System and method for shift schedule management

1. A computer-implemented method to improve shift coverage, the method comprising:

receiving a shift schedule template;

identifying a plurality of problem shifts in the shift schedule template, wherein identifying a plurality of problem shifts comprises:

obtaining a plurality of published shift schedules, each published shift schedule associated with a respective shift of a respective employer of one or more employers, wherein each published shift schedule includes a location attribute, industry code attribute, and week indicator attribute;

for each published shift schedule,

obtaining a corresponding historical time and attendance record; programmatically analyzing the published shift schedule and the corresponding historical time and attendance record to determine one or more unscheduled shifts; and

adding unscheduled shift data associated with the one or more unscheduled shifts to a first training corpus, wherein the unscheduled shift data includes two or more of an employer identifier, a location identifier, an industry identifier, an employee identifier, a job type identifier; and

applying a machine learning (ML) model to the first training corpus to identify the plurality of problem shifts by identifying shifts that lie at a threshold Hamming distance from a centroid of a cluster of problem shifts;

determining, using the machine learning model, a randomized incentive offer for each problem shift;

instantiating a graphical user interface (GUI) portion on a plurality of user devices;

displaying the randomized incentive offer for each of the identified problem shifts via the GUI;

receiving shift bids from each of a set of users via one of more user devices of the plurality of user devices;

measuring a lift value associated with the randomized incentive offer;

adjusting a shift schedule based on the received shift bids to generate a published shift schedule;

determining, based on the adjusted shift schedule, a shift compliance metric;

updating feature vectors based on the shift compliance metric;

creating a second training corpus based on the updated feature vectors; and

retraining the ML model using the second training corpus until a threshold level of shift compliance is reached.

2. The computer-implemented method of claim 1 , further comprising:

obtaining time and attendance data corresponding to the published shift schedule;

identifying a token associated with timely pay; and

accelerating payment to a user based on the identified token.

3. The computer-implemented method of claim 1 , wherein the incentive offer is a bonus pay offer, and wherein an amount associated with the bonus pay offer is based on a logistic regression value associated with the problem shift.

4. The computer-implemented method of claim 1 , further comprising providing a graphical user interface (GUI) on a user device of an employee, wherein the GUI enables a user to submit a shift bid.

5. The computer-implemented method of claim 4 , wherein the shift bid is an indication of acceptance of the incentive offer by the user.

6. The computer-implemented method of claim 1 , further comprising:

calculating a reception level for the each incentive offer based on a count of users that view one or more incentive offers.

7. The computer-implemented method of claim 6 , further comprising:

comparing the reception level for a particular shift to a first threshold level;

comparing received shift bids for the particular shift to a second threshold;

based on a determination of a reception level meeting the first threshold and the received shift bids meeting a second threshold level;

determining an updated incentive offer;

and displaying the updated incentive offer on the user devices.

8. A system comprising:

a memory with instructions stored thereon; and

a processing device, coupled to the memory, the processing device configured to access the memory and execute the instructions, wherein the instructions cause the processing device to perform operations including:

receiving a shift schedule template;

identifying a plurality of problem shifts in the shift schedule template, wherein identifying a plurality of problem shifts comprises:

obtaining a plurality of published shift schedules, each published shift schedule associated with a respective shift of a respective employer of one or more employers, wherein each published shift schedule includes a location attribute, industry code attribute, and week indicator attribute;

for each published shift schedule,

obtaining a corresponding historical time and attendance record; programmatically analyzing the published shift schedule and the corresponding historical time and attendance record to determine one or more unscheduled shifts; and

adding unscheduled shift data associated with the one or more unscheduled shifts to a first training corpus, wherein the unscheduled shift data includes two or more of an employer identifier, a location identifier, an industry identifier, an employee identifier, a job type identifier; and

applying a machine learning (ML) model to the first training corpus to determine the plurality of problem shifts by identifying shifts that lie at a threshold Hamming distance from a centroid of a cluster of problem shifts;

determining, using the machine learning model, a randomized incentive offer for each problem shift;

instantiating a graphical user interface (GUI) portion on a plurality of user devices;

displaying the randomized incentive offer for each of the identified problem shifts via the GUI;

receiving shift bids from each of a set of users via one of more user devices of the plurality of user devices;

measuring a lift value associated with the randomized incentive offer;

adjusting a shift schedule based on the received shift bids to generate a published shift schedule;

determining, based on the adjusted shift schedule, a shift compliance metric;

updating feature vectors based on the shift compliance metric;

creating a second training corpus based on the updated feature vectors; and

retraining the ML model using the second training corpus until a threshold level of shift compliance is reached.

9. The system of claim 8 , wherein the operations further comprise:

obtaining the historical time and attendance data corresponding to the published shift schedule;

identifying a token associated with timely pay; and

accelerating payment to a user based on the identified token.