Not Just Correlation, Causality Modeling is Key to Supply Chain’s Future

Over the past few months, we’ve had discussions with multiple clients about understanding risk causality within the supply chain. In preparing for these conversations, a clear pattern emerged in how organizations approach causal analysis. 

While companies with strong technical capabilities often leverage advanced causal modeling techniques, the majority still rely on more traditional methods like manual data crunching activities to map the root causes. The latter is time-tested, but tends to be time-consuming and heavily reliant on deep domain expertise. Some other organizations use SHAP (Shapley Additive exPlanations) values within their machine learning models to identify potential drivers. While SHAP provides helpful insights into feature importance, it is fundamentally based on correlation, not true causation. As a result, it may offer directional guidance but lacks the rigor needed for confidently identifying causal relationships. 

In today’s supply chain landscape, where organizations are constantly bombarded with data, observing correlations between these variables isn’t enough to build a resilient and efficient supply chain. We need to understand why things happen, and that’s where causality modeling comes in.

Limitations of Correlation

Traditional analytics often rely on finding correlations. For example, we might notice a strong correlation between a marketing campaign and a surge in sales. While this observation is valuable, it doesn’t tell us if the campaign caused the increase in sales. Perhaps it was a seasonal trend or a competitor’s stockout. Without understanding the causal relationship, we risk making flawed decisions.

In a supply chain context, relying solely on correlation can lead to:

• Ineffective Inventory Management: We might stock up on certain products based on past sales patterns, without understanding the underlying drivers of demand fluctuations.
• Poor Risk Assessment: We might fail to identify the root causes of supply chain disruptions, leaving us unprepared for future shocks.
• Suboptimal Operational Decisions: We might implement changes based on observed trends, without understanding their true impact on overall performance.

Power of Causality Modeling

Causality modeling goes beyond correlation to identify the cause-and-effect relationships between variables. It allows us to:

• Understand the “Why”: By identifying the causal factors driving supply chain events, we can develop more accurate predictions and make informed decisions.
• Simulate “What-If” Scenarios: We can model the impact of different interventions, such as changing suppliers or adjusting pricing strategies, before implementing them in the real world.
• Identify Root Causes of Disruptions: We can pinpoint the underlying factors contributing to supply chain vulnerabilities, allowing us to build more resilient systems.
• Optimize Operational Efficiency: We can identify the most effective levers for improving performance, reducing costs, and enhancing customer satisfaction.

Techniques for Causality Modeling

While there are several techniques for causality modeling, Bayesian Network modeling is among the most widely used. It is a probabilistic framework that represents the relationships between a set of variables. Establishing causal relationships using a Bayesian Network involves four key steps, as follows:

1. Build a causal graph: Start by creating a Directed Acyclic Graph (DAG) that visually maps how different variables influence one another and ultimately affect the target outcome.
2. Estimate Causal Inference: Estimate the causal effect of each input variable on the outcome
   1. Define treatment and outcome.
   2. Identify causal estimand.
   3. Estimate causal effect using regression or other methods.
3. Quantify the Impact: Convert causal estimates into simple business metrics such as “if variable X increases by one unit, the outcome will increase or decrease by Y units.”
4. Simulate scenarios for decision support: Use the causal model to simulate what-if scenarios to support better planning and evidence-based interventions.

Applications in Supply Chain

Here are some examples of how causality modeling can be applied in supply chains:

• Supplier Risk Management: Identifying the causal factors contributing to supplier disruptions, such as political instability, natural disasters, and financial instability, can help mitigate risks.
• Manufacturing Excellence: Understanding the causal relationships between various factors, such as scheduling, machine speed, etc, on the overall equipment efficiency.
• Logistics Optimization: Identifying the causal factors affecting transportation costs and delivery times can help improve logistics efficiency.
• Inventory Optimization: Understanding the causal drivers to understand inventory stockouts, excess, and slow-moving inventory.
• Fill Rate: Understand which factors have an impact on customer fill rate and by how much to improve overall customer satisfaction level.

Future of Supply Chain

As supply chains become increasingly complex and data-driven, causality modeling will play a crucial role in enabling organizations to make informed decisions and build resilient systems. By moving beyond correlation and embracing the power of causality, we can unlock new levels of efficiency, agility, and sustainability in supply chain management. By leveraging the right techniques, businesses can build smarter, more robust, and more future-proof supply chains.