Background: Reliable access to basic services can improve a community's resilience to HIV/AIDS. Accordingly, work is being done to upgrade the physical infrastructure in affected areas, often employing a strategy of decentralised service provision. Spatial characteristics are one of the major determinants in implementing services, even in the smaller municipal areas, and good quality spatial information is needed to inform decision making processes. However, limited funds, technical infrastructure and human resource capacity result in little or no access to spatial information for crucial infrastructure development decisions at local level.

Industrial manufacturing companies face the challenge of adapting to increasingly complex demands, especially with the influx of online ordering. One case of this is in the underperformance of end-effectors, limiting the adaptability of robotic arms in manufacturing functionality. To create proper gripper adaptability, intelligent gripper design is required to improve the sensibility and processing capability of the end-effectors. This will allow for grippers to perform effective decision-making and optimize production. This paper suggests a methodology that includes a step-by-step design process for an intelligent gripper and discusses how to develop intelligence utilizing key components of Industry 4.0 (Internet of Things, machine learning, and cloud manufacturing). This method was analyzed in a case study of a low-level intelligent vacuum gripper design. The methodology will be beneficial to intelligent gripper design from multiple levels of intelligence, creating a guide for engineers to follow to effectively design intelligent gripper solutions for their systems.