- Demand Planning
- Machine Learning
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.