Machine Learning For Omnichannel Businesses

Customer: Amazon Seller
Location: Us-West1,San Francisco, CA
Partner: Pluto7 Consulting Inc.
Google Cloud Platform's official blog featuring us: Read here

Customers Specific Challenges ‘pre-Google’ Solution:

The current solutions in place at Amazon Seller were inhibiting the organizations plan to scale. They were unable to anticipate and predict the demand changes which resulted in the factories following a more manual scheduling methods and the warehouses maintaining safety stock was significantly above the industry norm.

The entire planning was largely manual or Excel based.This resulted in revenue being left on the table as a result of not being able to run at a capacity that was desired by their customers who then gave that portion of the business to other suppliers. While keeping high safety stock levels enabled them to meet the desired service levels it resulted in heavy obsolescence and inventory holding costs that were eating into their profits.The lack of sophistication in demand management also resulted in their inability to scale their e-commerce channel. AMAZON SELLER was looking for an end to end solution that would enable them to do advanced demand management with industry leading forecasting methods and use the same as the driver for the distributed Supply chain planning that they needed to drive the factories and warehouses.

They are unable to tap potential synergies of demand for their product due to lack of information (they do not have resources to get such information). Their ability to optimize the distribution and delivery of their offering is constrained as a result of all or any of these factors

Time sensitivity around trends in customer sentiment for their products

Lack of efficient deliveries and supply management – economy of scale

Ability to predict the inventory from source to distribution centers to amazon fulfillment centers.

GCP Components Used by Planning in a Box running on GCP as well as AMAZON SELLER/ECOMMERCE GCP instance:

App Engine, Compute Engine, Google Cloud Storage, BigQuery, Cloud ML Engine.

They are already google customer leveraging gsuite offerings and Pluto7’s SaaS product Planninginabox solution for planning which uses GCP and Cloud ML. click here Case study

Customer Acknowledgement of the Work Done:

Founder from AMAZON SELLER/ECOMMERCE were pleased with the results of the Planning in a Box , the SaaS that runs on GCP and uses Demand ML, the solution built on top of GCP and will use the ML model everyday to make supply decisions. The company sees a lot of potential in the efficient demand planning and supply planning. Currently AMAZON SELLER/ECOMMERCE are centralizing all their data in GCP in CloudSQL and Bigquery.

Benefits Customer Has Seen so Far:

Pluto7 implemented end to end Cloud and SaaS solution that forecasts demand, which is then used to run the distributed plan for the factories and the warehouses with following key features:High Forecast Accuracy.Distributed Planning for Warehouse and Factories.Exception based alerts with multimedia collaboration for effective shortage resolution and excess reduction.Executives are able to have a Sales and Operations Plan and improve Revenues Factory and Warehouse Operations are able to operate at significantly lower inventory levels and actual improve service levels