Excess inventory can signal a lack of trust in forecasts, suppliers, and replenishment. Discover why teams stockpile and what leaders should ask before cutting.

Revenue growing but profits declining? Discover the underlying causes of this common CEO challenge and why the real problem may not be what you think.

Most supply chain programs do not fail because the software is weak, the process maps are incomplete, or the team lacks effort. They fail because the business never truly agrees on what should change, who gets to decide, and what tradeoffs it is willing to accept. A supply chain can look technical from the outside. Forecasts, inventory targets, network models, transportation lanes, planning systems, supplier lead times, and service levels all sound like operational details. Y

You know the symptoms: Inventory is too high. Working capital is tied up. Customer service is inconsistent. Margins are under pressure. So you decide to hire. But there is a catch: A Head of Supply Chain cannot repair decisions the executive team keeps making in isolation. Before hiring, ask the harder question: Are we prepared to change how our executive team makes decisions, or are we expecting one person to fix the consequences of decisions made in silos? That question mat

Improve forecast accuracy with AI by reducing demand planning latency, automating manual workflows, and turning real-time signals into faster supply chain decisions.

How to gain market share when competitors continue to discount price

How to automate demand planning the smart way

How do I automate demand planning without losing control?

A market analysis that sits still can make a moving market look safe, dangerous, or attractive for the wrong reasons. That is the core problem with Porter’s 5 Forces. It can help explain why an industry looks profitable at a given moment. It can show where pressure comes from, such as competitors, substitutes, suppliers, buyers, and new entrants. But for many companies, that is no longer enough. The deeper issue is not that the model is “wrong.” The issue is that it often sto

A tariff changes the cost of a key material overnight. A factory fire stops production with no clear restart date. A major customer leaves just as payroll comes due. No spreadsheet, policy manual, or leadership offsite fully prepares a business for that kind of week. These are wicked problems. They are messy, urgent, and full of tradeoffs. There may be no clean owner, no perfect data, and no single right answer. Solving them requires more than technical skill. It takes judgme

A process can be followed perfectly and still produce the wrong result. That is one of the hardest lessons for organizations to learn, especially after years of building controls, checklists, approval flows, and standard operating procedures. Process discipline matters. It gives teams consistency. It reduces avoidable errors. It keeps people from reinventing the work every time they touch it. But process discipline has a blind spot. It assumes the conditions around the proces

A forecast can look perfect right up until a supplier delay, a viral product trend, a price change, or a sudden shift in consumer confidence makes it wrong. For years, demand planning teams have tried to close that gap with spreadsheets, historical sales reports, and careful judgment. Those methods still matter, but they struggle when demand changes faster than the planning cycle. AI changes the job of forecasting by pulling more signals into the forecast, finding patterns ac

AI can help a supply chain move faster. That is true! But there are limitations! If the process is fragmented, the data is late, and teams do not trust the signals in front of them, AI may only accelerate the confusion. It can make bad handoffs faster. It can surface more exceptions than people can use. It can automate work that should have been redesigned first. I recently read IBM’s The New Supply Chain Equation, and one point stayed with me: the supply chain operating mode