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. 

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Topics: Machine Learning, Big Data, Google Cloud Platform

Business Transformation Leveraging Machine Learning and Analytics

Posted by Salil Amonkar on Jan 23, 2017 9:33:27 AM

In this blog I am going to call out the specific areas where machine learning has shown transformational results in a business context and highlight a few use cases that can benefit from leveraging machine learning.

Before we go further I would like to mention why I see machine learning becoming an important factor in business transformation leveraging analytics.

  • Traditional business transformation leveraging analytics relies on definition of static business rules that need to keep up with constant changes in business.
  • These are usually deeply embedded in the core analytic systems and applications and are difficult to change and thus become less effective over time.
  • The above two limitations are key reasons why business productivity typically stalls after the initial deployment of the analytical solutions.

With machine learning we can have a dynamic element to refining the business rules based on patterns derived from analyzing massive amounts of data with business real-time capability which avoids the above mentioned problems.

Below are few examples of use cases where enterprises have seen significant improvements in business indicators leveraging machine learning for driving business transformation without incurring regular maintenance of business rules for accuracy of reported business intelligence information.

Use case #1  involving difficulty in separating data points of seemingly similar but semantically different data patterns (traditionally solved by human managed business rules that require rigorous ongoing maintenance and costs).

  • Airbus Defense and Space, tested the use of Google Cloud Machine Learning to automate the process of detecting and correcting satellite images that contain imperfections such as the presence of cloud v/s snow formations. This business intelligence service that was derived from this was extensively by enterprises managing farming operations, construction operations across Europe. Using machine learning eliminated the deficiencies of the previous time consuming, error-prone solution that was unable to scale with the needs of the enterprises. Results of the improved solution were improved farming profitability due to increase crop yields, lower construction costs with the ability to predict environmental impact on construction projects.1

Use case #2  involving multiple drivers of demand information with or without complex offerings and multiple data management systems/applications/repositories with or without disjointed front end and back end processes in Value Chain.

  • Danone achieved significant business transformation by connecting disconnected front end complex demand and forecasting processes with their operational back end planning processes while adopting predictive commerce and forecasting methods leveraging machine learning. The key to Danon’s success was improving forecast accuracy by leveraging machine learning and using upstream and downstream data from both internal/external data sources. Results of the improved solution were a direct increase in forecast accuracy to enviable 92% with decrease of forecast error by 20%. The indirect benefits of this were reduction in lost sales by 30% and reduced obsolescence of products by 30%.2

Use case involving inability to figure out drivers or having to deal with unstructured data to drive business transformation in a Retail Aspect.

  • Ocado Technologies an online only retail grocery supermarket with 500,000 customers had to figure out how to service their customers better and yet improve profitability while dealing with mostly textual content. They applied machine learning to determine patterns of customer behavior that not only helped them improve the customer interaction in term of faster response to customers but also helped them make their distribution center efficient by automating the way they managed the storage and retrieval of their inventory for the most efficient distribution.3

With the above background here are a few use cases within specific industries that I would like to highlight as potential use cases where machine learning based business transformation could lead to significant business benefits.

High Technology/Discrete Manufacturing:

  • Propensity to buy including improved service attach rates
  • Demand forecasting
  • Supply Chain optimization especially for those enterprises with complex offerings and rapid changes
  • Predictive maintenance or condition monitoring
  • Warranty reserve estimation

Services including Finance:

  • Cross-selling and up-selling
  • Customer Segmentation
  • Sales and marketing campaign management
  • Credit worthiness evaluation
  • Risk analytics and regulation

Retail :

  • Recommendation engines
  • Upsell and cross-channel marketing
  • Market segmentation and targeting
  • Predictive inventory planning
  • Customer ROI and lifetime value
  • Managing Omni channel value chains

Key factors to keep in mind for achieving success with Machine Learning driven Business Transformation

  1. Right Use Case identification
  2. Identification of data and quality of data; data needs to have right representation to ensure that is representative of data that is required for achieving desired business results
  3. Optimize the algorithm for improving prediction accuracy (use step by step approach of training of models, validation of models and selection of models to set the initial set of business rules).
  4. Use a crawl, walk and run approach

In my next blog I will describe how to successfully adopt the crawl, walk and run approach for leveraging machine learning for business transformation.

 References:

1 From reference information publicly shared by Google on the work done with Airbus

2 From 2015 Material Handling and Logistics Conference

3 From Case study illustrating usage of machine learning at Ocado

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Topics: Machine Learning, Analytics, Business Transformation

What is Machine Learning in Big Data, really?

Posted by Manohar Powar on Jan 6, 2017 5:10:30 AM

What is machine learning in Big Data?

Are computer systems that learn from data. It is not explicitly programmed to handle a task. The amount of data and quality of data determines the  ability or performance of the machine learning model to handle the given task.

To summarize machine learning systems -

  • Learn from data
  • no explicit programming
  • discovering hidden patterns
  • data driven decisions

Machine learning is an interdisciplinary field:


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Topics: Machine Learning

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.

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Topics: Tableau, Google Cloud Platform

The SaaS Market - Looking Beyond 2016...

Posted by Murali Bojjala on Dec 15, 2016 10:07:50 AM

 

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A Perspective on what makes good Value Chain Planning Solution(s)

Posted by Salil Amonkar on Dec 8, 2016 10:01:20 AM

I have spent most of my professional career providing Business Transformational solutions. A significant amount of that time has been spent on providing enterprises with Value Chain and or Supply Chain Planning solutions based on software provided by well-known companies such as Oracle or even custom solutions in a few cases.

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Advancing Business Productivity - Digitization leveraging Analytics, Cloud and SaaS

Posted by Salil Amonkar on Nov 14, 2016 1:06:42 PM

The Confluence of Analytics, Cloud and SaaS has the potential to drive Business Impacting Results within weeks instead of quarters that most businesses especially enterprises are used.

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Planning in a Box showcased by Tableau at their Largest user conference as an innovative Supply Chain solution with embedded analytics

Posted by sangeetha reddy on Nov 9, 2016 8:11:49 AM

Planning in a Box - SaaS supply chain collaborative platform built on GCP recognized by Tableau at the user conference as innovative with embedded analytics .     Click Here to Request a Trial Licence

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Topics: Machine Learning, Tableau, Google Cloud Platform, Artificial Intelligence, Paxata

Better Supply Chain Management with Machine learning and Embedded Tableau Analytics

Posted by sangeetha reddy on Nov 1, 2016 3:31:02 PM

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Topics: Machine Learning, Big Data, Analytics, Tableau, Google Cloud Platform, Artificial Intelligence, Paxata

A Game Changing Year for IT Industry led by Cloud, Big Data, Social and Mobile Platforms

Posted by sangeetha reddy on Oct 23, 2016 8:21:26 PM

It has been almost 15 years since Silicon Valley in california has seen the current high momentum in innovation, technology and investments, last since during the dot com days in 1999.  The successes of social media companies and its impact on business and geo political landscape has made us only imagine further on the impact to the IT industry.

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Topics: Big Data