Simplifying Iterations in Cross-Functional Design Decision Making

1997 ◽  
Vol 119 (4) ◽  
pp. 485-493 ◽  
Author(s):  
V. Krishnan ◽  
S. D. Eppinger ◽  
D. E. Whitney

In this paper, we consider the cross-functional design decision making process and discuss how sequential decision making leads to a degradation in design quality even when downstream design tasks are not rendered infeasible by preceding upstream decisions. We focus on the problem of simplifying the design iterations required to address this quality loss. Two properties, called sequence invariance and task invariance, are introduced to help reduce the complexity of subsequent design iterations. We also discuss how these properties may be used by designers in situations where mathematical descriptions of the design performance characteristics are unavailable. We illustrate the utility of these properties by showing their applicability to the design of catalytic converter diagnostic systems at a major U.S. automotive firm.

Author(s):  
Herbert C. Puscheck ◽  
James H. Greene

A two-sided wargame simulation and four decision making models to play one side of the game were developed. The game and models were used to study the decision making process exhibited by 64 students at the U.S. Military Academy. It was concluded that these students utilized a simple strategy; decisions were unaffected, within the range indicated by opponent decision delays; students displayed a learning effect during the game; there existed a positive correlation between mean decision time and score; academically lower ranking students received higher scores than higher ranking players; and players received higher scores when opposing certain more sophisticated opponents than when opposing selected simpler models. The results are discussed. The wargame and associated decision making models were run on a GE-225 computer from remote Teletype terminals. The investigation suggests a number of additional applications for the wargame and decision making models.


2019 ◽  
Vol 61 (4) ◽  
pp. 66-83 ◽  
Author(s):  
Yash Raj Shrestha ◽  
Shiko M. Ben-Menahem ◽  
Georg von Krogh

How does organizational decision-making change with the advent of artificial intelligence (AI)-based decision-making algorithms? This article identifies the idiosyncrasies of human and AI-based decision making along five key contingency factors: specificity of the decision search space, interpretability of the decision-making process and outcome, size of the alternative set, decision-making speed, and replicability. Based on a comparison of human and AI-based decision making along these dimensions, the article builds a novel framework outlining how both modes of decision making may be combined to optimally benefit the quality of organizational decision making. The framework presents three structural categories in which decisions of organizational members can be combined with AI-based decisions: full human to AI delegation; hybrid—human-to-AI and AI-to-human—sequential decision making; and aggregated human–AI decision making.


2017 ◽  
Vol 143 (5) ◽  
pp. 05017002 ◽  
Author(s):  
Xia Wan ◽  
Peter J. Jin ◽  
Haiyan Gu ◽  
Xiaoxuan Chen ◽  
Bin Ran

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