Pluto7 Blog

Value Chain Planning moves local with machine learning

Posted by Salil Amonkar on Feb 16, 2017 9:28:23 AM

Technology leads the path for change, but success is dependent upon the ecosystem to adopt the change.


Industries will either move away from or gravitate toward a specific geography based on economics, geopolitical situations or basic demand and supply.  US manufacturing has gone through an interesting journey of starting local (e.g., automotive companies like Ford) and then expanding globally for both distribution as well and manufacturing. These companies are beginning to show that the path can also come full circle to boost manufacturing locally. Part of the need for all of this is driven by US political interests as well as the strong public sentiment and grass roots efforts to "buy local" which are fair expectations, given other countries are doing the same.

At the same time, there are some interesting shifts happening in technology as well, which are shaping the manufacturing industry in new was, powered by Data.  Cloud, when combined with the power of machine learning, now challenges the fundamental notion of the need for human resources at various stages in the value chain. When data is combined with advanced analytics and workflow automation, conducting various checks and balances in the end to end value chain (e.g. status or quality inspection ) are now in threat of being replaced. Will the political campaigns that promise manufacturing jobs really address this analytics problem? In my opinion, only partly. There is an extent to which governments can compel manufaturing companies regarding when, where and how they run their business before experiencing pushback and workarounds. The journey that will persist, no matter what incremental improvements are made in any given supply chain organization, the industry will efficiently explore new ways and means to drive human productivity and better customer experience.  

With the evolution of Cloud solving the new asks of supply chain planning is now becoming lot more critical and the expectations with Lead Time, Inventory Turns and Forecast Accuracy KPI managements have also changed. This is not only for the large enterprises but also for mid size and smaller customers. At Pluto7, we leverage the power of cloud, mobile and big data combined with machine leanrning to solve the very basic problems of demand and supply and drive above KPI improvements. Over decades of experience with Value chain we now have formed solutions to solve the same with Planning in a Box. 

Manufacturing consists of various stages including "Plan," "Make," "Delivery" and "Source. " At each stage the capability of relevant data "instantaneous" which we how see with cloud and mobile,  changes the way the goods and services flow within the supply chain. When you now extend the power of the same to your borader value chain, you have esentially helped improve the key KPI like leadtimes, OTS and other metrics that matter to your customers and partners. Bigdata continues to delvier tangible value which when combined with machine learning in a meaningful way challenges the current state of your value chain driving better SLAs. Thanks to and similar companies who have redefined the value chain with extensive use of machine learning.

In some of our previous posting you can refer to the various use cases with Value chain that effectively uses machine learning.  Refer to


 It is merely a matter of time when we will see wider adoption of machine learning.  Even if process improvements are not being explicitly called machine learning, ML technology will be seemlessly embedded in new explorations of processes and systems. Within our own SaaS offering Planning in a Box, we leverage machine learning for product recommendation for upsell and cross sell driving higher revenue to our SaaS subscribers. We are in active conversations on innovation with Retial omin channel enablement capabilities with market leaders like Google and their end customers leveraging google machine learning ( best known in the form of Google Search Engine). 2017 is an exciting and promising year at global level as well as more specifically for the US markets for the manufacturing segment. 


Topics: Machine Learning, Big Data, Google Cloud Platform