DZ-70

DZ-70 audits SAP maintenance plans by analysing completed work orders vs. plans, spotting unrecorded tasks to prevent costly double maintenance and ensure up-to-date, efficient mining operations.

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Project Scope: Intelligent SAP Maintenance Plan Auditor

Overview

This project delivers a Python backend system that reviews SAP maintenance work orders and strategies for non-compliance. The focus is on identifying when the actual maintenance schedule for an asset is out of sync with the planned schedule. The system produces a single-page HTML report with clear recommendations, enabling SAP master data specialists, planners, and engineers to manually update SAP as needed.

Problem Statement

In industrial maintenance environments—especially mining—scheduled maintenance is often disrupted by unplanned or out-of-sequence work. If these deviations are not reflected in the SAP maintenance plan, they can lead to redundant work, missed tasks, or compliance issues. There is a need for a tool that highlights these discrepancies so that maintenance plans can be kept in sync with reality.

Solution Description

The system analyzes work orders and maintenance plans for a specified asset, focusing on:

  • Changeout Analysis: Utilizes a JSON-defined asset hierarchy to recognize when lower-level parts (e.g., impeller) are included in higher-level changeouts (e.g., entire pump assembly). Changeouts are identified by analyzing maintenance items, plans, and work order data.
  • Schedule Compliance: Non-compliance is defined as any case where maintenance is performed significantly earlier or later than the planned schedule (actual interval deviates from the planned interval by more than 10%).
  • Data Handling: Operates on provided SAP-like data dumps (test/fake data). Only complete data is analyzed and reported; incomplete records are excluded.
  • Reporting: Generates a single-page HTML report for the selected asset after each analysis run. The report highlights:
    • Asset and schedule overview
    • Planned vs. actual (or forecasted) intervals for each component
    • Deviations from plan, with clear, actionable recommendations
    • Only issues (non-compliance) are shown; no changes are made to SAP

Technical Requirements

  • Backend Only: Python-based backend, no frontend/UI.
  • Libraries/Frameworks: No restrictions on Python libraries.
  • Report Format: Single-page HTML, generated on demand per asset.
  • Data: Consumes SAP-like data dumps (CSV/JSON). No live SAP integration.
  • No Automated Remediation: The system only flags issues; it does not make changes to SAP.

Users and Workflow

  • Intended Users: SAP master data specialists, planners, and engineers.
  • User Workflow: Users run the analysis for a specific asset, review the HTML report, and manually update SAP maintenance plans as needed based on the recommendations.

Success Criteria

  • The system correctly identifies and reports all cases where maintenance intervals are out of sync with the plan (non-compliance).
  • The HTML report is clear, actionable, and easy for planners and engineers to use.
  • The system works reliably with the provided (fake) SAP data dumps.
  • All flagged issues are supported by complete data; incomplete records are excluded.

Example Output

The HTML report includes sections such as:

  • Asset and Schedule Type Overview
  • Analysis of Time Between Last Work and Next Work
  • Deviation from Plan e.g., “The forecasted hours between changeouts (25,728) is over four times the planned interval (6,048). This is a significant extension and not within 10% of the planned value.”

Out of Scope

  • No changes are made directly to SAP.
  • No live data integration or real-time processing.
  • No frontend/UI beyond the generated HTML report.

Summary: This project delivers a backend Python tool that analyzes maintenance data for schedule compliance, focusing on changeout analysis and interval deviations, and produces an actionable HTML report for SAP maintenance planners and engineers.

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