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How Data Can Solve the Logistics & Supply Chain Issues

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Saahil Goel, Co- Founder & CEO, BigFoot Retail Solutions

Saahil Goel, Co- Founder & CEO, BigFoot Retail Solutions

The advent of big data analytics, Internet of Things and superior geo-analytics is here to define a new era of technical innovation in the entire supply chain dynamic. Managers of today can leverage a synergy created by these approaches and have at their disposal a host of new capabilities aimed at disrupting the status quo, enhancing the customer responsiveness and making the processes agiler, whilst reducing the costs and inventory.

Owing to the advanced technical know-how available today for data analysis and processing, major e-Commerce players and other companies stand a golden opportunity to optimize logistics, distribution and production networks. Furthermore, careful data analytics also provide companies with demand forecasts and future trends, hence making them more proactive and prepared for the upcoming times. Crucial tasks like expanding throughput, carrying out preventive maintenance, and asset uptime can be executed well ahead of time, further improving the prospects for the company.

Introducing change is seldom easy, especially when we are looking at a disruptive technology with enormous potential. Let us discuss certain pragmatic applications of data analytics for the supply chain and logistics issues.

Route Planning & Optimization
While the traditional routing systems and software have been assisting fleet managers, drivers and riders, they still leave a lot of unexplored room for real-time calibration and dynamic visualization. By combining data and geo-analytics, companies today can maximize the efficiency and bring down the costs of their on-fleet operations.

By dynamically computing the congestion on the roads, via data collected from IoT sensors, the technology suggests the best routes in real-time, saving time and fuel. Besides, by analyzing the geographical trends, buying patterns and other similar information, companies gain meaningful insights for smart routing accuracy.

The database helps in keeping the recipient's address records down to four levels, including the Street Name and block, which further assists with enhancing the route-planning efficiency and providing precise EDD and ETA. In fact, with proper investment in big data analytics or associating with the right service providers, the cost per shipment for the merchants has decreased by 30 percent.

Smarter Demand Forecasting
Demand forecasting tends to get pretty tedious and time-consuming. Either the relevant information or the adequate systems for processing the same are missing.
Besides, as we take into account the ever-increasing SKU volumes and demand volatility, tracking and forecasting the same becomes rather difficult. However, the technologies of today provide companies with dynamic data collected from both customers and suppliers, along with IoT Sensors. Analysis can be done on the basis of the same, along with other factors such as price, seasonal fluctuations, competition analysis, and others, to come up with the right demand forecasts.

Taking User Experience a Notch Higher
Smartly utilizing the collected data creates opportunities for efficiency and savings to the merchants. The insights from such analysis contribute towards optimizing the supply chain and fill the existing loopholes within logistics and shipping. Furthermore, the data reveals to sellers or buyers the points between two locations, where they can either pick up or drop off the couriers as per their convenience. Providing further assistance to the merchants is the ratings accrued by several shipping and analytics service providers. After carefully analyzing the influx of data, the platforms provide relevant information to merchants, crucial for decision making.

Smartly utilizing the collected data creates opportunities for efficiency and savings to the merchants and helps optimize the supply chain and bridge the gap within logistics and shipping

Service providers today are leaving no stone unturned in providing the most plausible service and value proposition to their customers. There exists newer innovations today, meant to leverage data analytics, whilst providing customers and merchants with actionable recommendations. For instance, at ShipRocket, we have engineered a recommendation engine that takes into account the previous shipping data provided by courier partners. Based on the past behavior and upcoming trends, the recommendation engine provides consumers and sellers with the fastest and most cost-effective means of shipping. Not only does it expedites the decision-making process, but also brings down the costs and time investment in end-to-end shipping typically by 20 percent. Furthermore, data regarding RTO (Return to Origin) delivers, lost couriers, and COD reconciliation further play a significant role in improving the shipping experience.

Database Solving Multiple Conundrums
Cainiao, taking care of logistics in China, is one of the leading examples of the efficient utilization of data analysis and its applications for optimizing processes and reducing costs. The company, as the logistical wing of the Alibaba Group, made the information transparent for both upstream and downstream, whilst assisting decision makers by predicting future trend and automating resource allocations as per their time and location.

With constant innovation and an industry-wide paradigm shift, the model can be replicated in India and other emerging markets. India has all the infrastructure and networking required in logistics & e-Commerce shipping. All that we require doing is accumulate all the data, and work on improving logistics and shipping. This will help in improving the shipping experience altogether, and make it much more efficient and feasible. With the increased emphasis on smart cities, Internet of Things and Digitalization, India stands poised today, at the cusp of immense possibilities and promise. In essence, a data-driven platform is a way to solve the logistics complexity issues in India.