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Operations analyst who turns messy data into decisions people act on

Jordan Taylor

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.

In numbers

8 → 3 days

Cut the month-end close from 8 days to 3

£45,000

Saved £45,000 a year on returns handling

60%

Built the stock forecast the warehouse now plans around

No. 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.

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

The outcome

The 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.

What I did

  • 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

Skills

SQL, Power BI, Process improvement

No. 02

Saved £45,000 a year on returns handling

Found and fixed the three causes behind most costly returns.

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

The outcome

The 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.

What I did

  • 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

Skills

Python, Stakeholder management, Workshop facilitation

No. 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.

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

The outcome

The challenge

Same retailer, two warehouses, 2021.

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

What I did

  • 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

Skills

Forecasting, Excel modelling, Python

What I bring

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

Contact

Say hello.

For roles, projects or a conversation about the work above.

Email JordanView profile

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