed before and after controls (hypothetical values agreed by the team), with thresholds high ≥ 15, medium 8–14, low < 8.

risks = [   # hazard, L before, I before, control, control level, L after, I after
    ("propeller strike", 3, 4, "prop guards and 15 m keep-out zone", "engineering", 1, 4),
    ("battery fire while charging", 3, 5, "fireproof charging box", "engineering", 2, 3),
    ("injury to bystanders", 2, 5, "cordon and flight over empty area", "administrative", 1, 5),
    ("heat stress of crew", 4, 3, "shade, water and work-rest schedule", "administrative", 2, 2),
]

def level(r):
    return "HIGH" if r >= 15 else "MEDIUM" if r >= 8 else "LOW"

for name, l0, i0, control, kind, l1, i1 in risks:
    r0, r1 = l0 * i0, l1 * i1
    print(f"{name:<28} {r0:>2} {level(r0):<6} -> {r1:>2} {level(r1):<6} via {kind}: {control}")
admin_only = [r[0] for r in risks if r[4] in ("administrative", "PPE")]
print("relies on people's behaviour:", ", ".join(admin_only))

propeller strike             12 MEDIUM ->  4 LOW    via engineering: prop guards and 15 m keep-out zone
battery fire while charging  15 HIGH   ->  6 LOW    via engineering: fireproof charging box
injury to bystanders         10 MEDIUM ->  5 LOW    via administrative: cordon and flight over empty area
heat stress of crew          12 MEDIUM ->  4 LOW    via administrative: shade, water and work-rest schedule
relies on people's behaviour: injury to bystanders, heat stress of crew

Every control lowers the risk, but two depend on administration, which works only if everyone follows it, so checks that it is really done are needed. Harm to bystanders keeps the highest impact even with lower likelihood, so it must be reviewed every time the flight site changes.

Example 2 Pre-flight checklist over 12 flights

Items skipped on each flight, from a 10-item checklist (hypothetical data).

from collections import Counter

ITEMS = 10
skipped = [[], ["compass check"], [], ["NOTAM/airspace check", "compass check"], [], [],
           ["compass check"], [], ["battery temperature"], ["compass check"], [], ["NOTAM/airspace check"]]
done = sum(ITEMS - len(s) for s in skipped)
print(f"compliance {done}/{ITEMS * len(skipped)} = {done / (ITEMS * len(skipped)):.1%}")
print(f"flights with every item done: {sum(not s for s in skipped)}/{len(skipped)}")
for item, n in Counter(x for s in skipped for x in s).most_common():
    print(f"skipped {n}x: {item}")

compliance 113/120 = 94.2%
flights with every item done: 6/12
skipped 4x: compass check
skipped 2x: NOTAM/airspace check
skipped 1x: battery temperature

The overall rate looks high, but half the flights skipped something, and the compass check was skipped most. Ask the team why, for example whether the step is too slow or seems unnecessary, and propose a fix, which could become the improvement project topic in Module 4.