Sourcing and Procurement
How using big data leads to proactive warehouse management
Warehouse management is a critical element in supply chain management. It is the central point of all manufacturing-related activities right from receiving raw materials, their storage, transformation, storage after transformation, and finally dispatch. Therefore, seamless warehousing is a must in a highly competitive marketplace.
Manual warehouse management was done based on experience and gut feeling.
And, there were several bottlenecks such as:
- Suboptimal space utilisation
- Inefficient order fulfilment, and more!
These not only caused delays and increased costs but also led to poor customer experience.
Digital technologies came as a welcome solution to overcome the challenges of manual warehouse management. Big data turned out to be a significant part of digital warehouse management.
What is Big Data?
Data that is high in volume, velocity, and variety is known as big data. Today a wide variety of data is being generated in large volumes at a great speed. Structured and unstructured data are available from multiple sources. And, this data has the power to boost the efficiency of processes leading to better outcomes. The onus is on businesses to leverage data effectively.
Big data is extremely relevant for warehouse management because warehouses generate a vast variety of data in large volumes from multiple sources. Traditional data processing tools cannot handle the data generated. Big data analytics processes real-time data, historical data, and data from other sources to streamline warehouse operations.
In warehouse management, big data analytics has the transformative power to convert inefficient warehousing to dynamic and data-driven centres of efficiency. It facilitates a systematic, data-driven approach to managing warehouses.
Data-driven warehousing entails the collection, analysis, and interpretation of vast amounts of data generated from warehouses and all across the supply chain. A data-driven approach helps supply chain professionals make precise decisions to optimise operations and respond to the variations in a dynamic market.
How does a data-driven warehouse work?
Data from various points is collected with the help of the Internet of Things (IoT) and Radio Frequency Identification Technology (RFID) sensors.
IoT sensors facilitate capturing real-time data from inventory, equipment, vehicles, etc. They provide data related to various environmental factors such as temperatures, humidity, location, etc.
RFID tags help in tracking individual items within the warehouse. RFID technology automates order processing enhancing its accuracy and speed.
Warehouse Management System
Warehouse Management System is the control centre for managing various warehouse operations. WMS gathers and stores data such as inventory levels, order processing, etc., from different sources.
Machine Learning (ML) algorithms process datasets to identify patterns and make forecasts. They also optimise routes for material handling and facilitate preventive maintenance of equipment.
Data analytics orchestrates this information to provide valuable insights that facilitate data-backed actions.
A logistics management report indicates that “WMS is used in 85% of warehouse operations.”
Why does Warehousing Management Require Big Data?
Data-driven warehousing is revolutionising inventory management practices, wielding the power to track inventory levels with surgical precision and predict future demands. This modern approach doesn't just stop at optimising numbers; it reshapes the entire inventory landscape. It turbocharges inventory turnover rates, deflates bloated carrying costs, and eradicates the dreaded specter of stockouts. Data reveals that “Using an integrated order processing system for inventory management can boost productivity by 25%, space consumption by 20%, and efficiency of stock use by 30%.”
Data analytics helps identify bottlenecks in various warehouse processes like order picking, shipping, etc. Timely action can be taken to enhance order fulfilment.
Data analytics fosters proactive risk mitigation. The insights about various warehouse processes help identify issues in their nascent stages. Therefore, warehouse managers can proactively resolve these issues before they get compounded.
Data also maximises capacity utilisation of the warehouse by optimising the allocation of resources material, labour, equipment, and storage space. This helps reduce wastage and idle time thereby saving cost.
Big data plays a vital role in warehouse management because it enhances decision-making and optimises several aspects of the supply chain. It leverages data from diverse sources and processes it to reveal patterns and make forecasts. Warehouse managers can make data-driven decisions to mitigate risks and boost the efficiency of the supply chain. Data-driven decisions reduce the cost of various supply chain activities and speed up the order fulfilment process with minimal accuracy. An efficient supply chain delights customers and boosts ROI.
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