"On-Time Delivery (OTD) Risk Predictions Using Bayesian Model"

Bayesian models are a powerful tool for managing delivery risks, enhancing the ability to meet delivery commitments, and improving overall customer satisfaction.

Implementing Bayesian models for OTD risk predictions would typically involve the following steps:

1. Data Collection: Gathering historical data on deliveries, including timing, delays, and contextual factors.
2. Model Specification: Defining the prior distribution and likelihood function based on the collected data and expert knowledge.
3. Inference: Using Bayesian inference to update the model as new data arrives.
4. Decision Making: Applying the predictive insights from the model to inform logistics and supply chain decisions.

The Bayesian model is a statistical method that subjectively interprets probabilities, representing a degree of belief in an event's occurrence based on prior knowledge. Here's a simplified explanation of how it works:

When applied to on-time delivery risk predictions, a Bayesian model would work as follows:

1.Prior Belief: You start with a prior belief about the probability of OTD based on historical data. For ex. if historically 80% of deliveries have been on time, your prior would reflect that.

2. New Evidence: As new data comes in, such as information about a storm disrupting travel or a strike at a supplier's facility, you gather evidence that could affect delivery times.

3. Likelihood: You assess the likelihood that this new evidence could impact your deliveries. For example, how often do storms or strikes delay shipments?

4. Posterior Belief: You then update your prior belief with this new likelihood to form a posterior belief. This new probability reflects both the historical data and the new information.

5.Prediction: Using the posterior belief, you predict the risk of delivery not being on time. This can be a probability distribution rather than a single value, providing a range of possible outcomes.

Example in reality:

XYZ company that delivers fresh product. The prior belief about OTD might be quite high. However, if a weather forecast predicts a significant storm along the delivery route, the likelihood of OTD decreases. The Bayesian model would update the OTD prediction to reflect this new information, providing a more accurate prediction that accounts for the storm.

In summary, Bayesian models are iterative, constantly improving as they ingest more data, which makes them particularly useful for dynamic and uncertain environments like supply chain logistics.

In Python, Bayesian models can be built using several libraries, one of the most popular being PyMC3 .