## 5 Discussion and Conclusions
In general, ML increases the \"clock speed\" [83] of an organization, and the decision structures must keep pace, for example, through decentralization that tends to reduce the number of levels a decision has to pass through (reducing delays). A further implication of bottlenecks preventing subsystem improvements cascading to the system level--discovered through a synthetic rather than an analytical view of performance--is how it provides a valuable frame for questions about the value of data. In case additional data (costly to acquire and process) push the system to an operating point that surfaces limiting constraints fixed in the short term (e.g., physical assets or leadtimes), it puts a cap on benefits. This, in turn, helps ascertain the value of data collection efforts. From a more technical standpoint, a systems lens strengthens the argument for a reasonable statistical baseline before attempting ML methods that usually require many predictors and complex nonlinear relationships (between predictors and the target variable) for their superior performance [84]. Treating data as instrumental to value (and not valuable in themselves) is a position that follows naturally from the causal modeling approach that is the bedrock of system dynamics simulation. Thus, the importance given to the data-generating process aligns with the position of the causal inference research community (gaining wider acceptance) that espouses the need for good explanations. In [PERSON]'s words, \"empiricism should be balanced with the principles of model-based science\" [62]. One can surmise the upshot of this from our simulations. By situating the forecasting process in the context of the end-to-end order-to-delivery process chain, the model makes prioritizing aspects of the explanation possible. For instance, one can focus on predictions that most impinge outcomes and pose \"why\" questions (see Figure 16) to understand if they are representative or a product of anomalous inputs. In this way, the proposed modeling approach contributes to the explanatory AI work by identifying what the \"completeness\" criterion (for evaluating explanations) must entail. **Author Contributions:** Conceptualization, G.S. ([PERSON]); Methodology, G.S. ([PERSON]); Software, G.S. ([PERSON]); Validation, G.S. ([PERSON]) and M.A.P. ; Formal analysis, G.S. ([PERSON]); Investigation, G.S. ([PERSON]); Data curation, G.S. ([PERSON]); Writing--original draft, G.S. ([PERSON]); Writing--review & editing, G.S. ([PERSON]), M.A.P. and G.S. ([PERSON]); Visualization, G.S. ([PERSON]); Supervision, M.A.P., M.K. and G.S. ([PERSON]); Project administration, M.A.P. ; Funding acquisition, G.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript. Figure 16: Causal modeling engenders asking relevant “why” questions to make algorithmic rationality less opaque. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The ML code, system dynamics simulation files, and data are available on GitHub under: [https://anonymous.4 open.science/r/aicollab-model-C108](https://anonymous.4 open.science/r/aicollab-model-C108). **Conflicts of Interest:** The authors declare no conflict of interest. **Credits:** The \"Excel\" icon on pages 8 and 9 is by [PERSON], the \"Mind\" icon on page 8 is by [PERSON], the \"Decision\" icon on page 8 is by [PERSON], and the \"Simulation Computer\" icon on page 8 is by [PERSON]; they were all sourced from thenounproject.com. **Appendix A**  
**Table A1. ** System dynamics parameters.