You will learn to use data-based discoveries to alleviate organizational risks and the skills to interpret large data sets. You’ll grow your career as you cultivate an analytics-based leadership mindset. You’ll develop both quantitative and analytical skills as you work with real-world problems from the business domain.
How does logistics analytics reduce costs?
The modules outlined provide examples of what you can expect to learn on this degree course based on recent academic teaching. As a research-led University, we undertake a continuous review of our course to ensure quality enhancement and to manage our resources. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand. Explore how tariff changes, key supply shortages and volatile logistics capacity will test network resilience in this roundup of deep dives from Supply Chain Dive.
What is the difference between logistics analytics and supply chain analytics?
The data flows automatically from daily operations into visual dashboards without exports, integrations, or technical configuration. For fleets under 100 vehicles, built-in analytics is the practical choice because it removes the barriers that prevent most fleet operators from using analytics at all. In retail and consumer goods, supply chain analytics is often used for demand forecasting and inventory optimization. By combining historical sales data with real-time information about promotions, seasonality and regional demand patterns, organizations can align inventory with customer needs.
Ukrainian Operations in the Russian Federation
They show up in fuel bills, on-time rates, driver productivity, and customer retention within the first quarter of implementation. And missed delivery windows become a pattern nobody notices until customers start leaving. The average delivery fleet loses 15 to 20 percent of its operational budget to inefficiencies that basic analytics would surface in the first week. Using advanced technologies to forecast demand and optimise supply chain operations, improving efficiency and competitiveness. Predictive demand models and lead-time forecasts allow teams to maintain “just enough” inventory reducing capital tied up in stock while ensuring service levels.
- By combining historical sales data with real-time information about promotions, seasonality and regional demand patterns, organizations can align inventory with customer needs.
- Leading 3PLs report 6-12% reductions in transportation cost after AI deployment.
- They fail because the goals were vague, the data was scattered, and nobody thought about whether people would actually use the thing.
- Across industries, organizations are using analytics to turn complex logistics networks into efficient, responsive machines.
- Data-driven supply chain leaders use advanced analytics to predict inventory needs, minimize delivery times, optimize warehouse operations, and ensure near-perfect delivery performance.
- Pelypenko added that Ukraine’s domestic systems also give commanders more operational freedom.
If a supply chain and logistics team relies on a patchwork of spreadsheets, documents, and Industry 3.0 systems, they are doomed to a lifetime of manual errors and delays. Customer service reps can waste hours reconciling critical data only to send an irrelevant response to the wrong customer. Perishable cargo goes to waste because temperature logs are buried in someone’s inbox. Logistics companies can use data analytics to identify potential risks and disruptions in the supply chain, including natural disasters, geopolitical events, or problems with suppliers.
Reduce your carbon footprint by optimizing transportation routes and modes, leading to reduced fuel consumption and lower greenhouse gas emissions. Organizations looking to improve delivery reliability can download the 2026 Future of Logistics Intelligence Report to uncover more insights. To learn about the suite of digital solutions designed to support smarter, more connected logistics, including recently announced post-purchase digital solutions, visit fedex.com/en-us/fdx. John Manners-Bell discusses what the “end of globalization” means for supply chains. Logistics companies are making progress in https://newsgary.com/car-numbers-wiser.html applying artificial intelligence to streamline back-office functions and enhance physical operations. Russian heavy bomber drone shortages are likely restricting Russian forces’ ability to use these systems on the frontline.
Cost Reduction
Machine learning models can incorporate up-to-date information to improve forecasts of future demand, lead times and potential disruptions. Other tools build on these insights by recommending actions—such as adjusting inventory levels or rerouting shipments—to reduce costs or avoid delays. To better manage all these factors, logistics professionals use data analytics to find trends and patterns in the big data produced by their supply chain. For example, a supply chain analyst working for the aforementioned shoe manufacturer might analyze historical sales data to predict when consumer demand for the shoes will rise and fall in the foreseeable future. Known as demand forecasting, this common supply chain analytics method ensures that businesses can effectively plan their material sourcing, manufacturing, and distribution to meet customer demand (a process known as demand planning). For example, during peak seasons, demand forecasting can help companies stock up adequately to meet customer needs without creating excessive inventory costs.
- Unstable APIs, delayed synchronization, or incomplete data transfers can further reduce accuracy and timeliness.
- Fleets that track stops per driver per day and compare performance across the team typically find that their bottom 20% of routes are producing 30 to 40% fewer stops than their top performers.
- We will continue to evaluate and report on the effects of these criminal activities on the Ukrainian military and the Ukrainian population and specifically on combat in Ukrainian urban areas.
- Bridging the gap between traditional supply chain management and advanced data analytics can be a hurdle for some.
- These industry leaders demonstrate how AI is changing logistics & supply chain management concretely in 2025 through measurable results that directly impact the bottom line.
Conversely, inventory forecasting models rely on the demand forecast as a key input. The two disciplines work together to align supply and demand in the most efficient way possible. Logistics analytics can greatly contribute to improving shipping of perishable items through integration of sensors for temperature monitoring in designated containers. This sensor data can be fed into logistics data analytics systems to track temperature fluctuations and identify any deviations from the optimal temperature range for the specific perishable goods.
While 90% of logistics executives express strong confidence in AI’s business value, they recognize that there are challenges, including legacy infrastructure and data limitations. During 2026, we expect that leading supply chain operations will move beyond a focus on resilience toward a focus on delivering ‘Total Value’. From a supply chain management perspective, Total Value shifts the organizational lens from merely navigating supply chain disruption to actively pursuing enterprise-wide value maximization.
Supply chain analytics relies on sensitive operational data from driver telematics to shipment details. With evolving global data protection regulations, maintaining strong privacy and security practices is essential. A recent study found that 81% of manufacturers and logistics operators report data quality issues that delay analytics initiatives. By analysing delivery density, traffic conditions, and customer availability, models cluster deliveries intelligently and route drivers through the most efficient paths. Dynamic rerouting ensures flexibility for changing addresses, gated communities, or weather events keeping delivery promises intact. Companies using supplier analytics report up to 15% improvement in on-time delivery and stronger cost control across vendor portfolios.
The most valuable analytics loop connects insight to action inside the same platform. When analytics identify an underperforming route, route optimization re-sequences the stops. When on-time rates dip on Fridays, scheduling adjusts driver assignments for that day.
