Supply chains produce large amounts of data. Supply chain analytics helps businesses understand this data by finding patterns and useful information. These insights can help improve product quality, delivery times, customer satisfaction, and overall profits.
What are analytics?
Supply chain analytics is the process of making decisions based on data. It involves looking at trustworthy, relevant information, often shown in graphs, charts, or other visual tools to help people understand it better.
Types of Supply Chain Analytics
There are several types of supply chain analytics:
- Descriptive analytics
This type shows what is happening across the supply chain. It brings together information from both inside and outside the company to give a clear picture of the current situation.
- Predictive analytics
This type helps a company guess what is likely to happen in the future. For example, it can help predict possible problems or delays and allow the company to take steps to reduce their impact.
- Prescriptive analytics
This type helps businesses find the best way to solve problems. It allows companies to work with their partners to respond quickly to challenges and reduce the effort needed to fix them.
- Cognitive analytics
This type answers complicated questions in a natural, human-like way. It helps companies think through problems such as, “How can we make this process better?”
Using Cognitive Technologies in the Supply Chain
Supply chain analytics is also used with technologies like artificial intelligence (AI). These technologies can understand, learn, and solve problems like humans do—but much faster and at a larger scale.
This advanced form of analytics is bringing major improvements. It can go through large amounts of information quickly to help companies improve forecasts, find problems, understand customers better, come up with new ideas, and make important changes.
Why Supply Chain Analytics Matters
Supply chain analytics helps companies make better, faster, and more informed decisions. Some of the main benefits include:
- Lower costs and better profits
Businesses can access complete data to create better plans and get a clear view of their operations. This helps them become more efficient and find helpful information.
- Better understanding of risks
Analytics can point out known risks and help predict future ones by spotting trends and patterns.
- More accurate planning
By studying customer data, companies can better guess future demand. This helps them decide when to stop making certain products or understand what customers may want next.
- Creating a lean supply chain
With analytics, companies can watch their warehouses, track how partners respond, and keep up with customer needs to make better decisions.
- Getting ready for the future
Advanced analytics tools can now process all kinds of data. They help businesses stay alert and respond quickly. These tools can also find patterns in data from different sources, helping to reduce risk and avoid waste.
As technologies like AI become more common, companies can expect even more advantages. Information that was once too hard to study, like written reports or customer feedback, can now be quickly understood. AI can read and connect data from many sources in real-time.
With this real-time analysis, companies will understand their supply chains better, improve performance, avoid problems, and support new ways of doing business.
Important Parts of Effective Supply Chain Analytics
The supply chain is often the part of the business that customers see the most. Doing supply chain analytics well helps protect a company’s image and long-term success.
Simon Ellis from IDC explains five important features of future-ready supply chain analytics, called the five “Cs”:
- Connected
The ability to bring together different types of data—from social media, smart devices, and traditional business systems.
- Collaborative
Working closely with suppliers through cloud-based tools that support communication and teamwork across businesses.
- Cyber-aware
Keeping the supply chain safe from digital threats, which is something the entire company should care about.
- Cognitively enabled
Using AI as a central system that helps make and carry out decisions across the supply chain. These systems often learn and improve over time.
- Comprehensive
The system must handle data in real-time. It should give complete and fast insights, because delays are not acceptable in modern supply chains.
How Supply Chain Analytics Has Changed Over Time
In the past, supply chain analytics mostly focused on numbers and performance data. Most information came from spreadsheets shared by different people in the supply chain.
By the 1990s, many companies began using electronic data exchange and planning systems to share data more easily with partners. These tools made it easier to analyze data and helped with planning and forecasting.
In the 2000s, companies started using software that offered deeper insights. These tools helped improve understanding of supply chain performance and allowed businesses to make better choices.
Today, the challenge is how to use the large amounts of data that supply chains now create. As of 2017, the average supply chain handled 50 times more data than it did just five years before. But only about 25% of that data was being used. Also, around 20% of the data is easy to read, but the rest is unstructured and harder to analyze. Now, companies want to figure out how to understand this hard-to-use data.
Experts believe the next step forward involves technologies like AI. These tools do more than just store or organize data—they can think, learn, and respond like people. AI can handle large volumes of both structured and unstructured data and provide quick summaries and insights.
IDC predicted that by 2020, half of all business software would use some form of AI. These systems not only bring together data from different sources but also allow companies to make sense of the information in real-time. Combined with new technologies like blockchain, businesses will soon be able to predict and plan for events before they happen.
Using Software for Supply Chain Analytics
Since supply chain analytics has become more complex, many software tools have been created to help. These tools offer real-time information and monitor areas such as sales and operations.
For example, IBM has developed many software tools that support supply chain analytics. Some of these use AI to help systems learn from changing conditions and prepare for future changes.
Case Study: FleetPride & Cresco International
A real-world example shows how FleetPride improved its supply chain by working with Cresco International. They used IBM analytics tools for descriptive, predictive, and prescriptive analytics. These tools gave their supply chain managers valuable insights that helped improve operations.
