Product · Systems · Decision-Support

Product Manager for Transport Systems and Operational Software

Most operational problems are systems problems disguised as software problems.

I am a senior product leader specialising in transport management systems, optimisation, and complex operational software. I help organisations understand, improve, and scale systems that manage millions of transactions.

20+ years' experience Enterprise transport systems MBA · MSc Computer Science · FCILT

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The problem I work on

Make the system visible before changing the software.

Most organisations do not understand the system they operate in. They rely on intuition, dashboards, inherited assumptions, and linear planning in environments that are anything but linear. Product decisions are then made around visible symptoms rather than the deeper structures driving behaviour. The result is predictable: misaligned incentives, hidden constraints, unintended consequences, and features that solve local problems while creating wider system friction.

I bring structure to ambiguous product problems, reveal the drivers of system behaviour, and help teams design products, models, and decision-support tools that support clearer decisions.

Every system is perfectly designed to get the results it gets

Deming

Turning complexity into usable decisions

The approach is practical: make the system clearer, test the assumptions inside it, and turn the findings into tools people can use.

Governance

Make risk traceable

A structured NHS IT risk tool connects hazards, causes, controls, and review evidence instead of leaving governance across disconnected documents.

Forecasting

Model delivery uncertainty

A simulation-first planning platform exposes queues, bottlenecks, dependencies, and capacity constraints before they become missed commitments.

Product discovery

Improve the specification

A controlled AI assistant helps teams challenge vague requirements, surface missing information, and produce clearer specifications without owning the product decision.

How I Work: The Clarity Cycle

A practical three-stage approach for making sense of complexity, reducing ambiguity, and turning messy operational problems into software that supports decisions that account for complexity.

Understand the System

Before designing a solution, I work to understand how the system actually behaves. That means mapping the domain, surfacing constraints, identifying dependencies, and capturing the mental models of the people who operate inside the system every day.

The aim is to move beyond assumptions and build a shared understanding of the system’s structure, behaviours, trade-offs, and failure points.

Model the Complexity

Once the system is understood, I turn complexity into something visible and testable. That might mean a conceptual model, process model, simulation, decision model, data model, or prototype.

Good models do not remove complexity. They make it easier to reason about. They help teams explore scenarios, compare options, expose uncertainty, and understand the likely consequences of different decisions.

Design for Decision-Making

The final stage is designing software that helps people think and act with greater confidence. The goal is not just to digitise an existing process, but to create tools that support judgement, prioritisation, planning, and operational control.

For complex domains, good software should not hide reality. It should make the right information visible at the right time, so experts can take appropriate action under pressure.

Causal diagram showing how system structure drives behaviour — Structure drives behaviour: mapping the causal loops that produce system outcomes.

Selected work

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Service Quality and Student Loyalty

Research into how perceived service quality influences student loyalty, satisfaction, and retention in higher education. The project uses the SERVQUAL model, which assesses service quality across five dimensions: reliability, assurance, tangibles, empathy,and responsiveness. It explores how gaps between student expectations and actual experience can affect trust, engagement, recommendation behaviour, and the likelihood of students continuing with an institution

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Genetic Algorithms for Design of Experiments

15 January 2026

Richard Faint on probabilistic forecasting for product management: why fixed deadlines mislead and how to forecast delivery with ranges and confidence.

Modelling

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Need clarity in a complex operational system?

Explore my approach to product thinking, modelling, and decision-support

Let's untangle your system