Submitted by admin on Thu, 08/18/2022 - 01:04

Inefficient management of stock levels and operations

Sequence Number 3 Industry Logistics Banner Inefficient management of stock levels and operations How 5G enabled

Manufacturing efficiencies can be greatly improved by exploiting AI (ML) to predict the optimum stock levels, utilising automation such as autonomous vehicles, etc.

Data Flows
Title Devices Icon Devices Description
  • Obtain information on stock levels through various sensors, e.g., drones, cameras, systems, etc.
  • Add data to support autonomous vehicles
  • Ideally 5G-based to support real-time
Title Connectivity Icon Connectivity Description
  • All data collected to be transmitted to cloud
  • Time series data
  • Asset data
Title Edge Compute Icon Edge Compute Description
  • Real-time required for autonomous vehicles and temperature control
  • Not required for stock level monitoring
Title Cloud Compute & Storage Icon Cloud Compute & Storage Description
  • All data is stored in enterprise data storage, where data ownership belongs to the company
Title Applications & Services Icon Applications & Services Description
  • Both MV and ML models to be developed and operated
  • Predict stock levels on data collected
Title Inform Decision Makers Icon Inform Decision Makers Description
  • Operators are alerted only in cases of issues that are unable to be solved by AI
Title Support Decision Making Icon Support Decision Making Description
  • End of process
Application Logic
Description
  • Collect data from as many sources as possible, where data sources need to be aligned to get the best information about stock levels.
  • Collect information on stock inflow and outflow trends, and conditions that could influence the trend, e.g., weather, seasons, holidays, market changes, etc.
  • Include all historical information.
Description
  • Data required for real-time processing will be used at the Edge and all data will be collected in the enterprise storage.
  • Develop the AI (ML) model based on all data gathered.
  • SME involvement working with data scientists is required to develop the models.
Description
  • Based on learnings from the models, improve the quality of the data sources, review changes of data sources for the model to provide predictions on required stock levels.
  • Over time, stock levels can be reduced as accuracy of predictions improves.
  • Move towards autonomous operations in steps, such as in deployment of AGVs.
Expected benefits Key value created