The Logistics Data You’re Missing: A Practical Event Taxonomy for Orders, Shipments, and Exceptions

The customer places an order. The customer refreshes the tracking page for the third time today. The system says “in progress.” The carrier status shows “in transit.” The support team adds, “We’re checking.” Isn’t there a massive disconnect between the three?

About 77% of supply chain experts say real-time data visibility is crucial. However, many platforms today struggle with fragmented logistics data management. While there’s an event, there’s still some lag in shared and structured data meaning. Integrating a practical event taxonomy will be of great help. It standardizes the process and brings order to the chaos. It turns raw logistics into a reliable source for automation. This builds customer trust and improves analytics.

Impact of Poor Logistics Data Management for Businesses

Around 76% of logistics transformations fail to meet the essential performance metrics. The failure of logistics data management often causes these issues. Here’s how poor logistics data management impacts businesses:

• Higher Operational Costs: Poor management of logistics data can lead to much higher operational costs. Excess inventory, inefficient route planning, and expediting the fees frequently affect the costs. If there’s no clear logistics strategy, overspends also contribute to delays and disruptions.
• Supply Chain Disruptions: Supply chain data visibility is highly crucial for businesses. However, poor logistics leads to disruptions, creating vulnerabilities across the supply chain system. So, businesses find it hard to adapt to change, especially when inventory is low. These disruptions affect the overall business performance and strain relationships.
• Missed Growth Opportunities: Businesses need clear plans to grow and enter new markets. Poor planning leads to limited capacity, damages brand reputation, and restricts expansion. Having a proper logistics data management strategy boosts growth opportunities.

Is Raw Logistics Data Enough for Businesses?

When it comes to making effective business decisions, raw logistics data isn’t enough. Businesses using advanced analytics can reduce logistics costs by 15%. They can also cut inventory by 35%. Data analytics and business intelligence (BI) tools turn raw data into useful insights. This helps improve operational efficiency. Raw data is often scattered and messy. This makes it hard for people to understand. Depending only on raw data can lead to:

• Lack of complete supply chain data visibility.
• Missed opportunities due to a lack of strategic planning.
• Numerous operational inefficiencies.
• Poor decision-making.

A Guide to Event Taxonomy for Orders, Shipments, and Exceptions

Goods flow is measured in logistics through several stages:

• Orders: This starts the process.
• Shipment: This tracks the movement of goods.
• Exceptions: This covers cases when orders are delayed.

Order Events

Order events track the lifecycle from when a customer places an order to when it’s ready to leave the warehouse. The key events include:

• Order placement: When the order is placed as requested by the customer.
• Order confirmation: Order details are verified, and payment status is checked.
• Order processing: Start activities like picking and packing items. Then, transfer them to the warehouse.
• Shipment: The order event ends, and the order is ready to be shipped.

Shipment Events

These events focus on transporting the package from the origin to the final destination.

• Shipped: The carrier picks up the package and sends it in transit.
• In transit: The package moves between sorting facilities and logistics hubs.
• Out for delivery: The package is out for delivery after reaching a local delivery branch.
• Delivered: The package is successfully delivered to the recipient’s address.
• Not delivered: The package wasn’t delivered. There was a delivery attempt, but unforeseen circumstances prevented it.

Delivery Exception Events

The delivery exception usually happens when the standard delivery process is disrupted. The shipping carrier has updated the status, indicating the package will arrive later. Common causes for delivery exceptions include:

• Incorrect address: An incorrect or missing address affects shipment status. This often leads to delays.
• Customs delays: For international shipments, customs clearance is essential to avoid delays. Missing documents or restricted items can result in shipment exceptions.
• Operational challenges: High shipping value often affects logistics exception data, especially during peak seasons. Disruptions in the workforce eventually lead to increased delivery exceptions.

Foundational Pillars: Data Quality, Scenario, and Visibility

To ensure reliable transportation data, focus on the key pillars. Using appropriate supply chain data visibility tools can improve data quality and enhance planning. It saves time and safeguards the service levels.

1. End-to-end Visibility Across Supply Chain

End-to-end dashboards combine sourcing, manufacturing, warehousing, and delivery into a single view. Real-time data from IoT, TMS, and EDI can spot delays and find their causes. Supply chain data visibility is crucial to managing returns and reverse logistics. This supports on-time performance during peak periods.

2. High-quality Master Data for Real-time Decision Making

Accurate item, location, and partner records are crucial for planning. Regular audits of raw data and governance, with golden record management, reduce errors and manual work. Clean lead times can help to improve cost-to-serve analysis, ASN accuracy, and ATP. A well-defined transportation data model standardizes how shipment events and timelines are structured across systems.

3. Predictive Analytics to Remove Disruptions

Effective lifecycle tracking and supply chain planning need predictive analytics. This helps manage disruptions. The team uses real-time logistics data to test a wide range of scenarios, including buffers, triggers, and playbooks. Integrating predictive analytics helps reduce forecast errors and speed up replanning cycles.

Common Mistakes in Logistics Data Management

Minor errors in logistics data can cause significant losses. They hurt efficiency and impact decision-making. Here are some common mistakes in logistics data management:

1. Maintaining Multiple Data Vendors and Fragmented Data Stacks

Juggling between numerous data vendors and disparate tools can create fragmented data stacks. It increases complexity and costs while preventing a unified view of data. Not streamlining the vendors and fragmented data stacks negatively affects order lifecycle tracking.

2. Overdependence on Excel Reporting

Excel is easy to use. However, it’s prone to a wide range of errors, affecting scalability and collaboration. Relying too much on Excel and spreadsheet analytics can waste time and lead to wrong insights.

3. Broken Data Pipelines

Logistics data management is one of the most crucial aspects to keep everything flowing. However, inefficient and poor planning can lead to data pipelines breaking frequently. There’s a constant struggle between data teams, which leads to delays.

How Strong Event Taxonomy Improves Visibility and Automation in Logistics?

Digital freight forwarding enhances visibility and automation within the logistics analytics framework. It sets a standard for capturing, categorizing, and using data at different stages of the supply chain.

Here’s why a strong event taxonomy is needed for improving visibility and automation:

1. Improves Customer Experience

As delivery volumes grow, customers expect more control over delivery visibility. A report suggests that around 93% of customers want to stay updated with the delivery process. It covers all shipment status events, from in-transit to final delivery.

Logistics management tools help keep customers informed about delivery ETAs. It increases customer engagement and availability at the time of delivery. The tracking link notification helps customers track their orders. This often boosts real-time deliveries to 95%. As a result, the integration of the latest technologies helps to improve customer experience.

2. Boost Sustainable Logistics Process

The trend of sustainable shopping has increased post-pandemic. Around 44% of customers prefer sustainable shopping. The report shows that 40% of customers are willing to meet basic criteria for faster shipping. All these findings are clear indications of changes in customer behaviour, especially in eco-friendly practices.

Brands must adapt to sustainable logistics. Customers want this change to help reduce carbon footprints. AI-powered sustainability dashboards can help brands monitor the carbon emissions for shipment lifecycles. They can pick their vendors and make informed choices to meet sustainability goals.

3. Better Cost Savings and Compliance

Constant logistics event tracking helps save costs and improve compliance. Guesswork doesn’t work when it comes to data-driven decision making. Opting for guesswork affects the business growth and can lead to losses.

Shippers can use intelligent freight procurement solutions to raise quote requests. The solution lets many freight forwarders and shippers handle tasks with just one click. Shippers can use intuitive bidding to secure competitive prices each time.

They can also adjust for changes in market prices. Digitizing every shipment cycle can help save costs and track transit time. Driving sustainable operations can also help with compliance.

4. Drives Last-Mile Efficiencies

Integrating end-to-end logistics data management into solutions can drive last-mile efficiencies. The customer will sign to confirm delivery. This will be recorded both offline and digitally. Therefore, all records for financial transactions will also be recorded.

The strong event taxonomy can help calculate CoD orders. This process can also be automated for cash reconciliation. It can be easily mapped into PoD. Hub managers can use it to allocate orders to drivers. They can also manage unsettled cash within a specific limit.

Visual integrity is also a key factor to consider at the hub level for reducing returns. When orders are packed correctly, it helps execute last-mile delivery. This can help reduce delivery costs and prevent SLA conflicts.

5. Fleet Performance Transparency

A clear event taxonomy boosts logistics visibility by organizing shipment status events. It captures all data related to what happened, where it happened, and why. It helps to overcome issues associated with taking delivery attempts and non-performing fleets. Rather than hiding the data, the generic failure statuses are constantly displayed.

It combines location-based events through geofencing and geolocation with customer feedback triggers. Brands can always check delivery attempts and measure driver KPIs accurately. It also helps to automate exception handling. The structured, high-quality events are also crucial for meeting control tower data requirements. As a result, it helps to optimize fleet utilization and enable fair driver payouts. Furthermore, improper event taxonomy also boosts operational decision-making.

Impact of Analytics, AI, and Predictive Systems

A solid event taxonomy supports logistics data management. It helps continue reporting to real intelligence, accurately measure analytics, and provide consistency. When orders, shipments, and exceptions align, metrics such as on-time delivery, failure rates, and dwell time are reliable. Businesses no longer have to rely on interpretation and guesswork. The shared data will help capture all the data. There won’t be any more debates about whether the product is ‘in transit’ or ‘delayed.’

Structured data is even more important for AI and machine learning models. These predictive systems depend on clean and labelled events for learning patterns. The standard taxonomy helps models to correlate early signals with downstream outcomes. The different signals include partial delivery attempts and repeated handoff delays. The use of AI for logistics data management can enable proactive exception alerts, innovative capacity learning, and accurate delay predictions. If this information isn’t available, AI will rely on unclear inputs. This will limit accuracy and trust.

Modern SaaS logistics stacks often require basic consistency. Generative AI in supply chain management helps capture real-time insights and improve forecasting. Shifting from reactive dashboards to predictive operations can help identify issues. This is usually when the customers even feel the impact.

Implementing Event Taxonomy in Your Stacks

Implementing event taxonomy usually begins by identifying the aspects that matter- orders, shipments, and exceptions. Focus on a consistent set of events that create meaning and ownership. Don’t get lost in every signal or small detail.

The events must be generated from various sources, including OMS, WMS, TMS, and carrier integrations. The event must be normalised as the data enters the pipeline. Centralizing data is key in a shared event model. It helps downstream systems like customer notifications, automation, and analytics. The structured approach simplifies integrations and improves data quality without complexity.

Final Words: Capture the Missing Logistics Data

Fragmented logistics data limits automation and reduces visibility. It’s vital to use a practical event taxonomy. This will create consistency in orders, shipments, and exceptions. Raw data needs to become reliable insights. This is essential for effective logistics data management.

Do not rely on guesswork; use reliable data with the help of experts like Intech. Intech helps to capture structured events and implement strong logistics event taxonomies to drive real-time visibility and automation. The advanced analytics system boosts supply chain efficiency. It supports growth and creates a standardized event model. This model leads to scalable, data-driven operations.

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