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Jordan Taylor

Operations analyst who turns messy data into decisions people act on

I've spent seven years in logistics and retail operations, rebuilding the reports and processes that teams wait on. I like untangling spreadsheets nobody trusts, building dashboards people actually open, and helping teams move from gut feel to numbers.

See my resultsGet in touch

Results

What changed because of my work

8 → 3 days

Cut the month-end close from 8 days to 3

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£45,000

Saved £45,000 a year on returns handling

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60%

Built the stock forecast the warehouse now plans around

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Selected work

Case studies

8 → 3 days

Outcome

The close went from 8 working days to 3, freeing two analysts for forecasting work.

Case study 01

Track record · 23 updates over 5 months

Cut the month-end close from 8 days to 3

Rebuilt a manual reporting pack so finance could close the month in half the time.

Challenge

Regional logistics firm, finance and operations team of 14, 2023.

Every month the board pack was rebuilt by hand from six spreadsheets. It took eight working days, arrived late, and nobody fully trusted the numbers.

Approach

  • Mapped every manual step with the people who did them
  • Moved the source data into a single SQL model with checks built in
  • Rebuilt the pack in Power BI, refreshed automatically each morning
  • Trained the team and wrote a one-page runbook
SQLPower BIProcess improvement

£45,000

Outcome

Return costs fell by £45,000 a year, and returns on the affected lines dropped from 18% to 7%.

Case study 02

Saved £45,000 a year on returns handling

Found and fixed the three causes behind most costly returns.

Challenge

Online retailer, 40,000 orders a month, 2022.

Returns were rising and costing more each quarter, but the data sat in three systems and no one could say why.

Approach

  • Joined order, courier and customer-service data for the first time
  • Traced 70% of costly returns to three product listings and one courier route
  • Ran a workshop with buying and customer service to agree fixes
PythonStakeholder managementWorkshop facilitation

60%

Outcome

Stockouts on the top 200 products fell by 60% over two quarters.

Case study 03

Track record · 11 updates over 2 months

Built the stock forecast the warehouse now plans around

Replaced guesswork with a weekly forecast the warehouse trusts.

Challenge

Same retailer, two warehouses, 2021.

Stock orders were based on last year's numbers, leading to frequent stockouts on fast sellers.

Approach

  • Built a weekly forecast in Excel, then moved it to Python as it grew
  • Reviewed it with warehouse leads every Monday for the first quarter
ForecastingExcel modellingPython

Skills

What I bring

Each one is backed by the case studies above.

SQL
Power BI
Process improvement
Stakeholder management
Excel modelling
Python
Forecasting
Workshop facilitation

Let's talk

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