Systematic comparison of digital maturity assessment models

IF 4 Q2 ENGINEERING, INDUSTRIAL Journal of Industrial and Production Engineering Pub Date : 2023-08-17 DOI:10.1080/21681015.2023.2242340
B. Cognet, J. Pernot, L. Rivest, Christophe Danjou
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Abstract

ABSTRACT Assessing the digital maturity of companies is essential to prepare for digital transformation in the context of Industry 4.0. Several digital maturity assessment models have emerged in the past few years to support this evaluation. One obstacle for companies is the impossibility of easily comparing themselves to one another quantitatively or qualitatively. This paper introduces a new way to compare digital maturity models through a quantitative framework that is compatible with a wide variety of models. Comparisons are performed in the space of the keywords used to characterize key performance indicators (KPIs) that are reverse engineered from the models. The matches are encoded in a keyword matrix that is used to automatically compute the match level of KPI pairs. The framework has been validated on 13 state-of-the-art maturity models whose analysis resulted in the identification of 451 KPIs characterized using 263 keywords structured according to 12 dimensions and 58 subdimensions. Graphical Abstract
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数字成熟度评估模型的系统比较
评估企业的数字化成熟度对于为工业4.0背景下的数字化转型做好准备至关重要。在过去几年中出现了几种数字成熟度评估模型来支持这种评估。公司面临的一个障碍是不可能轻易地在数量或质量上与其他公司进行比较。本文介绍了一种新的方法来比较数字成熟度模型通过一个定量框架,是兼容各种各样的模型。在用于描述关键性能指标(kpi)的关键字的空间中执行比较,这些指标是从模型中逆向工程的。匹配被编码在关键字矩阵中,该矩阵用于自动计算KPI对的匹配级别。该框架已在13个最先进的成熟度模型上进行了验证,这些模型的分析结果确定了451个kpi,这些kpi使用263个关键词,根据12个维度和58个子维度构建。图形抽象
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CiteScore
7.50
自引率
6.70%
发文量
21
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