Data and Analytics will Pave the Way for India’s Logistics Sector

By : |June 7, 2019 0

Logistics is being transformed by data-driven insights. The operating models of logistics companies have evolved to integrate emerging technologies into various processes. Analytics expertise, data-driven decision making, and the new services that can be achieved with the Internet of Things (IoT) represent an immense $1.9 trillion opportunity in logistics.

Thanks to digitalization, unprecedented amounts of data can be captured from various sources along the supply chain. Big Data offers massive potential to optimize capacity utilization, improve customer experience, reduce risk, and create new business models in logistics. Big Data has already begun to make an impact on the logistics industry by turning large-scale data volumes into a valuable asset. Moreover, advancements in analytics and artificial intelligence can unlock exciting new ways for logistics providers to monetize data-driven operating and business models.

These technologies will be useful at almost every step of the supply chain. Data analytics, particularly, holds great relevance for logistics. A company can take raw, real-time data and layer it with analytics to assess the relevance of the data to the business, and how best to utilize it. It also can extrapolate the far-reaching implications of real-time information.

The applications are many…

Dynamic, real-time route optimization through the intelligent correlation of data streams (shipment information, weather, traffic, etc.) can enable more efficient scheduling of consignments, optimization of load sequences, and very accurately predict the time of arrival.

Smarter forecasting of demand, capacity, and labor through Big Data analytics can significantly optimize planning and resource utilization, process quality and performance, and reduce unnecessary costs in the supply chain.

Online retailers, who have analyzed their customers’ purchasing behaviors, can use anticipatory shipping to predict an order before it occurs. This can then be used to move goods to distribution centers that are closer to a customer who is likely to purchase the products. It can enable retailers to offer same-day or even one-hour deliveries.

Companies can improve end-to-end supply chain risk management by evaluating current conditions with existing data pools. Big Data can be used to mitigate risks by detecting, evaluating, and providing alerts on potential disruptions caused by unexpected events, man-made or natural. This can be further enhanced through the integration of data from IoT devices.

As the first step toward leveraging the transformative power of such a predictive supply chain, companies must lay the foundation in the form of a descriptive supply chain – by improving its ability to collect and tap supply chain data to better identify trends and respond to change. Descriptive analytics are enabled by business intelligence systems, such as supply chain dashboards and scorecards, as well as data visualization and geographic mapping tools. With these in place, companies can manage the day-to-day operations of their supply chain to become more agile and cost-effective.

A predictive supply chain enables companies to shift from reactive to proactive management. Business leaders and managers have traditionally relied on historical data to make strategic decisions. Predictive analytics expands their visibility to seeing what’s coming. Organizations that have used data effectively stand to gain from higher revenue, improved customer service, successful product launches, and higher-quality products. Companies that are good at predicting demand can also improve their margins.

Moving forward, businesses that invest in technology-driven logistics will gain a tremendous competitive advantage, as they are equipped to make more informed decisions, increase efficiencies, and exceed customer expectations. Logistics companies that employ these technologies can offer vastly superior services with higher levels of safety and regulatory compliance, faster transit times, real-time visibility into the status of consignments, and much better customer service. This is particularly important for catering to the time-bound needs of industries such as e-commerce, FMCG, pharmaceuticals, automobile, and agriculture.

Businesses, whether they manage their own supply chains or engage with logistics providers, should make the most of data and analytics, for these will be two of the major drivers of success in the future.

By Manav Verma, Chief Digital and Marketing Officer, DHL SmarTrucking, India

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