A four-step plan for data excellence
In the follow up to his previous exploration of the so-called “asset swamp”, John Woodhouse outlines a four-point checklist for switching to a culture based on demand-driven data specification
Using the SALVO process, we can identify four methods that asset-heavy organisations should employ in order to reach a goal of optimal data identification and usage.
Step 1 “Identify problems and improvement opportunities” spells out the business impact criteria for which assets need what attention in the first place, and the desirable evidence to support this identification.
This includes definition of asset health indices, that is to say the relevant mix of performance and condition features and criticality measures.
Step 2 is the drill-down into the identified problems or improvement opportunities to ask why they are problems, essentially a root cause analysis.
This often reveals a mismatch between expectations and realities in the use of data to demonstrate patterns and correlations.
The noise in the system, the inherent limitations of data samples, and the volatile business environments in which data is collected – including consistency of collection methods – mean that pattern-finding or “non-randomness” is rarely provable, irrespective of the clever data analytics that are applied.
Except in very rare cases, the available data will normally be constrained and “censored” in various directions, so the collectable evidence needs to be used with great care.
This approach necessitates a healthy dose of realism and tacit knowledge from asset design, operations and maintenance experts.
Step 3 of SALVO covers the selection of potential actions or interventions, and these can be a far wider range of options than the technical tasks normally considered – such as inspection, maintenance or renewal.
SALVO has identified 42 practical options that might be applicable to solve asset management problems across the length and breadth of the business sphere.
Step 4 then covers the business value-for-money evaluation of the potential solutions, requiring assumptions and, if obtainable, evidence of costs and short-term and long-term consequences.
This step combines observable facts – mostly helpful in quantifying the “do nothing” implications – with external data needs and the tacit knowledge of the experts in forecasting and estimating the degrees of improvement that might be achievable.
This is a stage where reliance on collectable hard data is fairly limited, but at least we can be clear about the questions that need to be asked – that is to say, what data is desirable to support the decisions.
SALVO has mapped the information needs for all 42 common decision and intervention types – the information required to determine if the interventions are worthwhile and, if so, when.
For example, there are 13 specific questions or data elements that must be considered in deciding whether to buy a critical spare part and how many to hold.
These decision-specific checklists help to focus on the relevant and useful information within the background swamp of confusing evidence.
They, and a “what if?” approach within the evaluation process, reveal the role of the data to support decisions.
They demonstrate the business value of collecting the right stuff, by quantifying the “cost of uncertainty” when forced to rely on range estimates or assumptions.