Supply chains have seen the need for resilience exacerbated in recent times, given the massive global disruptions caused by the pandemic. This has triggered the industry’s response to look towards advanced technology, like artificial intelligence and machine learning, to optimize their current operations. AI is being used at every stage of the supply chain to improve efficiency, minimize the impact of global worker shortages, and find safer and smarter ways to get goods into the hands of consumers. AI applications can be seen from manufacturing to warehousing and delivery.
More than 60% of supply chain managers who adopted AI in their processes saw a decrease in their costs, according to research by McKinsey & Co. According to that same study, most supply chain management respondents are likely to report savings specifically from spend analytics and logistics-network optimization.
It’s a fact that AI/ML is a game-changer for most industries, especially supply chain and logistics. The following six use cases show how leaders can reach a more efficient and cost-friendly way of storing, handling and moving goods.
Demand forecasting. A McKinsey survey showed that 80% of supply chain executives expect to or are already using AI/ML in planning. This is a move in the right direction, as demand forecasting is essential for resilient and efficient supply chain management. The right implementation enables supply chain leaders to accurately predict and identify changes in future customer demand. By tapping into the data available in the existing supply chain process and software, supply chain managers can then make strategic business and purchasing decisions when planning inventory, without creating a surplus or understocking. This, in turn, boosts revenue, given the improved pricing and reduced inventory stockout that follow effective demand forecasting.
Warehouse management. ML assists in warehouse management by optimizing the flow of products in and out of the warehouse. By creating predictive models, warehouse managers can use the available warehouse space efficiently. A well-organized warehouse space streamlines the job of employees, like product pickers, enabling them to be more productive when it comes to order fulfillment. The benefits of optimized warehouse space extend beyond employees' productivity and efficient order fulfillment. Optimized use of warehouse space increases its storage capacity, enabling supply chain executives to purchase goods in bulk. Goods purchased in bulk cost less, resulting in lower expenditure and a higher profit margin.
Inventory management. With 94% of retailers seeing omnichannel fulfillment as a high priority, proper inventory management is a must-have. Implementing AI into the existing software infrastructure and data lakes gives supply chain managers real-time oversight of inventory control and stock levels. Feeding the right data to an integrated AI/ML system gives it the ability to predict the amount of stock needed, depending on the scenario. For example, a shortage of a material leading to the reduced production of specific goods. This lets supply chain executives accurately predict the amount of stock there should ideally be in their inventory to meet customer demand. This is helpful when planning inventory stock — and making business decisions based on data — to avoid over or understocking. Leverage AI/ML to analyze historical data to uncover trends and patterns for a well-stocked inventory.
Fleet management, route optimization. Make data-driven decisions based on data gathered from traffic conditions, weather and other external factors to manage your fleet. With relevant input, fleet managers have accurate data insights to pick the most optimal routes to get fleets to their destinations on time. Combining ML with data collected by IoT devices and sensors onboard fleets, fleet operators have the ability to make changes to routes in real-time. Driver and vehicle safety are also improved when making route decisions with input from real-time weather and road conditions. Downstream effects of a properly managed fleet include increased overall productivity and enhanced customer service.
Predictive maintenance. Imagine a supply chain workflow moving along like a well-oiled machine (as it should!). Now imagine a piece of machinery unpredictably breaking down, and others following suit over the next couple of months. Unplanned maintenance schedules disrupt the entire supply chain workflow, leading to delays and loss of productivity. Having equipment reliably up and running is key to ensuring a smooth end-to-end workflow. Predicting failures via advanced analytics can increase equipment uptime by up to 20%. By adopting a predictive maintenance approach, supply chains can keep their equipment running well, without unpredictable failures. With the help of AI and advanced analytics, a predictive maintenance strategy lets supply chains predict machinery failure. This gives them the ability to perform and schedule maintenance ahead of time, increasing downtime-related cost savings and monthly production capacities.
Production planning. AI has documented uses on the demand side of planning. Supply chain companies are now looking at how AI can help them optimize their production planning on the supply side as well. There is a large amount of data in the planning and scheduling software used by most companies. Let’s face it, such vast amounts of data cannot be analyzed as efficiently by a human. ML, on the other hand, can analyze this data quickly and in real-time. Therefore, the implementation of AI/ML, using this vast data made available, takes the guesswork out of production planning. Production managers can make accurate and efficient decisions on supply-side planning with data-driven insights. Ultimately, this leads to resources used efficiently, and a move toward a lean supply chain system.
There you have it. These are the most common issues and use cases that can be solved with AI/ML. AI is an investment that can drive your competitive edge, bringing about significant cost savings and efficiency gains so you can better meet growing customer demands. Having the data collection, storage and infrastructure is essential to begin implementing a ML strategy. The earlier companies begin planning, the sooner they can start reaping the rewards of ML. With an ever-growing list of AI/ML vendors making the development and deployment of models easier, getting started with AI/ML gets more accessible every day — and increasingly becomes a critical success factor for all stages of the supply chain.
Shaun Cotter is business development and sales manager at PI.EXCHANGE, a provider of AI and ML services.
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