Thesis/Capstone
Publication Date
Authored by
Ye Ma
Advisor(s): Maria Jesus Saenz
Topic(s) Covered:
  • Demand Planning
  • Digitization
  • Machine Learning
Abstract

The second machine age is reshaping the way we work, do business, and collaborate. Today

collaboration is switching from just among humans to between humans and machines. Mundane

and repetitive tasks will be done by machines automatically, while humans can develop insights

and make wise decisions supported by data streaming from intelligent machines. If and how

different human-machine teaming decision-making structures would influence the organization’s

performance is important to understand, so that human-machine teaming capabilities could

contribute the most to business outcomes.

 

By using the augmented inverse propensity weight estimator method, this research empirically

analyzes the average treatment effects of three different human-machine decision-making

structures: Full human to AI delegation, Hybrid AI-Human with adequate human intervention,

and Hybrid AI-Human with all steps of demand planning overrides. These three decision-making

structures are defined as treatment groups, and the traditional manual demand-adjustment

process is defined as the control group. Effects of switching human-machine teaming decision-making

structures from one to another are also analyzed. The performance of each treatment and

control group is measured by the long-term forecast accuracy, short-term forecast accuracy, and

customer inventory level. The project is based on an IT collaboration project between a large

fast-moving consumer goods company and one of its largest e-commerce customers. The project

implemented an AI-enabled demand-adjustment process to incorporate the external e-commerce

customer demand signals into existing demand-planning process. Demand planners engage in the

demand-adjustment process via web-based interfaces, to apply human judgment-based decisions.

All the stock keeping units are randomly assigned to treatment and control groups.

 

The results show that after the implementation of human-machine teaming decision-making

structures, both demand-forecast accuracy and inventory level are strongly improved by at least

47%. Overall, the Hybrid AI-Human with adequate human intervention model is the optimal

decision-making structures between human and machine, which improves the short-term forecast

accuracy by 53%, long-term forecast accuracy by 64%, and inventory level by 70%. The Hybrid

AI-Human with all steps of demand planning overrides model performed worse than the

previous model, because of the heavy human overrides. Additionally, those AI enabled decision-making

structures works better for low-turnover products than high-turnover ones.

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