In 2023, 2.64 billion online transactions were recorded worldwide. And it is expected to continue growing for years to come. For operations teams, this means finding ways to constantly optimize the supply chain and product inventory to keep up with demand while reducing operational costs.
But how do you achieve that when the data is siloed in separate systems which fail to communicate effectively?
The challenge is monumental.
You put in a crazy amount of energy to make sense of the data manually, only to end up with fragmented insights, operational inefficiencies, and missed opportunities.
The solution is consolidating data from all these sources to get a holistic view of the supply chain. It would help you optimize inventory levels, improve your demand forecast and even identify issues in your supply chain before they occur. However, without an integrated approach, leveraging that data can feel like an insurmountable task.
This article provides you practical solutions to improve your supply chain analytics leading to more efficient operations.
Supply Chain Analytics: The Complexity of Managing Disparate Data Systems
First, let’s take a quick tour of the systems required for an holistic supply chain analytics:
– ERP (Enterprise Resource Planning): Typically the backbone of retail operations, an ERP system integrates key business processes, including finance, HR, procurement, and supply chain management. However, the data within the ERP is often limited to back-office functions, leaving gaps in real-time supply chain visibility.
– WMS (Warehouse Management System): A WMS focuses on tracking inventory movement within the warehouse—receiving, picking, packing, and shipping goods. Yet, it often exists in isolation from other systems, creating challenges for end-to-end visibility into stock levels and fulfillment status.
– TMS (Transportation Management System): This system handles the logistics of moving goods, from tracking shipments to managing carrier relationships. While critical to ensuring timely deliveries, TMS data is often siloed from inventory data or sales forecasts, leading to poor alignment between shipping schedules and customer demand.
– POS (Point of Sale System): The POS system captures sales transactions at the store level, providing insights into what’s selling and where. However, the data here is often trapped in the POS platform and not linked back to inventory levels or sales forecasts, making it difficult to adjust supply chain activities based on real-time customer demand.
Each of these systems is essential, but their lack of integration poses significant challenges for retail operations.
When these systems can’t “talk” to each other, data discrepancies emerge, processes become inefficient, and decision-making becomes reactive rather than proactive.
The Impact of Disconnected Data on Your Supply Chain Analytics
Lack of Real-Time Visibility
When data is stored in multiple systems, it becomes nearly impossible to get real-time visibility into the entire supply chain.
I’m sure you won’t have any hard time picturing this scenario: your POS system shows that a product is selling out fast, but your ERP system still reflects outdated inventory levels because the WMS hasn’t updated it yet.
This leads to a disconnect in decision-making, as you’re unable to adjust procurement or logistics in time to meet demand. As a result, out-of-stock situations occur, leading to missed sales opportunities, frustrated customers, and potential damage to brand loyalty.
Inefficient Inventory Management
Inventory management is at the heart of retail operations, yet disjointed systems make it hard to optimize stock levels.
For instance, your ERP may show a surplus of items, but if the WMS is not accurately tracking movement, stockouts or overstock situations may occur. This lack of alignment can lead to bloated inventory, increased holding costs, or conversely, lost sales due to unfulfilled customer orders.
Poor Demand Forecasting
Demand forecasting relies heavily on the ability to analyze historical sales data, inventory levels, and shipment statuses across multiple systems.
When systems like TMS, WMS, and POS remain disconnected, it becomes incredibly challenging to build accurate models. Poor forecasting can result in overproduction, underproduction, or stock imbalances that directly affect a retailer’s bottom line.
Let’s take the example of an hypothetical sportswear brand, PowerStride.
PowerStridestruggles with demand forecasting for a seasonal product line because their ERP and POS systems weren’t aligned. The POS system showed high demand at certain locations, but this data wasn’t available in the ERP system for weeks. By the time the data was reconciled, the opportunity to adjust production levels was long gone, and the company was left with excess inventory they couldn’t sell.
Data Accuracy and Consistency
When your systems don’t integrate with each other, you’re sure to end up with data consistency issues.
One system might classify products differently from another, leading to errors in reporting and discrepancies in supply chain data analytics.
For example, the TMS may categorize shipments by delivery method, while the ERP uses a different metric, making it difficult to reconcile data between the two systems.
These inconsistencies makes it more challenging for you to make strategic decisions as you rely on accurate data. Without confidence in the data, it becomes nearly impossible to optimize operations.
Time-Consuming Manual Processes
When systems aren’t integrated, we all know what happens: everyone ends up exporting data manually from all systems and importing it into another – typically Excel.
This manual reconciliation is time-consuming, prone to errors, and unsustainable for growing your business operations. The retail and e-commerce industries are evolving and growing at the speed of light, so relying on manual processes is not an option.
Build a Holistic, Real-Time Supply Chain Analytics With ClicData
As mentioned above, you need a robust technology that can centralize and unify data from all your supply chain systems, ensuring real-time visibility, accuracy, and actionable insights.
ClicData is a powerful data integration and analytics platform that simplifies supply chain data analytics for retailers.
Learn how Carrefour streamlined their operations and collaboration with over 400 suppliers with real-time supply chain analytics and accurate demand forecasting.
Here’s how ClicData helps:
Integrating Data from Multiple Systems
ClicData allows seamless integration with any type of data source, including ERP, WMS, TMS, POS, and more.
By consolidating data from all these systems into one unified platform, ClicData provides real-time visibility into your entire supply chain. This means no more waiting for systems to update or manually pulling data from different sources. Instead, you have all your data at your fingertips, enabling you to make timely and informed decisions.
Real-Time Dashboards for Better Decision-Making
ClicData’s customizable, real-time dashboards offer a visual representation of your supply chain metrics, such as inventory levels, order fulfillment rates, and transportation costs.
These dashboards provide the insights you need to optimize operations, reduce costs, and improve customer satisfaction. By having a clear, unified view of your data, you can spot trends, identify bottlenecks, and take corrective action before minor issues turn into major problems.
Data Automation and Accuracy
By automating the integration and consolidation of data from various systems, ClicData eliminates the need for manual data entry and reconciliation.
This not only saves time but also improves data accuracy and consistency.
ClicData ensures that all your data is up-to-date, reliable, and easy to analyze, helping operations managers make better, data-driven decisions.
Improved Demand Forecasting with Comprehensive Data
With all your data consolidated into one platform, ClicData enables you to create more accurate demand forecasts.
By analyzing historical sales data, current inventory levels, and shipment statuses in one place, you can create predictive models that align production and procurement with actual customer demand.
This leads to optimized stock levels, reduced holding costs, and higher sales conversion rates.
Going Further: Leveraging Machine Learning & Predictive Analytics for Enhanced Supply Chain Operations
The integration of machine learning (ML) and predictive analytics into supply chain operations is a game-changer.
Machine learning models, when applied to supply chain data, can detect patterns, forecast demand, optimize inventory levels, and even predict potential disruptions.
For retail operations teams striving to stay ahead in a dynamic marketplace, this capability transforms traditional supply chain data analytics into a proactive, intelligent system.
Here’s how machine learning and predictive analytics can significantly improve supply chain operations:
Inventory Optimization
Struggling with inventory imbalances?
Machine learning models can help optimize inventory by analyzing real-time data from multiple systems, such as ERP, WMS, and POS, to identify patterns in inventory movement.
These models can predict the ideal reorder points and quantities for each product, ensuring that retailers have the right amount of stock on hand at all times.
For example, if a certain SKU shows consistently low turnover in certain regions but spikes during promotional periods, machine learning algorithms can identify these patterns and recommend more efficient stocking practices. This leads to lower inventory carrying costs, reduced stockouts, and improved cash flow.
Carrefour integrated their collaborative forecasting system directly with ClicData, enabling them to create a new service for their suppliers and an additional revenue stream with forecasting capabilities – unlocking the full potential of their data.
Predicting Supply Chain Disruptions
Anything could happen, at anytime.
Natural disasters, supplier issues, or logistics delays can severely impact a retailer’s ability to meet customer expectations.
Machine learning models can analyze data from multiple sources, such as transportation schedules, weather forecasts, and supplier performance history, to predict potential disruptions before they happen.
By proactively identifying these risks, retailers can take corrective action, such as rerouting shipments or finding alternate suppliers, before disruptions affect their operations.
For instance, imagine a scenario where a key supplier consistently delivers late. Using predictive analytics, a retailer could identify this pattern early on and either negotiate better terms with the supplier or find alternative vendors.
→ Learn more: 10 supplier quality metrics for better procurement
This approach not only reduces the impact of supply chain disruptions but also increases agility and ensures that retailers can respond quickly to unexpected events.
Let’s say you’re working for a European fashion retailer. You could use machine learning algorithms to predict supply chain disruptions caused by factory shutdowns in Asia. By analyzing data from various sources—such as geopolitical news, weather forecasts, and past supplier performance—you’d be able to adjust your procurement strategies and avoid significant delays in your product launches.
Transportation and Delivery Optimization
By analyzing data from the TMS, along with external factors such as traffic patterns and fuel prices, ML models can recommend the most efficient routes and transportation methods.
This helps reduce shipping costs and ensures faster delivery times, contributing to better customer satisfaction.
For e-commerce retailers, machine learning can be especially beneficial in optimizing last-mile delivery, where costs are high, and efficiency is crucial.
Predictive models can forecast potential delays in the delivery process and offer solutions, such as adjusting delivery routes or increasing delivery fleet capacity during peak demand periods.
Supplier Performance Analysis
Working with a reliable supplier network is key to operational success. However, suppliers are not always consistent in delivering on time, at the right quality, or in the right quantity.
Machine learning models can analyze past supplier data—such as lead times, delivery frequency, and quality issues—and predict future supplier performance. This information helps retailers assess which suppliers are most reliable and which may pose risks to the supply chain.
By monitoring these supplier quality metrics closely into a platform like ClicData, operations managers can easily monitor supplier performance, predict potential risks, and make data-driven decisions to improve supplier relationships.
Embrace End-to-End Supply Chain Data Analytics
The retail sector has always been a tough market, even more so with the explosion of volumes of data generated. Data silos are no longer sustainable to run your operations.
The pain points of disconnected systems—ranging from lack of real-time visibility to inaccurate data—can severely hamper operations and profitability. You must embrace a solution that can integrate multiple systems and provide real-time, actionable insights through effective supply chain data analytics.
With ClicData, retail operations managers can overcome these challenges by consolidating data from ERP, WMS, TMS, POS, and other systems into a single data analytics platform. By doing so, retailers can gain real-time visibility, improve operational efficiency, and make data-driven decisions that lead to better business outcomes.
If you’re interested in learning more about how we can help improving your supply chain analytics, let’s chat!