Supply chain analytics involves collecting, analysing, and leveraging data to optimise supply chain operations. Tools like data visualisation, predictive modelling, and machine learning to identify patterns, trends, and opportunities are used to improve the supply chain.

Notably, supply chain analytics is becoming increasingly important in today's global and interconnected economy, enabling companies to make data-driven decisions that drive competitive advantage and improve business performance. By using supply chain analytics, organisations can gain insights into their operations, improve efficiency, reduce costs, mitigate risks, and enhance customer satisfaction. 

Get a supply chain analytics certification to propel your career in supply chain operations.

What is a supply chain?

Supply chain refers to the network of organisations, people, activities, information, and resources involved in creating and delivering a product or service.

Supply chain analytics is applied to different areas of the supply chain, including procurement, manufacturing, transportation, warehousing, inventory management, and customer service.

Defining supply chain analytics

Supply chain analytics is the practice of using data and analytical tools to gain insights and optimise the performance of a supply chain. It involves collecting and analysing data from various sources, such as suppliers, manufacturers, distributors, and retailers, to identify patterns, trends, and opportunities for improvement.

How does supply chain analytics function?

Supply chain analytics functions by collecting and analysing data from various sources across the supply chain. It includes data on inventory levels, transportation costs, customer demand, supplier performance, and production capacity.

Once the data is collected, it is processed using various analytical techniques such as statistical modelling, data mining, and machine learning algorithms to identify patterns, trends, and scope for improvement.

Analytics can help companies optimise their inventory levels to reduce waste, track and manage supplier performance, identify bottlenecks in the production process, and forecast demand more accurately.

Importance of supply chain analytics in SCM

Here are some ways in which supply chain analytics can contribute to supply chain management (SCM):

Forecasting and demand planning: Supply chain analytics can help organisations accurately predict customer demand and plan production and inventory levels accordingly, reducing stockouts and improving customer satisfaction.

Inventory optimisation: By analysing inventory data, organisations can identify slow-moving products, curtail stockouts, and optimise inventory levels to reduce carrying costs.

Transportation optimisation: Supply chain analytics can help organisations optimise their transportation networks by identifying the most cost-effective routes, modes of transportation, and carriers.

Supplier performance analysis: Analysing supplier data can help organisations identify the best suppliers, negotiate better contracts, and improve overall supplier performance.

Risk management: Supply chain analytics can help organisations identify and mitigate risks such as supply disruptions, quality issues, and geopolitical events that could impact the supply chain.

Supply chain analytics is essential to modern supply chain management, enabling organisations to operate efficiently and effectively in a complex and dynamic global marketplace.

Future of supply chain analytics

The future of supply chain analytics looks very promising, with many exciting developments on the horizon. 

Here are some key trends that are likely to shape the future of supply chain analytics:

Artificial intelligence and machine learning: Advances in artificial intelligence (AI) and machine learning (ML) are enabling more sophisticated supply chain analytics. These technologies can assist in identifying patterns and trends in large datasets, predict demand, optimise inventory levels, and automate decision-making processes.

Internet of Things (IoT): The proliferation of IoT devices, such as sensors and RFID tags, generates vast amounts of data that can be used to monitor and optimise supply chain performance. Analysis makes identifying issues such as bottlenecks, delays, and quality problems in real-time easy.

Blockchain: This technology offers a secure and transparent way to track products and transactions across the supply chain. Organisations can improve visibility, reduce fraud, and enhance trust and collaboration with partners using blockchain-based supply chain analytics.

Data visualisation and dashboards: Data visualisation tools and dashboards enable organisations to quickly and easily visualise key performance indicators (KPIs) and trends in their supply chain. This can help decision makers to identify issues and white spaces and take action to improve performance.

Overall, the future of supply chain analytics is likely to be characterised by greater sophistication, automation, and real-time monitoring. 

Conclusion

In today's increasingly complex and global supply chain environment, supply chain analytics has become essential for organisations to remain competitive and meet customer expectations. By investing in supply chain analytics, organisations can improve their agility, responsiveness, and decision making, ultimately leading to increased profitability and long-term success.

You need to have a clear picture of supply chain analytics to advance your career in supply chain management. Imarticus Learning offers a comprehensive course for professionals seeking to boost their SCM career. The IIM Raipur supply chain management course offers live training by industry experts, a 3-day immersion programme, and the status of IIT alumni recruiters can’t ignore. So, enrol today!