The supply chain industry is growing at a rapid pace over the last decade or so. Data analytics has proved invaluable in revolutionizing how a traditional supply chain company handles its operations. Data analytics helps businesses improve their overall efficiency, bring down costs and optimise decision-making across the supply chain process. In this article, we take a closer look at how data analytics is helping companies unlock new levels for success and organisation.

How Data Analytics Improves Supply Chains

Data analytics enhances supply chain operations in several key areas:

  1. Demand Forecasting: Advanced capabilities in some analytics tools are able to read historical sales data, market trends as well as seasonal fluctuations to predict future demand accurately. This demand forecasting then helps businesses maintain optimal inventory and reduce the chances of stockouts or overstocking. 
  2. Route Optimization: Weather conditions, traffic patterns and historical data can all be analysed to chart the best possible routes while lowering fuel requirements and reduced transportation costs. 
  3. Supplier Management: Data from delivery times, quality and pricing is used to assess supplier performance. Doing so allows a business to make sound decisions regarding supplier relationships which in turn allows for better negotiations and a more efficient output of the supply chain management process.
  4. Real-Time Visibility: IIoT (Internet of Things) sensors when coupled with data analytics allow users and vendors to track shipments real-time, keep an eye on inventory levels and stay ready for any bottlenecks. Visibility like this is priceless as it allows businesses to tackle issues proactively and reduces the risk of disruptions.
  5. Risk Mitigation: Predictive data analysis is also helpful to analyse any potential risk from delays due to weather or geopolitical issues in the supplier’s local regions. Data analytics here helps streamline scm logistics and stay proactive with a robust strategy. 

Data Analytics Use Cases

Data analytics has become an integral part of the supply chain process for many businesses: 

  • Procter & Gamble was able to cut down costs across scm management touchpoints by 60% in India, which meant faster operations at a reduced cost. 
  • Amazon was able to clock in a 69% reduction in unplanned equipment downtime by using analytical data for predictive maintenance.
  • Walmart was able to remove 30 million unnecessary miles and lower carbon dioxide emissions by 94 million pounds. 

Looking Ahead 

With technology constantly changing, data analytics will become even more important for the successful execution of both local and global supply chain management. Here’s what’s on the horizon:

  • More AI and Machine Learning: AI and machine learning are poised to optimise data prediction which will further improve transparency and responsiveness of the supply chain. 
  • Integration of IoT Devices: IoT devices will be better integrated to offer real-time updates and add to the transparency and reliability of supply chain operations.  
  • Blockchain for Transparency: Blockchain technology can be used to improve the transparency and security of supply chains by sharing records of all big and small transactions. 

Top Tips to Integrate Data Analytics into Supply Chains

For businesses looking to seamlessly integrate data analytics into the supply chain process, consider the following:

  1. Foster a Data-Driven Culture: Modify your business culture to one that relies on data-driven decisions across the board. 
  2. Identify Key Use Cases: When integrating systems, prioritise areas where data analytics will be most beneficial, for example route optimisation or demand prediction. 
  3. Invest in a Data Platform: Partner with a robust system that can easily handle large datasets and share real-time insights. 
  4. Collaborate with Experts: Make sure you bring in specialists who can analyse the data gathered and interpret the insights to tweak strategies for optimal operational performance.
  5. Monitor and Adapt: Build a habit of constantly reviewing data analytics initiatives and modify strategies as and when required to ensure continuous improvement. 

Actionable insights from data analytics are set to revolutionise logistics and supply chain management. These can then be used to improve efficiency, lower operational costs and enhance customer experience. As technology grows and application becomes more and more common, data analytics will continue to evolve and its role in supply chain management and optimisation will only grow as time passes, paving the way for steady growth and innovation in the supply chain management industry.