Supply chain predictive analytics help companies to optimize their supply chain workflow by providing forecasts, identifying inefficiencies and driving forward innovation.
The main goal of using predictive models in the supply chain is to enable algorithms and intelligent machines to perform problem-solving tasks, increasing efficiency and productivity. These models, driven by location intelligence, can drive the entire supply chain without any manual involvement.
Advanced use of supply chain analytics can automatically enable companies to pursue innovative ideas and provide better customer needs and demands. Companies implementing these models within supply chains aim to make the logistics business more: orchestrated, intelligent and interconnected.
Also read: “Top benefits of Big Data in supply chains“
The new competition in the supply chain business includes demand planning, real-time inventory management and dynamic end-to-end margin optimization within the logistic industry. Therefore, to manage the complexity of the modern supply chain, your company needs to adopt these intelligently designed solutions aligned with your daily needs.
Why invest in machine learning and analytics-based solutions?
- You gain greater visibility and 360-degree responsiveness by assessing performance on a broader scale, predicting and minimizing risks and negative impacts on distribution channels.
- Improves customer experience by creating personalized products based on current user demands. A widely used example can be modern transportation and logistics using voice-activated means to track shipments and orders.
- It increases fleet efficiency by enabling better navigation and route optimization for freight and transportation. These tools access the most effective route for product delivery by processing driver, vehicle and customer data through machine learning. Simultaneously, they help you save both time and money for future shipments.
- Generates competitive advantage by leveraging real-time data from external resources such as industrial production, weather and employment history, helping to better gauge market conditions and assess upcoming demands for stable growth.
- Enables access to future insights by determining customer needs before they require them.
- Increases demand forecasting in warehouse supply and demand management, driving inventory optimization, where warehouse and inventory managers are informed about real-time control of parts, components and finished goods.
- Increases transportation and logistics longevity from vehicle-generated data that provides real-time information on the longevity of transportation vehicles, making maintenance recommendations and failure predictions based on past data.
- Improves the addition of portability to the supply chain loading process by gaining real-time, actionable visibility into the loading process. The data collected can also be used to design less risky and faster process protocols for handling packages.
- Saves costs and increases revenue by pinpointing changes in the supply chain profit process and managing courier contracts to negotiate better shipping and provisioning rates.
- Improves strategic sourcing based on data analytics in the supply chain, helping to standardize least-cost alternatives and predict supply performance indicators for compliance.
You might be interested in:” How to reveal hidden tiers in the supply chain?“
he benefits of artificial intelligence in supply chain management are indispensable. ML is a mainstream technology for supply chain now that companies of any scale and size have embraced its broad applications. Given the current scenario, every supply chain business model must be critically integrated with ML and analytics solutions for optimization and continuous improvement.
At PREDIK Data-Driven we help logistics leaders improve their supply chain visibility and identify hidden elements, breaking down all levels of the chain using Big Data and Data Science techniques.
