What does AI mean for supply chain management
Traditional supply chain management relies heavily on human input and manual processes, which can be slow and error-prone. AI, on the other hand, uses machine learning algorithms to analyse data, recognize patterns, make predictions, and automate decision-making processes. This means that AI can help you foresee demand fluctuations, optimize logistics, and even manage your inventory more efficiently. It adds speed, accuracy, and scalability.
Benefits of AI for supply chain management
AI can help supply chain managers in a number of ways. First, it can be used to forecast demand with a higher degree of accuracy by taking in past sales data, market trends, and even social media chatter. With more data, comes a better change at precision.
Going into more practical matters, AI can automate routine tasks such as order processing, inventory management, and shipment tracking. This not only frees up your team to focus on more strategic activities but also reduces the chances of human error. By saving time, optimizing routes, schedules, and inventory levels, AI can help you cut down on costs.
But perhaps the biggest benefit for supply chain managers is AI’s ability to detect and alert about unplanned events without relying on predefined rules. This capability allows AI to identify anomalies and potential issues in real-time, offering a proactive approach to problem-solving. Consequently, supply chain managers can respond more swiftly and effectively to unexpected disruptions, ensuring smoother operations and reducing the risk of significant delays or losses.
Big supply chain decisions can also benefit from AI. Whether it’s choosing suppliers, setting prices, preparing production schedules or planning your next move, AI can optimize the analysis and provide unique insights humans might miss.
Limitations of AI for supply chain management
The main problem companies face when trying to implement AI into their supply chain management is data quality. If your data is messy, incomplete or siloed across different systems, the results won’t be reliable.
Also, getting started with AI can be expensive. There’s the cost of the tech itself and the training needed to use it. AI systems can also be pretty complicated – to set up and to maintain. If you don’t have in-house expertise, you might need some outside help to get things up and running.
More data and more connectivity can mean more vulnerability, especially when processing vast amounts of sensitive data. Make sure you’ve got strong cybersecurity measures in place to keep your data safe.
Practical applications of AI in supply chain management
These are some practical, real-world applications of AI in supply chain management.
Demand forecasting
AI-driven tools can look at past sales, market trends, and even social media buzz to forecast demand. For instance, Salesforce, with its Einstein Analytics, uses machine learning to analyse historical data and predict future demand accurately.
Example: Unilever implemented Salesforce’s Einstein Analytics to enhance its demand forecasting capabilities. By analyzing historical sales data and market trends, Unilever improved its inventory management and reduced instances of overstock and stockouts. The AI-driven insights helped Unilever to anticipate demand fluctuations and adjust production schedules accordingly. Unilever utilized Salesforce Einstein Analytics for predictive demand forecasting, which led to better supply chain visibility and inventory optimization, significantly cutting down costs associated with overstock and stockouts.
AI in Demand Sensing
AI can enhance demand sensing capabilities by integrating real-time data from multiple sources, such as social media, weather forecasts, and economic indicators.
Example: Procter & Gamble (P&G) employs AI for demand sensing, using machine learning algorithms to analyse diverse data sets and improve demand planning accuracy. P&G employs machine learning algorithms to enhance demand sensing capabilities. They integrate real-time data from multiple sources such as social media, weather forecasts, and economic indicators to improve demand planning accuracy. Procter & Gamble uses AI to analyse diverse data sets for better demand forecasting, improving their supply chain responsiveness and inventory management.
Predictive Maintenance
AI-powered predictive maintenance systems can forecast equipment failures before they occur, ensuring timely repairs and minimizing downtime.
Example: General Electric’s Predix platform monitors industrial equipment and optimizes maintenance schedules, enhancing operational efficiency. GE’s Predix platform leverages industrial IoT (IIoT) and AI to predict equipment failures. Predix utilizes advanced analytics and machine learning models to monitor and analyze data from industrial machines. GE’s Predix platform is used to monitor industrial equipment, predict maintenance needs, and optimize repair schedules, which helps in reducing downtime and maintaining operational efficiency
Inventory management
Too much inventory ties up cash, and too little means unhappy customers. AI can automate tracking and reordering, so you always have just the right amount of stock. Salesforce’s inventory management solutions integrate AI to provide real-time insights into inventory levels and automate replenishment processes. It can even highlight slow-moving items, so you can run promotions to clear them out. This means you never run out of stock, and you don’t end up with excess inventory taking up space and tying up resources.
Example: Using Salesforce’s AI-driven inventory management, companies can maintain optimal stock levels, automate reordering processes, and ensure they never run out of stock while avoiding excess inventory
Route optimization
By analysing data from various sources, AI finds the most efficient routes and schedules. This means faster deliveries, lower fuel costs, and happier customers.
Example: A distribution company struggling with inefficient logistics, causing delays and high costs can adopt Salesforce Service Cloud with Einstein AI to optimize logistics and customer service. AI can analyze identify bottlenecks and predict delays. Faster and more reliable deliveries improves customer satisfaction while keeping down costs.
Supplier management
AI helps you evaluate their performance by looking at delivery times, quality, and reliability. This way, you can choose the best suppliers and negotiate better terms. AI can also predict potential disruptions, so you can take action before things go wrong. Salesforce’s AI capabilities also allow for real-time monitoring of supplier performance and proactive management of supply chain risks.
Quality control
Keeping your products top-notch is crucial. AI can analyse production data to spot patterns that might indicate quality issues. Early detection means you can fix problems before they escalate, reducing waste and keeping your customers happy.
Example: Siemens uses AI to predict and prevent defects in its manufacturing processes, ensuring high product quality and reducing waste. Siemens employs a combination of machine learning algorithms and data visualization techniques to enhance quality control processes. Specifically, they use their SIMATIC S7-1500 technology module, which integrates neural networks to process data in real-time and identify defects. Siemens’ quality control systems use visual data from Surface Mount Technology (SMT) assembly machines to detect defective parts and ensure component authenticity, minimizing recalls and enhancing traceability
Best practices to apply AI to supply chain management
Ready to dive in? Here are some tips to make the most of AI in your supply chain.
Start small and scale up
Don’t feel like you need to overhaul everything all at once. Start with a pilot project to test out AI applications and see what works. Maybe try AI-driven demand forecasting for one product line before rolling it out across your entire inventory. This way, you can learn and adjust without major risks.
Clean up your data
Good data is the backbone of AI. Invest time and resources in cleaning up your data and making sure it’s integrated across all systems. This will make your AI applications much more effective.
Team up with experts
If AI feels overwhelming, don’t hesitate to bring in the pros. Partnering with AI and supply chain experts, like Near Partner, can help you design and implement solutions that work for your business, and also give you access to cutting-edge tools and best practices.
Train your team
AI is as much about people as it is about technology. Make sure your team knows how to use AI tools and understand the insights they provide. Training is key to getting the most out of your AI investment.
Stay secure
Make sure you have robust cybersecurity measures in place to protect your sensitive data. This is crucial for maintaining trust and compliance.
Keep an eye on AI
AI isn’t a set-it-and-forget-it solution. Continuously monitor your AI applications and be ready to make adjustments as needed. Regular reviews will help you refine algorithms and ensure they continue to deliver valuable insights.
Near Partner, your partner for implementing AI in your supply chain management
AI is changing supply chain management. Sure, there are some challenges to overcome, but with the right approach, the advantages far outweigh the downsides. At Near Partner, we tailor specific solutions based on Salesforce that help businesses stay ahead of the curve. Get in touch.
