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How MTU Aero Engines has set the course for creating value from AI

October 26, 2021

A case study on AI strategy development

MTU CF34 MRO Ludwigsfelde

Initial situation

MTU Aero Engines is a leading aircraft engine sub-system provider and engages in the design, development, production, and support of aircraft engine modules in almost all thrust and power categories from propulsion systems for business jets to the world's most powerful commercial engines. Its core products include high-pressure compressors, low-pressure turbines, turbine center frames, and industrial gas turbines. Moreover, MTU is a leading provider of customized engine service solutions, and its maintenance, repair & overhaul (MRO) segment completes ~1000 shop visits per year for more than 30 different types of engines.

MTU employs more than 10,000 people at 15 locations worldwide. In the years ahead, MTU will focus its resources on its core business, seek stakes in emerging engine programs and expand its service offerings.

As an innovation leader, MTU recognized early on the vast potential of AI as a general enabling technology - with numerous possible areas for AI utilization identified and stand-alone AI pilots implemented in development, manufacturing, and repair processes. To capture the full value potential of AI, MTU has teamed up with appliedAI, Germany’s largest initiative for the application of AI technologies, to systematically develop a comprehensive AI strategy, encompassing all relevant dimensions: an AI ambition and use cases, the required enabling factors, and an execution framework for scaled AI application.

Project approach

To establish a joint baseline and prioritize fields of action, participants from various functional areas and hierarchical levels took part in the systematic appliedAI Maturity Assessment. Using the maturity assessment results, MTU and appliedAI were able to derive a data-driven representation of the status quo and to identify development potentials. They also had experts hold multiple structured interviews to gather supplementary data.

In parallel, appliedAI and MTU conducted an AI-focused analysis of the competitive environment, as the market landscape greatly influences the AI ambition: one has to understand the competitive advantage that AI brings and how it might impact the product and processes, business model, and the overall industry dynamics. The analysis revealed that players in the sector at upstream, core, and downstream value chain stages are massively increasing the use of AI and utilizing the technology at different levels of intensity in development, manufacturing, and maintenance processes. Moreover, the use of AI is already well established in adjacent high-tech industries in order to accelerate development simulations, improve quality control of components, and increase gas turbine control efficiency.

To form a coherent AI ambition for MTU, this market analysis was consolidated with MTU’s internal perspective, and an AI technology potential assessment performed by appliedAI.

Building upon a defined three to four year timeline and distinct strategic goals for the scale-up of AI, MTU and appliedAI determined three synergistic fields of application that provide the focus areas for the identification of AI use cases:

1. Product development (virtual engine and test support): acceleration of development times and utilization of development resources with the highest possible efficiency

2. Production (process simulation and automation, as well as work preparation, production control, and employee support): increase of production efficiency and improved accuracy in predicting distinct production process parameters' influences on product quality

3. Maintenance (digital inspection and advanced forecasting): greater customer proximity and better synchronization of internal planning processes

Based on the focus areas of the AI ambition, MTU and appliedAI conducted three use case workshops. For each department, use cases were systematically ideated and discussed first. Then, they were detailed along the dimensions of value contribution and ease of implementation, and lastly, they were thematically clustered together with department representatives and steering committee members. Each designated group then identified particularly promising use cases within their cluster to serve as potential lighthouse applications. Their underlying hypotheses will be confirmed and detailed before transferring the use cases into a proof-of-concept phase.

To sustainably anchor AI in the organization and to comprehensively utilize the value potential of the technology, MTU and appliedAI simultaneously determined a set of key enabling factors:

  • With regard to organizational and governance structures, a hybrid hub-and-spoke approach (with a centralized AI unit) was identified as particularly promising

  • To build up the required expertise, they developed thorough role profiles and discussed employee training as well as new recruitment strategies

  • They proposed first initiatives to begin the accompanying cultural transformation within the company

  • In workshop formats, they introduced the foundations for the development of a suitable AI infrastructure and data governance

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Project results and next steps

As a result of their AI strategy development project, MTU has established an AI Center of Excellence (AI CoE), which is considered part of their group-wide corporate development. The team aims to create a link between AI use cases and enabling factors (talent, infrastructure, organization, etc.) to ultimately develop the technology with long-lasting impact within the company. Therefore, the AI CoE will provide an overview of the possibilities of AI, advise MTU’s business units on potential AI use cases, and actively manage the existing portfolio of AI projects to support them during implementation and scaling.

The AI Center of Excellence will also enhance execution models, implement training concepts, and increase awareness of AI across the company through targeted communication measures. To complement the comprehensive internal activities, it will establish a network with relevant external stakeholders and join the appliedAI network, thus conjoining the vast wealth of experience of all partner companies.


Core success factors


Three main factors were responsible for the success of the project:

  • First, the project started with an introduction to artificial intelligence for all stakeholders. Often, a discussion about AI is held in organizations with no clear understanding of exactly what is behind the "buzzword"

  • Second, the various stakeholders from all relevant departments and divisions, including senior management, were involved in the entire project. This ensured that all areas relevant to the use of AI could be examined, and a joint understanding of the goal could be created

  • Third, the experiences of similar companies in the appliedAI network were incorporated into the project. This helped MTU to avoid common mistakes and to establish efficient structures based on the company’s AI maturity level

In partnership with

MTU Aero Engines

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