| Customer |
A nationwide chain of 800 service
centers |
| Users |
Retail store managers |
| Need |
At this chain of service centers, store
managers were responsible for making daily staffing decisions
to ensure that each store had enough staff to meet customer
demand and ensure short waiting times. However, having
too many extra staff led to excess labor costs. Thus,
optimal staffing required accurate, daily predictions of customer
demand for each store. |
| Solution |
Stottler Henke developed a case-based reasoning
system that predicts the daily sales volume for each of the 800 stores in the
chain. This prediction tool outperforms daily predictions
previously made by store managers, thereby reducing labor costs
while maintaining high levels of customer service. This system
also outperforms forecasting systems based upon statistical
methods. |
| Status |
Based on the system's predictions, each
retail outlet makes daily staffing decisions to minimize both
customer waiting and staff idle time. The tool relies
upon electronic point-of-sales (POS) data and does not require
store staff to carry out extra onerous data entry. |
| Related Applications |
This prediction technique can be used for
predicting daily, or even hourly, sales revenues for the near
future for chain service businesses such as restaurants or automobile
service centers. |
