The same planning leader, two very different strategies. This Perspective explores how ownership model, maturity, and constraints reshape what “good” planning looks like — and why copying peers is often a costly mistake.

Most large planning transformations fail long before technology is implemented. Drawing on practitioner experience, this Perspective explores why problem definition, operating model design, and ROI discipline determine success before vendor selection begins.

You may feel ready to invest in new supply chain technology, but progress often stalls much earlier — when data foundations are not aligned to real decision needs. Drawing on large-scale operational experience, Andy Devlin explains why decision-led data foundations matter, how to avoid “boiling the ocean,” and where to focus first to enable scalable change.

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.

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.

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.

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.

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.

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.

Supply chains designed for cost and efficiency are now operating in a permanently unstable world. In this conversation, Tim Richardson, CEO of Iter Consulting, explains why volatility is no longer cyclical, why confidence among supply-chain leaders has eroded, and why resilience must be designed into networks rather than bolted on after disruption hits. He argues that adaptability depends less on chasing new technology and more on clear thinking, sound foundations, and leadership capable of making decisions before the next crisis arrives.

James Moffatt of Baringa explains why supply chain transformation so often stalls before technology becomes the constraint. From fragile data foundations and misaligned outsourcing economics to short-term leadership incentives, he outlines the structural barriers that slow progress—and why the only viable route to automation, resilience and decarbonisation is a deliberate, step-by-step maturity journey rather than a single leap to an ideal future state.

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.