Managing the warehouse cost trade-off: space vs demand fulfilment through data - annotation

A retail warehouse is a crammed-up space: a lot of items are stocked in a limited amount of space. Yes, the space used is limited because usage of space has a cost attached to it. Your ultimate goal is to achieve a trade-off between -

the usage of space and stocking the space right enough with the optimal amount of inventory.

The cost of space usage should be right enough for you to bear. Also, the amount of inventory should be right enough in the given space to avoid customer turnout.

To achieve this trade-off, the operations in the warehouse should be tracked at the level of shelf, item etc. The count of items in the given warehouse space at any given time would help the warehouse operations manager know if the warehouse space is being optimally used, given the external environment - 2x customers lining up at the storefront are demanding that particular item.

There are two possibilities :

Case 1: The warehouse space is under-utilised and does not serve the huge demands of the customers lined up at the storefront.

• This increases the cost of space for the retail owner.
• This will lead to customer churning.
• This also increases the cost of restocking ( as restocking has to be done in a short time - interval )

Case 2: The warehouse space is over-utilised.

• This damages delicate items.
• Also, this increases the cost of warehouse operations as most items lying unused ( do not get to see the store ) require maintenance.

A case worth noting :

How can this be done? A data annotation platform like Labellerr can help an AI scientist specialised in the warehouse analytics space in a number of ways.

• Labelling warehouse image data/video data through Labellerr's manual/automated tools can help the AI scientist/product manager ( specialised in the warehouse analytics space ) know about the storage/movement/dispatch of items from the warehouse. This data when received at the item level can be of help to the warehouse manager. ( who has to optimise the cost of warehousing operations ).
• There are extended uses of this intelligent data that is labelled inside Labellerr platform. The data can be used in training AI algorithms which are the eyes of the Robots ( put to the particular use case where they have to scan the warehouse ).

What better when this data about real time movement of inventory from / to warehouse is connected to the vendor as well as the retail store.

This will create a system for preventing stockouts.

Some use cases where annotated data from Labellerr can be of help to the AI scientist in the warehouse analytics / retail analytics space :

Real-Time Inventory Tracking: The shopfloor robot trained on annotated data from Labellerr can continuously scan shelves and update the inventory count in real-time. This data can be synced with the warehouse management system (WMS) and the retail store's system, providing accurate information on stock levels and enabling timely replenishment.

Space Utilization Monitoring: By analyzing the layout of shelves and the placement of items, the robot (trained on annotated data from Labellerr ) can provide insights into space utilization. It can identify areas where space is underutilized or where items are not optimally organized, allowing the warehouse manager to reconfigure the space for maximum efficiency.

Demand Forecasting: Utilizing historical data and current inventory levels, the robot (trained on annotated data from Labellerr) can help predict demand for specific items. This forecasting capability enables proactive inventory management, ensuring that popular items are always in stock to meet customer demand without overstocking and incurring unnecessary costs.

Order Picking Optimization: The robot(trained on annotated data from Labellerr) can assist in order picking by guiding warehouse personnel to the exact location of items. It can optimize picking routes based on the order volume and warehouse layout, reducing picking time and improving overall efficiency.

Warehousing operation cost thus hangs on the low-hanging fruit of data annotation. It is very right when one says to look into the finer print to know the truth. The truth lies in the data at the level of the shelf !!

To optimising operations,

Labellerr