December 23, 2021

The Definitive Guide to Supply Chain Data Analytics

Because today’s global supply chain is so complex, it has many vulnerabilities, including natural disasters, weather events, and epidemics. At the same time, there are myriad opportunities for an organization to optimize its supply chain to improve customer satisfaction and increase profitability. Increasingly companies are using supply chain data analytics to provide the insight needed for solving problems quickly and acting on opportunities for a competitive advantage.

Table of Contents

What is Supply Chain Data Analytics?

Supply chain data analytics is a type of analytics designed to uncover insights into an organization’s supply chain by analyzing data from its various systems and applications. Data can provide visibility into every link in the chain: procurement, inventory management, order management, warehousing and fulfillment, and shipping.

Because of the complexities involved in the modern supply chain, there are many possible points of failure. If one link in the chain experiences a bottleneck or shutdown, the entire system following the point of failure will be affected. Data analytics for the supply chain can help companies identify where they’re vulnerable and how to avoid preventable problems. It can find ways to solve problems when they do occur. And it can uncover opportunities to streamline the supply chain to improve it even further.

4 Types of Supply Chain Analytics

Supply chains can benefit from four different types of analytics for a variety of insights: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

Descriptive analytics.
Descriptive analytics for the supply chain enables companies to collect and organize historical data for a clear picture of what happened in the past. It measures performance and looks at patterns across the supply chain, from suppliers to logistics to retailers and point of sale. For example, a national retailer might look at an analytics dashboard to track demand for particular SKUs across geographic locations throughout the past year.
Diagnostic analytics.
Identifying what has happened is typically valuable only if you also know why it happened. Diagnostic analytics steps in to identify the origin of problems and find possible solutions to prevent them from happening in the future. For this reason, diagnostic analytics is also called root cause analysis.

A manufacturer might see that an order from one of its suppliers is running late. A look at the analytics could show that the region where the supplier is located is experiencing severe flooding. Diving in further, the manufacturer might see that the flooding is expected to last three days before clearing up.
Predictive analytics.
Predictive analytics for the supply chain helps companies predict what could happen in the future and determine the probability of various outcomes. It enables better planning and realistic goal-setting as well as avoiding unnecessary risk. Ultimately, predictive analytics allows companies to more accurately anticipate future performance based on past performance and all the factors currently affecting it.

One of the most valuable forms of predictive analytics is what-if analysis, which involves changing various values to see how those changes will affect the outcome. Manufacturers implement predictive analytics to track consumer preferences and forecast demand for their products.

It’s important to note that predictive analytics is limited to identifying correlations, anomalies, and patterns — it cannot determine cause and effect. For this reason, complex critical thinking is crucial for accurate insights.
Prescriptive analytics.
Prescriptive analytics for the supply chain tells teams what they need to do based on the predictions made. It’s the most complex of these analyses, which is why less than 3% of companies are using it in their business.

This type of analytics might alert a retailer that one of its key vendors is likely to have difficulty sourcing materials due to political instability in the region where it currently obtains materials. The retailer could use explore alternate locations to obtain the material and work with the vendor to head off the problem in advance. Or the analytics might reveal that the safest option is to change vendors or even look at replacing the item with a different product altogther.

While using AI in prescriptive analytics is currently making headlines, the fact is that this technology has a long way to go in its ability to generate relevant, actionable insights. The use of AI at scale requires running thousands of queries in search of statistical anomalies. But randomly identified anomalies don’t always point directly to business opportunities. For now, human involvement is critical to achieving relevant insights.

For example, imagine you’re a sales director at a tech company. Here are a few examples of the types of insights each of the analytical tactics discussed in this section might reveal:

5 Supply Chain Big Data Analytics Use Cases

Today’s companies are using supply chain data analytics in many different ways. The following supply chain analytics examples demonstrate the potential for business transformation.

Predicting supply disruptions.
Supply chain data analytics gives companies the ability to predict supply disruptions and make adjustments before problems impact production. Companies can also look at trending data on weather events, political instability, or financial issues that may impact a supplier’s ability to deliver on schedule. With this information, teams can make alternate plans as needed so they can maintain normal operations.
Quality assurance.
Manufacturing companies use real-time data analysis for quality assurance. Using IoT-enabled cameras and measuring devices, a manufacturer can identify issues before a product is shipped to retailers or consumers.
Warehouse management.
Information gathered on warehouse temperature, shelf weight, and load weight can be used to optimize warehouse operations and improve productivity. Analytics can inform receiving, tracking, and storing inventory, as well as workload planning, managing shipping, and monitoring the movement of items in the warehouse.
Logistics.
Data analytics is used in logistics to plan more efficient delivery routes and reduce fuel consumption. Companies can use data to identify the ideal mode of transportation for their loads.
Sales, inventory, and operations planning.
Retailers can analyze point-of-sale (POS), inventory, and production volume data to identify misalignment in supply and demand. As a result, they can determine when to place orders with suppliers, which products to put on sale when, and when to launch new product offerings.

Sigma Pro Tip

See how a Fortune 500 manufacturer is using analytics to accelerate product development.

4 Benefits of Supply Chain Data Analytics

Supply chain data analytics enables an organization to access the insights needed to optimize their supply chain, becoming more resilient and competitive. Let’s look at five of the top benefits that supply chain analytics offers.

Identify and understand risks and address disruptions quickly.
Data analytics can provide a full-picture view of the supply chain and identify both known and potential risks. With this information, organizations can understand how various factors affect their operations and prevent problems from causing significant damage.

But not all problems can be prevented. When a disruption does occur, business teams can use supply chain analytics to quickly identify the source and learn why the problem is happening. Based on their data-informed understanding, they can then work to resolve the issue. Systems can also be set to trigger alerts to notify teams of potential problems before they become significant.
Improve the customer experience.
A smoothly-operating supply chain will improve the customer experience. When a company can keep its delivery promises, it will experience better retention rates. Additionally, data analytics helps sales and marketing teams better determine what products customers want when, so they can serve up relevant content. By implementing supply chain data analytics, organizations can stay competitive and keep customers loyal.

See how fashion retailer Olivela is empowering its marketing and merchandising teams to explore data themselves to learn what website content performs best.
Inventory management.
Customer data can be used to help predict demand. As a result, companies can more effectively determine how much of which products to stock and which geographic regions are likely to require what SKUs at any given time.
Increase profitability.
One of the key mandates of supply chain management is lean operations. Companies of all sizes are seeking to reduce costs and increase profitability. Supply chain analytics allows organizations to track KPIs and find opportunities to increase efficiency.

5 Challenges to Supply Chain Data Analytics

While supply chain analytics holds immense promise, getting real value out of your data can prove difficult. In a 2019 survey of C-level technology and business executives reported in the Harvard Business Review, 69% reported that they had not yet created a data-driven organization, and 77% said that business adoption of Big Data/AI initiatives was a major challenge. Here are five things holding companies back.

Data growth.
The rate of data creation is staggering: 2,000,000,000,000,000,000 bytes of data are generated each day. And due to the supply chain’s complexity, manufacturers, retailers, and logistics companies have more data to analyze than average. If companies want to make use of data, it must be securely stored in a centralized data repository, free of errors and duplications, vetted and ready for exploration.
Data integration.
Supply chain data is collected from a variety of disparate sources—enterprise applications, SaaS applications, sensors from IoT devices, third-party sources, and more. Unstructured and semi-structured data (like JSON) now make up an estimated 80% of data collected by today’s companies.
Expired data.
Using outdated information risks incorrect assumptions. Because supply chain data has a relatively short shelf life, organizations must analyze data in real time as it’s collected. Data extracts should also be avoided since they result in outdated information and open up security vulerabilities. This requires a robust data pipeline to collect data immediately after it’s created and transform and store it in an analytical database so that it’s queryable in minutes.
Governance.
Ensuring data’s integrity, usability, accessibility, and security across an organization is challenging. Data governance addresses both data quality and security concerns. Better quality data translates directly into better business intelligence, and ensuring security tools and procedures are followed will mitigate compliance liabilities.
Security.
Data security will always present challenges to businesses. With the amount of sensitive information involved in supply chain data analytics, there are security threats to mitigate. The vulnerabilities inherent in many of today’s distributed technology frameworks can open up opportunities for bad actors to breach systems. There is also pervasive use of false data, or counterintelligence information, that can be used to corrupt databases and hinder a company’s ability to decipher fact from fiction.

6 Important Features of a Supply Chain Data Analytics Solution

With the above challenges in mind, companies are seeking to identify the tools they need to get the most from their data. Here are the most important features to look for in a data analytics solution.

Robust security and governance.
It goes without saying that your tool must keep your data secure and streamline compliance. Look for features like roles-based access, row-level security, SSO/IdP integration, immutable hosts, container checking, and threat detection. The platform should be protected at every layer of the stack, and data should never be required to be extracted.
Interactive visualizations and dashboards.
Users should be able to easily turn analyses into interactive visualizations and dashboards so they don’t have to wait on data professionals for assistance. If a visualization generates a new question, authorized users should be able to simply click into the underlying, granular data for further analysis.
Intuitive interface.
For business teams to benefit, the user interface must be intuitive and not require technical skills such as SQL or proprietary programming knowledge. Anyone in the organization who is authorized to do so should be able to analyze live data down to the lowest level of detail using familiar spreadsheet formulas so insights are discovered easily and quickly.
Streamlined data modeling and datasets for busy BI teams.
BI teams are busy. They need intuitive, flexible visual data modeling options that allow them to work directly within the cloud data warehouse. With this capability, they can curate, simplify, or join raw table data and turn it into governed, user-friendly datasets for manipulation and consumption by business experts.
Simple yet secure collaboration.
The platform should enable simple, controlled sharing, commenting, and reuse of live analyses, visualizations, and datasets that harnesses the power of the team for faster, better decision making.
Embedded analytics capabilities.
Ideally, the platform will have embedded analytics features that allow authorized viewers to see visualizations and dashboards embedded in internal, external, and custom applications. Authorized users should be able to click into the underlying governed data and conduct analysis to answer new questions as they arise.

Sigma Pro Tip

See how Payload is using embedded analytics to securely share data with customers and partners.

What Does the Future of Supply Chain Analytics Look Like?

While it’s vital to understand the challenges and opportunities associated with the supply chain today, there are competitive advantages to gain by keeping an eye on the future. We can clearly see the impact that new developments have by looking at supply chain analytics historically.

Prior to the cloud revolution, analytics primarily consisted of tracking KPIs and conducting statistical analysis in spreadsheets. This worked well when there wasn’t a lot of data available. But in today’s world, companies need to take advantage of all the data available to them, and the limitations of spreadsheets hold them back.

Increasingly, data is coming from more and more sources in primarily unstructured and semi-structured formats. If manufacturers, retailers, and logistics companies want to use this data, they need not only the right tools but also a data-driven culture that encourages and empowers business teams to explore data on their own in a secure sandbox.

Eventually, we expect to see AI and machine learning evolve to the point where they can intelligently differentiate anomalies that have meaningful significance, making it more valuable for teams seeking true insights. While this development may still be several years away, setting up systems and processes that can handle extremely large data sets in an efficient manner will ensure you’re ready when the technology has these capabilities.

Why Use Sigma as Your Supply Chain Data Analytics Platform

Manufacturers, retailers, and logistics companies are using Sigma to glean the insights they need to optimize their supply chains and improve efficiency. Sigma has all the features necessary for effective supply chain data analytics, with the ability to directly connect with your cloud data warehouse — no extracts ever required. Your team can quickly and easily query multiple datasets and build interactive dashboards and visualizations to share with authorized team members, customers, and partners. And with Sigma’s robust security and governance features, you can do so safely and in a fully-compliant manner.