A Blend of Computational Theory and Business Expertise: Creating the Best Machine Learning Outcomes

Machine learning (ML) algorithms have revolutionized decision-making across modern businesses. In supply chain analytics, for example, these algorithms can detect the risk of product defects or flag issues related to long lead times with remarkable speed and precision.

However, a ceiling is beginning to emerge in terms of the value that computer scientists or software engineers can bring through machine learning alone. The core issue? Business intuition and domain expertise cannot be replaced—no matter how many certifications, advanced degrees, or hours spent learning new platforms and technologies.

At a certain point, software engineers need a deep understanding of the business context and collaborative work with business users to break through that ceiling. And the converse is true as well: MBAs are not typically trained to build machine learning models from scratch.

The next phase in the machine learning revolution lies in the meaningful integration of business theory/experience into machine learning algorithms. This is a powerful and under explored space—especially in domains like supply chain risk analytics and market analytics, where business theory is both well-established and highly applicable.

🧠 A 7-Step Framework: Infusing Business Theory into Machine Learning

Think of this process as a staircase—each step brings your model closer to real-world effectiveness and impact:

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1. Identify the Business Theory to Apply

Start with the business knowledge you want to incorporate. For instance, a supply chain expert may be aware of the bullwhip effect—the tendency for demand variability to increase as you move upstream in the supply chain, from retailers to suppliers.

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2. Translate the Theory into Measurable Outcomes

Next, you’ll need to turn abstract theory into quantifiable metrics. In the case of the bullwhip effect, you might track inventory volatility or build a feature that captures demand variance amplification.

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3. Ensure the Training Data Reflects the Theory

Your model is only as good as the data it learns from. So make sure your dataset includes variables that are relevant to the theory you're applying. Without them, your model can’t internalize the patterns the theory describes.

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4. Adjust the Model to Reflect Business Dynamics

Now, use the theory to fine-tune how your model works. If you’re addressing the bullwhip effect, for example, consider adjusting the model’s sensitivity to order volatility, or modifying the loss function to reflect costly prediction errors during unstable periods.

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5. Evaluate the Model Using Business-Relevant Metrics

Accuracy isn’t always the best measure of success. A model that performs well statistically may still fail to deliver value in the real world. Instead, evaluate your model based on business outcomes—such as fewer stockouts, smoother inventory turnover, or reduced over-ordering.

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6. Bring Business Experts into the Loop

After tuning the model, involve business stakeholders in reviewing the results. Ask: Do these predictions make sense? Do they reflect what we see on the ground? Their insights can be just as important as your validation metrics.

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7. Iterate and Improve

Finally, revisit the process regularly. Business environments evolve, and so must your models. Whether you're working with data or theory, continuous iteration is the backbone of effective machine learning.

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🎯 The Payoff

At AltaScient, we work hard to implement business theory into every model we build. That’s what gives us—and can give you—a true competitive edge in analytics.

It’s not just about building any model; it’s about building the right model, with the right information, for the right job.

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