The Ultimate Guide to Mastering Supply Chain Analytics

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The supply chain is a puzzle with hundreds of operational processes.

Now imagine adding a layer of analytics to streamline those processes to work smarter, not harder.

And, it is not mere speculation. According to Gartner, the global supply chain analytics market is set to balloon to a staggering $10.7 billion by 2026. With stakes that high, there's no room for inefficiency.

In this blog post, we will give you the lowdown on what supply chain analytics is, its importance in modern business, and how you can make it work wonders for you.

Are you ready to see how analytics can improve your supply chain?
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What is Supply Chain Analytics?

Supply Chain Analytics focuses on using data to improve and optimize every stage of the procurement and distribution process. It involves collecting and analyzing data from various sources, such as procurement, manufacturing, and logistics. With supply chain analytics, you can gain insights to make the whole process more efficient and effective.

For example, if you notice that shipping delays from a specific supplier are causing bottlenecks, you can act immediately to resolve the issue or find an alternative source.

Supply chain analytics helps you not just react to problems but predict issues before they even happen. It is crucial for any business looking to improve its operational efficiency, risk management, and customer satisfaction.

Related Read:

Importance of Analytics in Supply Chain Management

Analytics in supply chain management plays a crucial role by providing real-time data and insights that help companies make more informed decisions. This data can cover various aspects, from inventory levels to transportation logistics, which enables businesses to optimize their operations for efficiency and cost-effectiveness.

For example, if analytics indicate that a particular shipping route is consistently delayed, a company can make adjustments to avoid that route and improve delivery times.

Real-time data analytics also allows for quick response to unexpected situations like supply chain disruptions. With this kind of insight, a company can swiftly adapt its strategies to minimize the impact on operations and revenue.

Overall, analytics serves as the backbone for successful, efficient, and adaptable supply chain management.

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What are the Different Types of Supply Chain Analytics?

Each supply chain analytics type serves a specific function, and knowing when to deploy one is essential for optimizing your operations.

1. Descriptive supply chain analytics:

Descriptive analytics helps you understand what's happening in your supply chain by summarizing past events. This is the basic form of analytics where you look at historical data to understand trends.

For example, you could monitor last quarter's shipping times across various routes to find the most efficient paths. While it can't tell you why something happened or predict future events, it provides a necessary foundation for making informed decisions.

2. Diagnostic supply chain analytics:

Where descriptive analytics stops, diagnostic analytics takes over. It dives deeper into past data to determine why a particular event occurred.

Suppose your company missed several shipment deadlines last month. Diagnostic analytics would involve sifting through all the shipment and inventory data to pinpoint bottlenecks or inefficiencies that led to those delays.

3. Predictive supply chain analytics:

While the previous types focus on the past, predictive analytics aims to forecast future events based on current and historical data. This is where things start to get a bit more complex.

Using various statistical models and algorithms, predictive analytics can forecast how many units of a product you're likely to sell next quarter. It provides valuable insights that help you make data-driven decisions about inventory levels, workforce, and more.

4. Prescriptive supply chain analytics:

The most advanced of the four, prescriptive analytics not only forecasts future outcomes but also suggests specific actions to optimize those future outcomes.

Let's say your predictive model forecasts a spike in demand for a particular product; prescriptive analytics would suggest the best course of action, like increasing production or altering distribution routes.

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What are Some Key Metrics and KPIs in Supply Chain Analytics?

Understanding KPIs in supply chain analytics is a roadmap to your business success. They serve as indicators, guiding you where to speed up, slow down, or maybe even take a detour.

They help in transforming abstract operational challenges into tangible numbers, making it easier for you to take effective actions.

Order Accuracy: This metric shows you how many orders are processed without errors. High order accuracy means happier customers and fewer returns, which is good news for any business.

Inventory Turnover: This measures how many times your inventory is sold and replaced in a given period. A high turnover rate usually indicates your products are in demand and not just sitting in a warehouse collecting dust.

Cost of Goods Sold (COGS): This metric calculates the total cost it takes to manufacture a product that you sell. Keeping track of this can help you set your pricing strategy and find ways to lower production costs.

On-Time Shipping: This one's straightforward. It's all about how often shipments go out on time. Consistent on-time shipping enhances customer satisfaction and helps you maintain good relationships with suppliers.

Customer Order Cycle Time: This tells you how long it takes from the moment a customer places an order to the moment they receive it. The shorter, the better, as long as you're not compromising quality.

Fill Rate: This measures the percentage of customer orders that are fulfilled from stock on hand, meaning you didn’t have to backorder or lose a sale. A high fill rate signifies that your inventory levels are pretty much spot-on.
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How is Data Analytics Used in the Supply Chain?

Data analytics is a key component in modern supply chain management. It's a way to process and interpret large sets of information generated within a supply chain.

Identifying Trends and Patterns: Data analytics helps in understanding recurring events within your supply chain. By studying past sales and inventory data, companies can anticipate future demand. This allows businesses to prepare adequately for peak times.
Streamlining Operations: Data analytics provides insights into delivery routes, warehouse layouts, and other logistical details. These insights can lead to more efficient operations, saving time and money.
Customer Experience: Understanding customer preferences is another significant application of data analytics. Purchase history, customer feedback, and social media interactions can be analyzed to understand customers' wants. This information is then used to adapt supply chain processes to meet market demand.
Financial Planning: Data analytics offers insights into the costs associated with each stage of the supply chain. These insights enable more precise financial planning and resource allocation.
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Data Sources in Supply Chain Analytics

Data is the fuel that keeps the supply chain analytics running smoothly. Knowing what data sources are required for supply chain analytics is important.

Here's a quick rundown:

Transactional Data: It covers sales orders, invoices, and receipts—pretty much any document that signifies a transaction. Companies use this to track sales, inventory, and cash flow.
Sensor Data: From GPS and RFID tags to temperature sensors in a cold chain, this data helps in real-time monitoring and logistics.
Customer Data: While customer feedback and purchase history are directly tied to sales and marketing, they also offer insights for supply chain optimization.
Social Media and Web Data: If there's a sudden buzz about a type of product, the supply chain needs to be ready to meet that demand.
Supplier Data: It covers information like lead times, quality control metrics, and compliance records.
ERP Systems: From human resources to production schedules, they provide a holistic view of operations.

Collecting and analyzing this data enables the business to make smart, timely decisions that could make a difference.
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What Are the Benefits of Implementing Supply Chain Analytics?

Implementing supply chain analytics can significantly transform how a business operates and competes in the marketplace. Here's how it can add value:

Cost Reduction: Lower operational and overhead costs. A fashion retailer adjusts inventory based on predictive analytics, saving on warehouse costs.
Efficiency Boost: Streamlining operations. Real-time analytics help a shipping company reroute trucks to avoid delays, saving time and fuel.
Improved Supplier Management: Better vendor evaluation and selection. Analytics highlight a supplier's frequent delays, prompting a switch to a more reliable one.
Customer Satisfaction: Meeting or exceeding customer expectations. An online grocery store uses data to keep fresh produce in stock, increasing customer loyalty.
Strategic Planning: Aligning long-term goals with resources. Analytics guide a pharma company to focus R&D on medications in high demand in emerging markets.
Risk Mitigation: Identification and action against potential issues. A tech firm uses analytics to scan for geopolitical issues that could affect supplies, allowing timely pivots.
Competitive Advantage: Outperforming competitors in key metrics. Car manufacturers leverage social media analytics to develop in-demand features before competitors.
Enhanced Decision Making: Making better-informed choices. A logistics company uses real-time data analytics to choose the most cost-effective shipping methods.
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Challenges and Best Practices in Supply Chain Analytics

Challenges in Supply Chain Analytics

Data Silos: Different departments might not share information effectively.
Data Accuracy: Incorrect data can lead to poor decision-making.
Lack of Skilled Personnel: Not everyone understands how to interpret complex analytics.
Cost of Implementation: Advanced analytics systems can be expensive.

Best Practices in Supply Chain Analytics

Real-time Monitoring: Track supply chain operations in real-time. Use automated alerts for deviations in shipping times or costs.
Predictive Analytics: With machine learning algorithms you can forecast sales or detect anomalies in the supply chain.
Custom Dashboards: Make data easily accessible for decision-makers with user-friendly interfaces that can be customized to show key metrics.
Collaborative Networks: Share crucial data across departments or even with external partners using secure cloud-based platforms.
Automated Reporting: Reduce manual labor in generating reports. Use scheduled reporting tools that can generate daily, weekly, or monthly analytics reports automatically.

Databrain is an SDK solution which can help you build a custom analytics platform for your organization without any technical skills. It can pull supply chain data from data sources of your choice and build interactive supply dashboards that help you make data-driven decisions.

It supports white labeling to customize your interface and adheres with industry standard security protocols.
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Supply Chain Analytics Use Cases

In supply chain management, analytics helps you make sense of vast amounts of data. You can optimize stock levels, making sure you neither overstock nor run out of products.

Analytics also helps you identify the most efficient routes for transporting goods. Plus, you can assess supplier performance, tracking metrics like on-time deliveries and product quality.

With supply chain analytics, you improve every aspect of the supply chain, saving both time and money. It's a straightforward and powerful way to improve operations.

Conclusion

We have gone through the ins and outs of supply chain analytics, from what it’s significance, the types, key metrics, and benefits. It's clear that implementing supply chain analytics can add value to your business in numerous ways. Now, if you're looking for a customized solution, DataBrain is your go-to. It specializes in building tailored supply chain analytics solutions that will fit in your business.

Implement Supply Chain Analytics with Databrain
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FAQ

What is the difference between supply chain analytics and traditional supply chain management?

‍Supply chain analytics involves using data and analytical tools to optimize and improve the efficiency of the supply chain.
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Traditional supply chain management, on the other hand, is more about coordinating between different elements like suppliers, manufacturers, and distributors without necessarily leveraging data for decision-making. Essentially, analytics adds a layer of intelligence to the traditional process.