How Data Science Can Enable Solutions for and Help Organize the Logistics Industry

2020-11-24 15:51:29 Logistics News (Roadways & Railways)

( Major takeaways from his article)

Since the onset of the pandemic, the logistics sector has faced labour shortages, cargo capacity challenges, manufacturing slowdown, order delays, stuck shipments, etc. There have also been demand and supply shocks in the past few months, as the supply chains struggled through the pandemic.

The UN estimates that more than 40% of food produced in India is wasted before it reaches the consumer

These issues have an impact on other sectors, especially food and essentials. The UN estimates that more than 40% of food produced in India is wasted before it reaches the consumer. The pandemic exacerbated these losses around the world, as produce rotted in the farms, with no farm-hands to harvest or logistics to transport it, especially when retail chains/stores had to shut down at the peak of the pandemic.

Hence, it has become essential to address the logistics industry's challenges to continue the growth and survive shocks (pandemic, seasonal and otherwise).

How Data Science Can Enable the Next Level for the Logistics Sector

An Integrated National Logistics Grid

1.     Create an Integrated National Logistics Grid: The logistics sector in India spans more than 50,000 routes across 700+ districts, more than 10,000 manufacturing companies and 2 Lakh+ transporters, plying ~1 crore commercial vehicles. These large data sets exist largely in their silos with a manufacturer, transporter, or at location level with brokers/agents, but rarely in an integrated manner that would permit a network view.

Organizing the data into usable, interoperable matrices and integrating these into a national logistics grid will help create an information powerhouse that can be mined for analytics and insights and value creation opportunities. This first step itself will enable transparency and drive efficiencies by resolving information asymmetry and democratizing the logistics sector.

2.      Make Logistics Infrastructure Smart, at Scale

. Make Logistics Infrastructure Smart, at Scale: Data science is also being used by countries to create smart infrastructure at a scale which can drive a new level of cost and value creation, especially in pandemic times when bottom-lines are stressed. Physical logistics infrastructure including roads and warehouses are being embedded with telematics/ sensors planned for the next level of driverless trucks & drones, with driverless trucks moving non-stop over long distances. Availability of real-time data & analytics to optimize the demand-supply patterns constantly at scale is becoming a reality and creating agile supply chains, which can be integrated into the national logistics grid.

3.     Integrate Demand-Supply Patterns into the National Logistics Grid to Drive the Next Level of Efficiency & Visibility:


 Using the power of data and predictive analytics capabilities, we can plan more accurately, improve operational efficiency, automate processes and drive continuous innovation. Sophisticated demand forecasting algorithms can help manage production schedules to avoid customer impact. Uncertainty is one of the biggest challenges businesses face, and data science and predictive analytics can help businesses manage it much better. Historical data linked to seasonality, geographical variations and other variables, embedded in the National Logistics Grid, can be mined to reduce guesswork and ease out stress on the network in advance ensuring harmony in logistics flows.

This industry will rapidly organize in unprecedented ways

Organizing the logistics industry has been the dream of many for an upstart, and governments have made earnest attempts. But like a black swan, perhaps this industry will rapidly organize in unprecedented ways, as multiple powerful forces for change are now aligning, starting with the Goods & Service Tax (GST) reform which gave us a single national market, to the electronic e-way bill, electronic tolls and multiple other initiatives, all with a common denominator: they all move the logistics industry to digitize and enable transparency while building the foundation for leveraging data science.

The tipping point is now in sight

We may still be some distance from having an organized logistics industry, but the process is well underway, and getting faster. The tipping point is now in sight. We may not be there yet, but the speed at which Indian logistics will move may surprise all of us in the coming years.

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