Antifragile Supply Chains

Rethinking Our Relationship With
Uncertainty

Antifragile Supply Chains

Rethinking Our Relationship With
Uncertainty

Summary

What if supply chains could embrace uncertainty instead of bracing for impact? Or better yet, look forward to them? Sounds extreme. Yet, 9% of enterprises¹ are already living this reality, turning the concept of ‘anti-fragility’ into an actionable strategy. Unlike fragile systems that crumble under pressure or resilient ones that merely withstand, anti-fragile systems gain from disorder. They evolve and improve with every challenge.

How does a supply chain transform into an anti-fragile powerhouse? Is it even feasible with our current setup, or are we too entangled in outdated methods? The journey from fragile or resilient to anti-fragile is ambitious but not unreachable. The solution is multi-faceted but begins with technology and data – the building blocks of a strategy that welcomes uncertainty.

From tech debt and patchwork to a connected enterprise

Even as companies strive to modernize, technical debt¹ stands as an obstacle, entangling supply chains in a web of outdated tools and disjointed processes. Quick fixes have turned into long-term headaches, and the once solid foundations now resemble a patchwork struggling under its own weight. Crisis moments—like getting an end-of-life alert or grappling with obsolete code—often spotlight these issues, revealing the fragility of our systems.

Generative AI brings the power to screen and rejuvenate aging code and transform technical liabilities into assets. This is nothing like the patchwork that has been going on for decades. GenAI can reengineer the very DNA of our supply chain solutions and platforms.
From disparate data to a harmonized single source of truth

Supply chains are complex. They are not complicated like rocket science but are extremely complex because of their expanse, interdependencies, a sea of data, products, and partnerships. The industry also features high-volume sales and rapid product cycles that demand unmatched adaptability and speed. However, data disparity complicates the landscape. Take SKUs, for example. These product identifiers transform as they move from shop floor to logistics to retailers. They leave behind them a trail of disparate data with no reliable source of truth. This is just one use case to underscore the complexity of data processing.

Fortunately, GenAI and AI/ML platforms are making it possible to streamline data, constructing a solid foundation of cohesive data estate³ for sharper insights and clearer visibility.
From information to insights and visibility

Enterprises face challenges that vary in frequency and impact, from common yet manageable to rare but severe. Leaders need a deep understanding of these events’ implications before deciding. Consider a routine dilemma for supply chain managers: a material delay. They have plenty of options to choose from—prioritize a major client over a smaller, more flexible one, opt for costlier alternative materials, or switch suppliers. Extrapolate this dilemma to many more complex situations in the entire value chain. Figuring out the best option from all possible scenarios is a humanly difficult task, no matter how many people are deployed. But what if we could achieve this?

GenAI⁴ can go over the entire volume of internal data effortlessly and extend the analysis by incorporating factors like weather forecasts, traffic conditions, supplier reliability, sustainability, and even the financial and reputational effects of delivery delays. It then offers leaders the best possible decision paths to choose from. That is the role and power of insights and end-to-end visibility in facing uncertainties.

Priming the organization for successful GenAI implementation

GenAI is set to tackle the big obstacles and pileups in our digital ventures. While there’s plenty of talk about its potential, we’re also acutely aware that a majority of digital transformations fail—70%, to be exact⁵. Success hinges not just on the tech we adopt, like GenAI, but also on lessons learned from both wins and missteps. Drawing on years of experience, here’s the fundamental insight into what consistently achieves success:

Unify stakeholders around the goal

Technology deployments often miss their mark when stakeholders aren’t on the same page. Tools are introduced, but the anticipated results don’t appear. This scenario is all too common⁶, underscoring the importance of alignment internally and with external partners across IT, marketing, risk management, logistics, and beyond. The key lies in crafting a clear business case, pinpointing the source of impact, and acknowledging the essential non-technical elements needed for success.
Map out full-scale skilling approaches

The consensus among leaders is that building expertise over AI tools is a top priority, and so is ramping up the reskilling engine. GenAI is creating new positions, and they anticipate that nearly half of their workforce’s skill set will need a GenAI upgrade within three years⁷. We should also put processes in place to answer questions: How do we equip our team to use these tools effectively? What metrics and performance management systems are necessary to track compliance and outcomes?
Crystalize cost, impact, and ROI insights.

Gartner forecasts a significant slowdown in 90% of GenAI⁸ enterprise projects by 2025, as the balance tips with costs outweighing benefits. Over half of the enterprises embarking on building their own expansive models might hit a dead end due to the financial strain, complexity, and accruing technical debt.

Cost, time, and effort influence our choices. Should we opt for readily available GenAI solutions, with potential risks around data security and misinformation, or should we invest in developing, deploying, and fixing in-house models? Fortunately, experimentation is still in progress. FrugalGPT⁹, a new experiment, demonstrates that it’s possible to match top-tier LLM performance like GPT-4 at a staggering 98% cost reduction or even surpass its accuracy by 4% for the same investment.

The takeaway is clear: without strategic foresight, the GenAI leap could quickly morph from a strategic edge to a financial sinkhole.

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A new outlook on uncertainty

Thinking antifragility is just about tech is a sure path to disappointment. Tech kicks things off, but real success is a much bigger act between tech, people, and processes. More than anything, antifragility is a mindset. We cannot solve a problem with the same thinking that created it. Familiar ways of thinking and acting quickly might be what has created the patchwork of systems. So, we need to first innovate our mental models and change our relationship with uncertainty. Those who master this will discover disruptions can be the doorway to breakthroughs, not breakdowns.

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

References:

Antifragile Supply Chains

Rethinking Our Relationship With Uncertainty

Antifragile Supply Chains

Rethinking Our Relationship With Uncertainty