Machine learning models are powerful tools, but their true value lies in their accuracy. Without reliable predictions, even the most sophisticated models can fall short of expectations. This is especially true in dynamic fields like logistics, where precise forecasting based on real-world factors can unlock significant operational efficiencies and cost savings.
So, why aren't all ML models inherently accurate, and what can you do to improve their performance?
The Challenge of Accuracy in ML Models
Several factors can hinder the accuracy of machine learning models:
- Data Quality and Completeness: This is arguably the biggest determinant of model accuracy. If the data fed into the model is incomplete, inconsistent, or contains errors, the model will learn from these flaws and produce less reliable predictions. Missing variables, incorrect entries, or a lack of sufficient historical context can all contribute to inaccuracies.
- Lack of Sufficient Data: For a model to learn robust patterns, it needs a substantial amount of relevant historical data. If there isn't enough data, especially when dealing with new or unique scenarios, the model may struggle to generalize effectively and make accurate predictions.
- Dynamic Environments: Many real-world applications of ML, such as supply chain management, operate in constantly changing environments. New variables, shifting trends, and unforeseen events (like extreme weather) can quickly render older data less relevant, impacting a model's predictions if it's not continuously updated and adapted.
- Seasonality and Unique Patterns: Seasonal fluctuations and unique operational patterns significantly influence outcomes. If a model isn't exposed to enough historical data to capture these recurring trends, it won't be able to account for them accurately in its predictions.
- One-Off or Unforeseen Events: While models can learn from historical data, true disruptions (like a major weather event) are difficult for a model to anticipate without specific, real-time adjustments.
Strategies to Improve ML Model Accuracy
Here are some strategies to help improve ML model accuracy:
- Prioritize High-Quality Data: This is paramount. Ensure your data is:
- Complete: Include all relevant variables that could influence the outcome you're trying to predict (e.g., origin, destination, package dimensions, weight, day of the week, time of year for logistics).
- Consistent: Maintain a standardized format for data transfers to minimize processing errors.
- Clean and Valid: Remove or correct erroneous entries, duplicates, and data that doesn't represent legitimate events (e.g., mid-route injections that distort typical transit times).
- Timely: Provide executed shipment data on an ongoing basis so the model is always trained on the most current information.
- Provide Sufficient Historical Context: Aim to provide data that captures at least one full period of seasonality (e.g., a full year for seasonal shipping patterns). This allows models to learn from recurring trends and account for them in predictions.
- Account for Supply Chain Changes: When significant supply chain changes occur (e.g., a new distribution center, carrier, or route), understand that the model will need time to accumulate new historical data to learn the resulting patterns. For changes that overlap with existing patterns, adaptation will be quicker, but entirely new elements will require more data accumulation to reach peak accuracy.
- Integrate External Data for Unforecasted Events: For significant, one-off events like severe weather, models can benefit from real-time adjustments. This often involves integrating external data sources or having mechanisms to temporarily adjust predictions based on known disruptions. This moves beyond static predictions to more dynamic, real-time forecasting.
- Leverage Broader Datasets When Possible: While your unique data data is critical for tailoring predictions, access to a larger volume of relevant data (ex. carrier performance data) can enhance model performance, especially for scenarios where your individual historical data might be limited. This allows models to learn from a wider range of patterns while still prioritizing your unique operational characteristics.
- Understand Variable Importance: Recognize that not all data points have the same impact on a prediction. Factors like distance and package weight might naturally have a stronger influence on transit times than others. Understanding these variable weights can help in prioritizing data collection and validation efforts.
The Role of Platforms in Enhancing ML Accuracy
For many organizations, even aggregating the sheer volume and quality of data needed to train highly accurate ML models can be a challenge. This is where platforms like Shipium that specialize in ML-driven optimization can help.
The right optimization platform can help you:
- Accelerate Data Accumulation: By handling label generation or other operational processes, a platform can accumulate sufficient production data over time. For example, even 90 days of production data can provide a solid initial base for creating robust training and validation datasets.
- Leverage Pooled, Anonymized Data: As mentioned, platforms can aggregate, anonymize, encrypt, and train models on broader data to enhance performance. This allows models to learn from a wider variety of scenarios, improving accuracy even for shippers with less extensive individual historical data. This collective intelligence ensures that models are robust and adaptable to various challenges.
- Provide Expert Model Management: Specialized platforms have internal processes to manage the complexities of model training, validation, and continuous adaptation. This includes implementing strategies for handling unforeseen events, updating models with new data, and ensuring ongoing accuracy, freeing up internal resources.
Achieving high ML model accuracy is a continuous journey that requires a commitment to data quality, consistent data provision, and an understanding of how models learn and adapt.
If you're a shipper looking to optimize operations with data-driven insights, reach out to discuss your use case with our team here.