ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications   04/26/2024

Editor's comments and enhancements are shown in green. [✓ Reviewed]

The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • [1] Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • [2] Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • [3] Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • [4] Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • [5] Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
  • [6] Functional aspects, trustworthiness, and societal concerns
  • [7] AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.

No. 41 ID: Use Case Name: Improving Productivity for Warehouse Operation
Application
Domain		Logistics
Deployment
Model		On-premise systems
StatusPoC
ScopeBig data analysis for enhancing productivity
Objective(s)To improve productivity of warehouse operation by detecting and changing controllable factors
Short
Description
(up to
150 words) 		AI-driven operating system that uses big data from work performance information to issue appropriate work instructions has been developed. In PoC, picking operation improvement was conducted in a distribution warehouse. As the result, 8% work reduction was performed.
Complete Description Attempts are being made to increase the efficiency of work improvements through more widespread application of IT to work systems. However, as each new improvement is added or improvements are made with respect to environmental changes, it requires manual changes to the system, leading to increases in work improvement costs. This case has developed an AI system that uses big data such as work performance information, to understand worksite improvements and environmental changes and issue appropriate work instructions. It has conducted a demonstration test, which confirmed the effectiveness of this system for improving distribution warehouse work. In the future, we will continue to work on expanding the AI system to a wide range areas such as manufacturing and distribution.
Stakeholderswarehouse manager
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities		possibility of back action
Performance
Indicators (KPIs) 		Seq. No. Name Description Reference to mentioned
use case objectives
1 Number of labors reduced % of labors improvement of productivity
2 Number of complaints reduced % of labor's complaint improvement of productivity
3 Lead time time from order to shipment improvement of productivity
AI Features Task(s)Optimization
Method(s)modeling of relationship between explaining variables and outcome, and optimization
HardwarePC, wearable sensor
Topology
Terms &
Concepts Used		Human big data analysis, regression analysis
Standardization
Opportunities
Requirements		standardization of data format, sensors to be used, and API of IT and mechanical systems
Challenges
& Issues		understanding of workers' human factors (privacy, additional work etc.)
Societal Concerns Description solving labor shortage problem and improving labor related issues with aiming improving productivity.
SDGs to
be achieved 		Industry, Innovation, and Infrastructure
Data Characteristics
Description
Source
Type
Volume (size)
Velocity
Variety
Variability
(rate of change)
Quality
Scenario Conditions
No. Scenario
Name 		Scenario
Description 		Triggering Event Pre-condition Post-Condition






Training Scenario Name:
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Specification of retraining data
References
No. Type Reference Status Impact of
use case 		Originator
Organization 		Link
1 company's technical journal Published Hitachi, Ltd., Link

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013