# Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case
## 2 Literature Review  
The digital transformation in manufacturing mandates a transition of manufacturing business models from \"bolts\" to \"bits\", as mentioned in [7], where a framework for measuring the digital transformation of manufacturing industries is presented. In particular, the application of cyber-physical features in production systems holds the promise of enabling a smarter production paradigm with higher productivity, efficiency, and self-managing production processes allowing for innovative control and increased levels of automation, as highlighted in [8; 9]. Technologies such as cloud services and resource virtualization are considered important drivers [10] along with low-code development platforms [11]. Such a digital transition requires as well standardized implementation protocols and more important a realistic view of return on investment as highlighted in [12]. Recently, the COVID-19 pandemic has influenced dramatically the daily operations of manufacturing firms [13] mandating them to drastically increase their efforts in their digital transformation. Five industrial case studies showcasing the use of advanced information and operational technologies in manufacturing are presented in [14], demonstrating the increased automated support because of increased automation levels. Although the pandemic has intensified the need for manufacturers to incorporate Industry4.0 technologies, the barriers to this happening are still present. However, the capability of a firm to pursue and accomplish its digital transformation is dependent on its capabilities and dynamic management profile. The authors in [15] propose a systematic model for SMEs to better understand their capabilities and digital maturity and identify their competence elements. In a similar approach, a framework for the digital transformation of SMEs is provided [16]. The framework is also suggested for benchmarking the capabilities relevant to the digital transformation of SMEs. Considering that manufacturing companies including SMEs need digital solutions to demonstrate increasing levels of resiliency in today's market demands as well as disturbances, such as the COVID-19 pandemic, novel solutions for production planning are of major importance to improve their flexibility and adaptability. A literature review on production scheduling in Industry 4.0 is presented in [17] including the review of 53 papers. A conceptual framework for smart production planning and control in Industry 4.0 is discussed in [18], including scheduling, job sequencing, controlling and data management. A model for integrated planning and scheduling of engineer-to-order products is presented in [19] using a mixed-integer programming formulation. The results indicate a large computing time is required for large sized-instances. Also, a mixed integer linear programming extended with a Lagrange relation method is used [20] for energy-efficient scheduling under time-of-use pricing. The integration of industrial IoT with operational technologies to support the dynamic scheduling and rescheduling operations in a personalized production environment is proposed in [21]. Such approaches can enable data-driven decision-making and control facilitating the adoption of proactive strategies for a resilient and sustainable production system [22]. Regarding the knowledge that IoT data can reveal, and that traditional scheduling methods ignore, [23] propose a cyber-physical architecture for dynamic scheduling using reinforcement learning techniques. Also in that direction, a context-aware scheduling and control architecture is proposed by [24] and [25]. A manufacturing ontology is adopted to formalize raw data into structured information while rule-based control approaches are being followed in both. A Markov decision-making process for scheduling in semiconductor fabrication is employed in [26], while a control algorithm based on Kalman filters is discussed in [27] for non-stationary batch service production, with promising results.