Benefits of Integrating AI and Machine Learning Into Your OMS

Benefits of Integrating AI and Machine Learning Into Your OMS

Image of warehouse

Today, order management has become an integral part of supply chain operations. Recently, businesses have started utilizing AI and ML to expedite order processing and minimize the inefficiencies usually associated with inventory management. Managing inventory has been revolutionized for better customer experience, thanks to the innovations in AI and ML.

AI and ML are undoubtedly the big game changers, especially in automation, forecasting, and perfect order routing, significantly reducing human error. AI-enabled OMS will allow the next level of operational efficiency and effectiveness. McKinsey suggests that AI-driven supply chains may cut logistics costs by 15%, cut inventory levels by 35%, and increase operational efficiency by 65%.

Businesses, by its very influence, are to take up an AI-first approach; that is how the future of commerce will look like. With AI making grand breakthroughs, the order management space is on the brink of a massive shift. This blog briefly overviews the benefits of integrating AI and machine learning and provides some examples of AI and ML systems applied in the real world.

Streamlined Order Processing

AI automates the collection of order data from multiple sales channels, such as email, phone, and online. Further, it can automatically classify and processes orders based on item type, location, customer preferences, etc. — leading to faster distribution processes, reduced sales cycle time, and increased operational efficiency.

AI and ML systems are also able to automatically extract customer information based on their habits, sales, behavior patterns, and complaints without the need for service requests. This means enhanced employee productivity due to automated tasks.

Improved Inventory Management

AI and ML are revolutionizing inventory management with advanced solutions that enhance efficiency and reliability. Here are some key areas where AI and ML are making a significant impact:

Procurement: AI can analyze the performance data of suppliers, such as pricing, quality, and delivery time, to determine the most trustworthy and cost-effective suppliers, thereby streamlining the procurement processes.

Replenishment: AI-based inventory management identifies demand patterns, lead time, supplier sufficiency, storage capacity, and other factors to intelligently optimize the replenishment process. It seamlessly bridges the gap between suppliers, manufacturers, and retailers, resulting in faster and more efficient order fulfillment.

Optimal Stock Levels: It can also automatically adjust reorder quantities and points to maintain the right inventory levels. Balancing stock levels with customer demands in this manner frees up warehouse space, prevents overstocking/stockouts, and reduces holding costs.

Real-time visibility: Real-time inventory visibility streamlines all supply chain operations involving procurement, production, and distribution. AI monitors the movement of items across the inventory, their status, location, tracking quantities, etc.

Risk Mitigation: Lastly, accurate demand forecasts also help you proactively detect supply chain disruptions, such as sudden hikes in demand and delayed shipments. It helps you make further mitigation plans to ensure the smooth continuity of the order cycle.

Improved Demand Forecasting

AI-based forecasting algorithms offer multi-level insights into the frequency of return orders, efficiencies, sales history, customer wallet sizes, loopholes in order fulfillment systems, market trends, and external factors (such as competitor pricing, social media sentiment analysis, etc.) to generate accurate demand forecasts.

graphic of How AI impacts inventory management

AI can analyze large sets of data to identify patterns, fluctuating market dynamics, and seasonality and derive insights to help businesses maintain optimal inventory levels. This streamlined approach prevents overstock and understock situations, leading to better customer experiences.

Optimized Order Orchestration/Order Routing

Here is how you can implement intelligent order routing with AI systems:

  • Manage orders centrally from capture to order fulfillment with real-time multi-channel stock data.
  • AI and ML systems can identify market conditions and accordingly allocate the best fulfillment center for the order. It considers various factors such as closeness to customer location, product availability, shipping costs, delivery time, shipping carriers, etc.
  • Manage multiple product portfolios, even with numerous SKUs (stock-keeping units).
  • Categorize products based on various factors such as profitability, demand patterns, sales velocity, and seasonality. Thus, businesses can strategically prioritize their fast-moving or high-value products.
  • AI also detects gaps in the supply chain and helps make re-routing strategies.
  • Intelligently route orders to the best available fulfillment center via AI-based recommendations and accurate decision-making.

Reduced Human Errors and Operational Costs

Manual data collection is overwhelming and time-consuming, especially if the data volume is large — results in delayed order processing.

Automated order management tools gather data in no time without human errors. This, in turn, boosts your teams’ productivity levels, improves operational efficiency, and cuts down operational costs as well.

Better Customer Experiences

Real-time order tracking, automated payment/order notifications, and on-time delivery all lead to strong customer relationships and customer loyalty.

AI Chatbots further add to pleasant customer experiences by prompt replies via live chat.

AI can also suggest personalized recommendations based on customers’ preferences, purchase history, etc., to boost customer engagement and chances of sales.

Warehouse Optimization

AI and ML algorithms analyze order patterns and historical data to recommend the best warehouse layouts, picking strategies/routes, storage locations, and product placement. This significantly reduces inventory handling time across the warehouse.

graphic of warehouse process automation

Real-Life Use Cases of AI and ML systems

AI offers diverse use cases in order management systems, empowering businesses with essential tools to boost their supply chain operations. Here are some of the key use cases for AI in Inventory Management:

Graphic of top 5 agents of change within supply chain management

1. Automated Material Procurement

Material Procurement typically involves multiple suppliers/vendors, equipment, documents, etc., making it prone to errors and inflexibility. AI automates material procurement from collecting supplier data and sourcing best price quotes to managing the materials across the supply chain and detecting anomalies. This not only improves your inventory and service levels but also cuts down logistics costs.

2. Sentiment Analysis

AI-based Sentiment Analysis helps you sense changing demand patterns in the market by interpreting social media sentiments and customer reviews. Such data-driven insights help you cater to customer expectations better.

3. NLP (Natural Language Processing)

With Natural Language Processing (NLP), AI systems can derive insights from unstructured data such as customer feedback, social media posts, and market news. Such data analysis helps you understand your audience, market sentiments, and preferences better and plan product strategies accordingly.

Use of AI graphic

4. AI-powered Chatbots and Virtual Assistants

AI-based Chatbots and Virtual Assistants will help logistics staff attend to queries, make informed decisions, derive real-time insights, conduct item-level tagging, and so on. This, in turn, enables faster inventory management operations.

5. Computer Vision for Automated Inventory Checks

With Computer Vision technology, AI systems can capture visual data from sensors and cameras to keep track of stock levels, detect damaged goods, and track the movement of items in and out of the supply chain. This reduces human errors and eliminates the need for manual checks.

6. Autonomous Vehicles and Drones

AI-driven autonomous vehicles and drones can assist in logistics operations such as inventory picking, packing, handling last-mile delivery, etc. This enhances speed and efficiency, while also cutting down human labor.

7. AI-based Robots

AI-powered robots may play a critical role in warehouse management in the future due to their ability to detect damaged inventory and advanced scanning.

These robots can autonomously navigate across the warehouse to precisely pick, pack, sort, locate, and optimize the inventory, thereby accelerating the fulfillment process. As they can work non-stop, you will witness faster order fulfillment and reduced operational costs.

8. Blockchain Technology with AI/ML

Blockchain technology acts as a single, immutable source of truth for order records and transactions.

AI and ML, together with blockchain technology, will improve transparency and traceability of items across the supply chain.

Decentralized structure and intelligent contracts along with AI will make room for secure and verified transactions, as there will be a tamper-proof record of every transaction taking place. This eliminates chances of fraud or counterfeiting while building the trust of stakeholders.

This technology also allows you to securely share order-related data with stakeholders, reducing accountability and disputes.


Integrating AI and ML into OMS is not only a trend but a strategic imperative. AI capabilities will likely create a new world order by automating numerous tasks, optimizing processes, and generating insights. As competition becomes cutthroat, adopting change and keeping pace is essential. AI-based OMS can streamline order processing, manage inventory efficiently, and enhance customer experiences.

If you want to manage the complexities of your eCommerce business, consult our domain experts. Ignitiv, collaborating with Kibo, can help you integrate AI into your OMS and help boost scalability, flexibility, and ROI by delivering exceptional omnichannel experiences.

Rajib Das headshot
Rajib Das
Founder & CEO, Ignitiv

Rajib Das is a seasoned executive with 25+ years of experience in commerce technology. He is a proven business leader with a track record of bringing new organizations and products around commerce to market leadership, partnering with industry leading platforms such as Kibo, Oracle, Contentful and others. Rajib has expertise in building top performing and efficient go to market teams across strategy, solutions, sales, and delivery. He is a thought leader in Omni-channel commerce.