Automated Spare Parts Forecasting Using Historical Failure Data

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Solution Overview

Problem

Supply chain managers face challenges in accurately predicting the type and quantity of spare parts needed for new products without historical failure data, leading to operational delays and increased costs due to either insufficient or excessive inventory levels.

Innovation Solution

An automated spare parts forecasting system that integrates production, warranty, internal quality, and product information to analyze new products and generate forecasts based on comparable parts from existing models, using generic codes to determine failure rates and optimize inventory levels.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If spare parts inventory is increased to prevent operational delays, then reliability is improved, but cost increases

Engineering Contradiction:

Improveoperational continuityVSAvoidinventory cost

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system performs preliminary forecasting of spare parts demand by analyzing historical failure data, product usage patterns, and reliability information before operational delays occur. This allows proactive inventory planning and procurement, ensuring critical parts are available when needed while avoiding excessive inventory accumulation through data-driven predictions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts inventory parameters such as safety stock levels, reorder points, and lead times based on analyzed failure rates, product criticality, and demand variability. By changing these parameters according to actual data patterns, the system optimizes the balance between maintaining operational reliability and controlling inventory costs.

Inventive Principle:

Principle #35Parameter changes

2Quantity of substance

If spare parts inventory is decreased to reduce costs, then cost is reduced, but operational delays increase

Engineering Contradiction:

Improveinventory costVSAvoidoperational continuity

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual inventory estimation and guesswork with automated data analysis and forecasting algorithms. By substituting mechanical judgment with systematic analysis of failure rates, product criticality, and demand patterns, the system achieves more accurate inventory optimization that prevents both overstocking and stockouts.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If manual forecasting methods are used for new products, then adaptability is maintained, but measurement precision deteriorates

Engineering Contradiction:

Improveflexibility in forecastingVSAvoidforecast accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system replaces manual forecasting methods with automated data analysis and statistical modeling. By substituting human judgment with systematic analysis of historical failure data, product specifications, and usage patterns, the system achieves more precise and consistent forecasts while maintaining adaptability through configurable parameters and multiple analysis methods.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces data-driven intermediaries such as failure rate models, reliability algorithms, and forecasting algorithms that mediate between raw data and forecast results. These intermediaries process and interpret data objectively, providing precise forecasts for new products while allowing flexibility in selecting and configuring different analysis approaches.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS9971991B2Automated, new spare parts forecasting and demand planning system

Publication Date: 2018.05.15 ACCENTURE GLOBAL SERVICES LTD

AI summary

A system may receive an identifier for a new model product and identify a stored parts list associated with the identifier. The system may determine a code for one or more parts included on the parts list and may compare the code for a part, of the one or more parts included on the parts list, with codes for parts included in old model products. The system may determine that the part included on the parts list is comparable to one of the parts included in the old model products if the code for the part included on the parts list matches the code for the one of the parts included in the old model products. The system may use data associated with the one of the parts included in the old model products to generate a new spare parts forecast.