Improve Delivery Times for a More Streamlined Supply Chain

Jon Finerty | 3/23/2023

Topics: Predictive Analytics, Minitab Workspace, CART, Minitab, Supply Chain

Delivery lead time is a crucial supply chain management parameter. It’s critical for businesses to understand how long it takes a product to go through the pipeline from the original order to delivery, whether they are purchasing, selling, or moving goods and materials internally.

With consumers growing more and more accustomed to receiving their orders the next day—or even the same day—supply chains have been more and more focused on the delivery portion of their processes. Supply chains that want to remain successful, that is.

We can define successful delivery as getting your customers what they need—in the right amount, at the right time—and doing it consistently. Delivery now rates as the most fundamental requirement of any manufacturing or distribution business. Using the power of data analytics, Minitab can help companies optimize delivery, streamline their supply chain, and increase customer satisfaction.

Measure Speed of Delivery

The time taken to deliver a final product to the end customer is a key measure for supply chain professionals. Using a sample data set and some simple descriptive statistics, the example below shows that average (or mean) for the delivery time is between 54 and 55 hours. The data also indicates that the minimum time is 40 hours and the maximum time is 75 hours, so it provides a range of what the fastest and slowest times are which helps with goal setting.


Set a Goal and Brainstorm Possible Factors That Impact Delivery

A delayed delivery can cost the organization not only in terms of the customer experience, but it can also directly impact sales. If a company can’t deliver on their promise of a timely delivery, their customers will be far less likely to purchase again in the future. Set a strategic business goal to deliver goods within a certain timeframe. In this example, let’s set a realistic target of 50, which represents ~10% improvement in delivery time.

Next, brainstorm the possible variables that could be impacting the timeframe of delivered goods. This could be anything from package size, vehicle age, weather conditions, or even the driver making the delivery. The diagram below shows an example of a CT Tree, one of many powerful brainstorming and structured problem-solving tools included in Workspace.



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Quantify Impacts Using Predictive Modeling…

In general, predictive modeling is helpful in assisting in making predictions as well as understanding the factors that are influencing the response. By using Minitab’s Automated Machine Learning Tool, not only do we get to see the best model (in this case Random Forests®), but we also get to see how other models performed.r-squared-table

In this case, the popular and traditional regression method not only performs the poorest, but it also isn’t very accurate. However, the CART® model, ideal for visualizing relationships, performs relatively well.

Apply Improvements…

By looking at the CART decision tree below, it becomes clear that the fastest deliveries occur in sunny conditions with a newer vehicle, while older vehicles delivering in snowy weather take the longest. This provides the first area to address for improvement. While it’s impossible to control weather conditions, maintaining a fleet of newer vehicles could lead to some immediate improvement. Additionally, considering the weather forecast for a specific customer region could provide more accuracy when initially calculating and communicating delivery times.  



…And Operationalize the Model to Predict When Deliveries Will Arrive

Not only can this analysis help identify areas of improvement, but it can also help communicate with customers. By considering the factors at hand, and leveraging the most accurate Random Forests model (as determined by Automated Machine Learning), we can operationalize the model to automatically communicate with customers. Using solutions like Minitab Model Ops, as these data points are collected, the model can calculate the estimated delivery time and automatically communicate the timing to customers. This will ensure that your customers will be informed in a timely manner, so they won’t be guessing when their package will arrive. As you improve your performance, not only can you overdeliver on your customers’ expectations, but you can continue to refine your predictive model to provide more accurate timing to customers in the future.


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