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Work Process Flow Chart

For Informational Purposes to Bob and Michi
by

Diane Holmes-Curtice

on 13 May 2013

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Transcript of Work Process Flow Chart

Considerations for Implementing an Automated Staffing Approach. Work Load Scheduling of Student
Workers A Closer Look at the Scheduling Process General Objectives :

To align student worker scheduling with -

site traffic patterns
subject area needs
optimal staffing levels
available software platforms The proposed work flow system has seven component sub-processes. 1. Calculating and Optimum Staffing Ratio by Regression

2. Projecting a Schedule of Expected Attendance Values

3. Calculating Subject Area Ratios

4. Calculate Regression for Data

5. Calculate Anticipated Staff Needs Per Hour

6. Populate Projecting Schedule with Potential Tutors

7. Highlight Tutors with Best Fit According
to Preferred Staffing Patterns •An accurate, repeatable, and easy-to-understand projection of student attendance patterns

•An automated means to schedule students in accordance with projected subject area demand with institutional and Financial aid limitations

•A reduction in the time required to assess and set schedule of Tutor pool with weighted preferences, while maintaining element of site coordinator's selection

•The identification of optimal staffing targets for future scheduling Potential Outcomes In considering the potential way to
increase tutor availability during high demand periods, two details stood out as offering potential cost savings:

Offering an automated means to project the schedule- saving coordinator, site coordinator and supervisors time

Weighting the schedules of tutors who have the best fit for both subject demand and peak schedule Manual Automated - Flexibility - can be
in whatever format
site coordinator prefers

- Time Consuming - the
more student workers the
more time needed, as well as complications, and errors that can occur

- Decision making -
manual input offers
and requires more
direct decision
making at each
step - Standardization: Unifies the scheduling processes to all campuses

- Could be performed by a single operator for all or any campus as needed

- Can address necessary scheduling complications (schedule blocks, attendance patterns, Financial Ad limits, etc) consistently

- Can be updated to include recent attendance patterns without requiring additional manual calculation or alternately - guesstimates of expected demand Institute Of Education Science. (2009). Using Student Achievement Data to Support Instructional Decision Making. National Center for Education Evaluation and Regional Assistance, Department of Education. Retrieved from http://ies.ed.gov/ncee/wwc/pdf/practice_guides/dddm_pg_092909.pdf In 2004, the University of Maryland University College implemented an automated staffing tool that enabled them to

examine the entire pool of qualified faculty and staff according to particular needs

include an email responder with notification to the faculty members regarding teaching assignments/schedules.

"The implementation of this tool has proven to be an efficient way to meet the university's staffing requirements and utilize the academic talents and interests of the faculty... [which has been used successfully at UMUC .... After an initial pilot, the interface is now used in many disciplinary areas within undergraduate programs [with] tremendous time savings benefit associated with this practice, it also enables departments to identify any staffing gaps more quickly.

Robertson, Jim. (2012). Faculty Staffing Tool Saves Valuable Time. The Sloane
Consortium. Web. Abstract. Retrieved from
http://sloanconsortium.org/effective_practices/faculty-staffing-tool-saves-valuable-time. •Historical Data

oSite usage by hour by subject

oTotal site usage by hour

oTutors scheduled by subject by hour

oStudent ASC Return Visits

oStudent ASC Returns / Course Completion/Success

oCourse Schedules

•Current Data

oSite usage by hour by subject

oTotal site usage by hour

oTutors scheduled by subject by hour

oStudent ASC Return Visits

oStudent ASC Returns / Course Completion

oCoordinator and site coordinators’ staffing decision processes In addition the approaches noted, the following data and sources may be considered: The University of Maryland University College found that "the probable costs associated with this practice:

The time associated with development of the application represent the bulk of the costs.

The work involved an individual who contributed 4-6 weeks of development effort and time to the project. In addition, approximately one week was incurred during the pilot phase of the application.

Projected upkeep and maintenance costs are minimal, requiring only one person to generate the html forms for each department, each semester. This requires approximately 8-12 hours of time." "Simple model(s) or simulation(s) of how the workload varies and how staff hours can be deployed [are] surprisingly easy to create in a standard spreadsheet once some basic parameters about demand are determined." While the University of Maryland University College was an html-based staffing tool - Bryce, David & Taylor Christenson. (N.D.) Finding the Sweet Spot: How
to Get the Right Staffing for Variable Workloads. Retrieved from http://www.hfma.org/Templates/Print.aspx?id=25484 Diane Holmes-Curtice 10/16/12 Bryce and Christensen Identify a 9 step method for nursing schedules that may also offer application for Tutor Scheduling

A 9-Step Method for Striking the Balance in the ED, with a Focus on staff Hours

Step 1. Mine historical relative value unit (RVU) data to find the RVU workload average and standard deviation for each hour of the week.

Step 2. Determine the total number of RVUs a single staff member can achieve in an hour by looking at the RVU workload per staff for hours where callback hours were needed.

Step 3. Construct a spreadsheet that matches up the RVU data for each hour of the week with an experimental staffing model consisting of the number of staff scheduled.

Step 4. Use simulated demand to create the hypothetical workload for a week. This step is easily accomplished using a standard spreadsheet with the “norminv” function, which returns a specific “draw” or instance of demand based on the actual mean and standard deviation of workload for a time period. Recalculating a number of times will allow the organization to determine an average number of weekly on-call hours needed and an average total weekly cost. A good way to know whether the model accurately reflects actual circumstances is to insert the current staffing template, generate a prediction of important numbers, and compare them with actual data.

Step 5. Construct various experimental staffing models, varying each one’s balance of scheduled versus on-call and callback hours. A systematic approach to this step is to start by using only scheduled Staff to fill the predetermined percentile of demand..

Step 6. Graph all of the experimental staffing models’ average total weekly costs (including callback time) as a function of scheduled hours.

Step 7. Find the low point on the curve to identify your organization’s optimal number of scheduled hours.

Step 8. Separate that optimal number of scheduled hours into discrete shifts, ensuring adherence to all staffing policies and taking care not to allow the percentage of demand covered by scheduled staff members to vary too much from hour to hour.

Step 9. Convert the new shift template into the same format as the current shift template and implement. To achieve...
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