Visibility Insights

How Using Supply Chain Data Can Lead to Better Decision-Making

Supply chains play a critical role in the global economy, particularly during major global crises such as the ongoing Covid pandemic. Effective communication and uninterrupted supply chain operations are vital for delivering vaccines to different parts of the world, supplying provisions and weaponry to Ukraine, and providing humanitarian aid to people in need

None of that would be possible without data. Stakeholders now have the ability to track goods from the moment they are manufactured right up until delivery to the final, end user. Data is invaluable to supply chain planning, and in this blog, we are going to take a look at how you can boost your supply chain visibility by introducing some tweaks to data infrastructure within your organization, and offer a few of the most widespread use cases.

What Is a Data-Driven Supply Chain?

To meet the criteria of being data-centric, your supply chain needs to be utilizing the vast capabilities of retrieving, storing, processing, and transmitting data, including—but not limited to—breakthrough technologies such as AI (Artificial Intelligence) and ML (Machine Learning).

Due to the extensive range of data sources in supply chain management and their continuous changes and updates, many organizations struggle to automate their data capture processes. Yet with the proper infrastructure, strategic partnerships, and a well-designed plan, you can overcome these challenges and successfully implement a data-driven approach to your supply chain. The adoption of such an approach represents a strategic move that supports an ability to increase the accuracy of production forecasts, track changes in inventory in near real-time, and improve customer satisfaction at the same time.

Achieving a data-driven supply chain requires a solid infrastructure, streamlined automation processes, and high-fidelity data you can feed into data science and machine learning tools, which can help you mitigate risks, optimize efficiencies, identify areas for improvement, and stay ahead of the curve.

How to Create a Data-Driven Supply Chain

Data centralization, normalization, correlation, and automation are critical components for building a data-driven supply chain. Centralizing data sources from across the supply chain ensures that all stakeholders have access to the same data, facilitating collaboration and improving decision-making. Normalizing data ensures consistency and accuracy, enabling meaningful insights and trend analysis. Correlating data across multiple sources helps you unveil relationships between different factors and gather insights that would be difficult to identify with individual datasets. Automation also suppresses manual errors, streamlines processes, and accelerates data analysis, enabling you to make informed decisions faster

Together, these elements enable you to establish a robust data-driven supply chain.

Identifying Key Data Sources

Many companies face the challenge of having disparate internal systems that are not fully correlated with one another. For example, you may have multiple instances of SAP or Oracle and different warehouse management systems (WMS) that are only partially integrated with your enterprise resource planning (ERP) system

In addition, working with external partners with their custom, in-house systems can add further complexity to the supply chain data. This lack of cross-standardization can make it difficult to extract clear meanings from data, hampering your decision-making abilities.

First, you must identify critical data items within your supply chain. They may include:

  • internal: inventory levels, procurement, production, logistics, transportation, and customer orders
  • external: suppliers, customers, logistics partners, customs regimes, weather, port conditions, and other market trends

One way to centralize internal data is by using supply chain management (SCM) and visibility software to integrate different systems and data sources, providing a unified view of the supply chain. These platforms can provide insights into your performance, logistics network optimization, and visibility into company metrics.

To centralize external data, consider using application programming interfaces (APIs) to connect with external systems and collect data in real-time. On top of that, you can also use data mining tools to extract external data from such sources as social media, news outlets, and industry reports to observe ever-shifting global trends and customer preferences.

Differentiating Between Structured & Unstructured Data

Before proceeding to automate the process of capturing data, you need to make sure that you fully grasp the difference between correlation and causation to choose the proper tools and data sources themselves. Correlation shows a statistical link between two or more variables, while causation implies that one has an impact on the other.

Supply chain data is highly structured, transactional data, and it’s critical to maintain the relationships and structure to derive any meaningful insights. What you need to do is identify which data points truly matter concerning your supply chain as a whole and supply chain planning, in particular, making it available for real-time decision-making

Of course, it’s much easier to gain actionable insight and engage in supply chain planning when all of your data is structured, meaning it’s recorded into readable databases and stored there. Learning from unstructured data is much more difficult because it appears in an incompatible format. That said, it may equip you with very useful information if the cost to normalize the data is not excessive.

Consulting giant McKinsey names unstructured data as one of the essentials for establishing the Big Supply-Chain Analytics organization and provides the following list of core areas required to do so:

  • The current IT landscape (ERP/SCM)
  • Solution development
  • System integration
  • Analytical, mathematical, and statistical capabilities

To better understand the structured data construct, refer to the chart below:

Top Use Cases for Data in Supply Chain Planning

Below you will find three supply chain planning areas where data can be applied to the fullest extent.

Reporting, Analytics & Monitoring

Data is essential to accurate supply chain planning, as it provides valuable insights and information into your supply network. Take, for example, reporting and analytics—they allow you to gain a better understanding of KPIs and identify areas for improvement, while automated data monitoring can help you stay on top of critical supply chain issues and respond to them quickly to minimize disruptions.

Collaboration, Augmentation & Extensibility

Collaboration is another area where data has tremendous value. By sharing data across different teams and departments, you can ensure everyone is on the same page and avoid a silo mentality. In return, data augmentation and extensibility can provide your stakeholders with even more comprehensive insights into the supply chain, making it easier to anticipate potential issues, proactively address them, and identify hidden opportunities.

Data Science, AI & ML

Finally, data science is beginning to play an increasingly significant role in supply chain planning. By leveraging machine learning algorithms and predictive analytics, firms can more quickly identify trends and patterns in the data that may be invisible to a manual process review.

These data models can generate forecasts with various confidence intervals, allowing you to gauge the probability of specific events occurring and enabling better flexibility and real-time vision to the decision-making process. At the same time, enhanced data can speed up the time it takes your organization to respond to any kind of supply network disruption. But to truly capture the benefit of tech, the industry itself still has a long way to go as it is fraught not only with fragmented data sources but also poor data exchange and integrity processes.

Benefits of Data-Driven Supply Chain Improvement

To sum up, if you improve your supply chain’s data component, you’ll be able to:

  • enjoy quicker time-to-market
  • improve supplier communications
  • reduce operational costs
  • increase intra-organization collaboration
  • and much more

McKinsey provides the following infographics explaining how the digitalization of your supply chain can impact multiple performance dimensions:

If you’d like to incorporate data usage best practices into your flow, try Agistix for free. We provide infrastructure and automation to not only leverage data from key supply chain partners but also augment data from third-party applications and uncover hidden insights that help drive supply chain strategy. With our visibility platform, you can reduce supply chain costs by 25% and more with our supply chain data centralization and automation platform.


Trevor Read

President at Agistix based in San Francisco. I am an entrepreneur with a passion for data, and technology. I am results-oriented and committed to developing fast-deployment solutions to help customers seize the new opportunity coming from big data in the global supply chain.