ITMATTERS [ INSIGHT ]
by Asparuh Koev CEO, Transmetrics asparuh.koev@transmetrics.co | +359 299 64 696 (Sofia, Bulgaria)
3 Steps to Data-Driven Planning Outdated supply chain planning methods struggle to adapt to unpredictable customer demand, volatile commodity prices, and escalating political tensions. AI-powered solutions can bridge the gap.
3. Make data-driven incremental changes. Minor adjustments to existing supply chains can greatly impact stakeholders across the distribution network. Here are three areas where companies can use AI analytics to drive incremental change: • Predictive maintenance. Logistics companies and manufacturers can identify potential equipment failures by using AI to analyze historical maintenance data, sensor readings, and operational patterns. Once they can predict when equipment is likely to fail, they can proactively schedule maintenance. • Demand forecasting. To forecast future shipping demand, logistics planners and retailers need access to reliable historical customer data, market trends, and external factors such as e-commerce growth, trade restrictions, and in ation rates. Analyzing this information with AI can help them allocate resources more ef ciently. • Route optimization. Apps like TruckerPath and Sygic GPS make real- time route adjustments by monitoring traf c conditions, road closures, and live updates from users to optimize routes. Logistics planners can use this information to select the most ef cient routes and update customers in real time. A solid data foundation will enable resilience and agility in 2025. n
supply chains, they need to integrate data from various sources, such as ERP systems, WMS, TMS, and telematics. It’s essential to prepare a data storage and processing solution that can handle large volumes. Scalable web-based cloud storage services offer unlimited space with usage-based contracts. 2. Implement AI-powered predictive analytics. Once companies have implemented automated data capture devices, they can feed this data into their analytics tools. While automated data capture makes data reformatting and processing easier, however, it is still a job that needs to be done. Each data capture system is made to meet criteria that are strictly individual to its purpose. They collect precise data in particular formats at speci c times. Since the data quality varies, in most cases, additional processing is needed to synchronize with the other systems. The best way to implement these tools is to divide the implementation into phases according to objectives and priorities. Large systems such as the TMS and telematics require about one month each for full integration, so prepare your business accordingly.
Supply chain leaders and logistics planners who build a data foundation can maximize the latest analytics tools to identify capabilities and bottlenecks. This insight can drive data-driven strategies, implementing automation where they need it most. Here are three steps to implementing the right AI applications in 2025. 1. Build a data foundation. Supply chain leaders need visibility into their operations at all times to act with agility. Automated tools enable this visibility by collecting essential data at key points—drones can count stock levels, telematics can trace truck locations, and IoT sensors can monitor container conditions. By automating data capture, supply chain leaders bene t from regular, real-time updates. They also ensure standardized information, simplifying data synchronization across AI applications and enabling better interoperability so authorized leaders can access the required data. While data quality—accuracy, completeness, and consistency—is critical, companies also need the proper data infrastructure. Depending on their unique
18 Inbound Logistics • March 2025
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