Blog

How to ensure efficient supply chain management? Many manufacturers, retailers, distributors, and suppliers are unsure of the answer. Businesses are wondering how to make their supply chain less susceptible to disruption in the current environment of shifting supply chain market dynamics, evolving workplace practices, and increasingly volatile demand. Many well-known and new supply chain challenges can be solved by machine learning. 

Disruptive technologies such as Machine Learning (ML) and Artificial Intelligence (AI) offer excellent opportunities in a fiercely competitive market where businesses constantly strive to improve profit margins, reduce costs, and provide an exceptional customer experience. 

So, if you want to learn what machine learning can do for your supply chain business, this blog is a great place to start. Let's look at how machine learning affects the supply chain. 

Machine Learning in Supply Chain Management 

Machine Learning and Artificial Intelligence have recently become buzzwords in various industries, but what exactly do they imply for modern supply chain management? 

Machine learning in supply chain management can help automate various mundane tasks, allowing businesses to focus on more strategic and impactful business activities. 

Supply chain managers can use intelligent machine learning software to optimize inventory and find the best suppliers to keep their business running smoothly. Today, many businesses are interested in machine learning applications, ranging from their numerous benefits to fully leveraging the massive amounts of data collected by warehousing, transportation systems, and industrial logistics. 

It can also assist enterprises in developing an entire machine intelligence-powered supply chain model to reduce risks, improve insights, and improve performance, all of which are critical in building a globally competitive supply chain model. 

Why Use Machine Learning in Logistics? 

AI and machine learning in logistics may benefit the supply chain. It can be used to optimize operations, minimize human errors, and anticipate future possibilities and difficulties. 

An efficient and flexible supply chain is a significant advantage in a highly competitive industry. As a result, businesses are looking for tools to help them optimize their operations and make decisions that will increase operational efficiency and customer satisfaction while decreasing economic and environmental costs. The most significant advancement in this industry is the digital transformation of the supply chain, which remains an essential issue for many transport operators. 

By 2026, more than 75% of commercial supply chain management application providers will offer artificial intelligence (AI) and data science services, according to Gartner". 

Use Cases of Machine Learning in Supply Chain Management 

The top ten use cases of machine learning in supply chain management are outlined below, which can help drive the industry toward efficiency and optimization. 

Predictive Analytics 

Machine learning models can help businesses benefit from predictive analytics for demand forecasting. These machine-learning algorithms excel at detecting hidden trends in historical demand data. Machine Learning in the supply chain can also detect supply chain issues before they disrupt business. 

A solid supply chain forecasting system ensures the company has the resources and knowledge to respond to new challenges and risks. Furthermore, the effectiveness of the response is proportional to how quickly the company can respond to problems. 

Improved Customer Experience 

ML approaches, such as deep analytics, IoT, and real-time monitoring, can significantly improve supply chain visibility, allowing organizations to change customer experiences and meet delivery promises more quickly. This is accomplished through machine learning models and workflows that analyze historical data from various sources before identifying linkages between activities across the supplier value chain. 

Amazon is a good example of this because it uses ML techniques to provide excellent customer service. This is accomplished through machine learning, which enables the company to gain insight into the relationship between product recommendations and future consumer visits to the company's website. 

Streamlining Production Planning 

Production plans can become simpler with the aid of machine learning. On the basis of the production data currently available, sophisticated algorithms can be trained to find potential inefficiencies and waste areas. 

Furthermore, the application of machine learning in the supply chain to create a more flexible environment capable of dealing with any disruption is noteworthy. 

Reduced Cost and Response Time 

Many B2C companies use machine learning techniques to trigger automated responses and handle demand-supply imbalances, lowering costs and improving customer experience. 

Machine learning algorithms' ability to analyze and learn from real-time data and historical delivery records enables supply chain managers to optimize the route for their fleet of vehicles, resulting in reduced driving time, cost savings, and increased productivity. 

Furthermore, by integrating freight and warehousing procedures and enhancing connectivity with different logistics service providers, operational and administrative costs in the supply chain can be decreased. 

Warehouse Optimization 

Warehouse optimization is essential for logistics and supply chain managers because it allows them to use available space best, cut inventory costs, and improve order fulfillment. ML algorithms can analyze data from sensors, cameras, and other sources to optimize warehouse layout, inventory placement, and order-picking processes. 

Amazon, for example, uses machine learning algorithms to optimize its warehouse operations. Algorithms can optimize inventory placement and order-picking processes by analyzing data from sensors and cameras. As a result, operating costs have been reduced by half, and warehouse capacity has increased by 60%. 

Reduction in Forecast Errors 

Machine Learning is a powerful analytical tool that can assist supply chain companies in processing large amounts of data. 

Aside from processing such massive amounts of data, machine learning in the supply chain ensures that it is done with the most variety and variability possible, thanks to telematics, IoT devices, intelligent transportation systems, and other similar powerful technologies. This allows supply chain companies to gain insights and make more accurate forecasts. 

According to a McKinsey report, AI and ML-based supply chain implementations can reduce forecast errors by up to 50%. 

Logistics 

Last-mile logistics in supply chain management is prone to operational inefficiencies and can cost up to 28% of the total delivery cost. 

Some common issues in this field include: 

• Inability to find a parking spot for large delivery trucks near the customer's destination, forcing the package to be carried to its destination on foot 
• Customers not being available to sign for items, resulting in a delivery delay 
• Package damage during the final leg of delivery 

Companies often struggle to pinpoint exactly what is happening during this last mile. This final step is called the "black box" of the supply chain. 

A global brewing company recently collaborated with MIT Megacity Logistics Lab to leverage data and machine learning to address last-mile logistics operations and improve operational efficiency. In this scenario, machine learning tools analyzed historical route plans and delivery records to identify customer-specific delivery challenges for thousands of customers worldwide. Customers whose delivery constraints caused the most significant disruptions to the company's last-mile logistics operations were identified. The company then reconfigured its distribution services for a specific group of customers. 

Fraud Prevention 

Machine learning algorithms can improve product quality while lowering the risk of fraud by automating inspection and auditing processes and then performing real-time analysis of results to detect anomalies or deviations from standard patterns. 

Furthermore, machine learning tools can prevent privileged credential abuse, one of the leading causes of breaches throughout the global supply chain. 

Inventory Management 

Keeping the right amount of product in inventory in response to future market demand has always been a constant challenge for manufacturers. Manufacturers can use big data analytics to analyze various types of data such as past sales demand, channel performance, product returns, promotions data, and so on to gain insights around: 

• What is the optimal inventory level needed to meet demand while keeping stock levels to a minimum? 
• How can out-of-stock situations be reduced? 
• How to control the impact of product recalls? 
• How to enable cross-selling and improve the performance of slow-moving stocks? 

When fed the most recent supply and demand data, machine learning can continuously improve a company's efforts to solve the over or under-stocking problem. 

Automated Quality Check 

Automated quality checks have replaced manual quality checks on containers for any damage. The scope of automation in supply chain quality checks has increased as a result of developments in machine learning and artificial intelligence. 

In any case, this prevents faulty products from reaching customers and thus increases your reliability. 

Companies Using Machine Learning to Improve Their Supply Chain Management 

Some of the top firms use machine learning in supply chain management. 

Rolls Royce 

In collaboration with Google, Rolls Royce develops autonomous ships in which machine learning and artificial intelligence technology replace the jobs of entire crew members rather than just replacing one driver in a self-driving car. 

The company's existing ships use algorithms to accurately detect objects in the water and classify them according to how dangerous they are to the ship. Algorithms based on machine learning and artificial intelligence can be used to load and unload cargo, monitor security, and track the performance of ship engines. 

Microsoft Corporation 

Microsoft's supply chain system heavily relies on predictive insights powered by machine learning and business intelligence. 

The company's extensive product portfolio generates a large amount of data that must be integrated centrally for predictive analysis and operational efficiency. 

Machine Learning techniques have enabled the company to create a seamlessly integrated supply chain system to capture and analyze data in real-time. Furthermore, the company's robust supply chain employs proactive and early warning systems to aid in risk mitigation and quick query resolution. 

Amazon 

A well-known leader in the e-commerce supply chain, Amazon uses cutting-edge, ground-breaking AI and machine learning-based systems like automated warehousing and drone delivery. 

Because of significant investments in intelligent software systems, transportation, and warehousing, Amazon's robust supply chain controls critical areas such as packaging, order processing, delivery, customer support, and reverse logistics. 

Procter and Gambel 

The largest consumer goods company in the world, P&G, has one of the most complicated supply chains and a wide range of products.  

The company excels at leveraging machine learning techniques such as advanced analytics and data application for end-to-end product flow management. 

Alphabet Inc 

The well-known and incredibly innovative technology company Alphabet depends on a flexible and responsive supply chain that can work in unison with other regions. 

Alphabet's Supply Chain is entirely automated thanks to machine learning, AI, and robotics. 

Challenges in the Adoption of Machine Learning in Supply Chain Management 

Even though the global industrial landscape is moving towards adopting next-generation technologies to support digital transformation, adopting these technologies in niche areas such as supply chain management remains significantly low. The gap between the hype of technologies like AI and ML and their actual technological value is primarily due to limitations in adopting tech-driven tools in supply chain management. 

Most managers and business executives cannot comprehend and visualize the benefits and impacts of AI and ML in supply chain management on business growth. Furthermore, AI and ML tools necessitate regular maintenance to ensure flawless operation within the expected parameters of supply chain management systems, which adds to the cost. Such obstacles have severely hampered the spread of these technologies across all world's geographical regions. 

Despite these challenges, as awareness of the dramatically positive impact of AI and ML in supply chain management grows, its adoption will become unavoidable in the coming years. 

Conclusion

Any business must prioritize increasing supply chain efficiency. Any process improvement can significantly impact the bottom-line profit while operating within tight profit margins, 

Innovative technologies such as machine learning make it easier to deal with volatility and accurately forecast demand in global supply chains. However, to reap the full benefits of machine learning, businesses must plan for the future and begin investing in machine learning and related technologies today to enjoy increased profitability, efficiency, and resource availability in the supply chain industry.