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Order Routing, Beyond Rules: Embracing Data-Driven Intelligence in Fulfillment Optimization


The Three Options: Heuristics, ML, and Hybrid Models
1. Heuristics: Rules-based, optimized for simplicity and well-defined strategies.
2. Machine Learning (AI): Data-driven, dynamic decision-making based on real-time patterns.
3. Hybrid Model: Combines heuristics to limit options and AI for optimization within that subset.

The Power of Heuristics

In the realm of Order-Management Solutions (OMS), the use of heuristics has long been a reliable and common approach, especially in scenarios where simplicity and a well-defined strategy prevail. For companies with a limited number of distribution centers or those focusing on direct-to-consumer flows with minimal complexity, heuristics offer an efficient solution. This rules-based approach is often optimized based on one or two key variables, making it a practical choice for businesses that prioritize factors like shipping cost and the minimization of split-shipments.

Heuristic Optimization: A Closer Look

In the B2B landscape, companies employing heuristics often find success when dealing with a small number of distribution centers. The strategy revolves around optimizing geographical proximity to the end customer, considering factors like shipping calendars, labor capacity, and service levels (Carrier/Mode) to meet desired delivery dates. Similarly, in the B2C space, heuristics come into play for direct-to-consumer flows when routing needs for store fulfillment are relatively straightforward.

Key Considerations for Heuristic Approach:
1. Geographical Proximity: Prioritize locations based on proximity to the end customer.
2. Shipping Calendar: Consider shipping schedules to ensure timely deliveries.
3. Labor Capacity: Optimize based on the availability of standardized labor.
4. Service Levels: Evaluate carrier and mode options to meet customer service levels.

When to Choose Heuristics

Industries with lower average SKU counts at each location, standardized labor, and predictable shipping schedules are well-suited for heuristic-based routing. The variables influencing decisions are less likely to undergo significant changes over time or be heavily impacted by seasonality in demand.

Leveraging AI in Order Routing: Enhancing Decision-Making for Optimal Outcomes

Beyond Rules: Embracing Data-Driven Intelligence

While heuristics offer a practical and rule-bound approach, the advent of Machine Learning (ML) or Artificial Intelligence (AI) introduces a dynamic, data-driven dimension to order-routing decisions. ML models assess the most optimal site in real-time, leveraging patterns in data and assigning ‘weights’ to outcomes based on desired criteria.

Choosing Machine Learning: When Complexity Demands Precision

Machine Learning becomes indispensable when the order-routing landscape is characterized by complexity, variability, and a multitude of influencing factors. Here are key scenarios where ML shines:

  • High SKU Counts: Industries dealing with a vast array of products benefit from ML’s ability to adapt to changing variables.
  • Variable Demand: ML excels in handling fluctuations in demand, adjusting routing decisions based on real-time data.
  • Seasonal Dynamics: Businesses experiencing seasonal peaks and valleys find ML invaluable in navigating evolving patterns.
  • Dynamic Labor and Shipping Schedules: ML’s adaptability becomes crucial when dealing with variable labor and shipping constraints.

Striking a Balance: The Hybrid Model

Optimizing Efficiency Through Integration

Recognizing the strengths of both heuristics and ML, a hybrid model emerges as a compelling solution. By using heuristics to limit the number of possible locations and leveraging AI to optimize within that subset, businesses can achieve a balance between simplicity and precision.

Hybrid Model Advantages:
1. Reduced Complexity: Heuristics simplify the decision space, making it more manageable for AI optimization.
2. Adaptability: ML ensures dynamic adjustments within the subset defined by heuristics, addressing real-time changes.

Understanding the Tuning Process

For businesses opting for AI-based order routing, the question often arises: How should one view the tuning of AI decisions? Tuning is a critical aspect of maximizing the effectiveness of ML models in order routing. It involves adjusting parameters, refining algorithms, and continuously optimizing the system based on performance feedback.

Key Aspects of AI Tuning:
1. Data Quality: Ensure the quality and relevance of training data to enhance the accuracy of AI models.
2. Feedback Loops: Establish robust feedback mechanisms to continuously improve model performance.
3. Adaptability: AI systems should be designed to adapt to changing variables and evolving patterns in the order-routing landscape.

Conclusion: Crafting a Future-Ready Order Routing Strategy

In the dynamic world of order routing, the choice between heuristics, machine learning, or a hybrid model depends on the intricacies of each business’s operations. While heuristics offer simplicity and reliability, machine learning introduces adaptability and precision. The hybrid model strikes a balance, combining the best of both worlds.

As industries evolve, embracing AI in order routing becomes not just a strategic choice but a necessity for those seeking to navigate the complexities of modern commerce. The continuous refinement of AI models through robust tuning mechanisms ensures that businesses stay agile, responsive, and well-equipped to meet the ever-changing demands of the market.

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Zach Zalowitz

Zach Zalowitz is considered one of the leading voices in the post-purchase experience space, and has an expertise in order management solutions in the market. Prior to Perficient, he was VP of Digital Technology and Digital Experience at Foot Locker where he oversaw a global team of digital experts focused on the website and mobile app experience. He writes often on the state of digital experience online and instores, and all the parts of the customer journey in between.

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