Peak holiday seasons, like Cyber Monday, are often busy for eCommerce platforms like Amazon. In such a scenario, keeping up with demand and ensuring on-time deliveries is no small feat. But how does Amazon do it all? To manage all this rush, the brand usually relies on AI-driven data analytics to forecast demands and plan routes so that products are delivered on time.
Take Cyber Monday 2023, for example—Amazon analyzed demand for over 400 million products to determine where in the world they were likely to be ordered. This allowed them to manage inventory and ensure faster deliveries, with packages being prepared for dispatch within just 11 minutes of order placement! No small achievement, right?
However, Amazon isn't the only company that has used data analytics to improve its supply chain. Businesses across industries are using data analytics for inventory management, supplier monitoring, route optimization, and so on. Interested in knowing more about it? This blog will take you through the role of data analytics in supply chain management and optimization. Let's dive in!
How Can Businesses Use Data Analytics to Improve Supply Chain Management?
Data analytics helps reduce costs, improve operations, and speed up decision-making in various key areas of supply chain management. Here is how it can help you:
1. Demand Forecasting & Inventory Optimization
Almost 58% of the retailers and D2C brands have less than 80% inventory accuracy. Some of these brands face the issue of overstocking, while some struggle with understocking. Why? Because they fail to predict the future demand to maintain optimal stock levels. Let's see how this can be solved:
Use predictive models and feed them with data related to past transactions, purchase patterns, seasonal demand trends, website traffic, search queries, and abandoned cart users. This will help you identify recurring sales patterns and maintain the stock accordingly. Overall, you won’t have to worry about excess inventory costs due to overstocking and/or lost sales due to stockouts.
2. Route Optimization
In supply chain management, even small inefficiencies in routing can lead to major financial losses, like increased fuel costs, vehicle wear and tear, and delayed shipments. Has this ever happened to you? If yes, you need to leverage data analytics to optimize routes and ensure timely deliveries. But how?
Use real-time data related to weather, traffic conditions, and delivery deadlines so that data-driven AI models can recalculate faster alternative routes. This helps cut down unnecessary miles, which further reduces fuel consumption and fleet maintenance costs.
3. Risk Assessment and Mitigation
In any supply chain, there are always risks like fluctuations in currency, trade restrictions, political instability, natural disasters, compliance risks, and cyber security risks. If any of these situations arise, you can face product shortages, raw material unavailability, and even supplier-side delays.
By examining past and real-time data like supplier performance data, market trends, and weather & natural disaster forecasts, predictive analytics models can provide detailed analysis of deviations that are likely to occur. These models go one step further and provide recommendations for addressing such deviations. This will help you to have a contingency plan in place (like planning alternative shipping routes or supplier networks).
4. Supplier Performance Management
Poor supplier reliability, whether due to delays, quality issues, or cost fluctuations, can lead to stockouts, increased operational costs, and customer dissatisfaction. As a business owner, if you want to maintain high supply chain standards and get rid of such problems, you need to monitor and assess supplier performance from time to time.
By analyzing data related to on-time delivery records, pricing, and compliance with ethical and legal standards, you can identify underperforming suppliers. This will help you optimize your sourcing strategies by selecting the most reliable and cost-efficient vendors.
5. Warehouse and Fulfillment Operations
With almost two-thirds of global shoppers wanting to receive their items within 24 hours, we can say that faster shipping is a must-have for both shoppers and brands. That is why you need to focus on optimizing your shipping and fulfillment operations. But, exactly how?
By using data related to order fulfillment & processing, storage & space utilization, worker movement, task completion times, and picking accuracy, AI-driven data analytics models can predict the best fulfillment centers for faster shipping.
So far, you know the benefits of data analytics in supply chain management. But to get the most out of your data analytics endeavors, the important thing is having research-ready, up-to-date, and relevant data. This is where data management in supply chain comes into view.
Key Data Management Stages for Well-Optimized Supply Chain Analytics
Most of the data points we have mentioned above are unstructured. This means you cannot directly use them for data analytics. They need to go through stages of data management to be able to offer some valuable insights. Let’s go through these stages one by one:
1. Cleansing: Data experts find out discrepancies in supply chain datasets, like incorrect product & vendor names, billing mismatches, and duplicate inventory records. Through data deduplication and manual intervention, they can remove duplicate entries and correct misspelled details.
2. Standardization: Since supply chain data comes from multiple stakeholders like vendors, shipping partners, retailers, and manufacturers, it can have different formats, naming conventions, and measurement units. Data standardization ensures that there is uniformity and consistency in all data points for seamless integration into existing systems.
3. Enrichment and Validation: Sometimes, you might come across raw supply chain data that is insufficient (missing or outdated vendor/product details, unverified supplier performance data). Data management experts add missing information to these data points and verify existing records with external sources to ensure accuracy.
4. Maintenance: The supply chain data is quite dynamic due to fluctuating demand, supplier updates, geopolitical risks, and operational shifts. Data experts cross-check this data regularly to prevent outdated vendor/inventory records and add new suppliers, vendors, & warehouses.
If you want to get started with data management for supply chain analytics, you first need to establish a strong governance framework. To support this, you can either invest in master data management tools, hire people with industry experience for an in-house team, or outsource data management. Once your supply chain data is managed properly, it becomes a valuable asset for analytics.
Final Thoughts
Data analytics in supply chain management is a necessity today if you want to maintain optimal inventory levels, optimize warehouse operations, and manage supplier performance. As AI and real-time analytics continue to evolve, future trends in supply chain analytics, like digital twins, blockchain technology, and hyper automation, will make data even more critical. Thus, you will need accurate, up-to-date, and structured data that will make supply chain management more efficient - something that is possible with a defined data management approach.
