Analytics, Automation, and AI in Digital Supply Chains

Analytics, Automation and AI in Digital Supply Chains

The supply chain is an important part of business operations. It exists globally to assist ventures with data on certain business components, like sourcing, production, inventory, warehouse, transportation, distribution, etc. A supply chain ensures that all the business components are functioning properly so there is no delay in the fixed deadline.

However, there are certain instances where traditional supply chains still need to provide accurate data. In such a situation digital supply chain management comes to rescue. In the current technological era, a digital supply chain allows a company to leverage cutting-edge technologies for optimised performance and also ensure a functioning supply chain.

In this article, we will discuss more about the digital supply chain and how the incorporation of analytics, automation, and artificial intelligence can improve it further. 

What do you understand by digital supply chain?

A digital supply chain can be easily understood through a traditional supply chain. A traditional supply chain works as a linear chain interconnected with other chains. A product can not be successfully manufactured if any part of this linear chain fails.

Theoretically, a traditional supply chain model is pretty simple and can be executed quite easily. However, the execution of a supply chain is quite tedious and can generate numerous errors that result in delayed deadlines.

Therefore, the digital supply chain is used instead of the traditional one so that ventures can understand the requirements of the raw materials and demands of their consumers. A digital supply chain is also used to predict the real-time completion period of the various steps that are included in the supply chain.

Digital supply chain management tends to prioritise the customer, making it customer-centric. This system also uses the three pillars of excellence, namely choice, customisation, and speed, to rapidly fulfil customer demands. Simply put, this system merges all the external structured and unstructured information and the internal system to function efficiently. 

Data Analytics in Digital Supply Chain Management

Data analytics assists a venture in making accurate decisions regarding the supply chain. Here is how data analytics empowers businesses:

  • Predictive insights: Advanced analytics techniques enable businesses to anticipate demand patterns, identify market trends, and forecast supply chain disruptions. By analysing historical data and external factors, organisations can make data-driven decisions to optimise inventory levels, production schedules, and distribution strategies.
  • Performance optimisation: Analytics provide real-time visibility into supply chain performance metrics, allowing businesses to identify inefficiencies, bottlenecks, and areas for improvement. Through continuous monitoring and analysis, organisations can optimise processes, enhance resource utilisation, and streamline operations for greater efficiency and cost-effectiveness.
  • Risk management: Analytics empower supply chain professionals to assess and mitigate various risks, including supplier disruptions, transportation delays, and quality issues. By analysing data from multiple sources, businesses can proactively identify potential risks, develop contingency plans, and minimise the impact of unforeseen events on operations.

Types of data analytics

Numerous types of data analytics can be incorporated into a business’s digital supply chain management. These various types of data analytics are:

Predictive Analytics

Predictive analytics employs statistical graphs and regression analysis to determine the latest trends from previous data. This allows for the easy prediction of future trends and probable disruptions due to weather or political influences.

Cognitive Analytics

Cognitive analytics improves relationships between business providers and consumers. This is made possible by overseeing the feedback data collected after every purchase through various AI tools that skillfully understand consumer requirements.

Descriptive Analytics

Data mining is also known as descriptive analytics. In this method, huge amounts of data are analysed to track consumer patterns, eventually creating a summarised insight into any given situation. This analytics makes decision-making easier by simply studying past data and current trends.

Prescriptive Analytics

Prescriptive analytics assist a company in comprehending how a particular change will impact its digital supply chain. This will help a company make decisions that will create a positive impact on the outcomes, reduce production time, and maximise the value of that company.

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Digital Supply Chain Automation

Digital supply chain automation refers to a method where every step of the supply chain is guided by AI and ML automating repetitive human tasks. From the sourcing of raw materials to the distribution of finished goods, every step can be smoothly executed with the assistance of automation.

Following are a few things integrating automation in digital supply chain offers:

  • Process efficiency: Automation eliminates manual, repetitive tasks across the supply chain, such as order processing, inventory management, and shipment tracking. By automating these tasks, businesses can reduce errors, accelerate cycle times, and free up resources for more value-added activities.
  • Supply chain integration: Automation facilitates seamless integration and coordination among disparate systems and stakeholders within the supply chain ecosystem. Through automated data exchange and communication, organisations can enhance collaboration, visibility, and responsiveness across the entire value chain.
  • Scalability and adaptability: Automated systems are scalable and adaptable to changing business requirements and market dynamics. Whether scaling production volumes, expanding product lines, or entering new markets, automation enables supply chains to flexibly adjust and accommodate evolving demands.

Artificial Intelligence in Digital Supply Chain Management

To increase productivity, sustainability, and efficiency, companies are incorporating AI into their digital supply chain.

Here are some advantages of using artificial intelligence in the digital supply chain:

  • Demand forecasting: AI algorithms analyse vast amounts of data to predict future demand patterns with greater accuracy. By considering historical sales data, market trends, weather forecasts, and other variables, AI-powered forecasting models enable businesses to optimise inventory levels and reduce stockouts or excess inventory.
  • Optimised routing and logistics: AI-driven optimisation algorithms optimise transportation routes, warehouse layouts, and distribution networks to minimise costs and improve delivery efficiency. By considering factors such as traffic conditions, fuel prices, and delivery schedules, AI enhances logistics planning and execution for optimal outcomes.
  • Supply chain intelligence: AI enables intelligent decision-making by analysing complex supply chain data and generating actionable insights. From identifying cost-saving opportunities to detecting potential risks and opportunities, AI-driven analytics empower organisations to make informed decisions and drive continuous improvement across the supply chain.


By implementing a combination of analytics, AI, and automation, supply chain managers can transform their operations. The power of these three enables proactive decision-making, enhances operational efficiency, and fosters agility and resilience in the present dynamic business environment. The efficient implementation of AI, analytics and automation is essential for businesses to stay ahead of the competition, innovate and deliver superior value to their customers.

If you’re an aspiring supply chain professional, consider enrolling in the Advanced Programme In Digital Supply Chain Management offered jointly by Imarticus and IIM Raipur.

This 6-month course is curated and taught by industry experts. Learn about cutting-edge technologies, the role of IoT in digital supply chain management, strategic implementation and talent management and much more.

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