ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   04/24/2019
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The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
No. 47 ID: Use Case Name: Order-Controlled Production
Application
Domain
Manufacturing
Deployment
Model
Cloud Services
StatusPrototype
ScopeAutomatic distribution of production jobs across dynamic supplier networks
Objective(s)The objective is to enable automatic supplier contracting for optimized utilization of manufacturing capabilities at suppliers, novel degrees of flexibility in contract manufacturing, and enable (mass) customized customer ordering
Short
Description
(up to
150 words)
A network of production capabilities and capacities that extend beyond factory and company boundaries allows for a quick order-controlled adaption to changing market and order conditions. The result is a largely fragmented and dynamic value chain network that change as required by the individual order, and thereby make the best use of capabilities and capacities of existing production facilities. The goal is to allow for automated order planning, allocation and execution, thereby considering all production steps and facilities required to facilitate linking external factories into a company’s production process, as automated as possible.
Complete Description Use Case description taken from [1,2,3]. Many contemporary products are changing at an ever-in-creasing rate. Whereas up until just recently, smartphone displays were flat, the first curved displays are already on the market. The array of materials used in the automotive sector is also continually expanding – from aluminum, to high-strength steels and even fiber-reinforced plastics, today many types of materials are used.

Innovation and product cycles are getting shorter all the time, and new production technologies are putting pressure on manufacturing companies to react more and more rapidly and make quick investment decisions regarding both consumer goods and investment goods. In order to confront this trend and avoid lengthy investment decisions, companies are starting to increase the network of their production capabilities beyond their own company boundaries.

Key aspects
The Order-Controlled Production application scenario describes a flexible manufacturing configuration. Owing a network of production capabilities and capacities that extend beyond factory and company boundaries, this company can quickly adapt to a changing market and order conditions, and thereby make the best use of capabilities and capacities of existing production facilities. In this way the potential provided by a network to other factories out-side of the company’s own facilities is used to align the company’s own portfolio - and especially its production - to quickly changing customer and market demands. Specifically, manufacturing chains are optimized for various parameters, such as cost and time.

At its core, order-controlled production is based on standardization of the individual process steps on the one hand and the self-description of production facility capabilities on the other hand. This standardization allows for auto-mated order planning, allocation and execution, thereby considering all production steps and facilities required. This helps to combine individual process modules much more flexibly and earlier than previously possible, and to make use of their specific capabilities.

In this respect, companies offer their available production capacities to other companies and thereby increase the utilization of their own machinery. Other companies may access these capacities as needed, thereby temporarily expanding their own production spectrum. In so doing, available production capacities are utilized better and order fluctuations can be smoothed out. The goal is to facilitate linking external factories into a company’s production process, as automated as possible. In particular, the order placement process required for this should be executed automatically.

Effect on value chains
Today’s relatively rigid and separately negotiated relation-ships between companies along the value chain will be transformed into a largely fragmented and dynamic value chain network that changes as required by the individual order. This applies both horizontally over the entire manufacturing process as well as vertically, with regard to production depth. Manufacturing companies focus on value-added steps that distinguish them significantly from other competitors. The possibility of creating fast and global client-manufacturer relationships can lead to unexpected competitive situations, because companies may change their role from order to order. Dynamically integrating production capacities will lead to better machine utilization and, as a result, diminishing demand for machinery suppliers.

Value added for participants
On the one hand, manufacturing companies will be able to automatically expand their production capabilities and capacities ad hoc in line with demand, by utilizing external production modules. No investment is required. This enables companies to react very flexibly to changing market and customer demands. On the other hand, companies offering their machines on the market can optimize their utilization rates.

StakeholdersCustomer, Producing companies, Broker
Stakeholders'
Assets,Values
Systems'
Threats &
Vulnerabilities
Key
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives




AI Features Task(s)Automatic reasoning, AI (task) planning, distributed coordination and negotiation (cf. [5-8] for details and overview)
Method(s)
Hardware
Topology
Terms &
Concepts Used
Standardization
Opportunities
Requirements
Challenges
& Issues
Societal Concerns Description Enabling mass-customized production in global dynamic supply chains, and by that, ease production of small lot sizes for customized products.
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






Scenario Name Training
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 Working Group on Research and Innovation of the Plattform Industrie 4.0. Aspects of the Research Roadmap in Application Scenarios, Working Paper, German Federal Ministry for Economic Affairs and Energy, 2016. Link
2 Working Group on Research and Innovation of the Plattfom Industrie 4.0 and Alliance Industrie du Futur: Plattform Industrie 4.0 & Alliance Industrie du Futur : Common List of Scenarios. 2018. Link
3 Communication Promoters Group of the Industry-Science Research Alliance and German National Academy of Science and Engineering. Recommendations for implementing the strategic initiative INDUSTRIE 4.0, Final report of the Industrie 4.0 Working Group, April 2013. Link
4 Birgit Vogel-Heuser, Jay Lee, and Paolo Leitao. Agents enabling cyber-physical production systems. at – automatisierungstechnik 63(10). DOI: 10.1515/auto-2014-1153, 2015. Link
5 Weyer, Stephan, et al. "Towards Industry 4.0-Standardization as the crucial challenge for highly modular, multi-vendor production systems." Ifac-Papersonline 48.3 (2015): 579-584. Link
6 Lasi, Heiner, et al. "Industry 4.0." Business & information systems engineering 6.4 (2014): 239-242 Link
7 Monostori, László. "Cyber-physical production systems: Roots, expectations and R&D challenges." Procedia Cirp 17 (2014): 9-13. 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