Customer-centric asset management: an approach and applying it in practice
Bibliographic record
Abstract
Looking across the regulatory regimes and changes that have occurred across gas, electricity and water sectors, there is a clear shift in regulators' focus and ability to articulate the importance of the customer; and in a way that meaningfully will impact how asset management and operations function and ultimately their bottom line. Utilities are ever more connected to their customer through formal channels, but social media informal channels are increasingly playing a role. Compared across all sectors, utility customer satisfaction is one of the lowest, alongside retail banking and local government service provision. Place this data point alongside the clear intent of regulators and there is, for most utility organisations, a need to adopt a customer-focused attitude to enterprise asset management. This paper will argue that although there have been significant investments by asset and operations functions in improving how they operate (with significant investments in process, systems and trying various sourcing strategies), many are only now starting to seriously engage in the debate about customer needs and impacts from an asset and operational perspective. In discussions with clients, and others in the industry supply chain, it is clear that there is a need to shift the debate to investment planning and operational delivery which truly delivers an acceptable customer experience and is built on customer willingness to pay. This paper will explain, based on customer service improvement programmes in blue collar environments (across utilities and other comparable sectors) an approach taken by utility companies to determine and then enhance their customers' experience of how they invest, maintain and operate assets. This experience covers understanding customer journeys, how this impacts operational processes and systems, challenges the organisations' culture and behaviours and how ultimately it may need to reflect asset investment planning and investment priorities. (3 pages)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.027 | 0.022 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".