Enhanced Data Driven Decision Support

As items are increasingly being equipped with sensors, the applicability of data driven decision support will similarly grow. This paper surveys an endeavor to support decisions with sensor recordings that were coincidentally available. To become meaningful decision support, these sensor recordings should enable better causal inferences because decisions are intended to cause the future. However, data driven decision support is not trivial as normative decision theory is known to suffer from validation issues. This work attempts to alleviate concerns about (i) the assessment of preference, (ii) causal inferences from non-experimental data and (iii) the assessment of the uncertainty about the prospective outcome of a decision. This work will demonstrate that sensor recordings indeed can provide appreciable decision support by presenting two typical cases of human recorded events that were enriched with sensor recordings. From these sensor recordings, prima facie causes and effects of a decision maker’s concern were inferred. These type of inferences may potentially have a considerable impact on conventional maintenance policy assessments following a reliability centered maintenance process. Reliability centered maintenance merely anticipates on the believed consequences of failures by scheduled inspections, overhauls or discards. As sensor recordings are efficiently collected at a high sampling rate, scheduling inspections may become superfluous. Sight on the prima facie causes of failures may enable a kind of proactive control of failures that has not been addressed in the decision logic of a reliability centered maintenance process