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 (Supply Chain Management), 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".

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Tags: SCM, Supply, analysis, bigdata, chain, decision, dss, management, predictive, scm, More…supply, support, system


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Comment by Marius Alexander Schulz on July 21, 2019 at 11:37pm

Basicly the issue of SCM is the Bullwhip effect, so Big Data for matching supppliers Lacks Strategic partnership perspective. So it only is relevant for trade.

Inventory management is rule based, but uses demand predictions. The point wehre one geht's to Data Science is in mass customization or individualized  production - you then work with demand requirements to organise a flexible production SC.

Comment by Jean-Marc Soumet on January 20, 2015 at 12:38pm

Is supply chain data (inventory, orders) large enough to be qualified as Big Data?

I agree though that there are opportunities for doing data science with supply chain data.

Comment by Steve Cook on January 19, 2015 at 9:59am

I'm working with a company that does inventory optimization with data science - Right Sized Inventory.  They're using analytics to determine reorder points, min/max, etc. based on desired service levels and confidence levels.

Comment by VINU KIRAN .S on January 17, 2015 at 4:55pm

Thank you so much Vincent!  Going forward I will focus on individual key areas using algorithmic and analytic ways as highlighted below. Since each of them needs an individual attention for a good delivery of service or goods.

Comment by Vincent Granville on January 17, 2015 at 9:38am

Supply chain management (SCM) is operations research, and I consider it to be a field of data science. Lot's of interesting problems such as inventory management, pricing optimization and elasticity, assembling and vendor management to optimize costs of manufacturing, and distribution. Imagine the algorithm behind Amazon, to decide where to build warehouses, how to group book orders from millions of customers, how many copies of a book should Amazon purchase and stock (or should books be printed on-demand), how much should warehouse employees (or robots replacing them) be paid, to eventually deliver the book that you've ordered. 

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