Harnessing data for smarter logistics management

Photo: Getty images 1470664961, licenced for Unisys

For those navigating the complexities of global freight, data is more than mere digital ink on a ledger. It’s the bedrock upon which the interconnected supply chain stands. From shippers and freight forwarders to ground handlers, each relies on real-time insights to make critical decisions. In this context, unutilised or mismanaged data isn’t merely a lost opportunity; it’s a risk to timely deliveries, cost-efficiency, and, ultimately, customer satisfaction.

The economic toll of ignored data

In today’s interlinked and rapidly evolving supply chain, there’s no room for lapses in data management. The consequences extend beyond operational hiccups to eroding customer confidence. However, the rise of emerging technologies like artificial intelligence (AI), advanced analytics and quantum computing offers opportunities for a paradigm shift.

Navigating the three facets of logistics data

To effectively leverage your data, it’s crucial to differentiate among these three primary categories: dark, unstructured and structured. Understanding these will lay the groundwork for your data strategy.

1. The hidden value of dark data

Dark data, often overlooked in logistics operations, refers to the vast amount of unutilised or unanalysed data organisations possess that is undocumented or undigitised. Dark data is typically generated within a logistic organisation’s operations through various sources, including customer interactions, daily operations, sensor data and transaction records. However, it is undocumented, may even exist in someone’s head and therefore easily lost or not used. It represents a missed opportunity for logistics companies to uncover valuable insights and make informed decisions.

Leveraging AI and robust data governance, you can transform this idle data into actionable insights that enhance operational efficiency — improving resource allocation, demand forecasting and customer satisfaction.

2. Making sense of unstructured data

Unstructured data — like emails, handwritten notes and customer feedback — often defies easy analysis. Its irregular format makes it a challenge for traditional data models. Unstructured data lives within organisational boundaries but has not been effectively analysed. However, emerging AI technologies like natural language processing and computer vision are changing the game. They transform this labyrinthine data into structured insights, enabling logistics managers to discern patterns, forecast trends, and ultimately make decisions, such as optimised routing logic, that are both timely and data-driven.

3. Capitalising on structured data

Structured data stands as an accessible asset in logistics management due to its well-organised format. This type of data, found in databases and spreadsheets, presents an immediate opportunity for analysis and insight extraction. By utilising advanced analytics techniques, logistics companies can improve their decision-making processes, fine-tune demand forecasts, and take a more proactive approach to logistics management. With recent advances in next-gen technologies, logistics organisations can better use this information to streamline operations further.

Dismantling data obstacles for unified logistics

By embracing AI and quantum computing, logistics organisations can not only meet but exceed the demands of modern supply chain complexities. Combining these technologies can help structure unstructured data sets, capture and log dark data and process decision-making in near-real-time.

It’s time to leverage even more of your data to gain a competitive edge, trim costs, and skyrocket customer satisfaction. Your data is vital to transforming your logistics from a mere operational necessity to a strategic asset.

Want to overcome your data challenges? Unisys can guide you through the frontiers of next-gen capabilities in logistics. Contact us today to learn more.

This article is sponsored by Unisys.