This paper investigates digital traceability technologies taking careful consideration of the company’s needs to improve the traceability of products at the production of GPV Group as well as the efficiency and added value in their production cycles. GPV is primarily an electronics manufacturing service company (EMS) that manufactures electronic circuit boards, in addition to big metal products at their mechanics manufacturing sites. The company aims to embrace the next generation IoT technologies such as digital traceability in their internal supply chain at manufacturing sites in order to stay compatible with the Industry 4.0 requirements. In this paper, the capabilities of suitable digital traceability technologies are screened together with the actual GPV needs to determine if deployment of such technologies would benefit GPV shop floor operations and can solve the issues they face due to a lack of traceability. The traceability term refers to tracking the geolocation of products throughout the manufacturing steps and how that functionality can foster further optimization of the manufacturing processes. The paper focuses on comparing different IoT technologies and analyze their positive and negative attributes to identify a suitable technological solution for product traceability in the metal manufacturing industry. Finally, the paper proposes a suitable implementation road map for GPV, which can also be adopted from other metal manufacturing industries to deploy Industry 4.0 traceability at shop floor level.

Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

The use of digital technologies such as Internet of Things and advanced data analytics are central in digitally transforming manufacturing companies towards Industry 4.0. Success cases are frequently reported, and there is clear evidence of technology interventions conducted by industry. However, measuring the impact and effect of such interventions on digital maturity and on the organizational adoption can be challenging. Therefore, the research aim of this paper is to explore how the combination of the different methods of Industrial Internet Playground (IIP) pilots, Shadow Infrastructure (SI) and digital maturity assessment can assist in conducting and documenting the technical, as well as organisational, impact of digital interventions. Through an elaborate literature review of existing digital maturity assessment tools and key dimensions in digital transformation, we have developed a digital maturity assessment tool (DMAT), which is presented and applied in the paper to identify digital development areas and to evaluate and document the effects of digital interventions. Thus, the paper contributes with new knowledge of how the IIP pilot and SI combined with digital maturity assessment can support effective, transparent and documented digital transformation throughout an organisation, as explored through theory and a practice case.

Precisely estimating unidentified variables in photovoltaic (PV) cells/panels models is imperative to accurately evaluate solar cells/panels conversion performance to assist the sustainability, technology, and business aspects of societal benefits. With this motivation, in this paper, the Mountain Gazelle Optimiser (MGO) metaheuristic algorithm is proposed for the first time to discern the parameters of the various four standard benchmark photovoltaic cell/panel data sets, namely PV cell: amorphous silicon and monocrystalline silicon and PV panels: Sharp ND-R250A5, and PVM 752 GaAs. Evaluated performance analysis with root mean squared error (RMSE) using the prominently considered photovoltaic cell/panel models associated with the SDM (single diode model), DDM (Double Diode Model) and TDM (Triple Diode Model). This paper examines the subsequent performance effectiveness of the Mountain Gazelle Optimiser (MGO) for the Photovoltaic (PV) system application to identify five parameters (SDM), seven parameters (DDM), and nine parameters (TDM) in comparison with standard deterministic methods and other considered metaheuristic optimisation algorithms. According to the evaluation of the MGO performance in two solar cells/panels, extraction of SDM and DDM model parameters, MGO achieves excellent optimal parameters with minimal root mean square error (RMSE) of the amorphous silicon PV cell (SDM: 4.612322E−05, DDM: 4.094400E−05and TDM: 4.094395E−05), monocrystalline silicon cell (SDM: 5.630971E−0, DDM: 3.796747E−04, and TDM: 3.069266E−04), Sharp ND-R250A5 panel (SDM: 1.124464E−02, DDM: 1.1244639E−02, and TDM: 1.124468E−02) and panel PVM 752 GaAs (SDM: 2.278462E−04, DDM: 1.657951E−04, and TDM: 1.662235E−04) compared to other optimisation algorithms considered in the literature showing improvement in RMSE and competing performance. Furthermore, the Population sizes and iterations based on ablation sensitivity analysis, along with the statistical test-based validation and different fitness function experimental results, proved the superiority of the proposed work over the other existing works in the literature. The research is novel and informative, with the experimental results and statistical analysis providing insight into the technology and business development aspects of PV systems.