Variable labor optimization
In businesses that rely on part-time employees - such as entertainment venues, retail, ecommerce warehouses, etc. - being able to optimally staff the business is key to maximizing profitability while providing quality customer service.
Overview
Identify the optimal staffing plan to enable the business to serve the customers at minimum cost.
TARGET
Did the staffing level adequately serve the customers.
Challenge
Most companies have some idea of likely demand but turning that demand into an actual staffing plan can be challenging. Historical timecard data needs to be combined with the historical demand forecasts and information about the weather and events. Next, some measure of customer service needs to be extracted, either from sales, customer support or inventory systems. All this data needs to be cleaned, joined and transformed into valuable ML features before going into model training. This pre-modeling prep process can be frustrating and time consuming. We are here to help.
Modeling techniques and libraries
Machine learning demand
Build machine learning models to predict the staff required as a function of the independent variables. Use model interpretability packages to evaluate the impact of the independent variables on the prediction.
Packages:
- Sklearn
- ELI5
- LIME
- SHAP
Schedule optimization
Build and solve the optimization problem to find the optimal number of employees to schedule to maximize the customer service and timing to maximize sales.
Package:
- CVOXPT
- pyOpt
- scipy.optimize
