Bigdata Analytics and Supply chain management

I am a newbie to Bigdata and would like to highlight some significant advantages if incorporated in a company's supply-chain management strategies, expecting the reader's views and suggestions.
  
   Because, in recent past I have developed a online supply-chain management systems in which sellers and customers are matched using an algorithm. It acted as a decision support system and I needed to dig deeper on the available data to get more insights over the data pattern (even for evaluating its correctness).
   
  We know that data is generated throughout the supply chain – So, for sure the manufacturers of consumer goods or services analyze key performance indicators to ensure the resources are delivering at its capacity. So, there is a need for efficient data storage/management and information retrieval techniques.
    
 Current technology can track and capture data effectively and efficiently, even in vast quantities. Meaning that it can then be used to plan collectively to provide precise forecasting that ensures informed decisions can be made quickly. This type of analysis uses variety of data in huge quantities than ever before.

For example, a company can use big data to analyze petabytes of data on demand and supply, sales, identifying business insights etc.,

While thinking of SCM, Big data analytics can also be applied even on:
     Planning:Analysis can be done to predict market trends, demand & supply patterns over a period of time.
   
  Modelling and Designing :A web-based automated procurement system with a procurement model using efficient matching algorithms, searching, negotiation and evaluation may improve supplier selection, price negotiation and supplier evaluation and the approach for supplier selection/evaluation.
  
   Implementation: Volume of data can be used to ensure the correctness and effectiveness of supply-chain management by analyzing time to time. Key Performance Metrics can also be evaluated by looking for data patterns. These revolutionary analytics results in a predictable decision support systems.
   
  Because inaccurate forecasting may result in loss. If we make too much, we're wasting cash and losing profit. If we make too little, we're missing revenue. If you make it at the wrong time, we're probably getting hit by all three. It depends on the supply-chain factors. By implementing fluid demand and supply plans that are updated in real-time, based on true demand signals, material/resource availability and capacity, your revenue and profit potential is maximized.

     Industries get a huge benefit from analytics-driven insight that enable them to more intelligently track and manage. In recent trends, using automated data collection software to feed information into its big data analytics program, many merchandise retailer has dramatically boosted profits and more proactively met consumer demands.

     Since huge companies and industries are mainly focusing on big data analytics to improve their supply chain, primarily targeting to supply-chain management systems, since it is now better able to predict customer buying habits and track the effectiveness of special sales offers, last but not least "the demand".

- VINU KIRAN .S
Reference (http://www.datasciencecentral.com/profiles/blogs/bigdata-analytics-and-supply-chain-management)