COALA | COgnitive Assisted agile manufacturing for a LAbor force supported by trustworthy Artificial Intelligence

Summary

Humans are at the center of knowledge-intensive manufacturing processes. They must be skilled and flexible to meet the requirements of their work environment. The training of new workers in these processes is time consuming and costly for companies. Industries, such as the Italian textile sector suffer from the shortage of skilled workers caused, e.g. by the demographic change. A second challenge for the manufacturing sector is the continuous competition through high quality products. COALA will address both challenges through the innovative design and development of a voice-first Digital Intelligent Assistant for the manufacturing sector. The COALA solution will base on the privacy-focused open assistant Mycroft. It integrates prescriptive quality analytics, AI system to support on-the-job training of new workers, and a novel explanation engine - the WHY engine.

COALA will address AI ethics during design, deployment, and use of the new solution. Critical components for the adoption of the solution are a new didactic concept to reach workers about opportunities, challenges, and risks in human-AI collaboration, and a concurrent change management process. Three use cases (textile, white goods, liquid packaging) will evaluate the results in common manufacturing processes with significant economic relevance. COALA will contribute its results to the European AI community, e.g. via the AI4EU platform, and it will involve Digital Innovation Hubs to replicate its demonstrators for Europes first trustworthy digital assistant for the manufacturing industry. We expect to reduce the failure cost in manufacturing by 30-60% with the prescriptive quality analytics feature and the assisted worker training. For the change over time we expect a reduction of 15% to 30% by shortening the worker training time.

Results, demos, etc. Show all and search (39)
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Demonstrators, pilots, prototypes
Comment:

COALA has three application cases that cover discrete manufacturing and process manufacturing: White goods, Textile and Detergent production. Each case provides one challenge where a potential solution has a significant economic value. The cases are real production environments and they will provide 1) key requirements for the COALA solution and 2) performance indicators for its evaluation.

Attached files (1)
File Type
COALA_Flyer_v2.pdf PDF
More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/957296
https://twitter.com/Coala4Factory
https://www.linkedin.com/in/coala-your-factory-assistant/detail/recent-activity/
Start date: 01-10-2020
End date: 30-09-2023
Total budget - Public funding: 5 706 726,00 Euro - 5 706 726,00 Euro
Twitter: @Coala4Factory
Cordis data

Original description

Humans are at the center of knowledge-intensive manufacturing processes. They must be skilled and flexible to meet the requirements of their work environment. The training of new workers in these processes is time consuming and costly for companies. Industries, such as the Italian textile sector suffer from the shortage of skilled workers caused, e.g. by the demographic change. A second challenge for the manufacturing sector is the continuous competition through high quality products. COALA will address both challenges through the innovative design and development of a voice-first Digital Intelligent Assistant for the manufacturing sector. The COALA solution will base on the privacy-focused open assistant Mycroft. It integrates prescriptive quality analytics, AI system to support on-the-job training of new workers, and a novel explanation engine - the WHY engine. COALA will address AI ethics during design, deployment, and use of the new solution. Critical components for the adoption of the solution are a new didactic concept to reach workers about opportunities, challenges, and risks in human-AI collaboration, and a concurrent change management process. Three use cases (textile, white goods, liquid packaging) will evaluate the results in common manufacturing processes with significant economic relevance. COALA will contribute its results to the European AI community, e.g. via the AI4EU platform, and it will involve Digital Innovation Hubs to replicate its demonstrators for Europes first trustworthy digital assistant for the manufacturing industry. We expect to reduce the failure cost in manufacturing by 30-60% with the prescriptive quality analytics feature and the assisted worker training. For the change over time we expect a reduction of 15% to 30% by shortening the worker training time.

Status

SIGNED

Call topic

ICT-38-2020

Update Date

27-10-2022
Geographical location(s)
Structured mapping
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Comment:
  • Pioneering application of an open, privacy-focused digital assistant in manufacturing. The COALA Digital Intelligent Assistant (DIA) core software will base on the privacy-focused open assistant Mycroft, addressing industry-grade security, privacy, and ethics needs.
  • Introduction of digital-assistant-mediated prescriptive quality analytics using manufacturing resource data. The PREVENTION (PREscriptiVE aNalyTIcs for quality optimizatiON) service of COALA will address Prescriptive Quality Analytics.
  • Proof-of-concept for the effective augmentation of prescriptive quality analytics via a voice-first interaction.The DIA will use voice as a primary medium (voice-first) to receive user input and provide responses – this speeds up the interaction, is hands-free, and in general an advantage over, oftentimes, overloaded graphical interfaces.
  • Pioneering use of an explanation engine with a focus on manufacturing analytics. Fundamental research and the prototype development of the so-called “WHY engine” will enable the assistant to explain its responses (e.g. predictions and advices).
  • Introduction of a novel performance measurement procedure (“X”-WHY test) for the explicability of AI-systems, such as digital assistants in manufacturing. It identifies how many WHY questions the user expects to ask in a scenario in a row. Then, the test measures how many questions in a row the assistant is capable of answering.
  • Joint application of a Machine Avatar (Digital Twin). This feature is able to track and manage the information gathered by each IoT-connected machine with a Product Avatar, able to track the lifecycle of each product.
  • Availability of machine and product information through avatars in sharable environment, independent of the data format and totally product-agnostic.
  • A cognitive advisor with dedicated human-assisted AI methods for enabling transfer of tacit knowledge of experts to novice workers.
  • Introduction of a new didactic concept to teach workers AI competencies.
  • Pioneering use of digital assistant for on-the-job training with a voice-first interface.
  • Availability of a new concept for tacit knowledge transfer based on small training datasets.
  • Change management process to prepare the adoption of digital assistants at work.
  • Pioneering use of an explanation engine with a focus on on-the-job training.
Comment:
  • COALA highly involes in the Digital Innovation Hubs (DIHs) and other regional innovation infrastructures to address SMEs issues that may not be able to afford the education of their labor force, through COALA's didactic concept or related services.
  • A cost/benefit analysis will be included in the exploitation plan to verify the affordability for SMEs.
Comment:

Evaluation of the use case implementations will provide lessons learned and recommendations for further development and research of the COALA solution.

Comment:

The COALA vision for AI in manufacturing is the development of human-centered digital assistant that provides a more proactive and pragmatic approach to support operative situations characterized by cognitive load, time pressure, and little or zero tolerance for quality issues. COALA will help shaping the complementarity in the collaboration between the AI-based assistant and the human so that

  • the AI will take over time consuming and stressful tasks reliably and credibly, while
  • the human will focus on understanding and problem solving in complex, knowledge-intensive situations.

COALA’s AI-focused education and training concept will prepare the human-side of the collaboration by offering concept for teaching professionals systematically and, in the language of the workers, about the capabilities, risks, and limitations of AI in manufacturing. The COALA solution will transform how workers perform their jobs and it allows companies to maintain or increase the quality of their production processes and their products.

Result items:
Comment:

COALA will contribute in the ongoing discussion about AI ethics and potential standards, monitor the standardization potential for worker education under consideration of AI competencies, and will use and contribute to IT standards. We will take into account available IT-related standards and use them when applicable. This includes normative standards as set by ISO and its national bodies, which are of high importance to industrial companies. We will assess standards proposed by major influential de-facto standardization bodies like W3C, OASIS, and OMG. Standardization topics concern:

  • The technical system architecture level. Standardize communication protocols, data exchange formats, data exchange interfaces.
  • The content level. Ontologies for the semantic data integration and the WHY engine.
  • The process level. This includes innovation management system, terminology, terms and definitions, and tools and methods.
Comment:

COALA's three use cases (textile, white goods, liquid production) will evaluate the COALA solution in their manufacturing processes with significant economic value.

Comment:

The COALA solution will support the workers when performing process and product quality inspections with quality predictions and prescriptions for mitigation measures. The Augmented Manufacturing Analytics feature is A set of DIA functions interfacing the prescriptive quality analytics service connected to shop floor data sources. It will enable non-data-scientist workers to utilize and customize data analytics during product quality tests.

Comment:

COALA will contribute in the ongoing discussion about AI ethics and potential standards, monitor the standardization potential for worker education under consideration of AI competencies, and will use and contribute to IT standards. We will take into account available IT-related standards and use them when applicable. This includes normative standards as set by ISO and its national bodies, which are of high importance to industrial companies. We will assess standards proposed by major influential de-facto standardization bodies like W3C, OASIS, and OMG. Standardization topics concern:

  • The technical system architecture level. Standardize communication protocols, data exchange formats, data exchange interfaces.
  • The content level. Ontologies for the semantic data integration and the WHY engine.
  • The process level. This includes innovation management system, terminology, terms and definitions, and tools and methods.
Comment:

COALA solution for on-the-job-training using digital assistant allows the workers to meet the requirements of their work environtment: to be skilled and flexible. To keep up with continuously changing demands and to cope with altering product and process information, production workers need to adopt an approach that welcomes change. They need to be empowered by making customization transparent, enabling rapid adaptations, providing operational flexibility, and augmentation of their skills.

COALA aims to develop a cognitive advisor with dedicated human-assisted AI methods for enabling transfer of tacit knowledge of experts to novice workers. The transfer of tacit knowledge of production workers that grows with their experiences enabling them to cope with challenges of agile manufacturing.

For the change over time we expect a reduction of 15% to 30% by shortening the worker training time.

Comment:

COALA is expected to impact on the productivity of employees involved in the quality detection
and repair as well as on reduction of the not detected defects delivered.

Comment:
  • COALA solution aims to meet manufacturing requirements regarding, e.g. time criticality, reliability (e.g. deal with factory noise, number of defects), safety when giving advice to workers, and security in business environments.
  • The COALA didactic concept  will provide media materials, exercises, and competencies tests to teach factory workers and evaluate their learning progress regarding the opportunities, challenges (e.g. reliability, accuracy, and accountability of AI decisions), and risks (e.g. data security, ethical issues, and information quality) when working with AI-powered digital assistants.
Comment:
  • A Market Analysis (Value Chain analysis, competition evaluation, market segmentation and sizing, business expectations) will be performed to define a successful strategic positioning of COALA.
  • A Business Model and Plan will be developed in order to define a go-to-market strategy and demonstrate the financial interest.
Comment:

During the design and development of the COALA didactic concept, societal and environmental well-being is considered through impact analysis and the change of social skills of workers.

Comment:

COALA solution will support workers that need to use analytics tools and new workers that perform on-the-job training. Complementary to the technology, an education and training concept that focuses on building blue collar worker competencies in human-AI collaboration will be developed. The COALA solution will transform how workers perform their jobs and it allows companies to maintain or increase the quality of their production processes and their products.

Comment:

COALA solution is developed to consider following factore: factory noise, needed worker capabilities, variety of production machines, expected product and process quality, time-criticality, and worker safety. 

Comment:

COALA will contribute in the ongoing discussion about AI ethics and potential standards, monitor the standardization potential for worker education under consideration of AI competencies, and will use and contribute to IT standards. We will take into account available IT-related standards and use them when applicable. This includes normative standards as set by ISO and its national bodies, which are of high importance to industrial companies. We will assess standards proposed by major influential de-facto standardization bodies like W3C, OASIS, and OMG. Standardization topics concern:

  • The technical system architecture level. Standardize communication protocols, data exchange formats, data exchange interfaces.
  • The content level. Ontologies for the semantic data integration and the WHY engine.
  • The process level. This includes innovation management system, terminology, terms and definitions, and tools and methods.
Comment:

COALA calls for a new experience in human-machine collaboration: it fosters a vision of human-AI symbiosis in which AI augments its users instead of replacing and devaluing them. COALA aims to assist human reasoning, keep humans in decision making without directing them algorithmically.

Comment:

COALA will develop a Digital Intelligent Assistant (DIA) that can advise blue collar workers, such as machine operators and line managers, in complex, agile, and quality-focused discrete or process manufacturing industry. At this concern, human-centred interaction with the assistant needs to consider aspects such as the instant availability of information, support to problem solving of an adaptive system (e.g. context-awareness, internal information parsing, and impact evaluation).

Comment:

COALA has three application cases that cover discrete manufacturing and process manufacturing: White goods, Textile and Detergent production. Each case provides one challenge where a potential solution has a significant economic value. The cases are real production environments and they will provide 1) key requirements for the COALA solution and 2) performance indicators for its evaluation.