Fresh Food Distributor used ML to improve the distribution of perishable packages

“If we can continue to do what we’ve been able to do until now, and scale with growth, then I don’t have to worry about keeping up with demand, and can think about creating new business models.”

Chief Executive Officer, Fresh Food Retailer

Introduction

The client is the nations #1 provider of farm-fresh fruit to businesses. This family owned and operated company launched in 2008 as a way to help companies provide healthier food options to employees, quickly becoming one of the fastest-growing companies in the U.S. year over year. Their business model is heavily dependent on technology to ensure they can efficiently deliver perishable produce at the highest quality, using local delivery carriers.

Why We Chose Pluto7

With rapid growth placing a burden on current processes, the client looked to Google Cloud and Pluto7 to help them scale their distribution operations. By harnessing Pluto7’s expert Machine Learning support, the retailer was able to successfully leverage Artificial intelligence models to determine the most optimized routing for delivery to their customers.

Working with Pluto7, this client experimented with Google Cloud technology to leverage more data, analytics and Machine Learning. Ultimately, the objective is to achieve breakthrough innovation and business transformation by expanding the success of leveraging Google Cloud, Artificial intelligence and Machine Learning to related use cases and beyond.

Solution

Successful micro package distribution of perishable products depends on multiple factors like reaching the customer on time, avoiding congestion on routes and precise location delivery. Selecting the best route and the right local delivery carrier in the distribution network involves a lot of complexity, and rule-based logic must be updated continuously to reflect the constant changes in patterns.

This is a classic problem that Machine Learning and Artificial Intelligence based solutions can solve. In order to come up with a highly accurate predictive model for routing recommendation, past data needs to be readily available and properly labelled. Once this has been achieved, the data can then feed into a classification model that predicts the best routing for a given set of input parameters such as to and from location, number of packages, size of packages, delivery time, cost, distribution vendor, and so on. We helped the client utilize Google Cloud Platform to quickly and efficiently collect all related data and used Machine Learning and Ai to generate highly accurate routing recommendations for micro package distribution.

Optimized distribution and delivery for real-time supply chain planning

We harnessed analytics, Machine Learning and Artificial Intelligence to determine best routing for their perishable product delivery. With Pluto7’s expertise, they were able to identify the right Google Cloud solutions for their needs and quickly developed a Proof of Concept that demonstrated routing with over 90% accuracy- meeting the needs of the route and carrier selection for each target market.

Products Used

  • Google Cloud Platform
  • Google Cloud Dataprep
  • Google Cloud Dataflow
  • Google BigQuery
  • Google Cloud Storage
  • Google Machine Learning
  • Google Tensorflow