Here’s how a decision-centric model can fix a broken supply chain, according to Allan Dow, EVP/General Manager of Aptean Supply Chain.
When Steve Jobs stepped onto the stage at the Macworld Expo in August 1997, he wasn’t introducing a groundbreaking new product (he would announce the iPhone at the same event a decade later).
At the time, software was rigid. Systems were siloed. Data arrived late. People worked within the confines of the technology, content to be limited by its many shortcomings. Jobs was casting a vision for his company and the customers who would refuse to settle for the status quo.
It was a rejection of the way people were being forced to work with technology, and a promise and an invitation to think different and change the world. The modern supply chain took shape at the same time, and its software solutions were built around batch planning, static forecasts, and point-in-time data.
These weren’t the ideal solutions. It was simply what the technology could support. For a long time, it worked. Disruptions existed, but they were exceptions, not norms.
Today, global supply chains are more expansive than ever before, operating with more velocity and precision but vulnerable to disruption. As one survey of 1,000 senior supply chain leaders concludes, ‘Supply chain disruptions are no longer rare — they’re the new normal.’
Why Two Decades of Technology Spending Left Supply Chains Brittle
Two decades and $200 billion in supply chain management technologies have left many supply chains reactive and convoluted. This staggering investment has not delivered the expected resilience; global disruptions now cost the average company 8% of its annual revenue. McKinsey & Company estimates that extended supply chain disruptions lasting more than a month now occur every 3.7 years and can cost a business up to 45% of a year’s profit over a decade.
Despite this significant spending, most organizations are still operating on their heels, trapped in a cycle of:
● Making decisions based on fixed time horizons that ignore the fluidity of global trade
● Relying on data that is outdated by the time it reaches the dashboard
● Operating in silos, where teams are neither connected nor informed
● Reacting to crises rather than adapting to trends.
First-wave supply chain management solutions were designed to record and report, not to decide. They rely on fixed time horizons and historical data to inform the future. When disruption, uncertainty, and change are the norm, it’s clear that we need to think differently about our supply chain software.
Transitioning from Reactive Networks to Adaptive Decision Engines
Decision-making itself has become a first-class enterprise capability. It’s why a decision-centric approach is the defining framework of successful, agile enterprises.
Yes, it involves a new technology schema. Yes, it puts data at the centre of everything. It’s also more than that. It’s a new operating model where decisions are explicit, intelligence is continuous and adaptive, execution is connected, and humans and technology collaborate at scale.
Decision-centric organizations are not just focused on data collection, but also on applying this information to drive specific business outcomes. For supply chain entities, this means using available intelligence and analytical tools to become more forward-looking and responsive to market shifts before they become crises. These initiatives are undoubtedly powered by artificial intelligence (AI).
Making Intelligence Operational
AI is ubiquitous in the supply chain sector. A quick Google search reveals countless think pieces on the subject, and executives are eager to talk about how they are deploying the latest to achieve the elusive promise of total visibility.
What it actually does for them is a different story. AI-powered, decision-centric supply chains are defined by three pillars that produce real results.
1: Centralizing Data
Best-in-class supply chain entities are centralizing their data into a single, unified platform. AI-powered supply chain optimization doesn’t work if data silos and disparate teams are running the show. Integrate and unify data so AI models can train on a complete, vertical, end-to-end picture of the operation, rather than on conflicting or incomplete datasets.
2: Intelligent Responses
Decision-centric companies turn insights into action. They rely on clean, centralized information to identify problem root causes and respond in real time. Even better, generative AI solutions make information searchable, allowing decision-makers to query data to derive actionable insights, and machine learning helps teams arrive at complex, data-driven decisions.
3: Predictive Sales and Operations Planning
AI-driven demand sensing turns real-time data from the external world into insights that anticipate and understand subtle shifts in customer behaviour, market trends, and potential disruptions before they impact the bottom line.
Rather than relying on last year’s information, supply chain entities can use this technology to adapt to real-time, even unprecedented, circumstances, responding with robust solutions that clarify uncertainty and create opportunities from disruption.
For instance, 76% of fashion executives believe tariffs and trade volatility will be the defining issues of 2026, requiring this heightened level of agility. Generative AI-powered digital twins can help retailers understand the financial or operational implications of any given decision or scenario.
This AI-first approach connects planning, execution, and analytics in real time to deliver speed, resilience, and measurable business impact. When implemented effectively, it changes how supply chains work, converting reactive networks into adaptive decision engines.
A New Era of Strategic Advantage
When Steve Jobs challenged Apple and its audience to ‘think different’ he was redefining the relationship between creators and their tools, businesses and their processes and potential. It was a response to a status quo that desperately needed updating.
The logistics and supply chain sector is ready for a similar revolution. Specifically, the modern supply chain must be built to be actively anti-fragile. The transition to a decision-centric enterprise marks the end of an era defined by reactive management.
For decades, we required supply chain professionals to serve the limitations of their software. We’ve left expert planners firefighting exceptions in spreadsheets, while reaching the company’s strategic goals have remained elusive.
Adopting a decision-centric model changes this dynamic. It empowers people and their teams to think differently. They can be different, operating with a level of specificity and agility that meets this disruptive moment.
