The realization of digitalization in production companies – currently also referred to as Industry

4.0 – aims for reduction of internal value creation costs as well as costs for intercompany

collaboration and plays a key role in their current strategy development. However, related

strategy research still lacks to provide operationalized digitalization methods and tools to

practitioners with scientific rigor as well as real-world relevance. To challenge this status

quo, we present a scientifically grounded 14-step procedure model including 11 practically

tested tools, developed specifically for real-world application. The model leads practitioners

from their first contact with industrial digitalization, through the maturity assessment of

143 digitalization items, until the implementation of a KPI-monitoring system and a continuous

improvement process. We applied and re-worked the procedure model during three

years of application. Validation and Feedback from practitioners and scholars indicate, that

the model drives strategy development towards objective and data-based decision making

and increases stakeholder engagement in organizations considerably.

The main goal of this paper is to propose the probabilistic description of cyclical (business) fluctuations. We generalize a fixed deterministic cycle model by incorporating the time-varying amplitude. More specifically, we assume that the mean function of cyclical fluctuations depends on unknown frequencies (related to the lengths of the cyclical fluctuations) in a similar way to the almost periodic mean function in a fixed deterministic cycle, while the assumption concerning constant amplitude is relaxed. We assume that the amplitude associated with a given frequency is time-varying and is a spline function. Finally, using a Bayesian approach and under standard prior assumptions, we obtain the explicit marginal posterior distribution for the vector of frequency parameters. In our empirical analysis, we consider the monthly industrial production in most European countries. Based on the highest marginal data density value, we choose the best model to describe the considered growth cycle. In most cases, data support the model with a time-varying amplitude. In addition, the expectation of the posterior distribution of the deterministic cycle for the considered growth cycles has similar dynamics to cycles extracted by standard bandpass filtration methods.