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TABLE OF CONTENT
TOC
- The Evolution of the Supply Chain
- Why Your Supply Chain is NOT linear
- Building a Strong Data Foundation Means Moving Beyond Process Mining
- How to Embrace the Future of GenAI
Monday, February 3rd, the North American supply chain world held its breath. The tension was palpable. Were we facing another event on the scale of the COVID-19 pandemic? How would we adapt? What can we do to be ready? The questions were endless, the stakes high, and the time to act, limited.
The COVID-19 crisis underscored the crucial need for supply chains to be able to dynamically adapt to market fluctuations. It was a powerful reminder that, in this business, there are no dull days—often days filled with unpredictable challenges. What makes those challenges bearable—and even rewarding—is knowing that your organization is prepared to pivot and make the most out of the situation. As we say here, “Don’t let a good crisis go to waste”!
Disruptions, black swan events, and shifting market conditions create winners and losers. But one thing the winners all have in common is their ability to adapt quickly. The key to navigating uncertainty lies in the ability to harness data and technology effectively. It’s not just about surviving a crisis; it’s about positioning your business to thrive, regardless of what disruptions come your way.
The Evolution of Supply Chain (with a Big ‘S’)
In fact, we are entering an age where operational excellence in supply chains—not brand and marketing—has become the primary determinant of market success. Ensuring your company can run smoothly no matter what is thrown at it is no longer optional but a requirement.
Today, organizations must rethink how goods are sourced, produced, and delivered, all while maintaining profitability. Issues like value chain discrepancies, inflationary pressures, labor union negotiations, and rising production costs put significant strain on profitability. Add to that the growing expectation for personalized experiences, "liquid expectations" that require agility across teams, and the demand for on-time, flexible delivery—and the pressure intensifies.
So, how do organizations prepare to thrive in this ever-changing landscape? It starts with how you integrate data products and strategies into your supply chain. But be cautious—while some solutions may seem like the perfect fit, they may not always be the right choice. Let’s explore how the various operating environments you face should influence your strategy and why certain solutions might inadvertently lock you into limitations.
In this new environment, resilience is no longer a mere competitive advantage—it’s the benchmark for success. To thrive, supply chains need to leverage integrated platforms, real-time data capabilities, new workflow enablers such as GenAI agentic solutions, and custom systems that allow them to stay ahead of disruptions and continuously deliver customer-focused experiences.
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Your Supply Chain is NOT linear
The modern supply chain isn’t linear but rather dynamic and highly variable, driven by different customer segments and value propositions. There is no "one-size-fits-all" solution in today’s world.
The combination of various processes, products, customer expectations, and supplier behaviors often creates unique scenarios that can behave differently from the traditional experience an organization offers. Without data, managing this complexity is impossible.
As the saying goes, “you can’t manage what you can’t measure.”
If you lack the data, you cannot fully understand the nuances of your value chain, making it challenging to identify weaknesses or areas of resilience within your network.
Building variations and introducing nuance within the supply chain can be costly, which is why companies like Ford historically offered only one car color—to streamline their focus on quality and efficiency. The key, however, is understanding where to strategically differentiate the flow and experience while ensuring profitability.
In the pre-post-Fordism era, achieving this level of adaptability and nuance would have taken decades. Today, through the power of data—particularly big data—we can model our supply chain flows and subflows with unprecedented precision. This gives us the level of detail and insights that were once difficult to obtain intuitively, significantly enhancing both the richness and adaptability of our processes.
What once took 15 years to differentiate in the value chain can now be streamlined into a process completed in just 3 years, thanks to a solid data foundation. But what exactly does it mean to have a solid data foundation, particularly in the context of supply chains? In many cases, process mining seems to be the go-to approach for gaining visibility into supply chain flows and uncovering inefficiencies.
It can be tempting to rush into GenAI, Machine Learning, or Process Mining use cases head-on, but these approaches often come with significant drawbacks. Relying too heavily on them can lead to vendor lock-in, creating long-term dependencies on a single solution. Additionally, hyper-specialization can limit the flexibility needed to address a variety of use cases, resulting in a longer time to value. There are also risks of architectural gaps for system integration, as well as potential issues around data sovereignty and control.
This means focusing not just on short-term gains but on creating a long-term, adaptable data infrastructure that can evolve alongside your supply chain.
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Building a Strong Data Foundation Means Moving Beyond Process Mining
While process mining tools are incredibly powerful in modelling flows through their user interfaces (UI), the real value lies in the data itself and your ability to cascade it down and build your personalized flows from AI Models to Software integration for your operational users.
The process mining tools themselves are excellent for visualizing and understanding existing processes, serving as strategic decision-making support for managers. However, the real power comes when these tools are used operationally, with custom interfaces that allow operators and local affiliates to quickly understand predicted market shifts and adjust accordingly. Yet, as always, integration remains the most challenging aspect. It requires bringing all systems together, including legacy systems, which can be both time-consuming and costly.
The decision to build or buy a solution is pivotal. While external solutions may be tempting, relying on external, SaaS-based process mining solutions, you risk losing data sovereignty and control over your data. These tools can often be siloed, limiting their ability to be leveraged across the entire organization.
If your goal is to become genuinely data-driven, the success of these efforts will depend on how well the tools integrate into your existing systems, people and processes.
You might have guessed, but to unlock the full potential of your data and supply chain, it’s more effective (and cost-saving) to build your own platform, maintaining full sovereignty over your data, models, and metadata within the context of your business.
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Read full story
Just as McDonald's closely guards the Big Mac recipe, your unique supply chain setup is a strategic asset that drives market leadership. Strategically, you wouldn’t want to share this model or store it in a third-party or SaaS platform, as it would compromise your competitive advantage.
By investing in a strong, custom-built data foundation powered by data products and embedding robust data governance, you can create a more flexible, resilient, and scalable supply chain infrastructure that empowers your organization to stay ahead.
Embracing the Future of GenAI
Adding a layer of Generative AI—such as a semantic layer for data interpretation and visualization—ensures that data is not only available but also actionable. This empowers managers, local affiliates, and transportation partners and even enables automatic connections between operational systems and the data mesh.
By leveraging Gen AI, we can create dynamic, personalized user experiences that are tailored to the specific needs of each individual in real-time. As Arthur Mensch, Co-founder & CEO of Mistral AI, highlighted in a recent video, we are moving towards a future where AI models can dynamically generate user interfaces based on the context and needs of the user, transforming static interfaces into adaptive, customized experiences.
This shift is key to why relying on out-of-the-box process mining solutions may not be the best approach. Pre-built tools often come with rigid interfaces and specialized functionalities that may not adapt to the unique demands of your supply chain.
Instead, by integrating GenAI into your systems, you can create a truly flexible, personalized, and real-time data experience that aligns with the specifics of your business and operational context.
The result is an ecosystem where data is actionable, not just for strategic decision-making but for operational agility.
Looking to the future, the rise of Agentic AI, which takes action and orchestrates workflows based on data, will depend heavily on the quality and integrity of your data. This is where traditional process mining tools may fall short — they lack the capacity to handle the full spectrum of use cases in the Supply Chain.
Moreover, it is debatable, but relying on external, third-party and siloed tools can also raise concerns about data privacy and confidentiality, as they may not offer the level of control or security needed to protect your proprietary business data.
Stay tuned for part 2 of our series, where we will dive deeper into why focusing on edge cases is essential for building a resilient supply chain. By integrating data and data products, organizations can identify disruptions and black swan events that threaten the supply chain network. These unpredictable occurrences can have significant impacts, and understanding how to manage them is key.
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