ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications 04/10/2020
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:
 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.)
 Functional aspects, trustworthiness, and societal concerns
 AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.
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.
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.
Standardization needs for setting up this use case is currently under further investigation. Some initial intentions on standardization needs are the following: Standardization of data formats and semantic for exchanged data is enabler for this use case where multiple companies and institutions are involved (formal semantics for reasoning about 3d models, task decomposition and planning), standardization of interaction protocols between participants (esp. coordination and negotiation) enables automatic cross-company contracting.
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.
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.
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.
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.
Lasi, Heiner, et al. "Industry 4.0." Business & information systems engineering 6.4 (2014): 239-242
Monostori, László. "Cyber-physical production systems: Roots, expectations and R&D challenges." Procedia Cirp 17 (2014): 9-13.
Peer-reviewed scientific/technical publications on AI applications (e.g. ).
Patent documents describing AI solutions (e.g. , ).
Technical reports or presentations by renowned AI experts (e.g. )
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:
 B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.
 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.
 M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.
 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