AIXPERIMENTATIONLAB — Presentation of the use cases
Towards an institutionalized format for the design, development, use and diffusion of human-centred artificial intelligence applications
Augmented Intelligence framework for practical AI applications
The effective and efficient development of AI applications requires a detailed description and articulation of customer and business needs, together with a deep understanding of the underlying processes for the target application. Within our research project “AIXPERIMENTATIONLAB”, the Augmented Intelligence framework serves as a blueprint for developing AI applications in three defined use cases for every research project company member respectively. In view of this, the framework strives for a viable compromise between acceptable accuracy of off-the-shelf algorithms and estimable technical feasibility.
If you missed the article on the motivation behind augmented intelligence, the Augmented Intelligence framework developed at WZL of RWTH Aachen and its use within the research project “AIXPERIMENTATIONLAB”, you can check it here.
The envisioned users and relevant stakeholders of each company are actively involved in the development phase in order to map the daily activities by gathering information on user requirements, wishes and concerns. Intensive participation hence ensures a high degree of usability and acceptance, which in turn increases the effectiveness and efficiency of the later application. The proposed use cases are briefly presented and explained below.
Presentation of the use cases and companies
Companies are experiencing the challenges of the ever-increasing amounts of internally and externally generated data related to their products, activities and processes. Likewise, rising customer expectations, shorter response times and demanding market conditions pose an additional challenge for business operations. These circumstances put a strain on employees, especially when confronted with short-term decision-making situations, which lead to stress and defensive reactions among them. Particularly in the area of customer service, employees are constantly exposed to situations which require decision-making under time and uncertainty constraints. The research project “AIXPERIMENTATIONLAB” aims at addressing precisely these challenges within the project member companies. The main goal consists in developing human-centred AI applications for decision support to improve the employees’ work situation through efficiency gains during task completion, activity complexity reduction, and ultimately stress reduction. One of the main research interests within the project is thus the efficient interface design between the AI system and humans.
Three companies from different industry branches comprise the project consortium on the user side. The use cases were developed together with the companies through a series of workshops. While every defined case addresses a different business challenge, they all focus mainly on customer service and sales operations.
Use case 1: Request for tender and offer preparation — Aumüller Automatic GmbH
Aumüller GmbH is an internationally acting family-owned business with headquarters in Thierhaupten, Germany. Since its foundation in 1972, the company has specialized in the commercialization of solutions for buildings. The company’s core competencies include smoke and heat extraction systems as well as energy-efficient, natural ventilation systems. Aumüller’s systems are installed in buildings of various sizes and uses. Individual and innovative solutions are developed and implemented in cooperation with architects, building owners and specialist planners.
Aumüller usually receives orders and tender requests that vary in volume and degree of complexity, sometimes also with ambivalent technical specifications. Hence, the preparation of quotations and customer management can represent a challenging endeavour in daily operations. Particularly the analysis of project tenders and the subsequent preparation of quotations can be very time-consuming depending on the number of specifications.
The solution proposed for the above-mentioned situation consists in a matching tool of project tender requests and suitable products. The solution automatically identifies the requested specifications and matches them with existing products in the company’s product database. In this way, employees can process the request faster, and more context-specific information can be forwarded to support technical specification development.
Use case 2: Order assistant for assembly coordination deparment — Heim und Haus Bauelemente Produktionsgesellschaft mbH
Heim und Haus is a direct sales company for exclusive building elements such as awnings, windows and doors. The family-owned company is one of the largest manufacturers of its kind on the German market with over 1.4 million customers. The individual in-house production of the offered elements accounts for the company’s core business competence. For on-site assembly at the customer’s premises, Heim und Haus commissions independent assembly partners, most of whom have been working with the company for many years.
A major operational challenge is the daily balancing and coordination between built-to-order production, distribution warehouses and the service provided by third-party suppliers. This situation requires a constant striving for an optimal distribution of resources across the three business fronts. In addition, efficient warehouse utilisation by third-party suppliers and timely fulfilment of customer orders in the face of globally fluctuating supply chains represent highly complex planning tasks.
The envisioned solution involves the development of an sorting algorithm to assist the assembly coordination department in prioritising customer orders that may be affected by operational planning fluctuations. Although the company’s database contains all relevant key figures in a well-structured manner, the information overflow resulting from the increase in orders poses a challenge for the assembly coordination department to achieve an optimal task distribution at the aforementioned fronts of production, warehouse and third-party suppliers. By automatically delegating the prioritisation of customer orders with potential for delay to the sorting algorithm, the assembly coordination department can better plan their activities and the use of resources to ensure the delivery of orders.
Use case 3: Technical customer support — GRÜN aixtema GmbH
For more than 25 years, GRÜN aixtema GmbH has been providing after-sales support and service for manufacturers in the IT, communications and electronics industries. A technical hotline provides support for questions, advisory, complaint and return handling in seven languages. In addition, electronic devices are repaired and overhauled in a self-managed repair centre.
The daily business of GRÜN aixtema is characterised by a large volume of incoming customer enquiries. Fast response times by mail and telephone are key for an efficient day-to-day operation, and the number of processed orders is determinant for customer satisfaction. Due to a growing customer base and product portfolio, the content of customer enquiries increase in variance over time, which poses a major challenge for steady response and processing times. As a consequence, employees are faced with more complex tasks. This is a major challenge, especially for new team members who are still in the onboarding phase.
To address this problem, the internal, well-structured knowledge management system of the company is leveraged with text classification algorithms. Text classification enables the creation of clear, well-structured problem formulations. These formulations are then linked with the internal knowledge management system to provide the employee with a fast matching answer to the problem in question. In this way, searching and problem-solving times are significantly reduced, and customer support enquiries can be answered more quickly and in a more targeted manner, resulting in greater efficiency. Moreover, the matching system works as a guide tool for accelerating onboarding of new employees.
User interface between humans and AI
The presented use cases intend to highlight the importance of designing AI applications around human needs taking into account practical daily business problems and human cognition. During the development of the solutions, intensive participation supported by a variety of methods for the development of human-machine interfaces are planned. As mentioned before, a core research aspect of the project is the role of human involvement in the use of AI applications as a determinant factor for the sustainable implementation of these technologies. Stay tuned for upcoming project updates!
The research project AIXPERIMENTATIONLAB runs until the end of 2023 and is funded by the Federal Ministry of Labour and Social Affairs (BMAS) as part of the funding programme “Future-oriented companies and administrations in the digital transformation (room for learning and experimental AI)” — EXP.01.00016.20.
For more information please visit the project’s official website (not optimized for mobile, in German only).
