## 5 Conclusions  
The papers reviewed are not intended to represent an exhaustive list of all existing research on the subject. However, it is believed that the reviewed examples do provide a representative sample of the sort of research currently performed in this discipline. A conclusion that may be drawn from the first general observation in Section 4 is that there is value in having a standard set of terminology when speaking about the big data environment in order to distinguish when one is referring to organizational capabilities or individual competencies. In the reviewed literature, terms like 'framework' or 'data collection' carried wide variance in their meaning depending on the context. It is likely that standard terms will be settled on over time, either bottom-up from common use or top-down from professional organizations in industry, academia, or government. At this point, it may suffice simply to be aware of the different contexts in which the topic may be broached. Attention to detail is always a good rule of thumb in any endeavor, and that may be a good temporary solution for now. A second conclusion, following from the second general observation in Section 4, is that a generalized approach to provide clarity as to what input data is valuable and what input data is not valuable, perhaps with both a quantitative and qualitative dimension, can shape analysis decisions in the big-data environment. Those decisions might be localized to the problem of interest, as in deciding which features to include in the model. Those decisions might also extend to larger, resource-oriented decisions, such as start-up priorities for transitioning from a legacy manufacturing facility to a CE. From a knowledge management standpoint, there is value in building institutional knowledge regarding features that perform poorly as well as features that perform well. Knowing which features tend to habitually appear in good solutions and which features habitually appear in bad solutions, if such knowledge exists, would be tremendously helpful in long term data capture and storage decisions. Finally, the third general observation in Section 4 lends itself to the conclusion that there is room for additional research into practical means for feature filtration and prioritization. On the surface, there appears to be no reason why the single-layer filtration techniques employed in the reviewed articles cannot be extended into a series of hierarchical filters. One possible limitation would be that several of the techniques employ similarly-themed filters that may produce only limited improvement when performed in sequence. For example, filtering once by [PERSON]'s kappa and then by testing for statistical independence might not produce substantial improvement. However, following the initial filtration by way of Cohen's kappa with an optimization heuristic such as simulated annealing or genetic algorithm to find an optimal or near-optimal subset of features might be a promising avenue to explore. It is also worth exploring, from a knowledge management standpoint, feature reduction and selection techniques that preserve as much interpretability as possible. It has already been discussed that PCA is a common approach, but the reduction in dimensions from M to K, where K \(<\) M, will necessarily take away the physical meaning from those K features. Techniques in feature reduction that preserve the nature of the original features are a value-added contribution to this question. In closing, manufacturing in the 21 st century is a highly competitive enterprise, and the business value in exploiting 21 st century technologies in smart manufacturing, IIOT, Industry 4.0, and the CE cannot be overstated. At the core of this opportunity for the individual manufacturer is the untapped potential held in the volumes of smart manufacturing data collected and stored in its data repositories.