As at September 2022…
Enhancing Interpretive Models of Operational Efficiency using Measurements Derived from Big Data to develop Collaborative Multi-Modal Customer-Centric Key Performance Indicators for Transport
This doctorate is an extension of the author’s research into empirical measurement of public transport operations and passenger response. This research programme aims to assist all service partners in collaboratively implementing a Continuous Improvement Cycle (CIC) to deliver increased Value-for-Money (VFM) to the public, through using economy, efficiency, efficacy, & effectiveness to deliver transit that passengers perceive as Dependable, Timely, & Affordable.
That assistance will be provided through the development of Collaborative Multi-Modal Customer‑Centric Key Performance Indicators (KPIs) — which minimise operational behavioural distortions — to assist service partners in ❶ transitioning from Paradigm-Driven to Empirically-Driven approaches (from conceptually-driven to data-driven), in ❷ transforming from Product‑Centric to Customer-Centric approaches, and in ❸ planning Organisational Change.
These improved KPIs should support collaborative delivery of a unified continuous operational improvement cycle across all modes and service partners (including agencies, transit operators, and road operators); as distinct from current segregated and single-mode Product-Centric KPIs. These improved KPIs could use enhanced Big Data driven measure of the performance of delivered services, in such a way as to prevent behavioural distortions inherent in current KPIs.
These KPIs should represent the levers that service partners can collaboratively use to improve the customer experiences. These KPIs need to consider and reflect the actual responsibilities of the service partners, because change can only be implemented by those who are authorised and capable of taking the identified actions to improve the customer-centricity of the operations. These KPIs need to be usable by the service authorisers for the parliament to support their deployment. Staff at the service partners want to drive sustainable change. However, they need to know realistic points of leverage, in order to deliver collaborative changes that would be efficacious in improving the alignment of the combined operations with the customers’ needs. Improving values-alignment would address the gap in perspectives identified in the Service Quality Loop (SQL) and thus improve effectiveness.
As instruments, these KPIs will need enhanced Interpretive Models of Operational Efficiency using Measurements Derived from Big Data (MDfBD) focussing on Measuring, Stabilising and Reducing operational factors previously identified, such as Running Time and Running Time Variability.
These measurements should represent the transport service’s contribution to satisficing their customers’ desired objectives — that is customer-centricity. Businesses often claim to be customer-centric but merely present a product-centric operation behind a faux-customer-centric facade. For example, an emphasis on questioning customers about the transport they want — especially in the SQL — can only drive a tailored personal transport approach and is not viable for a city-wide utility-scale public transport system. Thus, there is a need to find the right questions to develop a new interpretive model of customer needs from the perspective of a utility.