How to Use Big Data to Drive Your Supply Chain (if You Have More Data Than You Can Handle)

Does it feel like your supply chain could snap in a strong wind – one weak link, and it’s all over?

Historically, organizations have tried to improve supply chain visibility to stay ahead of disruptions and address them before they snowball. However, this task has always been easier said than done.

With so many moving parts, you need to lead with the facts. Today, we’re sharing how to use big data to drive your supply chain.

Supply Chain Data Management: A Quick Look Back

Throughout the years, supply chain professionals have had a tumultuous relationship with data. They’ve needed it to streamline their operations, but they haven’t always had an ideal way to capture it.

The manufacturing industry was among the first to embrace the Internet of Things (IoT). Tapping into the connectivity of the IoT allowed businesses to use sensors and smart tags to track their inventory as it moved from one location to the next.

However, there was an issue. These solutions generated an enormous amount of data, and most manufacturers lacked the in-house processing capabilities required to make sense of it all.

The problem was exacerbated by the fact that most big data is unstructured. It’s comprised of virtually anything that can fit into a spreadsheet, from social media feeds to audio files.

Now, companies across industries are looking for ways to automate and digitize big data captures, and artificial intelligence (AI) is helping them get there.

The Basics of AI-Driven Big Data Analytics

At its core, AI-driven big data analytics works just as its name implies. This is the process of deploying machine learning to mine big data for actionable insights.

With these tools, organizations can identify historical patterns to plan future actions. They can also track real-time changes as they occur and predict future shifts.

While AI technology is multi-faceted and encompasses many different technologies, most of the analytics solutions fit within the following four categories:

• Descriptive Analytics: What logistically happened?
• Predictive Analytics: What do we think will happen next based on historical data?
• Diagnostic Analytics: Why did things go right or wrong?
• Prescriptive Analytics: What should we do next based on lessons learned?

How to Use Big Data to Drive Your Supply Chain: 4 Practical Use Cases

If your business is eyeing AI-driven big data analytics, you might wonder how to put it into practice. Which areas of your operations should you focus on to make the best use of these technologies?

Let’s look at a few areas where these solutions hold the most potential.

1. Optimize Inventory and Asset Management

Are your warehouses and hubs lagging in efficiency? With big data analytics, you can more clearly see where your inventory is stored and how it travels through each facility. You can also fine-tune exactly how your assets are utilized and managed.

With AI, warehouse managers can:

• Check inventory levels, shelf space availability, and warehouse assets via smart sensors
• Schedule preventative asset maintenance and prepare inventory to ensure uptime during busy seasons
• Reference historical data to optimize warehouse space and layout organization

When organizations combine the power of AI with a supply chain management system or an ERP system, they can transform their inventory management even further!

2. Improve Transportation and Delivery Scheduling

There might be many disruption risks across the supply chain, such as political unrest or natural disasters, so it’s important to pull AI insights from various data sources.

With AI-driven big data analytics, organizations can access service-level data to find real-time reports on where each transport vehicle is located. They can also use this technology to assess the condition of delivered goods.

Overall, it becomes easier to see where delays and bottlenecks are occurring, and how they can be prevented.

3. Boost Customer Satisfaction

A well-oiled supply chain improves the customer experience. When products are delivered as expected, customers are more likely to become brand advocates.

In this regard, AI can also help organizations uncover more customer information and improve each step of their journey.

For instance, these tools can group customers into categories based on certain attributes, such as:

• Age
• Industry
• Geographic region
• Order size
• Price point
• Order requirements

Then, the software can present a visual breakdown of each category, showing where most sales are coming from.

Organizations can also use this dashboard to understand how certain supply chain changes might affect each segment.

4. Lower Supplier Risk

Finally, AI tools can help organizations mine through big data related to suppliers and vendors. If they’re at risk, it could affect the organization’s performance, too.

Organization can use sensors to evaluate how each supplier is performing, based on pre-determined criteria such as:

• Quality of service
• Timely delivery
• Customer feedback

Organizations can then sort through this data to understand what makes a specific supplier stand out or what supplier is most likely to meet their needs.

Next Steps: Implement Big Data to Drive Supply Chain Results

Manufacturers have had a long and storied relationship with data. They need access to all the information they can get, but they can’t rely on legacy systems to make good use of it.

This struggle is familiar to any company needing to automate high frequency decisions.

If you’re in this boat, we can help. Schedule a free consultation with our enterprise software consultants to discuss your pain points and explore how to use big data to drive your supply chain.