Supply Chain Optimization Powered by Big Data and Analytics

Worldwide, a large amount of data is being generated in real-time.

We are experiencing a data explosion due to the high volume of data being produced every second by smart devices, artificial intelligence systems, and the internet.

This is where big data comes in. Big data analytics helps examine large swathes of data to find hidden patterns, trends, correlations, or any other insights. Using big data, organizations can bring in speed and efficiency. It gives them a competitive edge by helping them stay agile in this ever-evolving marketplace.

Big data analytics can also be an essential tool for supply chain professionals who also face data overload challenges. Using data-backed insights, organizations can achieve supply chain optimization.

But what exactly is supply chain optimization, and how can big data analytics help achieve it? Let's take a look -

What is supply chain optimization?

By optimizing their supply chain operations, organizations can maintain their supply chains at peak efficiency based on key performance indicators such as operating expenses and gross margin return on inventory.

Simply put, supply chain optimization focuses on delivering products to customers at the lowest total cost and highest possible profit.

This is where big data analytics can help in. It can improve supply chain management by resolving several key pain points at operational, strategic, and tactical levels. It can help balance the costs of manufacturing, inventory, sourcing, and warehousing.

Supply chain optimization powered by big data analytics

Let's discuss the specific benefits of using big data analytics in the supply chain.

Improved inventory management

Inventory management is critical to achieving supply chain optimization. But it comes with its own set of challenges.

Shoppers prefer to purchase the product they want, and at the time they want. If a seller is unable to offer it in time, then the customers will quickly switch to a competitor.

As per a report, 31% of online shoppers switch to a competitor at the first instance of the unavailability of a product with their preferred seller. This rises to 50% and 70% during the second and third instances, respectively.

So, the inability to fulfill this demand may lead to significant losses. That is why it is important to keep the inventory in stock. But it is easier said than done because having an excess stock that doesn't sell can also harm the business.

That is why big data analytics can play an important role in inventory management. Based on the analysis of trends and patterns of customer data, big data can help figure out –

·        How much minimum stock should be kept in the inventory?

·        How to lower down product recalls?

·        How to cross-sell the slow-moving stock?

Big data analytics helps retailers and online stores get a minute-by-minute overview of daily operations. It helps them identify the bottlenecks that might slow down the supply chain processes. It allows retailers to distribute their inventory as per the customer preferences in a particular area, thus reducing distribution costs.

Understanding consumer personas and behavior

Big retailers and eCommerce organizations are using big data analytics to understand the usage patterns and purchase habits of customers. The trends and patterns that are then revealed can be used by these organizations to model their strategy for retaining customers and increasing revenue.

A key aspect of understanding consumer behavior is to understand consumer personas. Specifying the demographics can help identify individual behaviors. By understanding customer demographics like age, marital status, ethnicity, employment, lifestyle, interests, and personal objectives, organizations can understand key metrics related to consumer behavior.

Analysis and insights about such key aspects help organizations understand -

·        Customer acquisition costs

·        Customer retention costs

·        Customer spending power

·        Customer value

·        Customer satisfaction and happiness

·        Customer average monthly spending

Once organizations understand these buying trends, it becomes easier for them to match their supply chain strategies with the specific consumer personas.

Smarter and more strategic sourcing

Global supply chains have become more complex in recent years. This puts pressure on the sourcing department to achieve maximum cost savings.

This is where big data analytics shines the most. It can help organizations analyze supply chain data like vendor performance, compliance history, and transactional data. This helps retailers get what they need at the time of their choosing, at the lowest possible cost.

The insights that big data analytics reveal can help sourcing teams to obtain the right quality and pricing for the raw materials.

Firms can also use these insights to identify deviations from normal delivery patterns and can also predict risk management.

Efficient manufacturing

As per a study, around 67% of manufacturing executives understand that big data and analytics are required to compete in this data-driven economy and, hence, are willing to invest in big data analytics.

Big data analytics can help manufacturers find new information that can be used to improve manufacturing processes. Using data insights, manufacturing processes can be scheduled to take advantage of fluctuating factors like electricity. Data related to manufacturing parameters can be used to analyze defects in the production process.

Predictive analysis can also help manufacturers schedule predictive maintenance preventing expensive asset breakdowns or unexpected downtime.

New opportunities in warehousing

Big data analytics can enable proactive warehouse management. It helps retailers and eCommerce firms in streamlining warehouse operations.

It can be used to get real-time analytics of the warehouse workflow. Big data analytics can also be used to drive product assortment in the warehouses. Firms can get information on new products, discontinued products, and those products which are longer in demand. Many organizations are also looking at using big data analytics to improve robotics and automated processes in the warehouses.

Conclusion

Big data analytics is already being used by leading organizations to optimize their supply chains. This technology has shown potential in almost every aspect of the supply chain, making supply chain optimization easier to achieve. Have you taken advantage of it yet?