Supply Chain Predictive Analytics: What You Need to Know Before Diving in

Optimizing supply chain activities has always been a laborious and time-consuming task. Now, those efforts are more challenging than ever before.

As a result, business leaders are looking closely at supply chain predictive analytics. These tools help businesses improve their competitive advantage and meet customer expectations by enabling them to avoid roadblocks to profitability and performance.

Today, we’re looking at how this technology works and how to assess whether it’s a smart investment for your company. 

How Does Supply Chain Predictive Analytics Work?

Supply chain predictive analytics helps executives predict future supply chain actions and patterns. This technology gathers data from supply chain management (SCM) applications, and then, analyzes that information to anticipate future scenarios.

What is an SCM application? An SCM system is an enterprise solution that automates many areas, including:

• Procurement
• Inventory management
• Warehouse management 
• Order management
• Transportation management 

Each of these functional areas may have its own reporting capabilities. For instance, warehouse management apps can report on orders filled per hour, so managers can gauge anticipated future output. 

Note: SCM systems are different than ERP systems. To understand the difference, read our blog post, ERP vs SCM.

Though you’ll find this technology everywhere from healthcare to retail, it’s especially important in the manufacturing sector. Accurately predicting future supply chain performance can improve manufacturers’ approach to myriad business activities, including:

• Inventory management
• Sales and operations planning
• Demand forecasting
• Freight price estimation
• Digital transformation
• Customer relationship management

Optimizing each of these areas can lead to measurable cost savings. For instance, more accurate inventory management helps manufacturers control procurement spending. This enables them to plan for future spikes or dips in demand and proactively respond.

Understanding Supply Chain Risks

Why are supply chain leaders interested in predicting the future? The answer lies in the unique risks and liabilities associated with operations that are constantly moving and that can change course at any time.

The journey from supplier to retailer to customer includes many different touchpoints. A small hiccup in one spot could have major consequences in another, so it’s important to have visibility into potential issues.

Monitoring an entire supply chain requires more insight than one person or even one team can provide. This is what makes predictive analytics so attractive and applicable in the supply chain world.

With enhanced visibility, managers can ensure that their final product arrives at the right spot, at the right time, in the right condition, and meets customer expectations.

In short, predictive analytics allows supply chain leaders to keep a close eye on their parts and products as they’re:

• Purchased
• Assembled
• Shipped
• Delivered

If there are any issues noted, they can take immediate action to remediate them. This ultimately saves their brand reputation and their bottom line. 

The Role of Data in Supply Chain Logistics

The world of supply chain logistics can be a complicated one. These leaders are tasked with managing deliveries and providing service updates. In addition, they’re responsible for directing teams as they ship, receive, and store goods. 

As such, they need around-the-clock access to different types of mission-critical data. In years past, this might have meant simply checking weather reports or road conditions to ensure against interruptions and delays. Now, however, the risks are much more numerous. 

To monitor all these risks, logistics managers not only need access to more data, but they need it to be more up-to-date and accurate. This is where sophisticated analytics tools come in. With the right technology, managers can understand how external forces affect every aspect of their strategy and performance. 

Big Data vs. Little Data

Predictive analytics centers on both big data and little data. It provides a 360-degree view of everything that might happen along a supply chain. This way, users can get a head start on mitigating issues and planning for future changes.

Big data usually includes more complex and comprehensive insights that help managers prepare a successful logistics strategy. It considers elements such as:

• Modes of transportation
• Available routes and lanes
• Transportation-specific risks (e.g., inclement weather, infrastructure issues, extreme temperatures)
• Social issues (e.g., labor actions, geopolitical struggles, riots) 

Little data is more granular and company-specific. Tracking it helps managers understand how big data insights will impact their organization. It includes key metrics such as:

• On-Time In-Full (OTIF) commitments
• Customer satisfaction ratings
• Total revenue
• Profit margin
• Total market share 

It can be impossible to keep up with these data points without robust analytical software because supply chains are often global in nature with hundreds or thousands of widespread touchpoints.

Predictive analytics tools automate the forecasting process across all these touchpoints, allowing managers to make quick decisions with confidence. 

Should Your Organization Implement Predictive Analytics?

Supply chain predictive analytics can help your team optimize every aspect of your operations. Yet, it’s still a complex, expensive undertaking.

ERP software can offer a cost-effective solution. By implementing a modern ERP system with SCM functionality, users can gain insights from different units in real-time with easy-to-use dashboards. 

To learn more about the role of analytics across the supply chain and how ERP can help, contact our enterprise software consultants below.