Benchmarking and optimising maintenance for turnarounds

A methodology can be used to benchmark scope and provide early and reliable forecasts of labour hours and costs during a turnaround

Asset Performance Networks

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Article Summary

Turnarounds are critical to the bottom line of refinery 
and chemical companies. Turnarounds are sometimes referred to as shutdowns or outages and are the periodic planned shutdown of a facility to perform maintenance work and install new capital projects. These can be major events involving more than a million labour hours and impact a company’s profitability through the cost of the event, the lost revenue due to the plant being offline, and potential harm to plant reliability if the turnaround is performed poorly. Turnarounds also entail significant safety and environmental risks.

The refining industry has struggled to execute large, highly complex turnarounds on budget and on schedule. Our data indicate that more than two-­thirds of turnarounds exceed their planned cost and schedule by 10% or have a trip after startup. Forty percent of turnarounds experience a cost over-run or schedule delay of more than 30%. The causes of these over-runs are numerous, but generally fall into the following categories:
• Poor scope control prior to the shutdown as significant work is added after the budget is developed
• High rates of added scope or ‘discovery’ during the shutdown
• Poor planning and preparation prior to the shutdown
• Unrealistic cost and schedule targets: planned turnaround duration and cost targets are often established by the business far in advance of the turnaround and not related to the scope that has to be actually implemented. Turnaround teams have no choice but to live with these targets realising that there is little chance of success.

The turnaround work scope is the most critical item related to performance outcomes, as it is the foundation for cost, schedule, and plant reliability. Minimising the amount of scope and the level of scope growth during the turnaround execution window is the primary driver of competitiveness. Yet, despite the importance of turnaround scope, historically there has been a critical hole in our collective toolset: how do we effectively benchmark and evaluate turnaround scope? There has not been an objective, quantifiable measure of scope that can help us answer this question — until now.

In this article, we will discuss the challenges to scope control, describe a methodology for benchmarking scope, and illustrate how this methodology can be used to benchmark scope and provide early and reliable forecasts of labour hours and costs.
Scope optimisation
Optimising scope selection helps companies reduce spending by minimising the scope, keeping scope at manageable levels, and enabling more effective turnaround execution by eliminating the typical industry scope ‘churn’ (or recycle). In order to realise these benefits and ensure more effective use of maintenance funds, industry has developed tools to optimise scope selection and to minimise turnaround scope. Risk based scope reviews (RBSR), for example, provide a systematic approach to economically justifying or challenging discretionary scope items.

While these tools have proven effective – teams can realise substantial savings by reducing turnaround scope up to 30% (or more) through the application of an RBSR – industry as a whole struggles to achieve scope freeze and to maintain scope discipline. Based on the AP-­Networks Turnaround Database – an industry database of over 1350 turnaround observations – industry average scope growth from scope freeze to execution is 19%. By contrast, top quartile performers experience growth of only 7% (see Figure 1). This gap tells us that there is more we can be doing as an industry to optimise turnaround scope.

While an RBSR provides significant benefits, taking the subjectivity out of scope selection still presents a challenge. Applying RBSR and other similar scope challenge tools requires reliability and integrity data, as well as historical data on scope and outcomes, to forecast equipment and asset conditions. These tools also require in-­depth analysis, and are time consuming and labour intensive to apply due to the need for multiple stakeholders to contribute to the analysis. While the effort in applying these tools is rewarded, the interpretation of the data is still somewhat subjective.

The Turnaround Scope Index (TSI) complements the RBSR and provides a quantitative means to validate scope selection, thereby removing the subjectivity that has historically plagued scope challenge methodologies. The TSI empirically quantifies the amount of turnaround scope, and enables the benchmarking of scope relative to comparable industry events. It allows industry to look at scope objectively and to compare scope against industry norms. For example, the TSI allows a team to understand if they have more or less mechanical scope (in terms of pieces of equipment) than their peers, and if they have established reasonably competitive estimates for the labour hours necessary to execute this mechanical scope.

Developing the Turnaround Scope Index model
The AP-­Networks Turnaround Database, referenced earlier, comprises more than 1350 turnaround observations covering turnarounds from the onshore and offshore upstream, gas processing, refining, chemicals, and power generation industries. When considering the complexity of the turnarounds represented in the database in terms of the number of labour hours, the amount of capital executed during the event, and the interval between turnarounds, over 60% of the turnarounds are high to mega complexity events (measured on a scale of low, medium, high, and mega). Of course, many of these larger events span multiple units.

The database has information on inherent turnaround characteristics, turnaround planning and preparation practices, and turnaround outcomes. Inherent turnaround characteristics described in the database are unit type, unit capacity, time between turnarounds, and equipment count by type opened, inspected, and repaired. The database also includes information on estimated and actual labour hours, costs, and schedule by unit and 
by overall turnaround event. AP-Networks used this database to develop the Scope Index Model. This model is applicable to typical refinery units, as well as many chemical processes.

As Figure 3 shows, there are two components of benchmarking turnaround scope: the Scope Index Model and the Direct Field Labour Hour (DFL) Model.

The Scope Index Model relates asset characteristics – unit types, unit capacities, and turnaround interval – to the maintenance scope in terms of number of pieces of equipment by type opened, inspected, and repaired. The DFL Model relates the maintenance scope to the maintenance labour hours. Together, these models allow us to benchmark:
• Scope relative to turnarounds for comparable assets
• Labour hours relative to turnarounds with similar amounts of scope
• Labour hours relative to turnarounds for comparable assets.

These empirical measures allow companies to understand how much mechanical scope they have relative to their peers, how their estimated labour hours compare to those for similar assets, and if their estimated labour hours are reasonable for the amount of scope.

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