Industrial Data Analytics Framework

Context

There is a widespread belief in companies’ management that analytics offers value and represents a fundamental source of competitive advantage within their own industry. For those companies that lack available analytics insights in their systems the awareness that the potential hidden behind available data is not fully exploited generates thrust to the adoption of advanced information approaches. This craving for analytics is incentivised by the many success stories publicised by media. However, turning a world full of data into a data-driven world is an idea that many companies have found difficult to pull off in practice.

Idea

The consideration that many companies struggle to build roadmaps for analytics and to understand how best they can get value from it has prompted IMR to develop an analytics framework that would guide companies in defining sensible analytics strategies.

 

This analytics framework is being developed based on IMR past experience and the direct involvement of eight manufacturing companies based in Ireland.

 

The IDAF consortium comprises companies of different sizes (i.e. SMEs to multinational) operating in different sectors (i.e., medical device, food, mechanical engineering, electronics), thus giving a comprehensive account of the Irish manufacturing landscape.

 

Aim

The industrial data analytics framework aims at abstracting the process that IMR has done with many companies to produce a knowledge base that could be used as a self-service or guided tool for companies, and providing:

 

  • An assessment of current analytics capability
  • A review of projects and techniques that are relevant for the present capability/infrastructure/culture
  • What incremental steps could be taken to bring value through new data analysis capabilities

Challenge

The main barriers to the adoption of analytics are generally managerial in nature and they probably stem from the lack of understanding around where to start and how to manage an analytics project, which also translates into a lack of understanding on how to go about solving problems using data science and inherent analytics capabilities. This also creates scepticism around the real value of analytics, which is also deterring management from investing in analytics initiatives.

Other

Exemplars of analytics projects (i.e. case studies) have also been developed for each of the companies involved in IDAF to make them appreciate the potential value of analytics through practical implementations in settings that they are familiar with. The use cases are inspired by typical manufacturing problem statements that could be addressed by analytics solutions and prove the concept that even small investments in analytics can generate immediate and long-term benefits.

PROJECT MANAGER

Anna Rotondo
Senior Research Scientist – Optimisation

THEMATIC PILLAR

Digitisation

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