Long Term Retail Task Scheduling With Fixed Baseline Hours
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing weekly scheduling of task resources at retail stores makes it difficult to plan activities unrelated to the store, due to the varying schedules.
Innovation Solution
A system and method for long-term scheduling of task resources, involving the storage of forecasted data over multiple weekly periods, transformation into baseline demand values, and generation of fixed schedules that remain consistent for at least eight weeks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If weekly scheduling of task resources is implemented at retail stores, then resource allocation can be adjusted frequently to meet changing demands, but it becomes difficult for task resources to plan personal activities due to varying schedules
Solution Approach 1:
The scheduling system segments the planning horizon into a fixed long-term component (8-12 weeks) and a flexible short-term component (weekly adjustments). The fixed schedule portion provides stability for personal planning, while the segmented weekly adjustments maintain adaptability for retail demands.
Solution Approach 2:
The system performs preliminary scheduling action by establishing fixed long-term schedules in advance (8-12 weeks ahead), allowing task resources to plan personal activities beforehand. This preliminary action reduces uncertainty while still permitting later adjustments.
2Ease of operation
If fixed schedules are implemented for at least eight weeks, then task resources can plan personal activities effectively, but the system loses flexibility to respond to changing retail demands
Solution Approach 1:
The scheduling system segments the planning horizon into a fixed long-term component (8-12 weeks) and a flexible short-term component (weekly adjustments). The fixed schedule portion provides stability for personal planning, while the segmented weekly adjustments maintain adaptability for retail demands.
Solution Approach 2:
The system applies dynamics by allowing the schedule to transition from a fixed state (long-term planning) to a flexible state (weekly adjustments). This dynamic approach enables the schedule to be rigid when needed for personal planning and flexible when retail demands change.
3Productivity
If weekly schedule variations are maintained, then the retail store can adapt to fluctuating customer traffic and operational needs, but task resources experience scheduling uncertainty that complicates life planning
Solution Approach 1:
The scheduling system segments the planning horizon into a fixed long-term component (8-12 weeks) and a flexible short-term component (weekly adjustments). The fixed schedule portion provides stability for personal planning, while the segmented weekly adjustments maintain adaptability for retail demands.
Solution Approach 2:
The system performs preliminary scheduling action by establishing fixed long-term schedules in advance (8-12 weeks ahead), allowing task resources to plan personal activities beforehand. This preliminary action reduces uncertainty while still permitting later adjustments.
Data Source
AI summary
In some embodiments, apparatuses and methods provide long term scheduling at a plurality of retail stores including storing a forecasted data over at least eight weekly scheduling periods; determining based on the forecasted data a first baseline hours demand value for each day of a week for each job function of a first set of job functions at a particular retail store; determining based on the forecasted data a second baseline hours demand value for each day of the week for each job function of a second set of job functions at the particular retail store; automatically generating a plurality of fixed schedules for a set of task resources at each of the plurality of retail stores; automatically instructing a user electronic device to display a notification indicating that a first fixed schedule associated with a first task resource is available.


