How Can Artificial Intelligence Optimize Supply Chain in the Retail Industry

September 22, 2023

In the past decade, the world of commerce has transformed completely. Gone are the days when shopping outlets and stores were the only way to get new clothes. With the development of e-commerce giants like Amazon, Flipkart, Myntra, Ajio, Nykaa and so on, consumers have convenient access to quality products across the spectrum, which explains why the volume of the online retail industry is increasing each day. This growth needs to be accompanied by advancements in supply chain management to ensure timely delivery at an optimized cost of operations.

Let’s take a quick look at the challenges faced in the supply chain management of retail industries in the current landscape:

  • Fluctuating Customer Demand: Retailers often struggle to predict and adapt to changing customer preferences and demands, leading to overstocking or understocking of products.
  • Inventory Management: Managing inventory efficiently is a constant challenge, as retailers need to strike a balance between keeping enough stock to meet demand and avoiding excess inventory costs.
  • Supply Chain Visibility: Limited visibility into the entire supply chain can result in delays, inefficiencies, and difficulties in tracking products from manufacturer to end consumer.
  • Seasonality and Trends: Retail is an industry where the demand for products change with season and time. For example, jackets and sweatshirts would be in high demand during winter and would drop highly in the summers. The demand is also influenced by sudden fashion trends of celebrities, social media trends and more.

Integration of Artificial Intelligence in the end-to-end supply chain can help in the optimization of operational costs to a huge extent. In this article, I’ll be covering some of the top ways to use AI in the supply chain and the benefits it would bring.

  1. AI for Demand Forecasting

Demand forecasting refers to the process of predicting what would be the customer demand for a particular product at a future date based on existing information. How is it done? We use information including historic sales data, demographics, and customer behavior patterns to predict this. For example, a particular customer may have the habit of purchasing clothes closer to festivals like Diwali. Many customers prefer shopping during the weekend, before the start of the summer season in their region, and so on. 

The different types of data that are crucial for demand forecasting are: 

  • Point of Sale Data of Transactions
  • Web Analytics Data (website traffic, pages, etc.)
  • Market Economy & Trends
  • CRM (Customer Relationship Management Data - orders, returns, feedback)
  • Weather Data
  • Social Media Data (to understand buyer preferences and so on)

A few pointers to help you execute demand forecasting:

  • The most important step here is to set up a data collection pipeline, store, and clean the obtained data. You can use E2E Networks for data management.
  • The next step is data pre-processing: removal of outliers, imputation of missing values, removing noise from the data.
  • Once you have the data ready, you can feed it to machine learning algorithms that can learn patterns from it and provide a prediction. Usually, regression-based models are used for forecasting. Regression is the method in which we try to find the best-fitting mathematical expression for the available data points. Linear regression is the simplest, and there are more advanced options like Random Forest Regression, XGBoost (Extreme Gradient Boosting algorithm), and LightGBM.
  • The type of model you choose should also factor in the size of the data, complexity of the task, and the trade-off between accuracy and computational costs. 
  1. Warehouse Management and Optimization

Warehouse management is a crucial process in the supply chain, and can be a bottleneck in case of concerns like understocking, inefficient layout, etc. Deep learning can learn from data in the form of images – with which we can perform various tasks like object detection, segmentation, and classification. Here are a few areas where we can implement computer vision to optimize warehouse management:

  • Visual Inspection: Computer vision algorithms like Convolutional Neural Networks (CNN) and YOLO can be used to detect the target product level and quality as well as the misplacement of items in real time. This would require the set up of cameras and sensors to provide real-time data feed of the warehouse. Detectron is an open-source framework developed by Meta AI Research for object detection and instance segmentation. It's useful for more advanced tasks that require precise localization and segmentation of objects.
  • Shelf Replenishment: AI can monitor inventory levels on store shelves and automatically trigger restocking orders when items are running low.
  • Layout Design: The layout needs to be optimal to reduce operation time. Reinforcement learning (RL) is a field of ML where algorithms learn the best path/result through a reward-based mechanism. The idea is that if the final output is good, we reward the algorithm; else we punish it. Through this, the algorithm will learn the best path through external reinforcement. These RL algorithms, such as Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO), can be used to optimize warehouse layouts. By simulating different layouts and rewarding efficient ones based on historical data, RL algorithms can learn optimal layouts over time.
  • Dynamic Reorganization: AI systems can dynamically adjust the layout based on changing inventory levels and demand patterns. Genetic algorithms can also be used here.

  1. AI for Predictive Maintenance

In any industry, there is the usual wear and tear of machines and equipment over time. If the quality is not regulated and maintained continuously, a sudden breakdown can lead to a shutdown of the factory. To reduce the total downtime and improve efficiency, predictive maintenance is a crucial aspect. Let me take you through the journey of implementing AI for predictive maintenance in retail.

The first step is data collection, which is done using various sensors and IoT devices to monitor equipment and systems. For instance, temperature sensors in refrigeration units, motion sensors in doors, and voltage sensors in electrical systems collect data about temperature readings, power consumption, and equipment status continuously. Why do we need these? For example, tracking power usage patterns will help us to detect irregularities that may signal equipment malfunction. Note that the raw data collected from sensors would have a lot of noise and cannot be used directly.

Once the data is preprocessed and the features are engineered, machine learning models are employed to make predictions. Common ML algorithms used for predictive maintenance in the retail industry include:

  • Time-Series Forecasting Models: Models like ARIMA, SARIMAX are popular time series forecasting algorithms. They can predict when equipment is likely to fail based on historical data.
  • Classification Models: Classification algorithms like decision trees or support vector machines can predict whether a specific piece of equipment will fail within a certain time frame.
  • Anomaly Detection: Anomaly detection techniques, such as Isolation Forest or One-Class SVM, can identify unusual patterns.

When an ML model is deployed, it will generate predictive alerts when it detects anomalies or potential failures.

Challenges & Conclusion

The success of all the ideas presented above depends hugely on the quality of the data collected. Obtaining high-quality, consistent data from various sources within the supply chain can be challenging. Implementing AI requires skilled data scientists and AI experts, which may be a hurdle for small-scale retail organizations lacking the necessary in-house talent. Complying with data privacy and security regulations, especially when handling customer and supplier data, is a critical challenge. Data privacy of customers needs to be respected; any mishandling could lead to legal issues.

Hence, it is recommended to set up end-to-end systems for data cleaning, validation and data governance. You should continuously monitor and fine-tune your AI systems to adapt to changing market dynamics and evolving customer demands. 

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