Applied case study

Apple packhouse planning software for better next-run decisions

Growth Mindset built custom Python and SQL software for a New Zealand apple packhouse. The application turned live production records, changing production plans, priority instructions, and historical run performance into practical views for daily production, fruit forecasting, and next-run packing line setup.

Apple packhouse production line used as an example of planning and operational decision support

Production day

The packhouse had data. The hard part was using it in time

Carton production, run performance, grower information, and production settings were already being captured during the operating day.

The planning work still depended on several moving inputs. Production plans were managed in SharePoint. Daily packing priorities arrived through PDF reports and calls. Priorities could change while a run was already under way.

The practical need was a working view for the next decision. Controllers needed to understand current production, expected fruit mix, likely rates, volumes, and equipment assignments before the next setup was committed.

System map

The application connected the information used by planners

The value came from turning fragmented planning sources into a practical next-run view.

Source information

What the application brought together

The tool joined the records and planning documents that shaped each production decision.

  • Production plans from SharePoint files
  • Daily priority instructions from PDFs and calls
  • Live carton and label production records
  • Historical run performance and production summaries
  • Grower summaries, fruit size patterns, class, and grade information

Planning logic

How the decision was prepared

The application used production history and operating rules to estimate the next run and prepare a practical setup.

  • Fruit size distribution, class split, grade split, and likely production volume
  • Fruit per minute, cartons per minute, and historical run performance
  • Packing rates, capacity limits, availability, and setup changes
  • Pack type changes, tray setup, class rules, size limits, and equipment constraints

Planning outputs

What teams could review and use

The output was designed for controllers preparing the next production run, not for a static report after the fact.

  • Current and historical production views
  • Grower and run summaries
  • Expected fruit mix, rates, and volumes for upcoming runs
  • Recommended assignments across available packing options and equipment groups
  • Next-run views that made setup choices easier to review before production was committed

The build

The build focused on daily production and next-run setup

The application gave controllers and planners a clearer way to monitor, compare, and prepare production decisions while the day was still moving.

Production view

Current and historical runs became easier to review

Users could review live carton production, fruit volumes, run performance, and grower summaries from one planning interface.

That reduced the need to reconcile plans, production records, and separate reports before deciding what to do next.

Plans and priorities

Changing instructions were turned into working planning views

Production plans and packing priorities were converted into views that could be filtered, compared, and used during the operating day.

This mattered because target products, markets, and run priorities could change after the day had already started.

Fruit size and quality

Upcoming runs could be assessed before fruit reached the grader

The tool used historical carton and fruit data to estimate likely size distribution, class split, grade split, fruit per minute, cartons per minute, and production volume.

That gave the team a useful pre-run view before the grader could provide a live estimate, when setup changes were still easier to make.

Setup recommendation

Assignments reflected practical packing limits

The optimisation logic recommended how work should be assigned across available packing options and equipment groups.

It considered expected fruit mix, product targets, rates, capacity, availability, material changes, pack type changes, tray setup, class restrictions, size limits, and equipment constraints.

Business value

Production planning became easier to see and act on

The application helped teams use real production history before the next run began, not only after the grader had already produced a clearer estimate.

Data became planning support

Production records were no longer only stored and reported. They helped shape the next operating decision.

Priorities were easier to manage

Plans, PDF instructions, calls, and production changes were brought into clearer working views.

Pre-run estimates improved

Expected size distribution, quality outcome, rates, and volume could be reviewed before the line was committed.

Setup became more repeatable

Recommended assignments gave teams a consistent way to prepare equipment around the likely run mix.

Transferable value

Decision support software for packhouse planning

This case study shows Growth Mindset's ability to build practical software for production teams working with valuable information that is hard to use in the moment.

The value came from synthesising plans, priorities, live records, production history, and operating rules into clear guidance before decisions were locked in.

Discuss a project

Use production data before the next decision is made

A focused consultation can clarify the operating decision, the information involved, and the planning output that would make the next run easier to prepare.

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