{"id":68485,"date":"2023-07-16T15:17:18","date_gmt":"2023-07-16T13:17:18","guid":{"rendered":"https:\/\/intellias.com\/?post_type=blog&p=68485"},"modified":"2024-07-23T14:56:31","modified_gmt":"2024-07-23T12:56:31","slug":"powering-supply-chains-with-machine-learning","status":"publish","type":"blog","link":"https:\/\/intellias.com\/powering-supply-chains-with-machine-learning\/","title":{"rendered":"Powering Supply Chains with Machine Learning"},"content":{"rendered":"
As a supply chain manager, have you ever considered predicting changes in consumer demand with greater precision and accuracy? Identifying unnecessary and redundant expenditures within supply chain processes? Finding a way to streamline transportation and warehousing?<\/p>\n
If your answer to any of these questions is yes, you might be looking for strategies to optimize your supply chain operations<\/a>. Indeed, according to a 2023 KPMG report<\/a>, 47% of supply chain organizations need to prepare themselves for the disruptions and challenges that arise.<\/p>\n A large number of supply chain businesses still rely heavily on legacy processes that served them well in the past. But over time, it becomes increasingly difficult for these businesses to keep pace with the evolving business environment. The fierce acceleration of technological progress over the last several years has prompted supply chain businesses to change their approach.<\/p>\n Relying on traditional methods is no longer enough to meet market demands. Utilizing machine learning in supply chain operations for processing huge amounts of data, identifying patterns, and providing actionable insights is an agile solution that helps businesses proactively prepare for the future.<\/p>\n Key takeaways:<\/p>\n Traditionally, supply chain decisions are based on a manager\u2019s intuition and expertise. Companies have relied on the experience of their managers to notice seasonal increases in consumer demand for certain products. However, as operations expand and there\u2019s more data, it becomes difficult and time-consuming to process all the information manually. An increase in the volume of transactions and the growing consumer demand have caused supply chains to become convoluted. Traditional approaches to predicting consumer demand have become inaccurate, inefficient, and not agile.<\/p>\n Supply chain businesses understand the limitations of traditional practices and have shifted to a data-driven and technology-adept approach. According to a 2023 KPMG supply chain trends survey<\/a>, six out of 10 supply-chain organizations plan to invest in digital technology to improve supply chain processes<\/a> and data analysis<\/a>. More and more organizations recognize the power of data to improve supply chain<\/a> operations and processes. The digital supply chain<\/a> market revenue continues to grow, confirming that more and more organizations opt to digitize their supply chain processes. The digital supply chain market, valued at $5.4 billion in 2023, is expected to reach $12.8 billion<\/a> by 2030. That\u2019s an increase of more than $7 billion in just seven years!<\/p>\n <\/p>\n Source: NextMSC<\/a><\/em><\/p>\n Machine learning adoption by supply chain businesses is expected to grow fivefold, from 15% in 2022 to 73% in 2027, according to an MHI and Deloitte survey<\/a>. Machine learning integration can offer many benefits to supply chain companies, including precise demand forecasting, optimization of logistics and transportation<\/a> processes, and accurate stock-keeping.<\/p>\n Moreover, it helps automate complex and mundane data analysis processes, streamlining operations<\/a> and eliminating potential errors. Supply chain organizations leverage machine learning to analyze historical data, learn from it, and become more effective and resilient to outsmart the competition.<\/p>\n As supply chain businesses implement machine learning for supply chain data, they have started witnessing the powerful transformation of supply chain operations. Let\u2019s dive into the key benefits of machine learning in supply chain that can make a big difference.<\/p>\n Even though supply chain organizations often possess large amounts of data, they find it challenging to extract meaningful insights from it to forecast trends and prepare for the future. Machine learning is a useful tool for anticipating future market and customer dynamics<\/a>. In particular, machine learning can model various market scenarios to forecast behavior with a high degree of accuracy, detecting patterns and interconnections that aren\u2019t apparent at first glance. For instance, a machine learning\u2013powered model that predicts consumer demand outperformed a previous model by more than 150% as indicated in a Gallup study<\/a>.<\/p>\n Use cases of predictive intelligence<\/a> provided by machine learning include accurate demand forecasting, proactive alerts on supply chain disruptions, and prediction of transportation delays.<\/p>\n A lot of decisions supply chain managers make involve high stakes and bear significant consequences for cost-effectiveness and overall business performance. These decisions often involve trade-offs, such as maintaining high inventory levels to make sure all products are available versus optimizing storage<\/a> space. Or delivering goods fast, which entails higher transportation costs, or choosing more thoroughly planned logistics, which may result in longer delivery times.<\/p>\n Machine learning can assist in these decision-making burdens by automating mundane processes such as order fulfillment, inventory renewal, and supplier selection. It can also aid in making complex decisions, supply chain forecasting<\/a>, or determining opportunities for better resource allocation. Machine learning algorithms can evaluate consumer behavior and competitor prices to suggest optimal pricing strategies so that businesses can improve their revenue and profitability. The growing reliance on machine learning for strategic decision-making is reflected in a Gartner<\/a> report that estimates 50% of supply chain companies will use machine learning to improve their decision-making by 2026.<\/p>\n <\/p>\n Traditional supply chains use static models and predefined strategies that can\u2019t fully embrace the constantly changing global trade environment. For instance, they can\u2019t promptly incorporate sudden changes in customer demand, which may lead to stockouts or overstocking. Machine learning reshapes this issue with adaptable systems that absorb all available information, such as market trends and customer preferences, to create powerful action plans that make supply chain companies more effective and resilient.<\/p>\n Examples of adaptive systems are end-to-end autonomous supply chain planning systems that aid in decreasing obsolete inventory by up to 20% and reducing supply chain expenditures by 10%, as reported by McKinsey<\/a>. In particular, such adaptive systems can aid with forecasting changes in product demand and automatically adjust all processes and decisions along the supply chain accordingly. As a result, supply chains function more efficiently in volatile circumstances, needing less human oversight and decision-making.<\/p>\n McKinsey<\/a> estimates that the biggest value IoT provides is managing operations, representing up to 39% of the entire economic value created by IoT in factories and equating to an estimated savings of up to $1.3 trillion by 2030. This is evidence of the decisive role IoT plays in improving operational effectiveness. The convergence of IoT and machine learning takes it all one step further. Machine learning provides up-to-the-minute visibility of different points in the supply chain, enabling proactive identification of equipment failures, stockouts, or transportation delays. One example of this is a predictive maintenance solution<\/a> developed by Intellias that integrates IoT<\/a> with machine learning to monitor oil and gas leaks.<\/p>\n IoT sensors can detect inventory on shelves, and machine learning algorithms can determine whether the identified stock levels are the same as those documented. Even more, machine learning algorithms are capable of forecasting required stock levels and automatically reordering goods when needed.<\/p>\n Machine learning aids with identifying environmental issues as they arise, such as excessive water use, pollution, or growing greenhouse gas emissions. Machine learning algorithms use data from different sources, such as water flow sensors and historical water usage patterns, to determine anomalies in water consumption that may indicate leaks or overuse. In addition, machine learning algorithms can track and analyze the carbon footprint of supply chain operations, determining possible sources of concern or atypical emission levels. An example of how big data transforms the supply chain is an equipment monitoring platform<\/a> Intellias has developed for a wireless sensor vendor in the Baltic states. This platform is used across a network of 125 stores, enabling efficient monitoring of refrigeration equipment and saving millions of dollars in potential food spoilage while reducing energy consumption by about 20%.<\/p>\n Machine learning algorithms assist with the automatic processing, categorization, and validation of supply chain documentation, such as invoices, shipping documents<\/a>, bills of lading, and customs declarations. Such automation shortens the time needed to process documents, as it\u2019s not necessary to review each of them manually. This leads to faster clearance times and speeds up the whole process. For instance, Klearnow<\/a>, a startup that automates document processing with machine learning, has helped a manufacturing company exporting products from Asia to Europe to decrease its customs clearance time by 50% with machine learning algorithms that automate document processing.<\/p>\n\n
Traditional vs modern supply chain practices<\/h2>\n
Why is machine learning important for supply chain management?<\/h2>\n
Enabling predictive intelligence<\/h3>\n
Automating supply chain decision-making<\/h3>\n
Creating adaptive systems<\/h3>\n
Integrating IoT and machine learning<\/h3>\n
Building ethical and sustainable supply chains<\/h3>\n
Simplifying regulatory compliance<\/h3>\n