📈 #50 Operations Research reimagined (Part I)

Let's learn together about the problems so we don't repeat history

Borja Menéndez

Nov 10, 2024

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Operations Research stands as:

A discipline with over 70 years of success [1]
A sophisticated field that solves complex business challenges [2]
A value generator worth a hundred million dollars for organizations worldwide [3]

However...

Most business leaders haven’t heard of it
Only large corporations can afford to build OR expertise
It's overshadowed by more popular fields like Data Science and ML

This contrast between OR's potential and reality raises the question: why hasn’t Operations Research become more widely adopted?

A Data Science Lead asked the following question in a recent LinkedIn post:

Why Operations Research opportunities are less common than Data Science and Machine Learning ones?

After 248 likes, 7 reposts, and 45 comments, it became clear these barriers are systemic issues holding back OR's growth.

In today’s first post of a six-part series, I will discuss the issues that have kept OR from reaching its full potential and help you avoid these patterns.

Together, we can elevate OR’s role in today’s data-driven world, making it a more accessible and impactful field.

Today you’ll read in Feasible:

🌀 Systemic issues and negative feedback loops
📜 Impact of historical context
🏔️ 4 central challenges

If you’re ready, I’m ready. But one more thing before we start:

I’m seeking sponsors for Feasible aligned with my values and your interests. If your company or a known one, university, or conference could benefit, let me know by replying to this email!

→ Let’s get started! 🪂

🌀 Systemic issues and negative feedback loops

In systems thinking, negative feedback loops are processes that balance or counteract change.

In the case of OR, these loops hinder the field’s growth and relevance in broader industry applications.

There’s a negative feedback loop that leads to reinforcing issues keeping OR out of mainstream business applications. It works like this:

I don’t know where it began, but it’s killing OR.

At the core of OR's limited adoption is a self-reinforcing cycle.

OR remains confined to niche applications, limiting impactful case studies and showcasing its value. Without these success stories, there is minimal motivation for companies to market OR solutions, keeping the field invisible.

This lack of awareness among business leaders reduces demand. The small market size discourages development of accessible tools, making OR feel exclusive and specialized.

Ultimately, this cycle of limited adoption, poor visibility, and high costs has consequences.

There are consequences.

The first one is structural rigidity.

The historical focus of OR on niche applications and academic rigor has created a structural rigidity that resists change.

The emphasis on theoretical depth limits flexibility and adaptability, hindering OR's evolution into a field focused on practical, business-friendly solutions.

The second one is misalignment with business needs.

There’s a misalignment between OR’s capabilities and modern businesses’ needs. They prioritize quick, actionable insights over rigorous, mathematically optimal solutions.

This misalignment creates a barrier to adoption in mainstream business contexts.

Since Operations Research is mainly a practical field, how could that happen?

📜 Impact of historical context

The history of OR significantly impacts its current challenges.

OR was developed by military strategists and scientists who applied rigorous mathematical techniques to solve urgent, high-stakes problems, after being born during World War II.

This origin story established OR as a field rooted in data-driven analysis and complex modeling, aimed at optimizing wartime decisions like convoy routing, resource allocation, and tactical planning.

The success of OR during the war demonstrated the power of systematic, quantitative approaches in addressing large-scale, mission-critical challenges. It sets a foundation of methodological rigor that persists today. [4]

📚 The post-war shift to academia and theory

In the years after WWII, OR moved from the battlefield into universities. It evolved from a practical wartime tool into an academic discipline.

The establishment of dedicated OR journals, like Management Science in 1954, formalized its status in academia and underscored a shift toward theoretical depth and methodological rigor.

Academic institutions prioritized mathematical proofs, algorithmic development, and optimization theories. They fostered a culture that valued analytical depth over ease of application.

This academic focus advanced optimization and modeling techniques; it distanced OR from the immediate, accessible solutions needed in business contexts. [5]

Over time, people began to see OR as an esoteric discipline, accessible mainly to specialists and less adaptable to the fast-paced, practical needs of industry.

🧩 A legacy of specialized applications

The adoption of OR in specialized industries—like defense, logistics, and manufacturing—reinforced its perception as a tool for solving specific, large-scale industrial problems rather than a versatile, general-purpose solution for everyday business challenges.

In these sectors, OR’s ability to handle complex, quantitative tasks was invaluable. This solidified its reputation as a sophisticated and niche discipline.

However, this focus limited OR’s visibility outside these domains. This led to a perception that it was suited only for specific sectors and not broadly applicable to business management or other industries.

This perception hindered OR’s recognition as a flexible, accessible problem-solving framework across diverse fields.

🔗 The lasting impact

The cumulative effects of OR’s history have created structural rigidity within the field.

This history includes the wartime origin, academic entrenchment, niche focus, and closed tools ecosystem (which I’ll discuss in a few posts).

OR is often seen as a "black box" discipline, highly theoretical and complex, accessible mainly to those with specialized training.

Unlike Data Science and Machine Learning, marketed as accessible, general-purpose tools for various business problems, OR remains constrained by its legacy as a discipline for highly specialized applications.

These historical factors have limited OR’s reach and created a feedback loop. The limited adoption discourages open-source development and user-friendly tools, restricting OR’s growth and mainstream acceptance.

OR has a proud history of tackling complex problems. However, its journey from WWII to academia and specialized industries has left it struggling to adapt to modern business demands that prioritize accessible, practical solutions over mathematical rigor.

The history of OR has led to central challenges that must be addressed to unlock its potential and become a widely accepted problem-solving approach.

🏔️ 4 central challenges

I selected the four biggest challenges impacting the field based on the previous sections and the LinkedIn post:

Accessibility and commoditization: The lack of standardized, user-friendly OR tools means businesses, especially small-to-medium enterprises, must invest significant resources to implement complex solutions. This high barrier to entry limits OR's reach to large corporations with dedicated analytics teams.
Transparency and "black box" mindset: Over the years, a culture of exclusivity and complexity has developed around OR, due to its origins and evolution. This mindset, rooted in specialized applications and academic rigor, creates a perception that it is only for deep expertise. It leads to solutions that are not intuitive or accessible to non-specialists, reinforcing the idea that OR is only for select, highly technical audiences. [6]
Marketing and outreach: Unlike Data Science and Machine Learning, which have enjoyed widespread marketing and success in business, OR has failed to communicate its value to a broader audience. Without efforts to showcase its practical applications and impact, the field remains unknown outside its traditional strongholds.
Balancing rigor and practicality: OR's academic heritage has instilled a culture that prizes mathematical sophistication over ease of use. This analytical depth is a strength, but it can create a disconnect between OR's capabilities and the immediate, user-friendly solutions that businesses demand. Bridging this gap will be crucial for it to gain wider acceptance in the corporate world.

These challenges create a negative feedback loop. The lack of accessibility, transparency, and practical orientation limits OR's adoption, reducing incentives for improving marketing, tooling, and applications.

Breaking this cycle and repositioning OR as a versatile, user-friendly framework for business problem-solving will be critical for the field's future growth and relevance.

🏁 Some conclusions

OR can optimize complex systems and drive data-driven decisions across industries.

However, several interconnected challenges hinder its widespread adoption, especially among SMEs.

OR can overcome challenges and become a mainstream tool for businesses of all sizes by learning from DS/ML successes, embracing transparency and user-friendliness, and showcasing practical applications.

Today you saw the systemic issues in the field, the main challenges, and their impact on modern OR projects.

But remember, this is just the start. You’ll learn about each central challenge in next posts, so together we can make OR a better place for everyone.

Let’s keep optimizing,

Borja.