I rather fell into B2B networking and sales shortly after university, largely by chance. In my early roles, what surprised me was how often senior people would ask what I was seeing elsewhere — what patterns were repeating, which ideas kept resurfacing. Being asked those questions as a relatively junior person sharpened my interest in spotting common dynamics across very different industries and organisations.
Alongside that, I spent three years working as an economic journalist and project director, based in Madrid. That work involved researching and interviewing political and business leaders across a wide range of economic and institutional contexts. What stayed with me was not individual viewpoints, but how similar forces showed up again and again: incentives shaping behaviour, informal power overriding formal plans, and decisions being constrained by context as much as by intent.
Over time, that fed a broader interest in systems thinking and in how people make decisions inside complex environments. Long before it became professionally useful, I was reading widely on business, mental models, and judgement under uncertainty — not as theory for its own sake, but as a way of making sense of what I kept observing in practice.
Since 2011, my focus has been on applying those patterns where the rubber actually hits the road. There is no shortage of webinars, white papers, and conferences setting out future visions or celebrating success stories. They can help establish direction. What they rarely help with is deciding what to do next while still running the day job.
Turning vision into reality means finding signal in noise, separating fact from fiction, identifying priorities, building credible business cases and proofs of concept, keeping stakeholders aligned, and scaling what works without losing momentum. Across industries, I kept seeing capable teams struggle not through lack of intelligence or ambition, but because decisions hardened before the problem was properly framed.
BestPractice.Club was created to address that gap. The work centres on creating space for leaders to compare notes with peers facing similar challenges, ask candid “how did you actually do this?” questions, and access practical, impartial perspectives. The aim is tangible progress built through clearer thinking, better decisions, and sustained follow-through.
This page shows all the current person engages with BestPractice.Club across:
Insights from a discussion hosted by Andy Devlin on how to test assumptions about data readiness in supply chain AI initiatives, focusing on use-case sufficiency and sequencing.
Insights from a practitioner discussion hosted by Andy Devlin on how supply chain leaders can align digital accountability with dispersed data ownership when shaping AI and automation initiatives.
Many supply chains appear stable but rely on planning assumptions that no longer hold. This article explains why average-based planning struggles in volatile environments and why failure often goes unnoticed.
The trade-off between service, cost and cash is widely accepted in supply chains. This article challenges whether it is truly unavoidable, or a consequence of how planning decisions are framed.
Industrial manufacturing supply chains face long horizons, capital-intensive assets, and decisions that are hard to reverse. This article explores how leaders should prioritise once data reliability improves—focusing on commitment points, optionality, and scenarios that change capacity, sourcing, and customer promises. It’s for manufacturing leaders moving from analysis to sequencing: choosing the first moves that reduce regret and build resilience.
Once you accept that transformation is a decision problem, the next step is testing what must be true for value to appear. This article sets out practical enabling conditions that turn better data and systems into better decisions—process clarity, actionable agility, trusted data, and customer-facing productivity. It’s aimed at leaders who are pressure-testing readiness and assumptions before prioritising initiatives or engaging vendors.
Even when priorities are clear, organisations often stall because they don’t know what ‘ready’ looks like. This article sets out calm, practical signals of decision readiness: ownership, evidence thresholds, alignment on risk, and the ability to adapt when assumptions change. It also explains why post-event momentum often fades, and how to structure follow-up so it supports decisions without pushing premature sales conversations.
Many transformation programmes stall not because the technology is wrong, but because organisations never align on which decisions matter most and what must change to improve them. This perspective introduces the 'value void' and explains why common explanations (data, change management, readiness) miss the underlying constraint: shared decision clarity. It’s for supply chain and transformation leaders who are orienting around where to start before investing in tools or programmes.
A decision-led perspective on where to start in food and beverage supply chains once data improves, focusing on prioritisation, trade-offs, and decision leverage.
The post argues that supply chain forecasting often fails because companies focus on tools rather than decisions. Drawing on multinational IBP and S&OP experience, it highlights how over-complex AI-driven systems are frequently adopted before organisations are clear on what they actually need to decide, over what time horizons, and at what level of detail. Clean, reliable data must come before technology, and simpler, iterative forecasting approaches—often piloted or custom-built—can deliver faster, cheaper value than defaulting to large, “safe” vendors. The real objective is not perfect forecast accuracy, but greater execution agility in an increasingly volatile environment.