Peak holiday seasons, like Cyber Monday, are typically hectic for e-commerce platforms like Amazon. Under these circumstances, meeting demand and ensuring timely deliveries are challenging. But how does Amazon cope? To cope with this flood of orders, the brand often turns to AI-powered data analytics to forecast demand and plan routes, ensuring timely product delivery.
Take Cyber Monday 2023, for example: Amazon analyzed requests for over 400 million products to determine where they were most likely to be ordered. This allowed the company to manage inventory and ensure faster delivery: packages were ready for dispatch 11 minutes after placing an order. Quite an attainment, right?
However, Amazon isn’t the only business that has rummage-sale data analytics to improve its supply chain. Companies across industries use data analytics to manage inventory, monitor suppliers, optimize routes, and more. Want to learn more? In this article, we’ll explore the role of data analytics in supply chain administration and optimisation. Let’s get started!
How Can Businesses Use Data Analytics to Recover Supply Chain Management?
Data analytics helps reduce costs, improve operations, and optimise decision-making across several key areas of supply chain management. Here’s how it can help:
Demand Forecasting & Inventory Optimisation
Nearly 58% of retailers and D2C brands have inventory management accuracy below 80%. Some of these brands face overstocking, while others suffer from stockouts. Why? Because they can’t forecast future demand to maintain optimal inventory levels. Let’s look at how to solve this problem:
Use predictive models and consider data on past transactions, customer demand, seasonal demand trends, web traffic, search queries, and cart abandonment. This will help you identify recurring sales trends and maintain inventory accordingly. In other words, you won’t have to worry about excessive inventory costs from overstocking or lost sales from stockouts.
Route Optimization
In supply chain management, even minor routing inefficiencies can lead to critical financial losses, such as augmented fuel costs, vehicle wear and tear, and delivery delays. Has this ever happened to you? If so, you must use data analytics to optimize routes and ensure on-time deliveries. But how?
Using real-time weather, traffic, and delivery time data enables AI models to recalculate alternative routes more quickly. This helps reduce unnecessary mileage, fuel consumption, and fleet maintenance costs.
Risk Assessment and Mitigation
Any supply chain is subject to exchange rate fluctuations, trade restrictions, political instability, natural disasters, regulatory compliance risks, and cybersecurity risks. These situations can lead to product shortages, raw material unavailability, and even supplier delays.
Predictive analytics models can provide detailed analyses of potential anomalies by studying historical and current data, such as supplier performance, market trends, and weather and disaster forecasts. These models go even further, offering recommendations for addressing these anomalies. This will help you develop contingency plans (e.g., planning alternative delivery routes or supplier networks).
Supplier Performance Management
Poor supplier dependability, whether due to delays, quality issues, or price fluctuations, can lead to inventory shortages, increased operating costs, and dissatisfied customers. As a business owner, to maintain high standards in your supply chain and address these issues, you must regularly monitor and assess supplier performance.
You can identify inefficient suppliers by analysing data on on-time delivery, pricing, and ethical and legal compliance. This will help you enhance your sourcing strategies by selecting the most reliable and cost-effective suppliers.
Warehouse and Fulfilment Operations
With nearly two-thirds of global shoppers expecting delivery within 24 hours, it’s safe to say that faster delivery is critical for both customers and brands. Therefore, it’s crucial to focus on optimising delivery and logistics. But how exactly?
AI-powered data analytics models can predict the best fulfilment centres for faster delivery by using data on order fulfilment and processing, space and warehouse utilisation, employee movement, task completion times, and order picking accuracy.
By now, you’ve become familiar with the benefits of data analytics in supply chain management. But to maximise the effectiveness of data analytics, it’s crucial to have relevant, up-to-date, and research-ready data. This is where supply chain data management becomes critical.
Key Data Management Stages for Well-Optimised Supply Cable Analytics
Most of the data points mentioned above are unstructured, meaning they can’t be used directly for analysis. To gain valuable insights, they must undergo data management steps. Let’s break these steps down one by one:
- Cleansing: Data experts detect discrepancies in supply chain datasets, such as incorrect product and supplier names, invoice discrepancies, and duplicate inventory records. They can remove duplicate records and correct spelling errors using data deduplication and manual intervention.
- Standardisation: Since source chain data comes from various stakeholders, such as suppliers, delivery partners, retailers, and manufacturers, it may have different formats, naming conventions, and units of measurement. Data standardisation ensures uniformity and consistency across all data points, enabling seamless integration with remaining systems.
- Enrichment and Validation: Supply chain source data may be insufficient (missing or outdated supplier/product information, unverified supplier performance data). Data management experts add missing information to these data points and reconcile existing records with external sources to ensure accuracy.
- Maintenance: Supply chain data is highly dynamic due to fluctuations in demand, supplier information updates, geopolitical risks, and operational changes. Data experts periodically validate this data to ensure supplier/inventory data remains up to date and to accommodate the addition of new suppliers, distributors, and warehouses.
If you want to start managing data for supply chain analytics, you must first establish a robust governance system. To do this, you can invest in master data management tools, hire industry-experienced staff for your internal team, or outsource data management. When properly managed, your supply chain data becomes a valuable asset for analytics.
Final Thoughts
Data analytics in supply chain management is critical today for maintaining optimal inventory levels, optimising warehouse operations, and managing supplier performance. As artificial intelligence and real-time analytics advance, future trends in supply chain analytics, such as digital twins, blockchain technology, and hyperautomation, will make data even more crucial. Therefore, accurate, timely, and structured data is essential to improve supply chain management efficiency, which is possible with a proper data management approach.