Demand Forecasting

Optimizing Supply Chain with AI and Analytics
Vaishnavi Patil
February 23, 2023

A supply chain is the network of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer. It includes the sourcing of raw materials, manufacturing, transportation, storage, and distribution of finished goods.

Supply chain has been one of the most promising industries to adapt the use of AI and ML. According to studies, the increasing use of AI in this sector has led to improved stock management, and real time transportation monitoring. The goal of using AI in this industry is to drive the flow between the intermediaries without manual participation of the people and processes.  

SCM without AI and Analytics:

Below are some of the pain points which Supply Chain businesses might face:  

  • Limited visibility and insights: Without advanced analytics, it can be difficult to gain a comprehensive view of the supply chain and identify inefficiencies, risks, and opportunities.
  • Inability to handle large amounts of data: Traditional supply chain management methods may not be able to process and analyze large amounts of data in real-time, which can limit the ability to make informed decisions.
  • Difficulty in forecasting and demand prediction: Without AI and ML, supply chain managers may not be able to accurately predict demand and forecast future trends, which can lead to stockouts or overstocking.
  • Limited automation: Without AI and ML, supply chain managers may not be able to automate repetitive tasks, such as tracking and monitoring inventory, which can increase the potential for errors and slow down the supply chain.
  • Difficulty in identifying and mitigating risks: Without advanced analytics, it can be difficult to identify and mitigate risks, such as natural disasters or supplier disruptions, which can disrupt the supply chain and negatively impact the business.

How AI is transforming the face of the supply chain?

  • AI is transforming the face of supply chain by improving efficiency and accuracy in various areas, such as:
  • Inventory Management: AI can help optimize inventory levels by predicting demand and identifying trends.
  • Supply Chain Planning: AI can assist with forecasting, demand planning, and production scheduling to improve responsiveness to changes in demand.
  • Logistics Optimization: AI can help plan routes, optimize delivery schedules, and reduce transportation costs.
  • Quality Control: AI can be used to inspect goods at various stages of the supply chain and identify defects before they reach the customer.
  • Predictive Maintenance: AI can help identify potential equipment failures and schedule maintenance before a problem occurs.
  • Risk Management: AI can be used to identify and mitigate risks in the supply chain, such as natural disasters or geopolitical events.  

Different areas where AI and Analytics can be used to help Supply Chain companies optimize their operations:

  • Predictive modeling: This involves using historical data to build models that can predict future demand for products or services.
  • Cluster analysis: This involves grouping similar items or customers together to identify patterns and trends in the data.
  • Anomaly detection: This involves identifying unusual or unexpected events in the data, such as equipment failure or supply chain disruptions.
  • Natural Language Processing (NLP): This involves using machine learning algorithms to process and analyze unstructured data, such as customer feedback or social media posts.
  • Computer Vision: This involves using machine learning algorithms to process and analyze visual data, such as images or videos. This can be used in quality control, warehouse management or predictive maintenance.
  • Time series forecasting: This involves using machine learning to predict future trends based on historical time series data.
  • Predictive Maintenance: This involves using machine learning to predict when equipment or machines will need maintenance, so that it can be scheduled in advance.
  • Sentiment Analysis: This involves using machine learning to analyze customer feedback and social media posts to understand the customers' sentiment towards the company and products.

We must consider three overarching benefits of AI:

  • Supply chain transformation: The ability to use AI in the supply chain significantly increases the likelihood that organizations will report significant value from their initiatives. This is because it allows them to improve their decision-making and productivity.
  • Enhanced decision-making: Through a comprehensive AI approach, organizations can improve their decision-making capabilities by bringing together various data sources and insights into a single platform. This allows them to make more informed decisions and deliver superior outcomes.
  • Systems modernization: Thirty-six percent of companies with a comprehensive approach to AI are planning to use it this year to help create a data fabric —a 360-degree view of all data that touches their organizations and drives end-to-end value from critical functions within the supply chain.

Machine Learning and Technical Architectural Setup for AI revolution in Supply Chain Management:

From the perspective of implementing AI and ML in a supply chain operation, a company may need the following technical architecture:

  • Data pipeline: A data pipeline is needed to collect, store, and process large amounts of data from various sources, such as sensor data, log data, and customer data. This can include technologies such as Apache Kafka, Apache Nifi, or Apache Spark for data ingestion, Apache Hadoop or Apache Cassandra for data storage, and Apache Spark or Apache Hive for data processing.
  • Cloud infrastructure: Cloud infrastructure, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure, can provide the necessary compute and storage resources to run the AI and ML models.
  • Machine learning platform: A machine learning platform, such as TensorFlow, PyTorch, or Scikit-learn, is needed to develop, train, and deploy the AI and ML models.
  • Containerization and orchestration: Containerization technologies, such as Docker, and orchestration technologies, such as Kubernetes, can be used to package the AI and ML models and deploy them in a scalable and resilient manner.
  • Model management and monitoring: A model management platform, such as MLflow, can be used to track the performance of the AI and ML models and make adjustments as necessary. Monitoring tools, such as Prometheus or Grafana, can be used to monitor the performance and health of the entire AI and ML system.
  • Integration: The AI and ML models must be integrated with existing systems and processes in the supply chain operation, such as inventory management and logistics. This can be done using APIs or by using integration technologies such as Apache Camel or Apache NiFi.
  • Security: Security measures, such as encryption, authentication, and access control, must be put in place to protect the data and models. This can include technologies such as AWS KMS or GCP Key Management Service to encrypt data at rest, and Identity and Access Management (IAM) to control access to the data and models.

ROI of AI Supply Chain Management:

Gartner predicts that within the next five years, the amount of automation within supply chain processes will increase significantly. Additionally, over the next seven years, global spending on Industrial Internet of Things (IIoT) platforms is expected to experience significant growth, with a projected compound annual growth rate of 40%. This is forecasted to increase from $1.67 billion in 2018 to $12.44 billion in 2024.

Some potential benefits that can contribute to ROI include:

  • Increased efficiency: AI and ML can be used to optimize and automate various supply chain processes, such as demand forecasting, inventory management, and logistics, which can lead to reduced costs and improved efficiency.
  • Improved decision-making: AI and ML can provide real-time insights and predictions about various aspects of the supply chain, such as demand patterns, inventory levels, and delivery times, which can help managers make better decisions and improve overall performance.
  • Enhanced customer experience: AI and ML can be used to analyze customer data and preferences, which can help companies personalize their products and services, improve customer satisfaction, and increase sales.
  • Reduced risk and uncertainty: AI and ML can be used to identify and mitigate potential risks in the supply chain, such as disruptions in the supply of materials or changes in customer demand, which can help companies avoid costly mistakes and improve overall resilience.
  • Improved operational visibility: With the right tools, AI and ML can provide a better understanding of the supply chain, enabling companies to identify bottlenecks and inefficiencies, and implement corrective actions.

The return on investment (ROI) for a supply chain company after implementing AI and ML to their operation can vary depending on the specific use case and the size and complexity of the company's supply chain.

We hope you found our blog post informative. If you have any project inquiries or would like to discuss your data and analytics needs, please don't hesitate to contact us at We're here to help! Thank you for reading.
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