The Role of Data in Logistics

Data isn’t just numbers. It’s the new driver of logistics success. Here’s why analytics matter in supply chains: Let me paint a picture. A leading e-commerce company reduced delivery delays by 30%. How? By using predictive analytics to forecast demand, optimize routes, and avoid bottlenecks before they happened. Their secret was not just having data but knowing how to use it. → Real-time tracking to predict delays before they hit. → Dynamic pricing models to control inventory flow. → Heatmaps to identify weak spots in their supply chain. Analytics turned logistics into a growth lever, not just a cost center. If you're still relying on intuition over data, you're driving blind. The logistics industry is evolving fast, and only those who embrace data-driven decision-making will survive. Are you ready to stop guessing and start scaling?

Your supply chain isn’t a list of vendors. It’s a network, so start treating it like one. Disconnected systems create blind spots.  Delays, shortages, and unexpected failures can ripple through operations. Graphs and graph databases provide a smarter way forward. Here’s how: 📍 Supply Chain Visibility ↳ Graphs connect suppliers, transport routes, and logistics hubs into a single, real-time view. ↳ This helps leaders detect bottlenecks early and take action before small issues escalate. 🚦 Optimized Route Planning ↳ Graphs analyze real-time conditions including traffic, weather, and transport availability to instantly compute the best alternative routes when disruptions occur. ↳ This minimizes delays and reduces costs. 🔍 Fraud & Anomaly Detection ↳ Graphs connect financial transactions, supplier activity, and shipment patterns to detect hidden irregularities. ↳ By seeing the entire network, businesses can identify risks before they become costly problems. 🤝 Supplier Network Intelligence ↳ Graphs uncover deep interdependencies in the supply chain. ↳ This helps businesses anticipate risks, reduce vulnerabilities, and negotiate from a position of strength. 🔧 Predictive Maintenance ↳ Graphs combine sensor data, maintenance logs, and historical trends to predict breakdowns before they happen. ↳ This prevents costly downtime and ensures a more reliable supply chain. 📦 Adaptive Supply Planning ↳ Graphs enable real-time “what-if” simulations that adjust sourcing strategies based on demand fluctuations, supplier availability, and external shocks. ↳ This allows businesses to stay agile and resilient. These reasons are why at data² we built the reView platform on the foundation of a graph database. Connected data is driving the future of logistics and supply chain planning. 💬 What’s the biggest challenge you’ve faced managing your supply chain? Share your thoughts below. ♻️ Know someone dealing with complex logistics? Share this post to help them out. 🔔 Follow me Daniel Bukowski for daily insights about delivering value from connected data.

Ever wondered how warehouses can handle brand new products for which they have no historical order? Where do we place the article in the field to pick most efficiently? Will it be an A-article? Or will it be an absolute slow mover? We don’t know since there is no order data. But there is some information we can leverage. This new t-shirt from the new collection? Is it completely new? Well, yes. But we might have shipped a similar product in the past. And for this article we do have order data. First, we identify these “similar” existing articles. Similarity could be based on the price, the season, colors and design features. If we were an operation with a limited number of articles, say 100, it would be relatively easy to do this analysis by hand. I look at my new SKU and find the most similar item in the existing SKU base and take its historical order pattern as an indicator of how well the new item will ship. But how do we do this at scale? When we talk about apparel products, this problem becomes much harder very fast. We have to deal with potentially hundreds of thousands of SKUs and a lot of seasons or continued introduction of new articles. AI algorithms can analyze patterns across thousands of similar products and consider multiple factors simultaneously. To overcome this cold-start problem, we need to find the similarities.? How? We could for example embed product descriptions and other attributes into vectors and find the existing article that is closest in terms of a similarity measure such as cosine distance. Rather than comparing the new product to all existing products, we could also cluster. When early order data comes in for the new SKU, we can then adapt the produced forecast. And this is how we can handle SKUs in the warehouse that we have not seen yet. Follow me Dr. Jana Boerger and #datainlogistics for more content on data science in logistics and my path into the field. #datascience #logistics #datainlogistics #warehousing