Design and development of an effective sensing and measurement procedure for tasks in System of Systems Engineering Management in the agro-seed nurturing industry.

The metric system deployed in identifying, sensing and measuring the level of competitiveness associated with a production system such as the agro-seed processing industry, where grains are nurtured and developed, is largely non-objective due to the chain of embedded and interconnected non-metric qualitative tasks and activities. However, in this research the diverse pool of activities inherent in the agro-grain nurturing system of systems (SoSs) network of functional and operational activities and the associated management centric tasks referred to as the System of Systems Engineering Management (SoSEM) processes, were sensed, analysed and measured. This was done in a bid to quantitatively understand the performing levels of a range of dynamic variables, including the skill level of employees assigned to specific tasks and how all of these culminated into decision making on the level of the SoSs competitiveness on a percentile scale.

Traditionally, the procedures available for the identification, sensing and measurement of operational activities in system of subsystems (SoSubs) or system of systems (SoSs) are often limited to verbal articulations, physical observations, bench marking of tasks with desired targets at the end of the tasks, amongst others. In this research, a holistic and integrated framework depicting a SoSs network in the agricultural grain culturing industry was developed. Furthermore, a metric system for the identification, sensing and measurement of tasks performance, skillset rating of employees and overall quantitative evaluation of the SoSEM was presented.

The proposed solution was developed by first designing and architecting a holistic framework that depicts a homogeneous SoSs in the agro-grain industry. Following this, a metric system comprising the sensing and measurement systems was developed. This metric system was premised on a developed time variant quantitative approach, and a continuous weight assignment based on a prioritisation model that utilised dependency theory of the tasks.