9 Ways Machine Learning Can Transform Supply Chain Management

These applications help merchants make smarter decisions around procurement, transportation, and final mile delivery. For example, companies are concerned about cycle times, lead times, downtimes, margins of error, costs, supplier reliability, and quantities of goods. AI can be used to improve these measures through its application in a variety of operations. AI is particularly useful for predictive maintenance, whereby businesses can deploy sensors to examine the condition of equipment on a continuous basis to automatically determine the optimal timing for servicing. This allows enterprises to conduct maintenance when it is most needed, rather than using scheduled times, which have a higher potential of reducing the productivity of equipment and personnel.

This not only improves customer satisfaction but also minimizes costs and gives businesses a competitive edge in the market. Another way in which AI can enable supply chain automation focuses on transportation planning. Such planning involves coordinating the movement of goods from suppliers to customers, and AI-powered systems can optimize this process.

Exploring the Power of AI In Supply Chain

Scenario modeling also can help companies optimize their network, processes and inventory—which not only improves overall operating and business performance, but also helps enable companies to achieve ever-higher responsibility goals. If you are in the business of perishable goods or are contractually bound by penalties upon missing delivery deadlines, inventory mismanagement can cut into your margins and bleed your reserves. Machine learning techniques can predict the optimal level of inventory for your warehouse. AI applications can even take the timing constraints as input parameters, to determine the optimal order of item storage and shipping, according to your FIFO or LIFO policies. AI can help sales teams prioritize their leads based on the likelihood of a lead making a purchase.

The most successful businesses today are relying on AI to access accurate data in real-time to effectively manage supply and demand, keeping the most optimal inventory levels for profitable operations. Today, only 12 percent of supply chain professionals say their organizations are currently using artificial intelligence (AI) in their operations. However, 6 in 10 of those same professionals expect to be doing so five years from now, according to a recent survey. Another report comes to a similar conclusion, with Gartner predicting that the level of machine automation in supply chain processes will double in the next five years.

Data Engineering

By providing early warnings and suggesting proactive strategies, AI-enabled risk management systems can minimize the impact of supply chain disruptions, allowing companies to maintain business continuity and improve resilience. Supply chains perform a series of actions starting with product design and proceeding to procurement, manufacturing, distribution, delivery, and customer service. “At each of these points lie big opportunities for AI and ML,” says Devavrat Bapat, Head of AI/ML data products at Cisco. That’s because the current generation of AI is already very good at two things needed in supply chain management. The first is forecasting, where AI is used to make predictions about downstream demand or upstream shortages.

H2O.ai is simplifying supply chain and manufacturing duties by encouraging businesses to embrace AI. Leaning on AI and a cloud platform, H2O.ai can forecast demands and returns, detect faulty machines and anticipate when maintenance will be needed. The company also supports logistics organizations with driverless AI vehicles to meet inventory and production requirements.

Production Planning

Bespoke ML-based software with built-in data handling regulations that fit your requirements is key to keeping your transportation operations compliant. ML-based sensors monitor critical assets, while predictive models analyze data to forecast maintenance needs. Supply chain managers are always seeking opportunities to enhance business processes. So let’s introduce the current trend of Artificial Intelligence that can lead to supply chain optimization and improvement. According to research reports, it is believed to use the latest AI-integrated GPS for logistics and supply chain delivery; users will save $ 50 million (Approx.) per year.

AI applications in the supply chain need vast amounts of data to feed systems and meet the diversified needs of supply chain operators. Depending on a business’ needs, NLP can solve a specific problem or provide a complete technology solution for automating an entire back-office function using natural language processing and custom software development. NLP solutions can be applied for automated reading and extracting specific data from documents (natural language understanding – NLU) or for generating new documents (natural language generation – NLG). Predictive modeling enables production and distribution optimization through better throughput, quality, safety, and yield improvements.

AI is like the ultimate quality control inspector in the warehouse, never gets tired and never makes a mistake. In logistics, AI is revolutionizing the way warehouses and distribution centers operate, allowing companies to improve efficiency, reduce costs, and increase profits. AI/ML signal engineers build AI models and algorithms to assess content performance, processes, and workflows.

• For instance, Coca-Cola has been using the technology to optimize its inventory and prevent stock outs since 2017 and more recently applied it to its overall procurement efforts.
• Improved accuracy is a significant advantage that these robots bring to supply chain management, as well as reduced costs and improved overall efficiency.
• At the same time, global spending on IIoT Platforms is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years.
• AI can help companies plan loads and create a more balanced transportation plan so they can work with preferred carriers and ensure adequate storage space and labor availability across their sites.

Read more about Top 3 AI Use Cases for Supply Chain Optimization here.