In many organisations, traditional planning methods focus on either keeping existing equipment working or renewing old unreliable equipment. This often involves top down budgeting, which is usually based on what was spent in the previous financial year with an added efficiency challenge to spend a little less each time round, writes Andy Hunt, asset management consultant.
In stable plants and systems, this may be all well and good, but if this continues on a like-for-like basis each year then, in the end, equipment will become less reliable, and operations and maintenance activities more reactive – with the inevitable result that customer service will suffer. Often it is only at this point – and when the need to spend more money on asset renewals is blindingly obvious – that the budget gets a much-needed cash injection.
It is in the international standard for asset management systems ISO 55000 that preventing this type of problem is specifically addressed, by requiring organisations to have systems in place that link the top down and bottom up requirements of the equipment managed. By top down it refers to the stakeholder requirements of the organisation: typically government, regulators, customers and shareholders.
These set the service performance standards, funding and revenue against which the organisation must manage its equipment. It is the assigned planner’s role to take a bottom up view and determine the investment requirements for the equipment based on its condition, performance, risks and future demands to ensure that the equipment is capable of delivering those top down stakeholder requirements.
So what tools do planners have at their disposal to help determine what is required, and where and when in terms of operational, maintenance and asset renewal interventions?
At the simplest end of the scale lie the basic condition assessment tools, usually with a combination of factors taken into account to provide an overall score. Typically, 1-5 with 1 being equivalent to an ‘as new condition’, through to 5, which represents ‘in need of urgent replacement’. The main advantages of this forecasting approach is that it can usually be developed in house and is easily understood by everyone.
On the downside, entry-level programmes are often resource intensive as they require someone to physically inspect the equipment. As a result, they can also be inconsistently applied. For example, someone who is not familiar with the equipment’s operating history may assess an old item of equipment to be in poor condition when it is in fact perfectly operational. It may even be performing better than a newer, apparently sound item of equipment located next to it to which the assessor mistakenly assigns a ‘good condition’ grade.
These types of approaches also only provide a snapshot in time and are not usually good enough to forecast what future expenditure requirements might be over longer planning horizons.
Next generation modelling
The next level of sophistication is provided by reliability models, which describe the probability of failure against age or usage. These are usually mathematical models based on some form of parametric distribution system with the Weibull distribution: a continuous probability distribution system named after Waloddi Weibull, being the most popular due to its ability to fit a wide variety of failure patterns.
These types of models are most useful when the modelled failure modes result in defined outcomes such as renewal or refurbishment of the equipment. These probability models must also be applied to groups of equipment of the same class and type, operating in the same conditions. For example, a low voltage air circuit breaker in an urban environment may have a failure pattern different from the same equipment operating in a rural environment due to a combination of different load operations and environmental conditions.
Similarly, there will be differences between externally and internally housed equipment. Ideally, these models will employ information derived from real failure and renewal data that is updated periodically as more data becomes available.
The main advantages of these models is that with some training in reliability theory and mathematics plus some guidance on data cleansing and preparation these models can be produced in house at relatively low cost. So long as there is a reasonable record of the ages of the equipment it is also relatively easy to apply these models to the complete equipment inventory to forecast when equipment renewals might be required.
On the downside, this type of approach is not suitable for complex assets with multiple failure modes and multiple intervention options from overhaul, refurbishment, upgrade and renewal.
Neither do they create a business case or address important issues like whole life cost. They are best utilised where the equipment failure results in a fixed like-for-like
outcome, such as overhaul or renewal, which in practice applies to a lot of electricity distribution equipment.
Catering for complex systems
Where more complex decisions must be taken such as where the planning decision is about a major investment or where it could have system wide implications possibly driven by a combination of growth in demand as well as equipment condition then the various investment appraisal techniques associated with life cycle costing or whole life costing must be undertaken.
These generally compare the long-term cash flows associated with the different possible solution options available to the planner with the aim of selecting the option that meets the planning objective at the lowest overall cost to the organisation. At the most sophisticated level these whole life cost models combine financial approaches such as cash discounting with the reliability approaches of the type discussed previously.
Such tools are usually spreadsheet based and many organisations develop their own. Again with appropriate training these tools can be developed in house and very sophisticated investment decisions can be analysed using this approach. Indeed, many organisations mandate the use of such techniques for all major capital investments. However, their downside is that they are time consuming to populate with data, can be very difficult to audit and validate, and often only a few people in an organisation really know how they work.
Top market approach
At the top end of planning decision support sophistication are the costrisk optimisation techniques such as those developed by the Strategic Assets: Lifecycle Value Optimisation (SALVO) project in the UK. These overcome the disadvantages of the spreadsheet based whole life costing tools discussed above by providing a highly flexible, but structured, approach to identify the information the planner requires for the different decision types. The technique then takes life cycle costing a step further by optimising the costs of different intervention timings, showing and providing the business case for not just what intervention to make but also when to make it.
Clearly these techniques are not easily developed in house so the SALVO project commissioned a set of software tools to do the cost risk optimisation analysis. With training in their use as well as facilitation skills to apply them, they can also improve planning efficiency. As with the whole life costing tools and investment appraisal spreadsheets discussed above, they are best applied to more complex decisions where there are multiple interacting failure modes and different risks, which all contribute to defining the correct planning decision.
So there we have it: a brief introduction to investment planning tools from the simple to the highly sophisticated. Of course, understanding what planning decision support tools are available to the planner to help them in their job is only the first of a series of steps an organisation needs to take in developing its planning capability.
Getting the right training on how to use, develop and apply the tools are the next steps and many organisations find it doesn’t stop there. The use of such tools to support the planning and decision process opens up ever more possibilities to improve the way organisations manage the equipment under their responsibility.