## 1 Introduction  
Recent market requirements, emerging technologies, and process autonomy have influenced the technological landscape of the manufacturing industry [1] and the business environment [2]. The ongoing digital transformation in the industry necessitates strategic guidelines for implementation, particularly when the goal is to simultaneously reduce costs, enhance quality, and promote innovation [3]. Such a type of transformation calls for new information technologies (IT) architectures capable of handling large data volumes [4]. Common barriers to implementation include, but are not limited to, a lack of knowledge, inability and uncertainty to quantify the return on investment, and a shortage of a skilled workforce [1]. The term Industry 4.0 was coined in 2011 at the Hannover Fair and is commonly associated with digital manufacturing systems [5]. Diverse perspectives exist regarding specific requirements [6] and specific roadmaps for Industry 4.0 implementation, encompassing equipment, products, new competencies, and smart systems [7]. Due to recent advances and disruptions, Industry 4.0 implementation remains an ongoing concern for scholars and practitioners, necessitating further studies, particularly on how to integrate digital transformation into existing processes [5]. Previous studies pinpoint challenges within Industry 4.0 implementations. One challenge involves integrating the workforce with technology [8]. A smart factory, constituting an integrated manufacturing system, necessitates skilled personnel for equipment maintenance and process optimization [9]. Another challenge is vertically integrating strategic systems, like manufacturing execution systems (MES), with shop-floor necessities, including automation, data collection, processing, and supervisory systems [4]. No generic roadmap exists for guiding Industry 4.0 implementations: a roadmap should be crafted for each company and problem type, considering competencies, motivations, capacities, intentions, objectives, priorities, and available budgets [5]. The author suggests adapting general implementation models to guide diverse businesses or industries. Nonetheless, the recent literature shows some attempts to try to generalize and standardize the adoption of Industry 4.0-based tools. For instance, [PERSON]. [10] proposed a general-purpose and Industry 4.0-based architecture to enable smart factories, with a focus on the smart operator. Furthermore, some other attempts, such as [11; 12], offer interesting perspectives in providing guidelines to future implementations. This study aims to address this research gap by proposing a general roadmap adaptable to specific Industry 4.0 implementation cases. The research question is, \"How can a manufacturing company implement smart, digital control systems in an existing, operational manufacturing system?\" The research method is design science research. An example to illustrate the method includes a roadmap to transition to a smart statistical process control (SSPC), departing from a conventional statistical process control (SPC) as usually observed in manufacturing systems. There are some key differences between traditional SPCs and SSPCs. In SPCs, operators manually collect and analyze data at intervals, using statistical methods to control the process. On the other hand, SSPCs use real-time, automated data collection and analyses [13]. SPCs may have limited integration with other systems, whereas SSPCs can be a part of a larger Industrial Internet of Things (IIoT) ecosystem. In SPCs, any changes may require manual adjustments, but in SSPCs, machine-learning algorithms and artificial intelligence can dynamically adjust control parameters based on changing process conditions [14]. Finally, SPCs primarily focus on monitoring and controlling the current state of the process based on historical data, while SSPCs include predictive and forecasting capabilities.