As supply chains are restructuring post-Covid-19, promises of faster delivery have increased manifold from the pre-pandemic times. The unpredictability induced by the pandemic runs so deep that any organization eliminating that automatically pushes down its competition. In fact, 41% of shoppers say they would pay a charge for same-day delivery, with 24% of them willing to pay more for delivery within a 1-2 hour window.
Retail supply chains in particular face a horde of supply chain challenges such as:
- Integration of multiple distribution channels
- Customer satisfaction
- Inventory and storage management
- End-to-end supply chain visibility
Amongst these challenges, inventory management impacts all others. With indexed and on-time inventory, it becomes easier to address the challenges of customer satisfaction and end-to-end visibility because they are directly dependent on the availability and delivery of goods. Multiple distribution channels are also more effectively managed when on-time delivery of inventory is ensured.
While on-time delivery for retail supply chains is an excellent driver of trust and brand loyalty, these challenges can stand in the way of ensuring on-time delivery. In the next section, we will look at 6 ways Artificial Intelligence (AI) can ensure on-time delivery.
6 ways AI can ensure in-full, on-time delivery of inventory
AI is equipped to make predictions that lead to accuracy in both anticipating and ensuring deliveries. AI engines have a wide pool of information at their disposal, which assists them in drawing insights and analytics to create delivery systems that satisfy the customer and create efficiency for the supplier.
1. Machine learning for anticipating bottlenecks and finding alternative routes
Machine learning is an essential component of AI in the supply chain because of its ability to detect patterns in data and come up with insights. This ability is especially useful while planning delivery and logistics. When fed enough data, machine learning systems can forecast bottlenecks and suggest alternative delivery routes. This is useful at each stage of the supply chain — be it delivery of inventory to the storage facility, delivery of the product to the customer, or delivery of goods to the retail store.
2. Demand forecasting
The trend-spotting abilities of AI make it a powerful tool while anticipating demand. For retail supply chains knowing trends ahead of time can lead to accurate demand forecasting. Demand forecasting ensures on-time in-full delivery of inventory by propositioning ahead of time they need for this inventory.
Demand forecasting also allows supply chain managers to pre-plan trucking and shipping routes, giving them a headstart in ensuring on-time delivery of retail inventory.
3. Data analysis to inform logistics
A wide net of collected data, when unstructured, can be inconclusive. Machine learning makes sense of unstructured data by spotting trends, clustering similar data, and pointing out anomalies. This data can be immensely helpful for selecting delivery routes, assigning goods, or choosing the right transport channel for efficient delivery.
When armed with data-driven analytics, decision-making for logistics becomes easier, more accurate, and more efficient.
4. Intelligent warehousing with AI analytics
AI analytics can take stock of the available warehouse storage and inform inventory accordingly. Whether these warehouses are intermediary storage solutions or are warehouses within the retail brick and mortar, intelligent warehousing can cut down both personnel as well as maintenance costs.
Intelligent warehousing pertains to all levels of data-driven warehouse management, such as storage, retrieval systems, and robotic automation of material handling.
Advanced analytics drawn with Big Data analysis can not only inform these processes but create better processes for ensuring in-full, on-time delivery.
5. Capacity buffers to ensure on-time delivery
Capacity buffering is the act of increasing capacity to fulfill elevated demand. Capacity buffers can be in the form of extra workers, outsourced logistics, or backup teams to ensure that delivery commitments stay in place. If demand can accurately be forecasted ahead of time, capacity buffers can be installed to ensure on-time delivery.
6. Real-time updates
Sensors and individual tagging make real-time updates for individual goods an achievable milestone. Real-time delivery updates raw material sources for manufacturing plants, storage facilities, and to customer’s data leading to an enhanced supply chain visibility. Real-time updates can eliminate the need for (sometimes expensive) capacity buffers by revealing data at the point that it is created. This process further informs vendors as well as customers about estimated delivery times, ensuring in-full on-time delivery of inventory.
Manually coordinating operations such as capacity buffering, intelligent warehousing, and logistics operations can be a cumbersome task for any supply chain manager. AI-based solutions have the capacity to deal with all aspects of automation under one umbrella and in one go.
AI-powered automation for your retail supply chain
AI-powered automation can yield long-term growth benefits for the retail supply chain by ensuring dependability. AI-powered engines enhance end-to-end visibility in supply chains by creating clear directives with measurable outcomes.
The applications of demand forecasting through AI also inform intermediary operations, like warehousing and logistics. AI analytics are applicable at every point in the supply chain and work well together to ensure customer satisfaction and repeat ordering potential.
ThroughPut’s AI Suite offers AI tools designed especially with consideration to supply chain challenges of congested bottlenecks or close margin profits. These tools work well with retail supply chains and can administer in-full on-time delivery of goods repeatedly. Read more about how ThroughPut can help you gain complete visibility across your retail supply chain operations or book a demo with us.