Artificial Intelligence and Machine Learning in Supply Chains


Many traditional IT systems/applications are dedicated to supporting various business processes in supply chain and logistics, such as ERP (Enterprise Resource Planning), MES (Manufacturing Execution System), PPC (Production Planning and Control), etc. However, these fragmented solutions are not ‘intelligent’ enough and not very suitable for current market scenario, due to the dynamic nature of the supply chain, rapidly changing customer demand, and the constantly changing business processes. To establish intelligent, rapid and effective business response systems, it is important to operate with the highest efficiency in all major activities and business flows in the supply chain. Artificial intelligence is one of those digital solutions that can bring the highest efficiency in all major activities and business flows in the supply chain.


Artificial intelligence (AI) and machine learning (ML) are not buzzwords anymore in the digitization world. When both technologies are adopted in the right way, businesses will benefit in many ways. If any company’s system is still not integrated with AI and ML, there is a chance that the organization might lag behind the competitors. The AI market is expected to reach the $500 billion milestone by 2024.


AI and ML are undoubtedly the present and the future of the digital landscape. It offers efficiency, automation and accuracy which are all directly related to achieving productivity, optimization, quality, and enhanced user experience. With time, AI is getting more sophisticated and more powerful, it is high time that organizations move away from legacy systems and integrate AI into their business operations. Organizations can integrate their supply chain solutions with intelligent technologies to improve business in terms of process automation, make smarter planning decisions, increase the agility of their digital supply network, reduce costs, and gain broader insights into their supply chains, with greater visibility into static and real-time data.


Here are some use cases where AI and ML are implemented in supply chain businesses.


UPS, the multinational package delivery and supply chain management company claims to deliver thousands of deliveries each day, and at an average each UPS driver makes an average of about 100 deliveries in each business day. To deliver the packages on time and with ease, UPS offers the most optimized navigation system called ORIAN (On-road Integrated Optimization and Navigation) to ensure that the drivers use the most optimized delivery routes in regard to distance, time and fuel. According to UPS ORION uses highly advanced algorithms to gather and process large amounts of data so that they can optimize routes for the drivers. The system relies on online map data, to calculate distance and travel time.


Bosch, the world’s leading engineering and technology company uses an AI analytics platform capable of reading terabytes of data in seconds and can achieve zero defects. Bosch uses AI to solve challenges in demand forecasting, inventory management, and optimization of packaging sizes.


Boeing, the world’s leading aerospace company uses AI solutions in its supply chain to promote operational efficiency and situational awareness in flight, use of a maintenance performance toolbox, and flight planning to optimize routes.


However, the potential for the application of AI has not yet been fully explored in the SCM area. Below are some supply chain processes in which AI and ML technologies can be leveraged.


AI and ML in Supply Chains – The Business benefits

AI and ML along with advanced analytics can process massive and diverse data sets from all functions to provide better visibility across the supply chain and have potential to transform various aspects of the supply chain. AI and ML have significant potential to deliver unprecedented value to supply chain in enhancing the supply chain forecasting, efficient inventory and demand management, logistics optimizations and improving the customer satisfaction.


·         Improved demand forecasting: AI and ML algorithms can help organizations analyze large amounts of data from various sources and factors to forecast future demand for their products or services. By analyzing factors such as sales numbers, consumer trends, economic trends, seasonality, weather patterns, social media sentiment, and holidays, AI can predict future demand patterns more accurately and quickly than traditional methods. This analysis can help companies optimize their inventory levels to meet the customer demand. This would also help companies to reduce waste, reduce stock-outs, and improve customer satisfaction.


·         Inventory optimization: AI and ML can be used to optimize inventory levels more efficiently by identifying which products are selling quickly and which are not by using real-time data to monitor demand and supply levels. AI and ML can also help organizations manage their inventory levels more efficiently by analyzing historical data, current demand, and supplier performance. This information can be used to make better decisions about which products to stock, when to reorder, how much to order and how much to build to meet the demand forecast.


·         Efficient Logistics management: AI and ML algorithms can analyze data on traffic patterns, weather conditions, shipping routes and other factors that affect transportation. This can help organizations optimize delivery routes, schedules, improve delivery times, reduce transportation costs, and enhance customer satisfaction.


·         Quality control: AI and ML can analyze data from various sources to detect the defects and anomalies, monitor product quality and predict the product failures before they occur. By this, organizations can monitor and control the quality of products at each stage of the supply chain which would result in increased product quality, reduced rework, reduced wastage and enhanced customer satisfaction.


·         Supplier risk management: AI and ML algorithms can be used to monitor suppliers’ performance and identify potential risks, such as late deliveries and quality issues with the products and services. This can be used by organizations to proactively address the issues with the suppliers, make informed decisions about their suppliers, negotiate better prices and delivery lead times, and reduce supply chain disruptions.


·         Predictive equipment maintenance: AI and ML algorithms can help to reduce equipment downtime by predicting when equipment will need maintenance or repairs. By analyzing data on equipment identifying patterns of failure and performance, AI and ML can help companies to take proactive measures to avoid costly breakdowns.


·         Real-time tracking: AI and ML can be used to track shipments in real-time, allowing for estimating the accurate and timely delivery. This can improve customer satisfaction and reduce the risk of undelivered shipments.


Improved Customer relationships: AI along with data analytical capabilities will be able to track the online sales and customer profile and be able to deliver customer tailored communications. With this personalized experience of the user, AI will increase the sales and customer relationship.


·         Enhanced supply chain visibility: AI and ML can improve the visibility of the supply chain by providing insights into demand and supply levels, supplier performance, equipment throughput, tracking shipments, delivery times, and other key performance indicators in real-time.  This can help companies to identify potential bottlenecks and other issues in the supply chain and take proactive measures to mitigate them.


·         Predictive analytics: AI can be used to analyze historical data and make predictions about future demand, supply chain risks, and other key factors. This information can be used to optimize inventory levels, reduce waste, and improve customer satisfaction.


AI and ML in Supply Chains – Challenges

The potential of AI and ML in the supply chain is undisputed. However, the path to become an AI powered supply chain comes with its own challenges. Following are some of the challenges that might be encountered along the way.

·         Organizations continue to focus more on basic, near-term priorities and less on actions and investments that can help transform supply chains and create long-term value.

·         AI technologies are usually cloud based, and require expansive bandwidth and hardware

·         Budgetary constraints to incorporate the technologies

·         Lack of digital skills among employees and the company’s interest in providing the training


Incorporating into business

Implementing AI and ML powered tools in business operations requires a concrete plan. Business leaders need to envision how to take things forward as a business owner. Here are some tips that would help every business leader integrate AI into their business.


·         AI cannot fix every issue or perform every operation. Identify specific use cases of AI that go well with the company’s overall feasibility and ROI.

·         Set realistic expectations and measure the possible outcomes of this strategy.

·         Determine whether AI can enhance business revenue, increase productivity and efficiency, reduce costs, enhance customer experience.

·         AI benefits can be  long-term, so ready to invest significant time into finding the outcomes.

·         AI is not just a technology that can be integrated with just a few organizational changes, prepare the manual workforce to embrace it.



AI and ML are the future for the growth of any industry. They have the potential to revolutionize the supply chain by improving efficiency, reducing costs, and providing greater visibility into supply chain operations. By using AI and ML to automate and optimize key supply chain processes, companies can reduce costs, improve performance, and gain a competitive advantage in their markets. Supply chain market leaders are required to invest in implementing AI and ML solutions in their businesses to make best use of these solutions’ massive data analyzing capabilities. However, the successful implementation of AI and ML in supply chain management requires careful planning, data preparation, and integration with existing systems. As AI technology continues to advance, we can expect to see even more innovative applications of AI in the supply chain industry.



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Author Details

Hari Prasad Reddy Naga

Hari Prasad Reddy Naga has 21 years of experience in consulting, solution architecture, project management, and pre-sales. He has vast experience in providing Oracle OnPrem & Cloud Supply Chain product-based Solutions, Global & Digital Transformations, Supply Chain product developments for Manufacturing & Hi-Tech clients in North America, Europe, and Asia. He is certified in Oracle Cloud and OnPrem Supply Chain products.


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