WHAT IS LOGISTICS ANALYTICS?

WHAT IS LOGISTICS ANALYTICS?

Logistics analytics is a term used to describe analytical procedures conducted by organizations to analyze and coordinate the logistical function and supply chain to ensure smooth running of operations in a timely, and cost-effective manner. The logistics industry might be the very sector that could make the most out of big data and business intelligence, as long as it knows how to take the best advantage out of them. The hugeness of the flows handled every day with all the shipments, their weights, sizes, contact details or returns is generating an incredible amount of data that has to be managed.

Logistics industry has identified predictive analytics as having the biggest impact on the supply chain this decade. The movement towards anticipatory logistics is already widely accepted among industry decision-makers: A study by the Council of Supply Chain Management Professionals revealed that 93% of shippers and 98% of third-party logistics firms feel like data-driven decision-making is crucial to supply chain activities, and 71% of them believe that big data improves quality and performance. So what exactly is predictive analytics, and why has it become so important in logistics and supply chain? Predictive models use historical and transactional data to identify patterns for risks and opportunities within a particular set of conditions, which helps to guide decision-makers and anticipate specific future events.

 A wide variety of predictive solutions have developed by DHL, Maersk, and UPS. Below are the most effective aspects of predictive analysis in Logistics and supply chain.

Better Supply Chain Visibility

In this new era, both shippers and suppliers have entirely updated ranges of visibility into the shipment lifecycle. Research has shown exactly how predictive analytics is creating new supply chain visibility – helping 3PLs avoid late shipments by monitoring devices; improving the visibility of shipment status and location; avoiding costs related to late or off-schedule shipments; and creating new business opportunities by meeting visibility requirements. 

Forecasting

Now, anyone can prepare weeks or even months in advance to plan inventory and shipments based on customer demand and buying behavior, thus ensuring less waste and more on-time deliveries. By using predictive solutions to generate supply and demand forecasts, companies will be able to make the right operational decisions in a proactive manner. This approach can also allow for the rebalancing of assets across any logistic network at a minimal cost.

Read more about the Logistics Demand Forecasting

Transportation Management Systems (TMS)

Logistics service providers largely depend on transportation management systems to track and manage shipments and lead times. With predictive analytics, many TMS can now predict future disruptions before they happen and help logistics companies manage their operations proactively, rather than reactively. Predictive analytics can also create new visibility into seasonal buying patterns and forecasts to help suppliers make more informed decisions.

Unexpected Conditions

Organizations can better prepare for short-term behavioral changes that affect the supply chain and logistics such as news, weather, shortages, and manufacturing promotions. By utilizing predictive analytics models to detect unexpected conditions, they can better adjust shipments and inventory in response to specific, time-sensitive changes in routes or inventory.

Predictive Maintenance

This is a new cost-effective solution gained by implementing predictive AI algorithms. Suppliers and logistics companies can detect failure patterns and anomalies, learn from those patterns and then predict future failures of machine components so that they can be replaced before they even fail. This is improving the supply chain’s efficiency and maximizing equipment uptime.

Last-Mile Delivery

The ever-troublesome last-mile delivery problem is another area in which predictive analytics can have a huge impact. Carbon dioxide emissions from freight transportation account for 30% of all transportation-related carbon emissions from fuel combustion. But by using predictive analytics solutions in the areas of route optimization, robotics, and anticipatory shipping, real and quantifiable improvements can be made on sustainability in last-mile delivery.

4 ways Logistics industry using Data Analytics to address the issue in the industry.

1. Advanced Transportation Analytics

Transportation agencies and authorities generate terabytes of data from an array of sensors and operational systems, but until recently, these data sources were not connected. Today, the challenge is figuring out how to use all this data effectively to inform planning activity, aid in managing transportation networks, improve operations, reduce costs, and better serve travelers. New tools are needed, such as scalable databases that can leverage compute dense devices such as the NVIDIA (NVDA) GPU for geospatial analysis, to allow transportation professionals to act on real-time transportation data in order to:

? Optimize freight movement and routing

? Optimize inventory management and capacity

? Optimize fleet operations

? Improve customer experience

? Increase safety

? Reduce environmental impact

? Optimize transit schedules by predicting the impact of maintenance, congestion, and accidents

2. Route Planning and Optimization

Fleet managers can integrate data from vehicles, scanners, sensors, personnel, and live weather and traffic reports, to more effectively manage and deploy assets. And by using machine learning and advanced analytics, they can discover and act on insights to optimize delivery routes in real time.

These insights power data analytics-driven fleet and personnel scheduling, route planning, rerouting, and supply chain optimization, which saves time, reduces fuel and overtime costs, and improves the customer experience.

One of the world’s largest logistics organizations optimizes the operations of its several hundred thousand vehicles and employees, using visualizations and analytics of real-time data to more efficiently deliver goods. The GPU-based insight engine they use merges the query needs of the traditional relational database developer with the scalability demands of the modern, IoT-centric enterprise.

3. Just-In-Time Inventory Optimization

Enterprises need real-time insights into logistics and transportation systems to view and track deliveries for stores. And they need inventory systems that can react swiftly to a variety of data feeds to make up-to-the-minute routing and inventory decisions. Customer sentiment data can also inform inventory management supply chain decisions. With an insight engine powered by GPUs, enterprises can better manage workforce, supply chain, inventory, overstocks and spoilage, and avoid stock outs.

Retailers have discovered that the more they can make data fast, actionable, and intelligent, the more likely they’ll be to build a long-term relationship with a customer. Retail and CPG organizations are shortening data processing time, visualizing more data, and uncovering patterns to reveal new knowledge and insights in sub-seconds.

Retailers can get tracking visibility and notifications of store deliveries, to provide store managers and distribution centers with just-in-time insights, even for thousands of trucks delivering millions of shipments every month.

One such retailer deployed Kinetica, a GPU database engine, to fuse their transaction data with their inventory text descriptions. With just two queries, the retailer could see where their highest organic food product sales were occurring throughout the nation. The retailer could then create targeted and customized fulfillment strategies by store.

4. Condition-Based Equipment Maintenance

By simultaneously ingesting, analyzing, and visualizing real-time sensor data from aircraft, cars, trucks, and ships, and combining that with more static data such as maintenance schedules, logistics organizations can gain contextual insight into the condition of their vehicles and assets. Performing predictive analytics on this data in real time helps organizations to detect patterns, anomalies, deteriorating performance, and future failures. They can proactively maintain equipment, improve fleet productivity, and avoid costly downtime.

Enterprises across industries such as retail, travel and transportation, healthcare, heavy equipment, utilities, logistics, and telecommunications rely on effective fleet management for uninterrupted flow of goods and services. Leading enterprises use real-time fleet analytics to simultaneously ingest, enrich, explore, analyze, visualize, and act on data within milliseconds to make critical decisions, as well as find efficiencies, reduce risk, improve productivity, lower cost, generate new revenue, and improve customer experience. And they’ll need to if they’re going to outcompete slower industry players and new upstarts, from logistics and supply chain players down to digitally savvy e-tailers.