Data Analytics is a powerful tool for understanding information and deriving actionable insights. However, it comes in various forms and is applied across multiple industries, each with its own challenges and opportunities. This blog post explores the role of Data Analytics in logistics and how it is leveraged at Logward.
The Role of Data Analytics in Logistics
While the core process of shipping a container globally has remained largely unchanged for decades, the ability to capture and utilize logistics data has significantly evolved. The sheer volume of logistics data is immense, as multiple stakeholders contribute to moving cargo worldwide, each with different perspectives on supply chain operations. Consolidating all this information into a single dashboard is both an exciting opportunity and a complex challenge—one that Logward tackles as a key part of its value proposition.
Below, we explore the different types of analytics that contribute to building effective supply chain dashboards.
Different Types of Analytics for Logward Users
Descriptive Analytics: What Happened ?
Descriptive Analytics provides a summarized view of past events, offering customers an overview of their operations to efficiently manage complex supply chains. The more data available, the deeper the insights.
Examples of insights from Descriptive Analytics:
- Changes in shipment volumes over a given time period.
- Tracking accuracy based on real-time shipment locations.
- Operational movements at certain ports can be analyzed through detention and demurrage charges incurred by customers.
Diagnostic Analytics: Why Did It Happen?
Beyond knowing what happened, businesses need to understand why it happened. Diagnostic Analytics identifies the root causes of supply chain events and disruptions.
Key questions Logward helps answer:
- Where do most shipments get delayed?
- What common disruptions impact the supply chain?
- Were empty containers picked up on time?
- Were shipments stuck at inland container depots for too long?
Transportation milestones
Understanding which milestones impact OTIF (On-Time, In-Full) delivery is crucial for consistent shipping operations.
Predictive Analytics: What Will Happen Next?
Once businesses understand past trends and root causes, the next step is forecasting future outcomes. Predictive Analytics utilizes historical data to identify patterns, correlations, and probable future events.
For example, Allocation Management helps businesses monitor how tenders are managed (“what happened”). Poorly planned tenders and bookings (“why it happened”) can disrupt supply chains, leading to higher freight forwarding costs (“what will happen next”).
With Predictive Analytics, Logward helps customers proactively manage shipments:
- How soon will they reach carrier capacity?
- Will they need to book containers at spot quotes?
- Are they on track to meet carrier commitments for the week, month, or year?
- Are they at risk of penalties for underutilizing agreed allocations?
Prescriptive Analytics: What Should Be Done?
Prescriptive Analytics provides actionable recommendations based on predictive insights. Even if forecasts indicate potential disruptions, businesses can still take proactive measures to optimize their supply chain operations.
Examples of actionable insights:
- Should tenders and bookings be better planned?
- Can they negotiate better freight rates?
- Which vessel/service should be chosen to reduce lead times?
Conclusion
Data Analytics is an indispensable tool in modern logistics, enabling businesses to navigate complexity with confidence. By leveraging different types of analytics—descriptive, diagnostic, predictive, and prescriptive—Logward provides customers with deep insights and actionable intelligence. From monitoring shipments in real-time to optimizing future logistics strategies, data-driven decision-making transforms the way supply chains operate. As the logistics industry continues to evolve, companies that embrace analytics will gain a competitive edge, ensuring efficiency, cost savings, and resilience in their operations.
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