By Julia Sanzharova, Tech Supply Chain Expert
Most corporations dream of digital transformation, yet few are ready for what it actually takes. New technology may promise precision, efficiency, and visibility, but real change rarely fails because of poor software. The reason for many failed innovation attempts? Habits — and the people, data and processes that are not ready to sustain the changes.
When Innovation Meets Reality
In large manufacturing and FMCG companies, systems grow over time — layer after layer. A piece of an ERP setup here, a local Excel file there, a quick fix to make something work faster for one market — a shortcut for the sake of fast localisation. Each country builds its own version of “how things are done,” and on paper, it all seems fine. But once you try to bring these pieces together into one modern platform, the cracks start to show. What looked like efficiency turns into friction.
Teams realise that the challenge isn’t technical at all. You’re not just adding a new tool — you’re changing how people make decisions. What counts as demand? What’s considered safety stock? How do you define an order that’s “late”? These questions sound simple until you put them side by side across markets. The hardest part of transformation is agreeing on how to think, not how to code.
Technology doesn’t fix that, but only reveals it. The dashboards make the gaps visible — but what they show is often the result of habits, not errors. And that’s why the fundamental transformation starts not with implementation, but with people understanding that their daily decisions are now part of one shared system.
The Human Factor: Between Resistance and Readiness
Every transformation has its Prometheus moment. When you are passionate enough to bring the “fire” of innovation, you’ll be flabbergasted to realise that not everyone wants it. Teams that have worked the same way for ages see automation as a threat to their expertise or even their job place. Managers could fear that data transparency will expose inconsistencies in their reports. Even the most rational business case could and will struggle against habit.
Successful leaders learn to act as translators between the old and new worlds. They do not preach the beauty of algorithms; they explain how technology simplifies work, speeds up feedback, and reduces uncertainty. Change management, when done well, is not forcing adoption. The best change management examples start with rebuilding trust in the tools.
Data Readiness: The Forgotten Foundation
Before any rollout, the less visible work begins. Teams must reconcile how they define products, customers, or service levels. It’s not difficult to imagine the transformation program, where simply harmonising product and customer hierarchies across five markets could delay progress for months. Each region had its own naming conventions and bill-of-material structures, and without alignment, no forecast could be trusted.
The issue wasn’t the software; it was the data itself. Merging those inconsistencies required a shared business language, clear ownership, and patience. Companies that start data assessments 6 to 12 months before implementation usually save twice that time later. You all have to start speaking the same language, and learning it takes time, so give yourself some time.
Why Adoption Fails More Often Than Innovation
Technology development happens in a controlled environment. Adoption unfolds in the chaos of daily operations. When a tool goes live, planners must not only learn new interfaces but also unlearn deeply embedded shortcuts. Many organisations underestimate how much time and communication this unlearning requires. Changing habits is never instant — if you’ve been eating with your left hand your whole life, you won’t suddenly switch to your right just because someone says it’s more efficient.
The same happens when new planning tools meet the real world. Dashboards may look impressive, yet the recommendations they generate often clash with how the business actually runs. Orders that appear optimal on screen contradict production realities or logistics constraints. Even when teams are ready to follow the plan, fragmented or inconsistent information makes execution nearly impossible.
Lessons for Leaders
1. Treat implementation as change management, not as an IT project.
The best technology won’t fix broken communication. Success depends on early alignment between business and technical teams and on shared accountability for results. Again, speak the same language and manage expectations.
2. Begin with clarity on decisions, not on data.
Decide which business outcomes you want to improve — then identify the data required to support those decisions. Cleaning everything at once only slows momentum.
3. Create ownership across levels.
Every metric, from inventory to forecast accuracy, must have a name. When people know they own part of the system, adoption follows naturally.
4. Respect the learning curve.
Users need space to experiment, fail safely, and rebuild confidence. Training is not a formality.
5. Keep the dialogue alive.
Post-go-live, feedback loops are essential. Weekly syncs between users, business leads, and developers prevent minor mismatches from becoming systemic issues.
From Fire to Light
Innovation can spark excitement, but it’s adoption that keeps the fire burning. In the old myth, Prometheus brought fire to humanity — and paid the price for challenging what was comfortable and familiar. Corporate change isn’t as dramatic, but the feeling is the same: bringing something new often means facing resistance first.
The real success of supply chain transformation doesn’t come from algorithms or dashboards. It comes when people, data, and decisions start moving together — when technology becomes not a control system, but a shared language. Fire alone doesn’t change the world; it’s what people choose to do with it that does.
