SYSTEM AND METHOD FOR OPTIMIZING STAFFING OF A WORKING-SHIFT DURING A DATE RANGE BY PREDICTING ADHERENCE PARAMETER OF THE WORKING-SHIFT BASED ON A SCHEDULING UNIT

1 . A computerized-method for optimizing staffing of a working-shift during a date range by predicting an adherence parameter of the working-shift based on a Scheduling Unit (SU), said computerized-method comprising:

(i) configuring, by one or more processors, a User Interface (UI) that is associated to a Workforce Management (WFM) application to receive: a. date range; b. SU; and c. activity code for the working-shifts, for the staffing,

wherein there are one or more working-shifts during the date range,

for each interval-time in each working-shift in the one or more working-shifts:

(ii) operating by the one or more processors, a forecast adherence engine to yield the predicted adherence parameter;

(iii) operating by the one or more processors, a coaching aggregation engine to yield a coaching parameter;

(iv) operating by the one or more processors, a time-off aggregation engine to yield a time-off parameter;

(v) operating by the one or more processors, a shrinkage calculator based on the predicted adherence parameter, the aggregated coaching parameter, and the aggregated time-off parameter, to yield a shrinkage parameter;

(vi) configuring by the one or more processors, the WFM to automatically schedule staffing for the interval-time based on the yielded shrinkage parameter; and

(vii) after all time-intervals in each working-shift has been scheduled staffing, storing the working-shift in a database that is associated to the WFM application and configuring the WFM application to automatically trigger a notification to each agent that has been scheduled the working-shift.

2 . The computerized-method of claim 1 , wherein the forecast adherence engine comprising:

(i) retrieving from the database historic working-shifts during a preconfigured period for the SU and the activity code;

(ii) aggregating adherence data of each historic interval-time in the retrieved historic working-shifts;

(iii) calculating an average of adherence percentage of each historic time-interval to yield an actual adherence percentage;

(iv) applying a plurality of statistical algorithms on each historic interval-time in the retrieved working-shifts to yield a predicted history-adherence parameter;

(v) calculating a Mean Absolute Percentage Error (MAPE) for each statistical algorithm;

(vi) selecting a statistical algorithm from the plurality of statistical algorithms based on the calculated MAPE; and

(vii) applying the selected statistical algorithm on the interval-time to yield the predicted adherence parameter.

3 . The computerized-method of claim 2 , wherein the plurality of statistical algorithms comprising at least one of: (i) Box Jenkins Arima model; (ii) Exponential smoothing model; and (iii) Curve fitting model.

4 . The computerized-method of claim 1 , wherein the coaching aggregation engine comprising:

(i) retrieving from the database coaching data that is related to the SU for the interval-time; and

(ii) calculating the average of coaching time during the interval-time to yield the coaching parameter,

wherein the calculating of the average of coaching time during the interval-time is according to formula I:

average of coaching time=total coaching duration*100/total duration,  (I)

whereby:

the total coaching duration is a sum of coaching duration during the interval-time of each agent that is related to the received SU, and

the total duration is the number of agents that relate to the SU in the interval-time.

5 . The computerized-method of claim 1 , wherein the time-off aggregation engine comprising:

(i) retrieving from the database time-off data that is related to the SU for the interval-time; and

(ii) calculating the average of time-off during the interval-time to yield the time-off parameter, wherein the calculating of the average time-off during the interval-time is according to formula II:

average time-off=total time-off duration*100/total duration,  (II)

whereby:

the total time-off duration is a sum of time-off duration during the interval-time of each agent that is related to the received SU, and

the total duration is the number of agents that relate to the SU in the interval-time.

6 . The computerized-method of claim 1 , wherein the shrinkage calculator comprising:

(i) calculating a total duration of the interval-time by multiplying duration of the interval-time by a number of agents in the SU; and

(ii) calculating the shrinkage parameter according to formula III:

shrinkage parameter=( W 1*predicted adherence parameter+ W 2*coaching parameter+ W 3*time-off parameter)/total duration*100,  (III)

whereby:

the total duration is the calculated total duration,

the predicted adherence parameter is the yielded predicted adherence parameter,

the coaching parameter is the yielded coaching parameter,

the time-off parameter is the yielded time-off parameter, and

the W1, W2, W3 are weights ranging from ‘0’ to ‘1’ and a sum of all weights is ‘1’.

7 . The computerized-method of claim 6 , wherein the computerized-method is further comprising configuring the UI that is associated the WFM application to receive the weights.

8 . The computerized-method of claim 1 , wherein the SU comprising a group of agents.

9 . A computerized-system for optimizing staffing of a working-shift during a date range by predicting adherence parameter of the working-shift based on a Scheduling Unit (SU), said computerized-system comprising:

a database;

a memory to store the database; and

one or more processors, said one or more processors are configured to:

(i) configure a User Interface (UI) that is associated to a Workforce Management (WFM) application to receive: a. date range; b. SU; and c. activity code for the working-shifts, for the staffing,

wherein there are one or more working-shifts during the date range,

for each interval-time in each working-shift in the one or more working-shifts:

(ii) operate a forecast adherence engine to yield the predicted adherence parameter;

(iii) operate a coaching aggregation engine to yield a coaching parameter;

(iv) operate a time-off aggregation engine to yield a time-off parameter;

(v) operate a shrinkage calculator based on the predicted adherence parameter, the aggregated coaching parameter, and the aggregated time-off parameter, to yield a shrinkage parameter;

(vi) configure the WFM to automatically schedule staffing for the interval-time based on the yielded shrinkage parameter; and

(vii) after all time-intervals in each working-shift has been scheduled staffing, store the optimized working-shift in a database that is associated to the WFM application and configure the WFM application to automatically trigger a notification to each agent that has been scheduled the working-shift.