Beyond the data silo: Logistics management takes a quantum leap   

And this valuable information can be pivotal in optimizing operations, enhancing efficiency and improving customer experiences. However, the logistics sector often faces challenges in managing and leveraging data effectively. Why? Data silos.  

 

Despite the large volume of continuously generated data, it is often fragmented and stored in isolated silos leading to point solutions being deployed, making it challenging to obtain a comprehensive view and extract meaningful insights. And then you must factor in data quality and accuracy problems, among other considerations. If data is inaccurate or incomplete, it can hinder decision-making and lead to operational inefficiencies. 

 

These logistics management obstacles are more than inconvenient. Data barriers result in longer shipping times, higher costs and unsatisfied customers — the antithesis of what logistics operations should be.  

 

By harnessing the power of technologies like artificial intelligence (AI), advanced analytics and quantum computing, logistics companies can unlock the full potential of their data and gain a competitive edge in the industry. Here’s how you can solve key data barriers and apply advanced technologies to address these hurdles. 

To fully appreciate the data obstacles in logistics, it is essential to understand distinct types of data and their impact on operations. There are three key data types: dark, unstructured and structured. 

 

What is dark data? Dark data refers to the vast amount of unutilized or unanalyzed data that organizations possess and is undocumented or undigitized.  

 

Dark data is typically generated within a logistic organization’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 cannot be used. It represents a missed opportunity for logistics companies to uncover valuable insights and make informed decisions. 

 

What is unstructured data? Unstructured data refers to information not adhering to a specific data model or format, including text documents, emails and images. 

 

Logistics operations generate a vast amount of unstructured data, including handwritten shipment notes, customer feedback from diverse channels and other fragmented pieces of information. Analyzing and extracting meaningful insights from unstructured data using traditional methods is challenging, making it difficult for organizations to derive meaningful insights. 

 

What is structured data? Structured data is organized and formatted data that can be easily stored, searched and analyzed, including data in databases, spreadsheets and well-defined data models.  

 

Structured data lives within databases and spreadsheets and holds significant potential. From transactional records and shipping manifests to customer order information, logistics organizations can use this information to streamline operations. But, without advanced analytics capabilities, decision-making remains suboptimal, impeding the ability to predict demand patterns, optimize routes and proactively manage inventory. 

By leveraging advanced technologies, logistics organizations can put these three data types to good use. Let's explore how: 

 

To unveil the hidden potential within dark data, organizations can integrate decision points at various stages of your operations, reinforcing your AI models with every interaction and learning opportunity. With robust data governance frameworks and meticulous data integration strategies, you can bring dark data to light without disrupting normal day-to-day operations. As a result, you can uncover previously elusive insights, enabling you to optimize processes and improve efficiency. 

 

To tackle the obstacles posed by unstructured data, turn to AI. By applying natural language processing (NLP) and computer vision, you can automate the extraction and structuring of customer feedback, shipment notes and other unstructured data sources. Doing so enables you to gain valuable insights, identify patterns and address customer concerns precisely.  

 

Logistics companies can automate data extraction and structuring by employing AI models, enabling better decision-making. For example, AI models can process customer feedback to identify patterns and sentiments, helping improve services and address issues more effectively. 

 

For structured data, advanced analytics can generate deeper insights and provide recommendations. By applying advanced analytics techniques — such as predictive modeling, machine learning algorithms and optimization algorithms — to structured data, logistics companies can gain actionable insights for optimizing operations. 

 

Organizations can predict demand fluctuations, optimize flight schedules and streamline inventory management through these methods. The result is improved operational efficiency, cost savings and a competitive edge in the industry. 

Consider the process of building a unit load device (ULD) or pallets for air or truck shipments. Structured data provides crucial information about shipments' dimensions, weight and contents. However, unstructured data — handwritten notes on damaged packages — and dark data — decisions made on shipment exceptions — often exist outside digital applications. Because this data is generated offline, the system does not receive feedback on improving its processes and ULD-building capabilities. Because the solution cannot learn without feedback, the process does not improve. 

 

But there's a fix. Logistics management systems can collect the data via intelligent process workflows and then use AI and advanced analytics to learn and improve the ULD and pallet-building process. And because logistics operations deal with so much data at any given time, adding quantum computing allows this data to be utilized in the timeframe the operations team needs to respond. With quantum computing, logistics organizations can process vast amounts of data in near-real-time, enabling you to make data-informed decisions better and faster via logistics optimization and analytics. 

Data challenges in logistics can be daunting but harnessing your different data types — dark, unstructured, and structured — is a critical first step. By applying the power of AI, advanced analytics and quantum computing, you can unlock the full potential of your data, optimize operations and thrive in the competitive logistics landscape.