Workforce & payroll inputs

Why paper-based wages no longer scale

In Chilean fruit growing, labor is usually the largest component of production cost, and with the minimum wage and labor costs rising every season, the margin for error shrinks. When attendance is on paper and piecework is calculated by hand on each foreman's own spreadsheet, payroll close lands late, with disputes and no evidence to defend it.

The problem scales with the crew: with 30 in-house pickers the spreadsheet holds; at peak season with contractor crews, piecework calculated by hand is a constant source of error and conflict. And because every mis-allocated wage distorts the block's cost, the problem doesn't stay in HR: it contaminates costing.

The real problem (no marketing)

Workforce control on a Chilean farm fragments across paper, per-foreman spreadsheets and the memory of whoever was in the field. The breaking points are always the same:

The underlying cost is twofold. On one side, labor conflict and HR time spent firefighting. On the other, that without assigning each wage to the block where the work happened, the largest component of production cost stays outside per-block costing —so the cost per kilo you see is incomplete.

How this module solves it

The module captures labor data at the source —picked kilos, clock-in time, block— so payroll close arrives verifiable and dispute-free. It isn't a payroll system: it prepares the signed data that system needs. The flow starts on the supervisor's phone:

1. 01

   Clock-in from mobile

   Clock-in and clock-out with photo and geolocation from the supervisor's phone, with no paper to key in on Monday.

2. 02

   Assignment to block and task

   Each clock-in is auto-assigned to the block and task, to charge labor cost to where it happened.

3. 03

   Live piecework

   Piecework is calculated against output reported at harvest, on a single criterion, not on a per-foreman spreadsheet.

Every clock-in and every piecework figure is kept with photo, time and foreman in a history of disputes and resolutions. The conversation stops being "I picked more" against a spreadsheet and becomes a signed record. Because block assignment shares master data with logbook & work orders, the wage flows straight into costing.

Integration with your current stack

The module doesn't replace your Chilean payroll system: it feeds it with verifiable data. It connects to the tools your HR and field teams already use:

That same block assignment is what makes costing complete: every allocated wage flows into cost per block, and per-crew performance is cross-referenced with output from harvest control over the same master data.

Payroll close, without surprises

In a seasonal Chilean fruit operation, the weekly payroll close is one of the highest-friction moments: hundreds of pickers, piecework by kilos, and a short window to pay correctly. When data arrives late and unbacked, the close becomes a negotiation; when it arrives verifiable, it becomes a formality.

The module flips the order of operations. Instead of capturing data at the end —keying papers on Monday— it captures it at the source: the supervisor's mobile clock-in, kilos reported at harvest, block assignment. For the close, HR reconstructs nothing: it exports already-signed, traceable data to Buk or Talana.

Labor compliance leans on this too. Working with contractor crews demands evidence of who worked, when and under what conditions; geolocated clock-ins and the immutable history are exactly the kind of record a social audit like GRASP expects to see. The traceability that protects the pay close is the same that protects you in an inspection.

And a clean close has an effect rarely counted: it lowers turnover and conflict. A crew that trusts it will be paid well and on time comes back next season, and in a market where labor is scarce, that continuity is worth as much as the savings. Signed data doesn't just reconcile the spreadsheet: it sustains the relationship with whoever harvests your fruit.

There's also a management return that shows up before the close. When per-person and per-crew performance is visible live, the foreman stops rewarding whoever shouts loudest and starts recognizing whoever yields, which lifts the whole group's productivity. And the operation can plan headcount with data: if you know the historical kilos per workday of each task, you size the crew you need for the harvest window instead of over-hiring for safety or falling short at peak. That same data, crossed with block cost, answers the question that really matters: not how much you paid in total, but which tasks and crews move the cost per kilo.

Metrics you'll move

The module moves productivity metrics that used to live in the foreman's head: kilos per workday (KPL) per person and crew, workdays per hectare by task, and wage disputes, which with evidence trend to zero.

Each indicator enables a decision:

That productivity, cross-referenced with cost, is what lets you know which crew and which task really move the cost per kilo.