How AI is Used in Warehousing

A Guide To How Artificial Intelligence is Transforming Logistics

Top performers in supply chain management are putting more than twice as much money into artificial intelligence and machine learning (AI/ML) than their less efficient competitors. That’s according to research by global analyst firm Gartner, who studied nearly 1,000 supply chain organisations.

Gartner’s other top finding was that industry leaders prioritise productivity over cost savings or efficiency. These firms focus on using AI to unlock potential from their existing assets, keeping them ahead of lower-performing organisations.

One way or another, AI is going to find its way into almost all supply chain and logistics operations. It’s being integrated into apps and systems for warehouse design, demand forecasting and resource planning. What sets top performers apart is that they’re choosing to seek out, assess and implement AI where it benefits their business.

But where and how is AI making a difference? This is your guide to how AI is used in warehousing.

Your guide to how AI is used in warehousing

We have extensive experience of using artificial intelligence in our apps for warehouse design, and we see firsthand how AI and ML are making a massive difference to our clients. That’s why we’ve put together this short guide, to help you make informed choices about AI and ML in your organisation.

The language of AI in supply chain

Artificial Intelligence: A machine’s capability to absorb information, assess it and make decisions, like a human being.

Machine Learning: Apps that analyse massive amounts of data very fast, learning from patterns in the information.

Robotics: Machines that perform tasks, based on instructions supplied by external systems.

Internet of Things: A network of sensors, computers, robots and other devices that communicate with one another.

Virtual Twin: A digital version of a physical object that can be used for testing the impact of changes to its design or layout.

Extended Reality: A term to cover virtual reality, augmented reality and mixed reality, where the digital and real worlds are blended.

Algorithm: A specific process used by an app. Artificial intelligence is achieved through multiple algorithms working together.

SMART warehouse: A highly-automated warehouse with minimal human staff.

How AI improves productivity in warehouse processes

While AI can be presented as improving many individual processes inside a warehouse, it’s important not to lose sight of the warehouse as a whole. Many of the benefits of using AI, in warehouse and process optimisation, are interrelated, spanning multiple systems.

Demand forecasting and optimisation of inventory

Knowing what your customers will want, ahead of time, can help keep productivity high. You’ve got the right products in stock, and stored where they can be accessed quickly. In the same way that you can predict what people will want based on the time of year or other factors, AI can make the same predictions faster and, often, much more accurately. Using machine learning it can absorb massive amounts of data very fast, and take into account other external factors about trends, procurement, transportation and management of inventory.

Order fulfilment and logistics

The productivity of a warehouse depends on your team, both human and automated, working together to perform tasks and make journeys as quickly and efficiently as possible. This can involve AI analysing how items move around your warehouse and designing more efficient routes based on factors like product attributes, picking frequency and priority of orders in real-time. This can bring benefits such as improved accuracy and lower labour costs.

Automation and robotics

A lot of warehouse work involves the repetitive picking, moving, sorting or stowing of inventory. These tasks can often be done faster and more accurately by machines, freeing up your human team for less manual duties. Automation and robotics comes in many forms, including autonomous mobile robots (AMRs) automated guided vehicles (AGVs) and automated conveyor systems. All these can be integrated with AI, allowing warehouse workflows to be adjusted automatically as required, with minimal human intervention.

This can deliver significant productivity benefits, as inventory is located faster, picked and packed faster, and all with a high degree of accuracy and with reduced risk of human error.

Asset visibility and tracking in real-time

Tracking technologies, such as Radio Frequency Identification (RFID) and scanning tools are making it easy to monitor movement of inventory and devices around a warehouse, in real-time.

AI systems monitoring this information can identify process bottlenecks and other potential issues, allowing for fast decision-making and proactive problem-solving.

Optimising equipment and maintenance planning

Maintaining high productivity in an automated warehouse depends on devices being available when required, and not breaking down or being taken offline for maintenance at busy times.

AI linked to sensors in devices can monitor their condition and make predictions about maintenance requirements. It can schedule downtime for periods of low demand, or redeploy other devices where available.

This can help boost productivity by minimising downtime and avoiding unplanned disruptions to workflow. It can also help reduce maintenance costs and keep warehouse infrastructure operating at peak performance.

Continuous process improvement

Top-performing supply chain organisations nurture a culture of continuous improvement. Innovation is encouraged and its benefits are assessed in order to make incremental gains in efficiency, while remaining focused on serving the customer.

Using AI can make this process faster and offer perspectives and insights not previously available. The ability of AI to process large amounts of data accurately, without human bias or assumptions, can help it identify operational inefficiencies and suggest process improvements not previously considered.

Improved safety

Reducing the level of manual labour in a warehouse should immediately cut the risk of staff injuring themselves in the process of manual handling of inventory. Where automation is linked to AI, risks can be identified faster and activities automatically planned to avoid them. Proactive maintenance also helps to cut the risk of accidents.

How to implement AI in warehouse planning

There’s a risk that comes with implementing AI in your business. The risk is that you introduce AI-enabled technology in a way that makes it less effective and more costly than it needs to be. Getting the best return on your investment in AI means looking at your warehouse as a whole, rather than as a series of processes that can be tweaked individually.

This is how we work with clients using our AI-enabled warehouse design platform, DIDO (Data In, Design Out). DIDO designs fully-optimised warehouses using cutting edge technologies such as machine learning, virtual twinning and extended reality. These tools, combined with your unique needs and strategic objectives, can create a warehouse and operations that can significantly boost your productivity. It’s also designed to help ensure it remains optimised after being implemented.

DIDO’s warehouse design process

The first step is to capture your operational data. This information allows DIDO to learn how your operation works and understand it in detail. Your strategic priorities are also fed into the system. This allows DIDO to align its learning with your key business objectives. Phase two is the creation of thousands of potential warehouse layouts. In this Conceptual Design Iteration, DIDO works with three AI platforms to design and evaluate the designs.

ELSA performs sophisticated ABC analysis

ELSA (Everything Lives Somewhere Appropriate) uses machine learning to classify every SKU into one of six bands of products with similar picking needs, in terms of location and size. In essence, fast-moving products are placed into larger apertures and slow-moving ones into smaller apertures. The information is passed to CLARA.

CLARA creates thousands of designs

CLARA (Creates Layouts Algorithmically and Redesigns Appropriately) is an AI system trained to build warehouse layouts using the same approach as human designers, only much faster. One design typically takes less than one second to produce.

CLARA works through thousands of iterations to achieve the optimal design.

ELISA crafts a simulation of the design

ELISA takes the output from CLARA and sets up a simulation of the warehouse and assesses this against the strategic objectives of your business. It calculates performance metrics and gives the design a score out of 100.

This process is repeated over and over until the optimum warehouse design is achieved. It’s then modelled digitally using our SEC XR Extended Reality engine.

How to improve your use of AI in your supply chain

Organisations of all sizes now use AI to improve productivity in their supply chain and warehouse operations. Some are enjoying huge benefits, while others are still discovering how AI can make a positive difference. Our team of designers, data analysts and project managers have helped numerous businesses across many different industries. We have the experience and knowledge to help your business make better use of AI.

Get in touch with us today.

Sources used include:
https://www.gartner.com/en/newsroom/press-releases/2024-02-20-gartner-says-top-supply-chain-organizations-are-using-ai-to-optimize-processes-at-more-than-twice-the-rate-of-low-performing-peers
https://www.etp-logistics.eu/wp-content/uploads/2024/05/AI-White-Paper-web.pdf