Editorial Feature

Managing Data in an Autonomous Robot Warehouse

The logistics, supply chain, and warehousing sectors are currently undergoing a significant digital transformation. Robots and autonomous vehicles, Industrial Internet of Things (IIoT) networks, and big data analytics are all being brought to bear on the industry. Forward-looking firms can expect to leverage a sizable advantage in warehouse operations by effectively managing all of this data.

Managing Data in an Autonomous Robot Warehouse

Image Credit: Chesky/Shutterstock.com

Managing big data well – along with industrial automation and smart networks – can result in substantial productivity improvements for warehousing, supply chain, and logistics companies.

Big data analysis in the sector helps businesses to identify subtle or nonintuitive areas for improvement that yield larger than expected performance gains.

It works by identifying and monitoring identifiers for numerous small details in the logistics chain: from consumer trends to the price of local utilities and fuel to workforce availability.

Adding up many of these small detail identifiers and recording data on them over time can quickly generate large data sets containing lots of useful business planning and management data.

However, managing and analyzing these large data sets is necessary to achieve productivity gains.

Industrial Internet of Things

The Internet of Things (IoT) refers to a network (or networks) of things or objects that can be linked together via the internet. The term conceptualizes a system of objects (think sensors, lights, speakers, locks) that are all linked by the internet.

When this concept is applied in industrial settings, it is referred to as the Industrial Internet of Things (IIoT).

IIoT networks combine smart technology (devices with data processing chips and internet connectivity built in) with various industrial processes, including warehousing operations.

How Do Warehouses Acquire Data?

Warehouse operations that deploy IIoT systems quickly generate huge amounts of data on their business. RFID data shows where individual inventory items are physically located, sensor data records environmental factors in a warehouse facility like humidity and temperature, and clocking-in systems keep track of the workforce.

In warehouses, sensors and scanners deployed in key locations like forklifts, conveyor belts, and racking systems record and automatically transmit large amounts of useful operational data.

Connected to a live IIoT network, these help warehouses operate autonomous monitoring and management of through speeds, stock levels, and storage capacity.

Installations for individual warehouses are unique to the requirements of that warehouse. For example, a cold food or pharmaceutical storage facility will monitor temperature and humidity levels more closely, while distribution centers may invest more in autonomous storage management networks.

Forklift sensors include impact sensors to monitor collisions (a reliable indicator of too much pressure on some aspect of the operation) and load sensors that can detect how far a forklift travels with and without loads throughout the shift (which indicates the effectiveness of route planning).

The key to acquiring big data sets in warehouses – and any operation – is making the data acquisition process as passive as possible. This is where IIoT concepts come into play. Wireless connections and automated data acquisition throughout the day enable warehouses to grow big data sets without creating extra responsibilities for staff.

Managing Big Data

Putting that big data to work requires investment in skilled personnel, either through new recruitment or retraining programmes for workers displaced by automation.

Analysts closely study big data sets captured by warehouse operations to find ways to improve efficiency and productivity. For example, revising maintenance schedules to avoid equipment failure before it happens or optimizing air conditioning and refrigeration units to maintain correct temperatures with as little energy as possible.

Large facilities, complex operations, and international companies operating warehouses worldwide create data sets so vast that managing and analyzing them manually is next to impossible.

In these cases, artificial intelligence (AI) technologies like machine learning and deep neural networks take on the work of bulk analytics. Computers can parse digital information much faster and more accurately than humans can, and with enough processing power, they can do it instantly.

Advanced Data Management for Autonomous Robot Warehouses

A key benefit to connecting warehouse functions up in an IIoT network with algorithmic data management in place is that it lays the groundwork for more automation.

Increasingly, many functions in modern warehouses are being automated with robots. From robots with extremely limited degrees of freedom like conveyor belts and thermostats to fully mobile autonomous forklifts and pickers, industrial automation is transforming warehouses today.

Big data that has been captured passively and analyzed automatically with AI can inform robot control programs, creating a virtuous cycle of optimization in the warehouse.

Data sets are acquired through day-to-day warehouse operations and then transmitted to a central computer (or cloud service). There, algorithms run to find ways to optimize processes in the warehouse. New instructions can then automatically be created for autonomous functions in the warehouse to realize the potential optimization.

All of this can happen almost instantly, enabling advanced warehouses to respond to changing circumstances immediately, dynamically reacting to ensure operations are as productive as possible at all times.

Continue reading: A Data-Driven Approach to Industrial Innovation

References and Further Reading

Pilkington, B. (2022). How is the Digital Transition Benefiting the Planet? [Online] AZO Materials. Available at: https://www.azom.com/article.aspx?ArticleID=21652 

Viswanathan, N. (2018). Using Prescriptive Analytics to Decrease Supply Chain Disruptions. [Online] Supply Chain Management Review. Available at: https://www.scmr.com/article/are_you_using_prescriptive_analytics_to_decrease_supply_chain_disruptions_y

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Ben Pilkington

Written by

Ben Pilkington

Ben Pilkington is a freelance writer who is interested in society and technology. He enjoys learning how the latest scientific developments can affect us and imagining what will be possible in the future. Since completing graduate studies at Oxford University in 2016, Ben has reported on developments in computer software, the UK technology industry, digital rights and privacy, industrial automation, IoT, AI, additive manufacturing, sustainability, and clean technology.

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