AI-powered supply chains


Thirunayan Dinesh

June 11 2020

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Have you ever wondered about the journey of a product which you ordered online - starting from its manufacturer till it reaches your doorstep? How pivotal is the role of a supply chain in this complex network involving suppliers, distributors, manufacturers and retailers in this process? In essence, a supply chain is a network of vendors, manufacturers, suppliers, and retailers who work together to deliver products or services to consumers as efficiently as possible.

Plus, the benefits of applying AI to the supply chain can be many such as, enhanced visibility into the supply chain, faster decision-making, reduced cycle times, predictive analysis of big data, improved quality, productivity and throughput, greater supply chain resilience, and the ability to continuously tweak and improve your processes based on real-time insights into system performance

Companies can also benefit from intelligent predictions about items such as the diversity of demand for a particular product, when to plenish stocks of raw materials for the processes, to reduce the amount of time spent on gathering data from different systems, using them on BI tools to develop strategies. However, with the past turn of events related to the pandemic and the fluctuations in various sectors, the global supply chain was also affected; stay-at-home orders, contrasted gaps in supply and demand, and many more vulnerabilities related to resilience. However, AI-driven practices were able to backbone the supply chain as a response to this change.The motive of this article is to acknowledge the role of AI in supply chain management and its importance. Below mentioned are five main applications of AI in supply chain management.

So how can AI help?

Demand Forecasting

The prediction process of the number of products that need to be shipped, the process of estimating customer demands based on predictive analytics are the main objectives of AI-driven supply chain management. Compared to traditional forecasting methods, AI-driven forecasting models consider historical, current, and external information. One of the most common problems that organizations do face is the overestimation and underestimation of demand. However, AI-driven demand forecasting looks into more variables that impact demand and recognize data patterns that may have been hidden.

Predictive analytics algorithms make it possible to analyze consumer shopping behaviors and habits, which further helps with better inventory replenishment planning. By deploying machine learning algorithms, the distributor can aggregate and evaluate a holistic spectrum of demand factors. This results in more accurate demand forecasting and well-informed demand decisions.

Inventory Management

AI has reshaped inventory management in terms of supply chain management by executing many new appliances such as helping supply chain teams optimize their inventory planning based on the actual and predicted results achieved, showing the inventory from all of your warehouses, despite their geo-location, leveraging AI to optimize the production cycle more efficiently, and adding efficiency between suppliers thereby enhancing customer experience.

Meanwhile, deep learning-based vision tracking systems also enable businesses to precisely identify damaged inventory, recognize misplaced items, detect under-utilized storage space and precisely track production volumes autonomously in factory environments, and suggest proactive measures to optimize inventory management and production control; tasks that may consume way more human labor and time if AI isn’t utilized.

Apart from these factors explaining increased visibility and accuracy of inventory management, the ability to analyze the performance of suppliers, products, and employees can help organizations reduce costs. Integrate AI systems that deliver alerts on scenarios such as low inventory levels, delays in delivery, or simply any sort of unexpected incidents as they occur.

Production Planning

A good production plan is crucial for any organization to best plan the allocation of raw materials, human resources, and other production resources. Organizations can decrease production planning from being a 6 hours task to a 90-second task through AI-enabled production planning systems. These systems leverage reinforcement learning-based sequential planning algorithms to create a decision-making engine. The simulation engine will first simulate the impacts of multiple production plans across different factors and then pick the plan that produces the best net outcome.

Besides, AI makes real-time planning possible which enables businesses to comb through and extract insights from data much faster than a human planner could possibly manage. Taking all facts into account, the benefits of using AI for production planning in supply chain management can be reduced disruptions on the production line, fewer late orders, utilization of resources more efficiently, with the potential for an increasingly lean supply stream, and use of time more efficiently.


AI is transforming warehousing processes and many more areas that fall under the category of logistics. A significant challenge faced in supply chain management is long lead times and the delivery of inventory on time. AI-enabled logistics systems allow companies to greatly save time and money with regard to logistics. For example, bee colony optimization algorithms help balance priorities with regard to supply chain logistics by helping decrease travel times through route optimization.

Computer vision will help manage quality control without the need for human intervention. Furthermore, if the supply chain is networked with multiple warehouses, AI can connect them to determine the optimal option for transferring the inventory. Real-time ETA estimation and tracking systems enable companies to track where truckers are at all times and their availability to conduct particular tasks. This allows logistics operations to be more efficient. Plus, AI is expected to make freight management a lot easier. AI-driven logistic practices such as real-time route optimization, back-office automation, warehouse automation, efficient planning, and resource management can benefit businesses worldwide in their logistic aspects.

Supplier Selection

Specifying the right supplier is mandatory for sustainability in a business. Supplier discovery tools enable companies to identify the right suppliers and optimize supply-based management. Through NLP (natural language processing) algorithms, millions of supplier escriptions in a combined data-set can be filtered to match an organization’s supplier selection criteria. A large range of variables such as cost, quality, delivery, flexibility, reliability, etc. can be considered, and patterns can be recognized between these variables. This enables companies to make better, informed decisions regarding supplier selection and enhance the overall customer service.

The supplier-identification process, scanning millions of suppliers in a fraction of the time and cutting-edge supplier capabilities do help businesses to differentiate products, boosting their market share.

As the practical use of the above-mentioned practices, utilizing AI solutions had resulted in most businesses making more increased revenues and decreased costs. The rapid advances in AI automation, big data analytics, cloud computing, robotic process automation, and IoT will help many supply chain roles and businesses to meet future demand.