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Data-Driven Supply Chains: Analytics for Efficiency and Resilience

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Data-Driven Supply Chains: Unleashing the Power of Analytics for Efficiency and Resilience

Estimated reading time: 15 minutes

Key Takeaways:

  • Data-driven supply chains leverage analytics to optimize decision-making, enhance resilience, and improve efficiency.
  • Key techniques include descriptive, predictive, prescriptive, and cognitive analytics, each offering unique insights.
  • Real-world examples showcase the transformative power of data analytics in various industries.

Table of Contents:

Introduction

In today’s changeable world, it’s important for companies to be ready for anything. Did you know that 72% of companies had problems with their supply chains in the past year? (Resilinc). A data-driven supply chain can help solve these problems.

A data-driven supply chain uses information to make better choices. Instead of guessing, companies can use data to know what’s happening and what might happen next. This makes the supply chain work better, stronger, and helps companies make smarter decisions.

As discussed in our comprehensive guide on AI in Logistics and Transportation, data analytics is a crucial enabler for AI-powered supply chain solutions. This post will explore the main ideas, advantages, and ways to use data analytics in supply chain management. This will help you learn how to make your operations more reliable and efficient. We will answer the question: How can data analytics improve supply chain efficiency?

Let’s learn how to use data to make your supply chain better!

What is a Data-Driven Supply Chain?

A data-driven supply chain is like having a super-smart helper for your business. It means using information and facts to make every decision, from getting materials to delivering products. Think of it as using clues to solve a puzzle, making sure everything runs smoothly.

The main ideas are simple: first, you collect lots of information from different places. Then, you look at the information to find patterns and understand what’s happening. Finally, you use what you’ve learned to make smart choices about what to do next.

This is different from the old way of doing things, where people would just react to problems as they came up. With a data-driven supply chain, you can see problems coming before they happen and fix them before they cause trouble.

It’s also important for everyone in the company to believe in using data. This means that everyone should be willing to share information and use it to make decisions. When everyone works together with data, the supply chain can be its best!

The Evolution of Supply Chain Analytics

Long ago, companies only had basic reports to understand their supply chains. But now, we can do much more with supply chain analytics! We’ve gone from simple reports to using super-smart techniques that help us make better decisions.

As we got more information and computers became more powerful, we could use more advanced ways to look at data. We learned to find patterns and make predictions that were impossible before. Instead of just talking about “Big Data,” we started focusing on the specific types of information that could help us the most.

Cloud computing also made a big difference. It made it easier for even small businesses to use these advanced tools. Now, everyone can use data to make their supply chains better!

The future is even more exciting! Soon, computers will be able to make decisions on their own, using what they’ve learned from the data. Mention AI and that for a broader overview of how AI is transforming the logistics industry, see our pillar post.

Key Data Analytics Techniques for Supply Chain Optimization

There are four main types of data analytics that can help make your supply chain better: Descriptive, Predictive, Prescriptive, and Cognitive. Each one helps in a special way!

Descriptive Analytics: Understanding Past Performance

Descriptive analytics is like looking back at what has already happened. It uses old information to understand how things were in the past. For example, you can look at past sales, how much you had in your warehouse (inventory management), and how long it took to deliver things.

But it’s not just about making reports. It’s about finding clues in the information that can help you make better choices. For example, you might find out that you sell more ice cream in the summer than in the winter. That’s a clue that you should order more ice cream before the summer comes!

Analyzing old sales data can help you see when things sell the most. This way, you can be ready and have enough products when people want them.

Predictive Analytics: Forecasting the Future

Predictive analytics is like using a crystal ball to see what might happen in the future. It uses math and computers to guess what will happen with things like demand forecasting, like how much of something you will sell, what problems might happen, and how much stuff you should keep in your warehouse.

For example, you can use special math to guess how much of something you will sell next month. You can also use it to guess when machines might break down (predictive maintenance) or when there might be a problem that stops you from getting materials. Mention AI and that for a broader overview of how AI is transforming the logistics industry, see our pillar post.

One way to do this is by looking at how things have changed over time (time series analysis). This can help you see patterns and guess what might happen next.

Prescriptive Analytics: Optimizing Decisions

Prescriptive analytics is like having a guide that tells you the best thing to do. It uses what we learn from guessing the future to tell you the best way to make decisions about your supply chain decision-making.

For example, if you know how much of something you will sell and when you will need it, prescriptive analytics can tell you how much of it you should keep in your warehouse and when you should order more. It uses computer programs to find the best way to do things.

Using computer programs can help you find the best way to keep enough of something in your warehouse. This way, you don’t have too much or too little.

Cognitive Analytics: AI-Powered Decision-Making

Cognitive analytics is like having a super-smart computer that can think like a human. It uses AI in supply chain to make decisions about difficult things. It’s like having a computer that can solve problems and make choices just like a person would.

It’s important to make sure that the computer’s decisions are easy to understand. This is called Explainable AI (XAI) for Supply Chain. Making AI easier to understand helps people trust it and make sure it’s doing a good job. It builds trust and allows for better human oversight.

Link to pillar post: Mention XAI and that for a broader overview of how AI is transforming the logistics industry, see our pillar post.

With cognitive analytics, computers can help us make the best choices, even when things are complicated.

Data Sources Powering Supply Chain Analytics

To make a data-driven supply chain work, you need information from different places. Here are some of the most important data sources:

  • ERP systems: These systems keep track of everything that’s happening in your business, from money to materials.
  • Transportation Management Systems (TMS): These systems help you plan and manage how you move things from one place to another.
  • Warehouse Management Systems (WMS): These systems help you keep track of what’s in your warehouse and how it’s organized.
  • IoT devices: These are small computers that can sense things like temperature, location, and movement. They can be attached to equipment, vehicles, and goods to give you real-time information.
  • External data: This includes information from outside your company, like weather, the economy, and what people are saying on social media.

It’s important to put all of this information together so you can see the whole picture. This way, you can make the best decisions for your supply chain.

Building a Data-Driven Supply Chain: Implementation Strategies

Building a data-driven supply chain takes work, but it’s worth it! Here’s a step-by-step guide to help you get started answering the question: How to build a resilient supply chain using predictive analytics?

  1. Decide what you want to achieve: What problems are you trying to solve? What do you want to make better?
  2. Find the right data: What information do you need to make better decisions? Where can you find it?
  3. Get the right tools: What computer programs and technologies do you need to analyze the data?
  4. Build a team: Do you have people who know how to work with data? If not, you might need to hire some.
  5. Create a data-driven culture: Make sure everyone in your company believes in using data to make decisions.
  6. Start small: Don’t try to do everything at once. Start with a small project and see how it goes.
  7. Expand when you’re ready: If your first project is successful, you can start using data in other parts of your supply chain.

By following these steps, you can build a data-driven supply chain that helps you make better decisions, save money, and be more reliable.

The Benefits of Data Analytics in Supply Chain Management

Using data analytics in your supply chain can bring many good things to your company!

Improved Efficiency and Reduced Costs

Supply chain optimization is achievable through data analytics. By using data, you can find ways to make things work better, waste less, and lower your costs. For example, you can find the best routes for your trucks to save on gas, or you can figure out how to keep just the right amount of stuff in your warehouse so you don’t have too much or too little.

Enhanced Supply Chain Resilience

Supply chain resilience is boosted through data analytics. Data can help you see problems coming before they happen. For example, you can see if there’s a storm coming that might delay your deliveries, or if a supplier is having trouble getting materials. This way, you can be ready and find a solution before it causes a big problem.

Better Decision-Making

Supply chain decision-making becomes easier with data analytics. Data gives you the information you need to make smart choices. Instead of guessing, you can look at the data and see what’s really happening. This way, you can make choices that are more likely to be successful.

Increased Visibility and Transparency

Real-time visibility is increased through data analytics. Data lets you see what’s happening in your supply chain all the time. You can see where your products are, how long it takes to deliver them, and if there are any problems. This helps you fix problems quickly and keep things running smoothly. Organizations that adopt real-time visibility see an average 10-15% reduction in inventory costs and a 5-10% improvement in on-time delivery (Project44).

Real-World Examples of Data-Driven Supply Chain Success

Many companies have already used data analytics to make their supply chains better. Here are a few examples:

Beverage Company: Optimizing Distribution Networks

A big drink company used data to make its delivery system better. They saved 15% on transportation costs and delivered things on time 10% more often. They did this by looking at old sales data, weather, and traffic to guess how much of each drink people would want and to find the best ways to deliver them.

Major Retailer: Real-Time Inventory Management

A large store used computers and sensors to keep track of everything in its warehouses. This helped them reduce the number of times they ran out of things by 20% and waste less by 10%. They did this by changing how much of each thing they had based on what people were buying right now.

Automotive Manufacturer: Digital Twin Simulation

A car company used a computer program to make a copy of its entire supply chain. This helped them find problems and make their production schedule better. This made them 12% more efficient and reduced the time it took to make cars by 8%.

Fashion Retailer: AI-Driven Demand Forecasting

A clothing store uses computers to look at what people are saying on social media and guess what clothes they will want to buy. This helps them keep just the right amount of each item in their store and waste less. They can now guess what styles will be popular weeks before they become popular, which reduces waste.

Pharmaceutical Company: Blockchain-Enabled Traceability

A medicine company uses a special computer system to track where its drugs come from. This makes sure that the drugs are real and not fake. This makes patients safer and helps people trust the company.

Challenges and Considerations for Implementing Data Analytics

Using data analytics in your supply chain can be hard. Here are some things to think about:

  • Data quality: You need to make sure that your data is good and correct. If your data is bad, your decisions will be bad too.
  • Integration: You need to put all of your data together in one place. This can be hard if you have old computer systems that don’t work well together.
  • Talent: You need people who know how to work with data. If you don’t have these people, you might need to hire them or train your current employees.
  • Privacy: You need to make sure that your data is safe and that you’re not sharing it with people who shouldn’t see it.

By thinking about these things, you can make sure that your data analytics project is successful.

Conclusion

Using a data-driven supply chain is a smart move for any company that wants to be more efficient and reliable. By using data analytics, you can improve how you make decisions and make your supply chain stronger. A key element to supply chain optimization.

We talked about many new things that are happening in the world of data-driven supply chains. By using these new technologies, you can stay ahead of the competition and make your supply chain even better.

Start using data analytics today to make your supply chain better and gain an advantage in today’s world. This post expands on the topic of data analytics, a key component of the AI-driven supply chains discussed in our main guide on AI in Logistics and Transportation.

FOR FURTHER READING

To learn more about how to protect your supply chain from unexpected events, check out our guide on Supply Chain Risk Management.

Discover how new technologies like AI are changing the way logistics works in our post on The Role of AI and Machine Learning in Logistics.

Find out how blockchain can make your supply chain more open and trustworthy in our article on Blockchain Applications in Supply Chain Transparency.

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