Supply chain and logistics generate a vast amount of data continuously. To extract valuable information from this complex and siloed data, organizations need to implement rigorous data cleaning and validation processes. Plus, data-driven decisions are the key ingredient today for achieving success and facilitating greater control over the business direction.
Hence, organizations need to delve deep into data, which is stored in data lakes. Implemented through the pay-as-you-go model, data lakes help companies store raw data without any limitations.
Here’s how data lakes are beneficial for supply chain and logistics companies.
What is a Data Lake?
A data lake represents a centralized repository to store vast amounts of structured, unstructured, and raw data in any format. The beauty of a data lake is that it can store data coming from various other databases, sensors, ERPs, CRMs, social media, etc.
As the data is stored in its original format, unprocessed, you can process and analyze it using different tools and technologies. However, don’t confuse them with data warehouses because the former have better-structured data optimized for specific reporting and analysis.
Benefits of a Data Lake for Supply Chain and Logistics
Data lakes have multiple applications in this industry. Taking into account every logistics and supply chain operation is different, data lakes become the one common link for every department to work as a single unit. Let’s see how.
- Improved Visibility and Real-Time Tracking
Imagine getting up-to-the-minute data on the inventory levels, fleet, etc., across all the warehouses and shipments through a single source. Data lakes store all the data generated, which can be used for fleet management, warehouse management, POS terminals, etc. When you get unparalleled visibility, it’s easier to track every truck, shipment, product, etc., and also identify bottlenecks in the process.
- Enhanced Planning and Forecasting
Data lakes enable an organization to implement sophisticated forecasting techniques. As they integrate data from multiple sources, including weather patterns, social media trends, competitor activity, etc., you can get accurate forecasts and predictions.
This knowledge can be used to plan ahead and make data-driven decisions. For example, adjusting inventory levels according to demand or changing delivery routes according to existing conditions is possible.
- Precision with Granular Data Analysis
With data lakes, you can take data analysis to the granular level, capturing information about product types, delivery locations, weather conditions, etc. Using this wealth of data it facilitates precision planning and resource allocation.
For instance, you can use precise data to detect frequent traffic delays in areas with congestion, adjust routes for delivery vehicles to go through low-traffic areas, and improve delivery rates.
- Integrate Data for Holistic Insights
Data in supply chain and logistics is often separated and stored at disparate locations or systems. A data lake will become your central repository to integrate this information from different sources, effectively eliminating data silos and fostering better collaboration across different departments.
- Proactive Problem Solving and Risk Mitigation
A data lake helps discover hidden patterns and trends through historical data analysis on all sorts of operations. It lets you connect the dots and identify potential risks before they can become a major problem.
For example, by analyzing data from the fleet of refrigerated trucks, you can source information on potential equipment failures due to temperature fluctuations or, engine performance, etc. Hence, you can run preventative maintenance measures to ensure the timely delivery of perishable goods.
- Unleash the Power of AI and Machine Learning
Data lakes form the foundation of deploying AI and ML tools to improve the supply chain and logistics. With these technologies, you can;
- Quickly analyze sensor data from vehicles to minimize downtime and reduce maintenance costs.
- Identify suspicious activities in the logistics network, like fraudulent invoicing, shipment rerouting, etc.
Analyze real-time market data and customer behavior to set dynamic pricing strategies.
A well-planned utilization of data lakes can turn your supply chain and logistics operation from reactive to proactive. Higher visibility and enhanced planning will be standardized, and advanced analytics will empower you to streamline and optimize resource allocation.
Building and Implementing a Data Lake for Supply Chain and Logistics
To build and implement a data lake for logistics and supply chain, you need to work on three core aspects: data sources, data integration, and choosing the right technologies for the implementation process.
1. Data Sources
Identify all the data sources related to your operations;
- ERP Systems: The Enterprise Resource and Planning system houses a vast wealth of data, including inventory levels, purchase orders, sales data, etc.
- Sensor Data: IoT-driven sensors collect data from trucks, warehouses, production lines, etc., and send real-time information.
- Customer Data: Bring in data from CRMS, social media handles, and website analytics for a holistic customer experience analysis.
External Data Sources: Data related to traffic, weather, congestion, etc., are valuable to future planning.
2. Data Integration
Data integration from diverse sources is a logistical challenge, which can be tacked with the following methods;
- Understand Data Formats: Identify the ideal format for storing data. For instance, ERP data should have a structured format where sensor data can be unstructured streams, and for this, data ingestion pipelines should be built.
Data Standardization: Build in-house data governance policies to ensure data consistency.
Data Quality Management: The Garbage In and Garbage Out data cleansing policy works with data lakes. It identifies and addresses missing values and inconsistencies.
3. Choose the Right Technology
The selection of technology depends on your requirements, and there’s no one-size-fits-all solution. You can either choose Cloud solutions or orchestrate an on-premise data lake.
For architecture, choose between data lake houses or federated data lakes, again choosing the one that suits your requirements. Lastly, invest in automation-enabled data management, like ingestion, transformation, and quality control.
To Sum it Up
Building a data lake is a continuous process rather than a one-stop solution. Once the basic structure is set, you will need to evaluate the changing configuration of data, data sources, analytical methods, etc. This continuity is equally important for your business as it is to set up a data lake.
INTECH can help you maintain robustness in data management and align the data lake and warehouse structuring with your requirements and standards. Since a data lake can augment your organization in different ways, leverage our experience to build an efficient system to scale, stay resilient, and be reliable.