9 Ways Machine Learning Can Transform Supply Chain Management

This empowers field operators with data-driven operations to approach in maintaining the optimum levels required to meet current (and near-term) demand. One of the biggest challenges faced by supply chain companies is maintaining optimum stock levels to avoid ‘stock-out’ issues. At the same time overstocking can lead to high storage costs, which on the contra, don’t lead to revenue generation either. An efficient warehouse is an integral part of the supply chain and automation can assist in the timely retrieval of an item from a warehouse and ensure a smooth journey to the customer. AI systems can also solve several warehouse issues, more quickly and accurately than a human can and also simplify complex procedures and speed up work. Also, along with saving valuable time, AI-driven automation efforts can significantly reduce the need for, and cost of, warehouse staff.

AI Use Cases for Supply Chain Optimization

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.

The future of intelligent, self-driving supply chain networks

Let’s face it, such vast amounts of data cannot be analyzed as efficiently by a human. 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.

Companies have so many vendors, technologies, and solutions to choose from that all sound like they promise the same thing, which makes it difficult for companies to determine which one is right for them. Despite recognizing the power and value of data and AI, companies will likely continue to find it difficult to leverage their investments more broadly. In fact, a full79% of COOsacknowledge they know how to pilot AI, but struggle to scale it across the business. Bloomberg report suggests that in the past two years, the overall cost in the supply chain has reduced to 12% leading to profits. Supply chain management comes with a great deal of detail-oriented analysis, including how shipments and goods are loaded and unloaded from the shipping containers. Both data modeling and AI precision are needed to determine the most efficient ways to get the goods on and off the containers.

Ways Machine Learning Can Transform Supply Chain Management

According to Gartner, 85% of businesses are still relatively immature in adopting analytics into their workflow. Data analytics can deliver actionable insights incredibly quickly, and even if executives aren’t familiar with techniques, machine learning & predictive analytics can drive insights from data streams within weeks, not years. In supply chain, on time product delivery to the destination matters the most. It takes just a minute to make or break your credibility towards winning a customer trust.

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It is also predicted that manufacturers prioritize AI on top of anything for supply chain and asset management. The primary reason to leverage this tool is AI can track every activity AI Use Cases for Supply Chain Optimization with ease from factory to warehouse and retail store to customer door. It enables real-time monitoring of shipment and predicting delays in product delivery while it is en route.

Challenges In Logistics and Supply Chain Industry

An agile approach enables organisations to begin implementing AI in cost-effective ways. By integrating third-party vendors, they can start where they are, learn what works for their businesses, and scale up as needed. This tactic allows for much faster AI integration than building a new platform from the ground up or building on top of legacy solutions. If you’re not ready for transformation, prepare a plan to implement artificial intelligence in supply chain. The manufacturing sector is changing fast, and you can’t afford to sit still. Looking ahead, you’ll also want to think about where your new tech stack will be located —on-site; in a data warehouse; in a private, hybrid or public cloud; or some combination of those.

What are the benefits of using AI in logistics?

The benefits of using AI in logistics include improved efficiency, reduced costs, and enhanced customer experience and satisfaction.

We enhance user interaction and deliver experiences that are meaningful and delightful. Stay ahead of the curve by implementing mobile applications or machine learning in your healthcare organization. Check how machine learning has undergone a massive transformation to facilitate fraud detection. Alphabet’s Supply Chain leverages machine learning, AI and robotics to become completely automated.

Use case 1: Inventory optimization

Let’s not forget that well-organized inventory management is the foundation of the supply chain business. The analytics-based machine vision software can minimize the standard manual input and create accurate forecasts. The AI systems also interpret real-time machinery data that continuously monitors the inventory and stock in the warehouses. The advanced AI-based GPS tools enable better navigation and optimization of the route for fleeting and transportation. These tools access the most effective route for product delivery by processing the driver, vehicle, and customer data through machine learning.

How can AI be used in logistics?

AI can be used in logistics to automate and improve many tasks, from lead generation and customer segmentation to pricing and product recommendations. In addition, AI can provide valuable insights into customer behavior, preferences, and trends.

The challenge here is that due to the possible cost and energy involved, the operational investment could be quite high. Manufacturers would also need to replace these which can shoot up the cost of utility bills and could directly impact the overhead expenses of keeping them running. AI systems are usually cloud-based, and require expansive bandwidth which is needed for powering the system. Sometimes, operators also need specialised hardware to access these AI capabilities and the cost of this AI-specific hardware can involve a huge initial investment for many supply chain partners. Speed in decision-making, speed in reducing cycle-times, speed in operations, and speed in continuous improvement. Let’s look at the examples of companies that have already adopted AI for supply chain management.

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That is why many supply chain professionals are predicting that the use of AI is bound to expand rather than contract for the foreseeable future. In other words, it is intelligence that is operationalized by machines rather than human resources. Autonomous delivery robots and drones are being used for last-mile delivery, slashing costs, reducing the traffic burden on roads and improving delivery times. These machines can handle navigation, trajectory adjustment, moving obstacle detection and avoidance — all in near-real time, says Desirée Rigonat, PhD., optimization and machine learning consultant at DecisionBrain.

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When supply chain components become the critical nodes to tap data and power the machine learning algorithms, radical efficiencies can be achieved. The value is realized through the application of machine learning in price planning. The increase or decrease in the price is governed by on-demand trends, product lifecycles, and stacking-the-product against the competition. This data is priceless and can be used to optimize the supply chain planning process for event greater efficiencies.

  • AI-powered software can analyze large amounts of data, define trends at a granular level, and react immediately.
  • Artificial Intelligence and Machine learning together have long contributed to digital transformation in supply chain.
  • Expanding the reliance on artificial intelligence in the supply chain even further,, businesses can create so-called digital twins — virtual simulations of all corporate assets, warehouses, routes, and materials and product flows.
  • It is difficult to plan production levels with everchanging forecasts, raw material costs, labor constraints, and shipping costs.
  • By partnering with third-party AI vendors, supply chain businesses can move away from the cumbersome old model of waiting for legacy platforms to catch up with new technologies.
  • Machine learning helps derive actionable insights, allowing for quick problem solving and continual improvement.

These solutions allow the supply chain to create personalized products based on the current user demands. One widely used instance can be modern transport and logistics using voice-activated means of tracking shipments and orders. This goes both ways where even the customers can perform the voice-activated query search using Alexa or Google assistant. 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.

  • The challenge is in identifying those aspects of operations that could benefit from artificial intelligence.
  • Before integrating Artificial Intelligence because it’s hype tech, take a look around.
  • Mosaic Data Science carries unique expertise around predicting demand and optimizing supply inventory across various business units.
  • This is where computer vision technology, one of machine learning in supply chain use cases, comes in handy.
  • Artificial intelligence can decrease operational costs by analyzing data and determining which actions are necessary.
  • And once the base solution is rolled out, you could evolve further, both vertically, expanding the list of available features, and horizontally, extending the capabilities of AI to other supply chain segments.
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