We have already taken a brief look at the potential changes and uses currently for artificial intelligence. And briefly looked into potential disruption in the supply chain and logistics space. Now we will delve a little deeper and explore six potential uses for AI specific to supply chain that could have a massive impact in the space.
Production Planning: Supply chain companies are looking to AI to help them optimize production planning. There is a vast amount of data associated with the planning and scheduling software used by many companies. Vast amounts of data cannot be analyzed efficiently by people alone. Machine Learning can analyze this data infinitely more speedily and in real-time. The implementation of AI/ML, can thus remove the guesswork from production planning. Production managers are able to make accurate and efficient decisions on supply-side planning with data-driven insights. This leads to resources being used more efficiently and is a significant step toward a lean supply chain system.
Fleet Management and Route Optimization: With the use of Machine Learning mixed with the vast amounts of data collected by IoT devices and sensors onboard fleets, fleet operators can make changes to routes in real-time. Driver and vehicle safety are also improved when making route decisions with the addition of real-time weather and road conditions. One of the knock-on effects of a properly managed fleet is increased overall productivity and better customer service.
Inventory Management: According to a recent survey, 94% of retailers see omnichannel fulfillment as important, this makes effective inventory management essential. Being able to implement AI into existing software infrastructure and data pools can give supply chain managers real-time oversight of inventory control and stock levels. Getting the right data to an integrated AI/ML system can result in supply chain managers being able to predict the correct amount of stock required, depending on the scenario. A shortage of a material that leads to reduced production of specific goods, lets supply chain executives correctly predict the amount of stock there should be in their inventory to meet the demands of customers. This is helpful when planning inventory stock to avoid over or understocking. The use of AI/ML to analyze historical data can uncover trends and patterns to optimize inventory.
Warehouse Management: As seen above, ML assists in warehouse management by optimizing inventory. With the use of predictive models, warehouse managers can use warehouse space efficiently. A warehouse that efficently uses space makes the job of employees working therein easier and more effective, enabling them to be more productive when it comes to order fulfillment. The many benefits of optimized warehouse go beyond helping employees' productivity and also improve efficient order fulfillment. It has cost benefits too, because it enables bulk orders which in most cases cost less.
Predictive Maintenance: Unplanned maintenance schedules can disrupt the entire supply chain workflow. This can be hugely costly. Ensuring that equipment is reliably working is key to ensuring a smooth workflow. Being able to predict failures with analytics can increase equipment uptime by nearly 20%. A predictive maintenance approach, lets supply chain operators keep their equipment running well. AI and advanced analytics help to this and can ensure a predictive maintenance strategy. This means that they are able to perform and schedule maintenance before problems occur, the cost benefits of which are self explanatory.
Demand Forecasting: A McKinsey survey recently showed that 80% of supply chain executives expect to or are already using AI/ML in planning. Demand forecasting is hugely important for resilient and efficient supply chain management. Implementation in the right way, lets supply chain operators accurately identify and forecast changes in future customer demand. By using the data available in the existing supply chain process and software, supply chain managers can better make strategic business and purchasing decisions when planning inventory levels. This boosts revenue, given the improved pricing and reduced inventory stockout that follow effective demand forecasting.
Source: (Supplychainbrain)