Most supply chains today aren’t short on data; they’re short on decision time. Sensors, carrier updates, warehouse transactions and supplier feeds generate a constant stream of signals, yet organisations still react to disruption rather than anticipate it. In this conversation with CIO&Leader, Phil Lewis, Senior Vice President, Solution Consulting International (EMEA & APJ) at Infor, explains what separates a merely visible supply chain from a genuinely intelligent one — and why that distinction matters more as networks turn multi-site, multi-modal and increasingly complex.
Drawing on real-world examples, including Infor’s work with Molex, Lewis explains how predictive intelligence creates “decision time” rather than simply predicting disruption for its own sake. He also shows why connecting systems should mean removing complexity, not adding another layer of it, and addresses why digital transformations in logistics stall, how resilience and agility demand different strategies, and what will truly separate high-performing logistics operators over the next few years.
CIO&Leader: The theme you’ve proposed is “from visibility to intelligence.” In practical terms, what’s the difference between what a genuinely intelligent supply chain platform does that a merely visible one can’t?
Phil Lewis: Visibility tells you what is happening. Intelligence helps you understand what it means, what is likely to happen next and increasingly, what should be done about it. That is the real distinction behind the shift from visibility to intelligence.
A visible supply chain can give you a clear picture of where a shipment is, which supplier is running late or how much inventory is available across locations. That is valuable, but the responsibility for interpreting that information and deciding what to do still sits with the individual.
An intelligent supply chain brings those signals together and puts them in context. A delayed shipment, for example, is not simply a logistics event. It could affect a production schedule, a customer commitment, or inventory availability elsewhere in the network. The technology should be able to identify those connections, assess the potential impact and help teams determine the best course of action.
That is where predictive analytics and AI become particularly useful. They can move organisations from monitoring exceptions to prioritising them, understanding their consequences and, where appropriate, supporting or automating the response.
The progression is not simply from seeing more data to analysing more data. It is from knowing what is happening to understanding what it means and being able to act on it. That is how supply chain visibility begins to become operational intelligence.
CIO&Leader: Most logistics and 3PL providers still run on a patchwork of legacy and point systems. What does the journey from fragmented operations to a connected, end-to-end ecosystem actually look like, and where do organisations typically get stuck?
Phil Lewis: Most logistics and 3PL operations do not choose fragmentation; they accumulate it. A transport management system may have been implemented for one region, a warehouse system may have been inherited through an acquisition, and spreadsheets may have been added over time to bridge the gaps. Each system may work perfectly well on its own, but collectively, they can make it difficult to build a reliable picture of what is happening across the network.
The journey to a connected ecosystem, therefore, should not begin with replacing everything. It should begin with understanding how information and decisions actually flow across sourcing, warehousing, transportation and delivery, and then connecting those processes so that information can move between them in real time.
That is where a network approach becomes important. With Infor Nexus, for example, the objective is to connect the different participants and processes involved in moving goods, while allowing organisations to work with the systems they already have rather than forcing every partner onto the same technology.
Where organisations typically get stuck is when the focus shifts from connecting systems to changing how the organisation operates. Consistent data standards, governance, ownership and adoption across multiple teams and partners are much harder to solve than the initial integration.
The organisations that make progress are usually the ones that treat connectivity as an operating-model change, not simply an IT project, and build it progressively around clear business outcomes.
CIO&Leader: Predictive analytics and AI are often pitched as tools for anticipating disruption. Can you walk us through a real scenario — a port delay, a demand spike, or a carrier failure — where they changed the outcome compared with a traditional reactive response?
Phil Lewis: A good example is Molex, where the complexity of its global supply chain means that a disruption in one part of the network can quickly have consequences elsewhere. The important shift for them was moving from simply knowing where shipments were to having a much clearer understanding of what was happening across the network and where attention was required.
Consider a port delay. In a traditional, reactive model, the problem often becomes apparent only after a shipment misses its expected arrival. By then, the organisation may have very limited options: expedite another shipment, change the mode of transport, reallocate inventory or potentially risk missing a customer commitment.
With greater network visibility and predictive capabilities, the organisation has more runway. If a shipment is likely to be delayed, teams can assess which orders or downstream requirements could be affected and consider alternatives while there are still choices available. That could mean adjusting the sequence of fulfilment, reallocating available inventory or finding alternative transportation capacity.
That is the real value of predictive intelligence. It is not about predicting disruption for its own sake. It is about creating decision time. The earlier you understand the likely impact of an event, the more options you have and the less likely you are to resort to the most expensive response simply because it is the only one left.
Ultimately, AI creates value when it helps an organisation move from reacting to disruption to managing it before it becomes a crisis.
CIO&Leader: How do you connect warehouse, transportation and broader supply chain operations without simply adding another layer of complexity on top of what’s already there?
Phil Lewis: This is one of the more common misconceptions in the market, that connecting systems means adding another layer on top. Done well, it is closer to removing layers. If warehouse management, transportation management and supply chain planning are speaking a common data language, you do not need a separate integration team stitching them together every time something changes, and you do not need a control tower built purely to compensate for the fact that nothing else talks to anything else.
We run Infor WMS on our Industry Cloud Platform. The same unified foundation carries our AI, analytics, data, and integration capabilities across the supply chain portfolio, precisely so that warehouse execution, transportation, and planning are not three separate products bolted together after the fact. When a transportation delay happens, that information should reach warehouse operations and planning automatically, without a manual handoff or a spreadsheet in between, and on a shared platform; that is what happens by design rather than by integration project.
Complexity tends to creep in when organisations treat each new capability as an additional application to manage. The better question is not “what do we add?” It is “what can we now retire, because this capability does it natively?” Genuine connection reduces the number of places a person has to look; it does not increase them.
CIO&Leader: Resilience and agility get used almost interchangeably in this conversation. Are they the same capability, or do organisations need to build for them differently, and does one tend to get prioritised at the expense of the other?
Phil Lewis: I would not treat resilience and agility as interchangeable. They are closely related, but they answer different questions. Resilience is about an organisation’s ability to absorb disruption and continue operating; agility is about how quickly it can change course when circumstances change.
Organisations have traditionally built resilience through buffers: more inventory, additional capacity, alternative suppliers or redundant infrastructure. Those measures can be important, but they can also come at a high cost and, if taken too far, make the supply chain less efficient and less flexible.
Agility requires a different capability: the ability to recognise change early, understand its implications and adjust plans, resources or routes quickly. That is where better visibility and intelligence become important. If organisations can identify an emerging disruption early and understand which parts of the network are actually exposed, they can respond more selectively rather than building excess protection across the entire supply chain.
So, one should not be prioritised over the other. The real objective is to build a supply chain that is resilient enough to absorb disruption and agile enough to adapt to it. Increasingly, technology can help organisations achieve both by giving them the information and intelligence to make those trade-offs dynamically, rather than relying on static buffers or assumptions about where the next disruption will come from.
CIO&Leader: As networks become multi-site and multi-modal, how does the calculus around visibility change? Is it more about the volume of data being captured, or the speed at which it’s connected and interpreted?
Phil Lewis: It is much more about the speed at which information can be connected and interpreted than the volume of data being captured. Most large supply chains have never suffered from a lack of data. There are sensors, carrier updates, warehouse transactions, supplier information and other signals constantly being generated across the network. The challenge is making sense of all of that information quickly enough to influence a decision.
As networks become more complex, a single shipment can involve multiple sites, carriers, modes and handoffs. A delay that appears minor in isolation could be critical if it affects a time-sensitive customer commitment or an inventory position elsewhere in the network. That context is what turns data into operational visibility.
I would therefore distinguish between knowing where everything is and knowing what matters right now. The latter requires information to be connected across the network, interpreted in context and presented in a way that helps teams prioritise action.
Capturing more data without improving how quickly it is understood creates a bigger dashboard. The real advantage comes when organisations can move from signal to decision in minutes rather than days.
Ultimately, visibility should not be measured by how much information an organisation can see. It should be measured by how effectively that information helps it manage the network.
CIO&Leader: Many organisations say they’re “drowning in data” but starving for insight. What’s the actual gap between collecting supply chain data and turning it into decisions that improve cost, service or efficiency?
Phil Lewis: The gap is rarely the data itself. It is the ability to connect that data to context, generate meaningful insight and translate it into action.
Most organisations already generate enormous amounts of supply chain information across ERP systems, transportation and warehouse platforms, suppliers, carriers and other external sources. The challenge is that having all of that information does not necessarily tell you what matters, what the potential business impact is, or what needs to happen next.
As supply chains become more complex, the challenge is increasingly about connecting signals across different parts of the network and putting them into business context. A piece of information may appear insignificant on its own but become important when viewed alongside inventory levels, customer commitments, capacity constraints or cost pressures.
This is where analytics and AI can make a meaningful difference. They can help identify patterns, prioritise exceptions and surface the information most relevant to a particular decision, rather than asking people to work through thousands of signals themselves.
There is also an organisational dimension. Even a good insight has limited value if it reaches the wrong person, arrives too late or is not connected to an actionable response.
For me, the progression is therefore data to context, context to insight, and insight to action. The real competitive advantage comes from shortening the time between an event and the right person making a better decision.
CIO&Leader: From your work with global enterprises, what’s the most common reason a digital transformation initiative in logistics stalls: is it technology, organisational buy-in, or something else entirely?
Phil Lewis: Technology is rarely the reason a logistics transformation stalls. In many cases, the bigger challenge is a lack of alignment around what the transformation is actually meant to achieve.
When a programme starts with “we need new technology” rather than “we need to reduce transportation costs, improve warehouse productivity or increase on-time delivery,” it becomes difficult to sustain organisational buy-in. A logistics transformation typically touches IT, operations, procurement, finance, warehouse teams and external partners, all of whom have different priorities. A clear and measurable business outcome gives those stakeholders a reason to move in the same direction.
The other issue is pace. Technology can often be implemented faster than an operational organisation can realistically absorb change. That is particularly true in logistics, where warehouses and transportation networks cannot simply stop while a transformation takes place. Programmes that try to change everything at once can create disruption even when the underlying technology is sound.
The strongest transformations therefore tend to be outcome-led and progressive: define the business outcome first, build a roadmap the organisation can absorb, demonstrate value in stages and then scale.
CIO&Leader: How should a CIO or supply chain leader think about sequencing this transformation? Do you start with visibility infrastructure, connected operations, or AI-driven intelligence, and does that order change by industry?
Phil Lewis: I would start with the business problem rather than a prescribed technology sequence. There is no universal starting point because the priorities will differ depending on the industry, network complexity and the organisation’s existing maturity.
That said, there is a logical progression. You need a reliable flow of information before you can consistently derive intelligence from it, and that intelligence needs to be connected to execution if it is going to influence outcomes for many organisations. That means establishing a strong foundation of visibility and connectivity first. From there, predictive capabilities can help identify risks and opportunities earlier, followed by greater automation where the underlying processes are mature enough to support it.
I would not, however, treat this as a rigid three-stage programme. Organisations can develop these capabilities in parallel, starting with a specific visibility or execution challenge, introducing intelligence around that process and then automating parts of the response as the technology and organisation mature.
Industry certainly influences the starting point and pace. A distributor with relatively mature systems may move quickly towards predictive intelligence, while a 3PL with a fragmented legacy environment may first need to focus on connecting its operations.
The guiding principle should be outcome-led sequencing: start where better information or faster decisions can create measurable value, prove that value, and then build from there.
CIO&Leader: Looking ahead, which capability do you think will separate resilient logistics operators from the rest over the next two to three years: real-time visibility, predictive intelligence, or something else altogether?
Phil Lewis: The differentiator will be the ability to turn intelligence into action, rather than any one capability in isolation.
Real-time visibility will remain fundamental, and predictive intelligence will become increasingly important as organisations look to anticipate disruption rather than respond to it. But both will become more common. The real advantage will come from what an organisation can do with those capabilities.
We are moving towards a model in which technology can identify exceptions, assess their potential impact, and recommend the most appropriate response, while allowing people to focus on decisions that require judgement. In some cases, technology will also be able to execute routine responses within defined parameters. That represents a significant shift from people constantly monitoring systems and coordinating responses manually.
This will require the right foundations (connected data, integrated processes, and appropriate governance), but it also requires organisations to be willing to redesign decision-making.
That also changes how we think about resilience. It will be less about building enough buffers to absorb every possible disruption and more about building the intelligence and flexibility to respond when conditions change.