AIMO

About AIMO

AI-assisted Tools for Modular Product Strategy Design and Implementation

With approaches from AI and modularization, the main objective of AIMO is to develop tools and techniques that help small and medium-sized companies optimize decision-making in product development processes. Having relevant knowledge and tools at hand increases the prerequisites for creating more sustainable and competitive businesses.

A core task in the project is to create a life cycle overview of the financial consequences of various product and process decisions in the design phase of products. Possessing this information helps companies make strategic decisions on a more informed basis. In the project, valuable knowledge becomes available in daily decision-making through a state-of-the-art model for variant costing that is integrated into the IT systems of four case companies. 

During the project, the four case companies will gain valuable insight into and better understanding of their products and the products’ life cycles, as well as of their IT systems, value chains and modular potential. In close collaboration with the case companies, the AIMO project team will identify the needs and opportunities of each organization and incorporate the tools and techniques developed in the project’s initial phase in the companies’ IT systems and business processes.

Subsequently, the most prominent findings from the project will become publicly available for the benefit of other Danish companies with an interest in business optimization, sustainability, and modularity.

Funding
AIMO is co-funded by Danish Industry Foundation and Thomas B. Thriges Fond.

Execution
AIMO is executed by Technical University of Denmark (DTU) and Aarhus University (AU).

Duration
AIMO was launched in 2022 and is to conclude in 2025.

Ownership
AIMO is owned and supported by NEM.

Project priorities

To develop methods for estimating variant costs for life cycle costs across the value chain and at different product levels.

To promote sustainability and suggest superior alternatives in the product design phase using AI techniques.

To implement and test prototypes of design assistance tools in collaboration with four case companies.

Project phases

Phase 1: Developing methods and models that promote the project objectives
  • Design and implementation of product and process architectures and considerations on how to design basic structures of products, which are optimal in relation to processes and recycling.
  • Variant cost estimation principles for life-cycle costs across the value chain and at different product levels – e.g., what is the value-chain and life-cycle cost of a given product if choosing between variant A or variant B in the design process.
  • Variant cost modeling in IT systems.
  • Methods for pattern recognition, classification, and segmentation of parts, modules, and products from digital product drawings.
  • Using deep learning to determine relationships in 2D digital product drawings, generate 3D product drawings from 2D, and provide recommendations to product engineers in the design phase.
Phase 2: Implementing the methods and models in the four case companies
  • Using a framework tool for estimating life cycle costs for one or more product lines in the company.
  • Implementing a model that integrates the above-mentioned cost model into the specific IT systems found in the case companies.
  • Testing methods to summarize life cycle costs of product based on 2D product drawings and the costs of each individual component.
  • Comparing efficiency levels.
  • Testing methods to produce more cost-effective alternatives to products or parts by comparing with similar designs or with the company’s existing portfolio of parts.
Phase 3: Communicating the project findings to broader audiences

The communication efforts in the final phase will, among other topics, consider the following questions:

  • How do the economic and competitiveness results from the project land?
  • What application possibilities are available through the project?
  • How are efficient solutions implemented and what does the investment case look like regarding costs and benefits?
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