Pluto7 Blog

Planning In A Box - My thoughts On scalability

Posted by sangeetha reddy on Dec 20, 2016 12:38:50 PM

As this week draws to a close, and I continue to work with beta customers, I thought I'd share a bit of my own thoughts on our Planning in A Box SaaS solution and the relevance to Supply Chain.

Generally speaking, modern cloud platform initiatives are providing innovative paradigms and possibilities in supply chain management industry programs. To date most of these business models in the supply chain management industry are aligning to adopt to the growing possibilities of data driven variations. This in particular is seen as more viable than ever before.  Further, the evolution of cloud platforms has laid out transformational trends in how data volatility is perceived, analyzed and the overall flexibility of process visibility the contemporary SaaS systems are able to provide. As we analyze the historical precedents in this aspect, the need for integration of supply chain models with a reliable SaaS solution built on a cloud platform is more than a mere necessity. This is where Pluto7’s new Planning in a Box solution steps into play.

To state the highest level nature of Planning in a Box, it is a supply chain collaborative analytics application, which is built on google cloud platform. The Planning in a Box app can scale to virtually any volume of data and is enriched with embedded Tableau dashboards. The data can be very simply imported in a number of different formats/types such as excel, .csv and google sheets, and it focuses specifically on helping planners to proactively drive supply and demand planning and forecast accuracy. The analyzed data we provide can be exported for further analysis if required or for offline sharing, though the tool has collaborative capabilities built in. Planning in a Box helps users to focus on backlogs, supply commits, on hand inventory, manufacturing forecast, projected PO’s etc., which can all be customized based on the company’s specific needs.

Planning in a Box also has machine learning embedded which enables product recommendations, displaying all the similar products in sync with the products viewed, it also exhibits options of recently viewed items, what other customers have viewed, which helps to better understand all the other products.

As I continue working with our earliest beta customers, I am looking forward to helping many more companies explore the possibilities of exception planning at massive scale.

Interested in joining our beta effort?

SIgn up for beta!

 

Topics: Tableau, Google Cloud Platform