Six Ways Companies Can Use Artificial Intelligence in Supply Chains

In recent years, supply chains have become substantially more challenging to manage. Longer and increasingly interlinked physical flows reflect the rising complexity of product portfolios. Market volatility, which has been exacerbated by the COVID-19 pandemic, has elevated the need for agility and flexibility. And increased attention on the environmental impact of supply chains is triggering regionalization and the optimization of flows. As a result, companies and stakeholders have become more focused on supply-chain resilience.

More than 60% of supply chain managers who adopted artificial intelligence 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.

In this article we will explore six ways how companies can reach a more efficient and cost-friendly way of storing, handling and moving goods using artificial intelligence.

An integrated end-to-end approach can address the opportunities and constraints of all business functions, from procurement to sales. AI’s ability to analyze huge volumes of data, understand relationships, provide visibility into operations, and support better decision making makes AI a potential game changer.

1. 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 stock out that follow effective demand forecasting.

2. 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.

3. 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.

4. 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 IT 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.

5. 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.

6. Production planning.

Artificial intelligence 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.

Supply-chain management has never been more formidable, but help is on the way. Artificial intelligence will be able to provide teams with deeper insights at a much higher frequency and granularity than ever. However, this visibility alone will not be enough to capture more value from AI-based supply-chain solutions. Any sizable technology investment must be matched by organizational changes, business process updates, and upskilling efforts. Only then will companies capture the expected ROI.

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70 commentaires

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Hello there! This post could not be written any better!
Going through this article reminds me of my previous
roommate! He always kept preaching about this. I’ll send this post to
him. Pretty sure he will have a very good read. Many thanks for sharing!