The AI-driven Supply Chain Revolution
AI adoption offers a solution to meet the rising challenges in supply chain management.
Introduction
Supply chains are built on the principles of scale, consolidation, and centralization, each of which grows increasingly complex over time. These complexities in the system make supply chains extremely vulnerable to disruption and dysfunction, as we witnessed in the aftermath of the Covid-19 pandemic and beyond.
Compared to their competitors, early adopters of AI in supply chain management have seen logistics costs reduce by 15%, inventory and service levels improve by 35% and 65%, respectively. Agility and flexibility are the twin engines of an AI-powered supply chain that can improve efficiency and combat disruptions.
The eCommerce supply chain
Within eCommerce, the desire to offer seamless omnichannel customer experience drives the integration of Artificial Intelligence (AI) in supply chains. Earlier, the different aspects of supply chain operations were siloed, which led to inefficiencies in the overall functioning of the system.
AI streamlines the supply chain by integrating order management, supplier quality management, inventory management, freight, warehouse management, automation, and transportation. The prevalence of AI in the supply chain is underscored and set to become more pronounced, as indicated by McKinsey & Company, who expect businesses to gain between $1.3tr and $2tr a year in economic value.
Use-cases
The key segments that stand to make the most from the increasing adoption of AI and ML in the supply chain are the following:
Inventory Management
Predictive analytics for demand forecasting allows businesses to avoid poor inventory management and optimize stock replenishment, reducing customer churn. AI can ensure decreased holding costs and optimal inventory levels using real-time data. The increasing adoption of computer vision in inventory management is key to detecting damages and classifying objects efficiently.
Using analytics, AI can perform supplier quality management to optimize inventory by distinguishing high-performing suppliers from low-performing ones. Through object recognition, AI can even prevent restricted items from being sold on your platform.
Analytics-driven insights and Supply chain monitoring
Dynamic pricing algorithms can now make informed decisions on purchase patterns and seasonal trends. Analytics can also help understand returns and help reduce them. Predictive analytics can help reroute freight in real-time to avoid delays and notify customers in case of unexpected changes.
AI’s ability to analyze large volumes of data in real-time allows it to provide greater transparency into the minutiae of supply chains. This can bring about an end-to-end visibility of the supply chain and help prevent fraud.
Demand forecasting
Machine learning algorithms can perform sentiment analysis of social media using Natural Language Processing (NLP) to make accurate price and demand forecasting in real-time and ensure the availability of products. It can also perform demand sensing to leverage short-term changes.
The role of high-quality labeled data in the supervised training of the ML algorithms that enable this change cannot be understated. With better training data, the accuracy of the AI is fine-tuned to perfection.
Warehouse management
Automating repetitive manual work in warehouses can result in fewer on-floor accidents leading to increased safety for workers. AI is also able to make predictive maintenance of equipment, saving costs.
Image recognition software can perform automated QC, which is shown to increase defect detection rates by up to 90% compared to human inspection. before items are dispatched. They can help prevent overstocking and understocking in warehouses.
Logistics
With relevant input, AI can chart the most optimal routes for delivery and logistics, predicting peak hours and ensuring it doesn’t affect delivery timings. Dynamic, real-time route optimization can be done by understanding weather and road conditions. Processes such as carrier selection and last-mile delivery can be streamlined using Robotic Process Automation (RPA), optimizing routing, freight, and rail.
The Taskmonk Advantage
According to the recent Supply Chain Complexity survey by Körber, only 1 in 10 businesses can stay ahead of their supply chain challenges. Warehouse robotics, inventory management, reliable customer support, and delivery infrastructure are all areas that employ AI. Taskmonk helps centralize the procurement of high-quality labeled data crucial for enabling ML algorithms. A myriad of ML algorithms, each of which requires use-case-specific training data are critical for the widespread integration of AI in supply chains.
Developing use-case-specific AI across the supply chain often becomes increasingly complex with the plethora of stakeholders involved. With the veracity of AI hinging upon the quality of training data, it becomes imperative to have a labeler-centric platform that is uniquely positioned to adapt to the requirements of the enterprise and the evolving AI.
By having a customizable No-Code platform like Taskmonk in their arsenal, enterprise AI teams can create use-case-specific workflows and maintain a bird’s eye view of the quality of data being trained, ensuring better, faster, and more consumer-centric Production AI.
Conclusion
Ecommerce platforms that have integrated AI in their supply chains have raised standards among customers who now expect better on-time delivery and products with fewer damages. The pandemic and the ensuing supply chain crisis have prompted renewed calls to integrate AI in end-to-end supply chain operations. Bringing AI-driven data insights to supply chain management is key to accelerating the dawn of Ecommerce 4.0
