Data Analytics in Logistics and Supply Chain: Predictive and Prescriptive Analytics

What is Big Data?

The term Big Data is generally used for a massive volume of structured and unstructured data. It becomes extremely difficult to process or analyze these large datasets using traditional analytic tools and techniques. With the growing number of transactions, the subsequent amount of data being generated is also growing exponentially. Many enterprises are experiencing huge data volumes and it’s growing so rapidly that it’s exceeding their current processing capacity.

Why is Big Data Analytics Needed in Supply Chain?

With the advent of IoT-based devices the manufacturing is changing rapidly from event-based planning to real-time sensing, even leading to automated maintenance and process adjustments. Data from various mobile devices, social media platforms, blog comments can be integrated with enterprise data to better understand the customer needs.
Data is growing tremendously in volume, variety and velocity and can prove to be of immense potential if exploited the right way. Big Data has enormous capability to change the Supply Chain Industry by creating substantial changes in efficiency, operational cost, visibility and customer experience.

The Difference — Descriptive, Predictive and Prescriptive Analytics

Descriptive Analytics is the most primitive type of business analytics technique that is used to analyze the historic data. Generally, In case of logistics and supply chain what happened in the past is not as informative as analyzing the future outcomes. Analyzing historic data like shipment volumes, delays, shipment losses, and so on can be termed as an example of Descriptive Analytics.
Predictive Analytics is an advanced business analytics technique used to predict future outcomes based on historical data input. This type of data is generally used for resource planning, inventory optimization, sales forecast etc.
Prescriptive Analytics can be termed as an enhanced version of Predictive Analytics. Prescriptive Analytics provides the best possible solution for achieving the desired objective taking other critical factors into consideration. Prescriptive Analytics help create smarter value chain through decision making.
Prescriptive Analytics can play a key role in allowing organizations to set better and scalable objectives.

Investing in Analytics can prove to be extremely vital for an organization as it clearly impacts the efficiency and profits. Also, it allows the stakeholders to get actionable insights from the data leading smarter decisions and better risk management.

For further reading please refer to following links
1. http://www.quintiq.com/blog/to-predict-or-prescribe-analytics-in-logistics-supply-chain-operations/
2. http://www.riverlogic.com/blog/predictive-and-prescriptive-analytics-supply-chain-needs-both
3. http://www.ebnonline.com/author.asp?section_id=1364&doc_id=277270