IAMM: A Maturity Model for Measuring Industrial Analytics Capabilities in Large-scale Manufacturing Facilities

Abstract

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility.

Digital readiness assessment of Italian SMEs: a case-study research

“…Generally, maturity and readiness models are limited to measuring a particular aspect of a domain. Even though multiple models can be aligned to facilitate assessments at a broader level, these multiple models can create challenges when different dimensions and levels exist (O’Donovan et al , 2016). The choice of assessment dimensions and their content is a crucial phase in the development of maturity models (Becker et al , 2009).…”

An Industry 4.0 maturity model proposal

“…In the center of all these emerging concepts and technologies are data, each time more precise and acquired in real time, allowing more data-driven decisions instead of those based on experience or feeling. Donovan et al (2016) assert that there are many challenges associated to the development of industrial analytical capacities, including the management of technologies and heterogeneous platforms, composition of multi-disciplinary teams, trainings and others. Some challenges are amplified when there are no methods to measure the level of current capability and strategically identify the areas that need improvements.…”

“…The methodology adopted for the development of the maturity model in this study was based on the process of maturity model development of De Bruin et al (2005), which is applicable to diverse knowledge domains, not restricted therefore, to the Industry 4.0 domain. This methodology was utilized by several authors in different domains, like, for example, Donovan et al (2016), for the Industrial Analytics Maturity Model, Mamoghli and Cassivi (2018) for the business processes support through human and IT factors, and Asdecker and Felch (2018) for the delivery process in supply chains.…”

“…Maturity models reflect aspects of the reality to classify capabilities of certain interest domains which can be used for internal analysis, competitor analysis and comparisons with the references in the domain (benchmark). These models typically contemplate dimensions and levels (Donovan et al, 2016).…”

“…The content of each dimension can be derived of qualitative research methods, including bibliographic reviews, case studies analysis, focal groups and other methodologies of idea generation and decision making, as the Delphi method (Donovan et al, 2016). The levels are ordinal labels that mean stages of maturity, while the dimensions represent specific capabilities of an interest domain.…”