Reflexion

The Reflexion project aims to support the emerging high-tech industry paradigm shift from selling ‘boxes’ to supporting ‘integrated solutions’ by using operational data to improve the development lifecycles, maintenance and troubleshooting of products. This leads to a faster time to market, lower costs and greater competitiveness.

Reflexion’s objectives are:

to react to unforeseen problems or emerging needs in a speedy and cost-effective way by augmenting products with an introspective layer of data sensing and data analytics;

thereby creating value out of the high-tech system’s operational data, which is (for now) still characterized by legacy choices on infrastructural and analytical approaches; and

to (automatically) propagate this knowledge back into the product development lifecycle and the service and maintenance flow.

In order to attain/maintain (global) leading positions in today’s globalized competitive markets, OEMs need to bring the latest technology products to market with a highly efficient time to market, limited costs and a controlled quality level. There are a number of challenges and opportunities for the Reflexion project:

• A better understanding of a customer’s needs, way of working and operational context will enable a focus on R&D investments.

• Early market introduction leads to a better competitive positioning. Note: only the top three players find success; however, this is only true when the market introduction meets adequate quality criteria.

• The industrial costs of poor quality sometimes amount to up to 5% of total turnover. Typically, more than 20% of R&D budgets is spent on resolving quality-related problems.

• There is a strong industrial need to employ scarce human resources to the R&D activities that generate the most business impact.

• Access to the OEMs’ development processes directly translates into new primary business capabilities, such as by creating opportunities to develop new tool functionalities and services for an as-of-yet underexplored area of application.

• The possibility to incubate and validate proof-of-concepts for (general purpose) tool functionality and services in an industrial context.

• Improving the visibility of the OEM industry in terms of advancing the tool and service providers’ market penetration.

To address the above challenges, high-tech systems companies must take the next step in integrating operational data into a product’s development lifecycle.

ESI is developing a generic methodology that integrates domain knowledge and data into a system-level reasoning framework. As shown in the above figure, ESI uses domain-specific languages for knowledge engineering in order to systematically model domain knowledge, which is often scattered across documents or in the heads of experts. For data-driven business applications, knowledge engineering supports the effective analysis of operational data by applying (for example) feature selection or constraints in learning algorithms. The exploitation of profiling, process and data mining techniques allow for the generation of context-specific operational models that can either support the automated testing or customization of system operations (among other things) or be used to develop new systems.

The Reflexion project understands that the main challenges are valorizing the emerging operational data in high-tech systems and reacting to emerging needs in an efficient and cost-effective way by augmenting products with an introspective layer of data sensing and data analytics. 

The lessons learned include:

• The exploitation of a high-tech system’s operational data is characterized to a high degree by legacy engineering choices in infrastructural and analytical approaches.

• The high-tech industry (R&D) still has an immature view on the exploitation of data science for the valorization of operational data. Building up an awareness of how the integration of data into smart engineering processes can be a valuable and competitive business asset is currently more important than building up expertise.

• The focus should be on bridging the world of data science with the world of high-tech systems development by exploiting data analytics expertise/techniques to extract value from existing operational system logging.

• High-tech systems are quite diverse: there are little to no out-of-the-box generic (data science) approaches; in reality, serious domain insights/modeling are required, mixed with real system understanding and craftmanship.

• 4 conference papers

• 7 invited talks

• 4 articles

• 7 workshops

• 7 master theses

• 7 demos at innovation markets
  

• ITEA OXILATE project

• Secured 25 data science specialist positions for industry

• ITEA Award of Excellence 2019

• EUREKA Award 2019