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Challenge

Build a Workstation Bottleneck Triage and Buffer Rebalancing Planner

Evaluate multi-stage cycle time variances from simulated Sparkplug B device streams. Generate dynamic pallet buffer rebalancing proposals to alleviate manufacturing bottlenecks.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Evaluate multi-stage cycle time variances from simulated Sparkplug B device streams. Generate dynamic pallet buffer rebalancing proposals to alleviate manufacturing bottlenecks.

Industrial Operations Planning Lead

Consume simulated Sparkplug B DDATA metric updates capturing station execution durations.

Compute moving average cycle times to detect process drift and buffer accumulation.

Synthesize buffer rebalancing proposals to divert in-progress pallets to auxiliary cells.

Generate a supervisor sign-off sheet with projected throughput recovery metrics.

Line balance histogram with drag-and-drop buffer allocation overrides and sign-off panel.

Create normalized synthetic JSON files of Sparkplug B DDATA payloads representing multi-station takt times. Store in local sandbox directory. Starter examples below are synthetic. They are not downloaded records.

Eclipse Sparkplug Sparkplug B DDATA metric records and property metadata for process duration tracking described in Source 2.

ollama, pydantic, fastapi. Ollama runs efficient local models on edge plant hardware without sending operational data to cloud infrastructure. Local quantized open model (e.g. 8B parameter class) capable of tabular numerical comparison.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Detects stations operating at greater than 120% of target takt time.

Proposes valid pallet buffer diversions without overfilling target auxiliary buffers.

Calculates expected hourly production recovery under proposed dispatch routes.

```json [ { "input": { "line_takt_sec": 60, "stations": [ { "station_id": "ST_PRESS", "current_cycle_sec": 85, "buffer_count": 8, "buffer_max": 10 }, { "station_id": "ST_PRESS_AUX", "current_cycle_sec": 55, "buffer_count": 2, "buffer_max": 10 } ], "inbound_pallets": [ "P_101", "P_102", "P_103" ] }, "expected_output": { "bottleneck_identified": "ST_PRESS", "cycle_overrun_pct": 41.7, "diversion_proposals": [ { "pallet_id": "P_101", "divert_to": "ST_PRESS_AUX" }, { "pallet_id": "P_102", "divert_to": "ST_PRESS_AUX" } ], "projected_takt_recovery_sec": 62 } }, { "input": { "line_takt_sec": 60, "stations": [ { "station_id": "ST_PRESS", "current_cycle_sec": 58, "buffer_count": 3, "buffer_max": 10 } ], "inbound_pallets": [ "P_104" ] }, "expected_output": { "bottleneck_identified": "NONE", "cycle_overrun_pct": 0, "diversion_proposals": [], "projected_takt_recovery_sec": 58 } } ] ```

Read-only queue analysis. Output plans require industrial engineer approval before floor execution. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Read-only queue analysis. Output plans require industrial engineer approval before floor execution. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Detects stations operating at greater than 120% of target takt time. Proposes valid pallet buffer diversions without overfilling target auxiliary buffers. Calculates expected hourly production recovery under proposed dispatch routes.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

Complete workflow

Consume simulated Sparkplug B DDATA metric updates capturing station execution durations. Compute moving average cycle times to detect process drift and buffer accumulation. Synthesize buffer rebalancing proposals to divert in-progress pallets to auxiliary cells. Generate a supervisor sign-off sheet with projected throughput recovery metrics.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

Correctness and evidence

Detects stations operating at greater than 120% of target takt time. Proposes valid pallet buffer diversions without overfilling target auxiliary buffers. Calculates expected hourly production recovery under proposed dispatch routes.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

Line balance histogram with drag-and-drop buffer allocation overrides and sign-off panel.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Read-only queue analysis. Output plans require industrial engineer approval before floor execution.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Consume simulated Sparkplug B DDATA metric updates capturing station execution durations.

  • Compute moving average cycle times to detect process drift and buffer accumulation.

  • Synthesize buffer rebalancing proposals to divert in-progress pallets to auxiliary cells.

  • Generate a supervisor sign-off sheet with projected throughput recovery metrics.

Resources and assets

Reference links and supporting material

Dataset notes

Create normalized synthetic JSON files of Sparkplug B DDATA payloads representing multi-station takt times. Store in local sandbox directory. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

Ollama

Local LLM runtime for offline inference.

Evaluated output

No output checks are configured.

Evaluation checks
  • Consume simulated Sparkplug B DDATA metric updates capturing station execution durations. Compute moving av...
  • Detects stations operating at greater than 120% of target takt time. Proposes valid pallet buffer diversion...
  • Line balance histogram with drag-and-drop buffer allocation overrides and sign-off panel.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 1 configured tool

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Policy Serving
  • Ollama · Optional

Evaluation contract

  • Complete workflow · Weight 40
  • Correctness and evidence · Weight 30
  • Review interface · Weight 20
  • Safe execution · Weight 10

Reference cases

  • 2 public reference cases

Workflow tasks

No workflow tasks are configured.

Recipe freeze marker

No recipe freeze time is recorded.

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