The Mantua Group

Simple Black and White Asset Management, Reliability Expertise, and Maintenance Execution Perfection.

Reliability

Quality Statistics, Reliability Statistics

Two Statistical Disciplines, Two Different Questions: How They Differ, How They Combine, and Why an Engineer Should Master Both

The two statistical traditions that reliability engineering inherits, the quality-statistics tradition descending from Shewhart and Deming and the reliability-statistics tradition descending from Weibull, Fisher, and the post-war life-testing community, are often spoken of as interchangeable extensions of a single discipline. They are not interchangeable. They were built for different questions, they make different assumptions about the data, they use different mathematics, and they support different decisions. An engineer comfortable in both moves fluently between the shop-floor control chart and the field-fleet survival analysis. An engineer who knows only one will, sooner or later, apply the wrong tradition to the wrong problem, and the analysis will produce results that are mathematically correct and engineering nonsense.

High Precision Planning, High Precision Maintenance

How High Precision Job Planning, Complete Kitting, and Tooling Discipline Multiply First Pass Quality and Maintenance Productivity Several Times Over

Maintenance organisations that consistently outperform their peers do not, in the main, owe their advantage to better technology, better instruments, or larger headcounts. They owe it to a small number of operational disciplines, applied without exception, over a sustained period. High Precision Planning and High Precision Maintenance, the practices that emerged from process-industry attempts to eliminate maintenance variability and that integrate naturally with the lean tradition of waste reduction, are among the most consequential of those disciplines. The numbers reported by sites that have implemented them in earnest are striking: first-pass-quality improvements of 2.5 times, productivity ratios of 3:1 or higher between planned and unplanned work, and equipment running for years rather than months between interventions. The numbers, however, are the consequence and not the cause. The cause is the discipline of precision planning, kitting, and tool readiness that produces them.

The Right Tool, at the Right Time, at the Right Depth

Elevating Reliability Engineering Decision-Making Through Disciplined Diagnosis, the Right Tools, Proportionate Method Selection, and Organizational Readiness

Reliability engineering offers no shortage of tools. The discipline has accumulated, across roughly a century of formal practice, an arsenal that includes Weibull life-data analysis, fault tree analysis, FMEA and FMECA, root cause analysis, control charts, modal analysis, design of experiments, accelerated life testing, condition monitoring, reliability-centred maintenance, and dozens of variants. The persistent failure mode in the field is not a shortage of tools but a mismatch between the tool and the problem: an over-engineered analysis applied to a question a five-minute hypothesis test would have settled, or a sophisticated technique deployed in an organisation that has not yet developed the foundational disciplines required to absorb its output.

Statistical Rigour in the Regulatory Arena: Weibull Analysis Certification and the Victorian AER REPEX Challenge

How independent Weibull MLE certification, aligned with the AER’s 2024 Asset Replacement Planning guidance, contributed to the evidentiary strength of regulatory proposals across Victorian electricity distribution network submissions.

Victorian electricity network service providers are engaged in one of the most consequential regulatory processes in recent memory. The Australian Energy Regulator (AER) has reviewed the combined revenue and capital expenditure proposals of five Victorian distributors, AusNet Services, Jemena, Citipower, Powercor, and United Energy, for the five-year regulatory control period commencing 2026.

The AER’s draft determination trimmed the distributors’ collective capex claims by approximately $3.7 billion, with the contested quantum approaching $2.9 billion once the revised proposals were filed. Central to the regulatory debate is the quality and defensibility of probabilistic asset replacement expenditure (REPEX) modelling, and, in particular, the statistical validity of the Weibull-based Probability of Failure (PoF) functions that underpin each distributor’s replacement case.

The Mantua Group (TMG) provided independent Weibull Analysis certification and expert advisory services to selected network service providers engaged in this regulatory process. Our work, conducted in alignment with the AER’s 2024 Asset Replacement Planning (ARP) Practice Note and the AER REPEX Model Framework, strengthened the statistical foundation of REPEX submissions across key asset classes, including distribution transformers, substation power transformers, and overhead conductors. This white paper describes the regulatory context, the methodology we applied, and the outcomes attributable to TMG’s involvement.

Weibull Statistical Analysis for Transmission Asset Reliability

Applying Weibull for Maximum Likelihood Estimation to Left-Truncated and Right-Censored Lifetime Data

This white paper presents a rigorous statistical methodology for analyzing transmission line asset reliability using Weibull distribution analysis. The approach specifically addresses the analytical challenges inherent in utility asset data: left-truncated observations from legacy system migrations and informatively right-censored data from inspection-driven replacement programs.

A study of approximately 15,000 transmission structures across an 11,000-kilometer network demonstrates the Weibull methodology’s practical application. By employing Maximum Likelihood Estimation (MLE) rather than ordinary least squares regression, the analysis produces unbiased parameter estimates even under complex censoring conditions that would render traditional approaches unreliable.

Key findings reveal that unique classification of assets exhibit similar but different reliability characteristics, with characteristic lives (η) of 60 and 75 years, respectively, and shape parameters (β) indicating wear-out failure modes in both populations.