Periodic Reporting for period 1 - COALA (COgnitive Assisted agile manufacturing for a LAbor force supported by trustworthy Artificial Intelligence)
Reporting period: 2020-10-01 to 2022-03-31
Knowledge-intensive manufacturing processes require qualified employees. Their training is very time-consuming and cost-intensive for companies. The current shortage of skilled workers, for example in sectors like textile manufacturing, also makes this even more critical. Another competitive challenge for the manufacturing industry is the constantly shortening production cycles and the increasing variety of products.
COALA addresses these challenges through the innovative design and development of a human-centred and trustworthy voice enabled Digital Intelligent Assistant (DIA) for the manufacturing industry 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 1) the AI will take over time consuming and stressful tasks reliably and credibly, while 2) the human will focus on understanding and problem solving in complex, knowledge-intensive situations.
COALA AI-based digital intelligence solution will contribute to make manufacturing companies more sustainable through the best use of digital technologies and development of employees’ skills which allow companies to increase their production process’ and products’ quality.
COALA aims to improve social skills of workers through the development of an AI-focused education and training concept which will help the workers to build competencies in human-AI collaboration. We will develop a concept for teaching professionals systematically and, in the language of the workers, about the capabilities, risks, and limitations of AI in manufacturing. This will help the workers to improve their professional, personal, learning and methodological competences and strengthen their control and responsibility during the use of COALA solution. This will allow the companies shorten training time of new workers significantly.
COALA offers an AI-assistant on-the-job training for new workers, which will give advice and support novice operators in their learning and working activities while reconfiguring and operating production lines. The training approach focuses on introducing changes through the advice of and dialogue with the digital intelligent assistant resulting in measurable changes in operator’s behaviour.
COALA’s feature Augmented Manufacturing Analytics aims to support workers in tasks that formerly required expertise in data science. Digital voice assistants and chat bots interact intelligently with humans via natural language – these AI-driven programs guide their users through complex analytics processes. This will support the non-data scientist workers in performing knowledge intensive activities that have significant impact on product and process quality, e.g. in one of COALA application cases, allowing the workers to utilise and customise data analytics during product quality test.
The combination of all these efforts is expected to provide a positive economic impact of companies, among other in improvement of productivity of the workers and new workers, reduction in poor quality cost and hence the total quality failure costs.
COALA addresses these challenges through the innovative design and development of a human-centred and trustworthy voice enabled Digital Intelligent Assistant (DIA) for the manufacturing industry 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 1) the AI will take over time consuming and stressful tasks reliably and credibly, while 2) the human will focus on understanding and problem solving in complex, knowledge-intensive situations.
COALA AI-based digital intelligence solution will contribute to make manufacturing companies more sustainable through the best use of digital technologies and development of employees’ skills which allow companies to increase their production process’ and products’ quality.
COALA aims to improve social skills of workers through the development of an AI-focused education and training concept which will help the workers to build competencies in human-AI collaboration. We will develop a concept for teaching professionals systematically and, in the language of the workers, about the capabilities, risks, and limitations of AI in manufacturing. This will help the workers to improve their professional, personal, learning and methodological competences and strengthen their control and responsibility during the use of COALA solution. This will allow the companies shorten training time of new workers significantly.
COALA offers an AI-assistant on-the-job training for new workers, which will give advice and support novice operators in their learning and working activities while reconfiguring and operating production lines. The training approach focuses on introducing changes through the advice of and dialogue with the digital intelligent assistant resulting in measurable changes in operator’s behaviour.
COALA’s feature Augmented Manufacturing Analytics aims to support workers in tasks that formerly required expertise in data science. Digital voice assistants and chat bots interact intelligently with humans via natural language – these AI-driven programs guide their users through complex analytics processes. This will support the non-data scientist workers in performing knowledge intensive activities that have significant impact on product and process quality, e.g. in one of COALA application cases, allowing the workers to utilise and customise data analytics during product quality test.
The combination of all these efforts is expected to provide a positive economic impact of companies, among other in improvement of productivity of the workers and new workers, reduction in poor quality cost and hence the total quality failure costs.
The main technical development activities between M1 to M6 concentrated on the elicitation of the initial technical, social and ethics requirements and definition of use case scenarios of the three business cases (Textile, white goods and detergent productions). By M9, a COALA system architecture and its interfaces have been specified. A first prototype of the core demonstrator, COALA Digital Intelligent Assistant (DIA), and some key components/services, including the Prescriptive Quality Analytics and Cognitive Advisor services, were developed.
By M15, second prototype of DIA core demonstrator components was released, along with the first prototype of some other services such as Product Avatar and Data Collection service, Data Anonymisation service, Dialogue and Interface Localization service. The first principles for the Why Engine, as a new, experimental solution component that will allow the assistant to answer “why” questions have been identified. Accordingly, an integration and deployment plan of the COALA components/services into the COALA Solution was developed as well as testing and evaluation plan to verify the proper functioning and performance of the integrated COALA Solution was defined. In the non-technical aspects, a first version of the didactic concept for the factory workers as well as the initial change management approach have been developed.
By M18, the technical components are in their final configuration. A second prototype of the key components such as Prescriptive Quality Analytics, Cognitive Advisor and Dialog and interface localisation service have been developed. A first prototype of the Why Engine has been developed. Two COALA main demonstrators, (1) “DIA for Augmented Manufacturing Analytics” demonstrating implementation of the main components of COALA within the White Goods Production (Whirlpool) use case, and (2) “DIA for on-the-job factory worker training”, demonstrating implementation of the main components of COALA within the Detergent Production (Diversey) use case and Textile Production (CITTA/PIACENZA) use case, have been developed. An evaluation of use cases implementation has been defined and its first assessment results are available. In the evaluation framework, an involvement of regional innovation infrastructures, such as Digital Innovation Hubs (DIHs), is planned, since they play an important role in strengthening the AI competencies of workers, due to they support local companies in evaluating and using new technologies. Specific contributions to Europe’s AI communities in manufacturing have been defined and carried out. COALA presence at the AI4Europe Platform has been established. Last but not least, numerous activities on dissemination, communication, and exploitation have been active since the project started.
By M15, second prototype of DIA core demonstrator components was released, along with the first prototype of some other services such as Product Avatar and Data Collection service, Data Anonymisation service, Dialogue and Interface Localization service. The first principles for the Why Engine, as a new, experimental solution component that will allow the assistant to answer “why” questions have been identified. Accordingly, an integration and deployment plan of the COALA components/services into the COALA Solution was developed as well as testing and evaluation plan to verify the proper functioning and performance of the integrated COALA Solution was defined. In the non-technical aspects, a first version of the didactic concept for the factory workers as well as the initial change management approach have been developed.
By M18, the technical components are in their final configuration. A second prototype of the key components such as Prescriptive Quality Analytics, Cognitive Advisor and Dialog and interface localisation service have been developed. A first prototype of the Why Engine has been developed. Two COALA main demonstrators, (1) “DIA for Augmented Manufacturing Analytics” demonstrating implementation of the main components of COALA within the White Goods Production (Whirlpool) use case, and (2) “DIA for on-the-job factory worker training”, demonstrating implementation of the main components of COALA within the Detergent Production (Diversey) use case and Textile Production (CITTA/PIACENZA) use case, have been developed. An evaluation of use cases implementation has been defined and its first assessment results are available. In the evaluation framework, an involvement of regional innovation infrastructures, such as Digital Innovation Hubs (DIHs), is planned, since they play an important role in strengthening the AI competencies of workers, due to they support local companies in evaluating and using new technologies. Specific contributions to Europe’s AI communities in manufacturing have been defined and carried out. COALA presence at the AI4Europe Platform has been established. Last but not least, numerous activities on dissemination, communication, and exploitation have been active since the project started.
The expected impact of COALA on the industry will cover two major levers:
(1) Improvement of the production performance: agile production processes and improved quality of products and processes.
(2) Improvement of human integration in the production system of manufacturing companies through:
- Education and on-job training will help workers to improve their skills and AI-related competencies.
- Cognitive advisor, Prescriptive Quality Analytics, and Product Avatar functionalities will: guide workers to manage problems they face, reduce intrinsic and extraneous cognitive workloads and the resulting stress, increase individual and shared situation awareness,increase in speed of gaining situation awareness about issues at hand, and promote germane cognitive load by redirecting their attention to cognitive processes that are directly relevant to the construction of schemas (e.g. understanding of the functioning of the process, and factors impacting quality).
- The WHY engine will provide some explanations about the DIA predictions and advices to support decision and sense making, and to reassure workers on the way AI elaborates conclusions (trustworthy AI).
(1) Improvement of the production performance: agile production processes and improved quality of products and processes.
(2) Improvement of human integration in the production system of manufacturing companies through:
- Education and on-job training will help workers to improve their skills and AI-related competencies.
- Cognitive advisor, Prescriptive Quality Analytics, and Product Avatar functionalities will: guide workers to manage problems they face, reduce intrinsic and extraneous cognitive workloads and the resulting stress, increase individual and shared situation awareness,increase in speed of gaining situation awareness about issues at hand, and promote germane cognitive load by redirecting their attention to cognitive processes that are directly relevant to the construction of schemas (e.g. understanding of the functioning of the process, and factors impacting quality).
- The WHY engine will provide some explanations about the DIA predictions and advices to support decision and sense making, and to reassure workers on the way AI elaborates conclusions (trustworthy AI).