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Staffing and Scheduling – Part II HCM 540 – Operations Management.

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1 Staffing and Scheduling – Part II HCM 540 – Operations Management

2 Primary Objectives 1. Staff scheduling is a difficult, time consuming managerial problem 2. Many flavors of staff scheduling problems 3. Staff scheduling inextricably linked with determining total amount of staff 4. Tactical and operational staff scheduling 5. Computerized staff scheduling systems

3 High Level Staffing Framework Budgeting and Planning Annual or as needed Planned capacity Staffing/scheduling policies Operational staffing/scheduling Every 2-6 weeks Target staffing levels Create employee schedules for core staff Daily allocation Ongoing Reacting to staffing variances Floating staff, overtime, contract staff, agencies Adapted from Abernathy et. al. (1973), Hershey et. al. (1981), Warner et. al. (1991) Budget, staffing plan, policies Staff schedule Realized shortages and surpluses Tactical Staff Scheduling Analysis

4 The Challenge of Staff Scheduling So…, how much staff is needed and how should they by scheduled? 1 3 2

5 Staff Scheduling - It’s a Problem Policies and practices affect total labor cost. little “tactical” scheduling analysis done Overstaffing increases labor costs while understaffing may impact quality of care or service Presents difficult combinatorial problems. Consumes costly managerial time and effort; ad-hoc methods are the rule. Bias often to favor employee over institutional needs. Large impact on employee dissatisfaction and turnover Not only in healthcare - police, fast food, call centers, airlines Computerized systems under-utilized and often require inputs which themselves are the solution to a difficult scheduling analysis problem.

6 Elements of Scheduling Environments Planning cycle is the number of weeks in the scheduling horizon 1, 2, 4, 6, 8, etc. Each day is composed of planning periods 15 minutes, half-hours, hours, 8-hr shifts staffing or “coverage” requirements by planning period  where did they come from?  hard constraints vs. soft constraints (e.g. understaffing costs) A shift has a start time, a day of week, and a length (8hr shift, starting 7:30am) allowable start times Tour Types: (periods/shift-shifts/week) (8-5) is someone who works 5 8-hr shifts per week (12-3, 12-3, 12-4) works three 12-hr shifts for two out of three weeks and four 12-hr shifts for one of three weeks (12-3, ) works three 12-hr shifts every week + one 8-hr shift every other week

7 Elements of Scheduling Environments Days-off Patterns Su Mo Tu We Th Fr Sa =working, 0=off So, how many different patterns are there for working 5 out of 7 days? 2-weeks A tour is a combination of days worked and shifts worked workstretch - # days worked consecutively time between consecutive worked shifts – e.g. 16 hours The “standard 3-shift nurse scheduling problem” day, afternoon, midnight shift each shift for each day of the week can have unique staffing requirement multiple week issues  covering “off-shifts” (permanent, rotation)  weekend rotation issues (A out of B weekends off)  some tour types, e.g. (12-3,12-3,12-4)

8 Elements of Scheduling Environments Employee preferences for various schedule characteristics A challenge of scheduling problems is to balance schedule quality with coverage

9 Tactical vs. Operational Scheduling Specific employees identified. Schedule current staff to meet TOD/DOW staffing targets subject to scheduling policies, staff preferences and availability. Done every two to six weeks. Done by department staff. Not concerned with specific employees. Determine minimum staff needed to meet TOD/DOW staffing targets subject to various scheduling policies. Done periodically as part of planning or a special study. Done by department staff or operations analyst OperationalTactical

10 Performance of Schedules Overall scheduling efficiency Distribution of under and overstaffing usually more desirable to “spread out” under and overstaffing than concentrate it costs of understaffing Schedule quality / implementability Fairness Ongoing manageability

11 Approaches to Solving Scheduling Problems Trial and error + basic scheduling principles self-scheduling within management set parameters Get a “master cyclic schedule” built and try to follow it making modifications as needed Various specialized heuristics or algorithms have been developed for different versions of scheduling problems lower bounds on staff size then build a schedule Website devoted to Excel based templates for scheduling  Mathematical optimization models Artificial intelligence based techniques suited for finding good solutions for problems with many complicated constraints Many different commercial scheduling systems exist with widely varying capabilities and incorporating one or more of the above approaches

12 Classes of Scheduling Problems Days-off scheduling staffing specified at daily level (1 or more “standard shifts” per day) by DOW find min staff size to meet coverage and other constraints on weekends worked, workstretch, allowable patterns traditional nurse scheduling Shift scheduling usually posed as a 1-day problem with staffing requirements specified by time of day (e.g. hourly) Tour scheduling basically a combination of days-off and shift scheduling over some planning cycle (1 or more weeks) Countless industry specific variations on all of these problems

13 Tactical Staff Scheduling Analysis Used periodically as part of planning Concerned with capturing the essence of staff scheduling problems TOD/DOW specific staffing targets allowable mix of tour types (shift lengths and # days worked per week) allowable shift start times and flexibility budget constraints days worked constraints (e.g. no 3 consecutive 12hr shifts) Determine minimum staff size needed to meet coverage requirements subject to scheduling related constraints Quantify cost of scheduling policies Example - Shift Length Flexibility All full time {

14 Dantzig’s Linear-Integer Programming Based Scheduling Optimization Model “A Comment on Edie’s Traffic Delays at Toll Booths”, Dantzig, G. (1954) (Total staffing cost) (Staffing coverage in each period (e.g. hourly)) Provided basis for 35 years of scheduling research and practice. Many extensions: –understaffing costs –varying skill levels and productivity –breaks and lunches –industry specific side constraints

15 What is Optimization? In a business problem context Loosely – Finding the “best” solution to a problem More precise – Finding the answer to a problem that minimizes (maximizes) some objective or goal of a decision maker while taking into account business constraints Mathematical version – Finding the values of a set of decision variables that minimizes (maximizes) some objective function subject to constraints (equations or inequalities) on the decision variables

16 Some Optimization Concepts A potential solution is feasible if it satisfies all the constraints we build in the model a model is infeasible if no solution satisfies all the constraints A potential solution is optimal if it is feasible AND it is better than all other feasible solutions in minimizing (or maximizing) our objective a model is unbounded if we can make the objective as big as we want (assume we’re maximizing) and still satisfy the constraints So, how do we search among the (potentially huge number of) feasible solutions to find the optimal solution? that’s what optimization algorithms such as those built into the Excel Solver do

17 Linear Programming Many useful, important problems can be formulated as: Maximizec 1 x 1 + c 2 x 2 + … + c n x n (objective function) Subject toa 11 x 1 + a 12 x 2 + … + a 1n x n  b 1 (1 st constraint) a 21 x 1 + a 22 x 2 + … + a 2n x n  b 2 (2 nd constraint) … a m1 x 1 + a m2 x 2 + … + a mn x n  b m (m th constraint) x i  0, i=1..n, (decision variables) The c i and a ij are just numeric coefficients that are multiplied by the values of the decision variables (x i ) LP LP=linear program

18 Yet Another Observation Many useful, important problems can be formulated as: Maximizec 1 x 1 + c 2 x 2 + … + c n x n (objective function) Subject toa 11 x 1 + a 12 x 2 + … + a 1n x n  b 1 (1 st constraint) a 21 x 1 + a 22 x 2 + … + a 2n x n  b 2 (2 nd constraint) … a m1 x 1 + a m2 x 2 + … + a mn x n  b m (m th constraint) x i  0, i=1..n, (decision variables) Some of the x i must be integers MIP MIP=mixed integer-linear program So, what is different?

19 Some of the toughest mathematical problems solved routinely in business today are optimization problems

20 Example 1: Simple 1 week, days-off problem Formulated model in Excel and we will solve it using Solver Goal 1: give flavor of optimization applied to scheduling Goal 2: illustrate fact that scheduling policies affect staffing needs Goal 3: real scheduling problems can lead to huge optimization problems SchedulingDSS_Northpark.xls Scheduling_AdvancedDaysOff1.xls Scheduling_AdvancedDaysOff2.xls

21 Example 2: Simple 1 day, shift scheduling problems Formulated model in Excel and we will solve it using Solver Goal 1: see difference between shift and days-off scheduling Goal 2: treat staffing requirements as both hard and soft constraints Goal 3: real scheduling problems can lead to huge optimization problems ShiftSchedulingModel1.xls ShiftSchedulingModel2.xls

22 Example 2-Week Schedule Creating a sample schedule is good “test” of whether you’ve come up with an implementable solution Schedule can be reviewed by staff for undesirable characteristics, errors, other ideas for improvement Sample schedule helps sell scheduling policy changes because people can visualize the end product

23 Cyclic Schedules Idea is to create a set of schedules that employees cycle through. Various mathematical methods, computerized and trial and error approaches to creating cyclic schedules Pros – schedules can be specified well in advance, fair, once created relatively easy to manage for stable workforce Cons – very rigid, difficult for mix of full/part time staff, difficult when varying shift lengths, difficult for 24/7 operations

24 Coverage Report – Comparison of Targeted to Scheduled Staff Levels Target=Min staff requirements Sched=Staff scheduled +/- = Over/understaffing

25 Sample summary report from a tactical scheduling analysis FTE implications of Constrained vs. Flexible scheduling policies Summary of FTEs and # of positions These solutions were derived from user specified scheduling policies and a scheduling optimization model Note also the variance pooling effect that an LDRP gives

26 Sample Applications Surgical nurses/techs Communications operators Appointment scheduling clerks Short stay unit nurses Recovery room nurses Medical transcriptionists Radiation oncology technicians Obstetrical nurses How much staff needed? Can current staff absorb increased demand through rescheduling? What are the potential savings from increased flexibility in shift lengths and start times? By how much can we improve customer service through scheduling changes? Isken, M.W. and W.M. Hancock, 1998, “Tactical Staff Scheduling Analysis for Hospital Ancillary Units”, Journal of the Society for Health Systems, Vol. 5, No. 4, pp

27 Comments on Tactical Scheduling How do shift start times and shift lengths match the work flow of the department? can’t make general statements that certain shift lengths or scheduling practices are “good” or “bad” look for opportunities to smooth workload to ease the scheduling burden Pay attention to policies and procedures regarding the definition of OT >40 hrs/week vs. >80 hrs/pay period Schedule desirability can vary widely by employee don’t assume what people will and will not like Important to involve staff in analysis of scheduling policies easy for them to undermine intangibles not captured by scheduling models

28 Another Link between staffing and scheduling Time of day staffing targets are really decision variables, Simultaneous solution of staffing targets and schedules may lead to better solutions from cost, service, and schedule quality perspectives. Preliminary experimental results are promising. Considers workload smoothing, buffering, and scheduling schemes. Operational setting drives the model building process (Lab and Transcription). Challenge is resulting problems more difficult to solve (research ongoing)

29 Operational Personnel Scheduling The ongoing process of creating and managing staff schedules Balancing system needs with staff availability and preferences Several methods: computerized scheduling systems self-scheduling manual scheduling done by committee or manager A difficult, time consuming process it’s like doing a really hard jigsaw puzzle

30 Nurse Scheduling Challenges 24/7 coverage needed Workload varies by shift by skill level by unit Rotation to off-shifts? Multiple skill levels (RN, LPN, aide, etc.) Covering weekends Shortage of personnel Dealing with daily fluctuations in supply & demand OT, agency, part-time, float on/off unit, contingent, send home, call-in ANSOS

31 Typical architecture of Computerized Personnel Scheduling Systems Supports day to day scheduling of current staff. Wide range of system capabilities and cost. Healthcare, retail, police, fire/EMS, telesales, tech support, fast food, banking

32 ANSOS – Per Se Technologies ANSOS - One Staff Created in 1970s by Warner, tested at UMMC The standard for nurse scheduling software Staffing requirements, scheduling policies, and nurse preferences optimization model based Integrates with 3 rd party PCS Numerous add-on modules Shift centric as opposed to time of day centric Extent of use varies widely among institutions glorified typewriter vs. sophisticated auto-scheduler See “ Automated nurse scheduling” by Warner et al that was passed out last time

33 A few scheduling packages ANSOS - ActiveStaffer - AtStaff - AcuStaf - Pathways Staff Scheduling - Shiftwork Solutions - ShiftMaker - 7/247modules.htm 7/247modules.htm ESP eXpert - InTime - VSS Pro - Kronos - ScheduleSource - ORBIS - Various packages - - web based scheduling

34 Evaluating Computerized Scheduling Systems How are staffing requirements specified (TOD or Shift)? Auto-scheduling or just a schedule manager? Schedule editing Support for self scheduling? Single vs. multiple weeks Easy access to emp. data Employee requests and preferences Skeleton rotation patterns Archive past schedules Reporting – built in and ad-hoc capabilities Does it handle YOUR scheduling environment? Can be integrated with 3 rd party workload systems? Can be integrated with 3 rd party timekeeping, payroll, and/or HR systems? Cost and licensing consulting, installation, training, sofware, hardware, maintenance, add-on modules Tech support Strong user base Hardware and software requirements How applicable to multiple departments within the same institution?

35 Flexible Scheduling Ideas Mix of different shift lengths >8 hr shift gives more days off per week easier to match fluctuating workload Increase number of allowable start times easier to match fluctuating workload more complex to manage; rotation issues Mix of full and part-time tour types part-timers can provide invaluable flexibility in dealing with vacations, odd shifts, absences, workload variation by day of week and time of day

36 Flexible Scheduling Ideas Float pools (internal agency) cross training sufficient voluntary “floaters”? How big should the pool be? How should the “core” staffing levels be set? Temp agencies pay a premium for staff on demand issues with integration with permanent staff Contingent usually from the employees perspective On-call Forced TO (time-of) and Forced OT not a super staff satisfier

37 Miscellaneous issues Circadian rhythms researchers study effect of shift work Shift overlap communication improvements 12hr tour types 334, 3334, 33-1, cost and scheduling implications Self scheduling need to have a good staffing plan and set of scheduling policies

38 Learning More Professional association trade journals and academic journals Nursing Management, Medical Laboratory Observer, Nursing Times, and numerous other Interfaces Search Medline for “staff scheduling” Google it – “healthcare staff scheduling” Introduction to Employee Scheduling: Issues, Problems, Methods Introduction to Employee Scheduling: Issues, Problems, Methods – Nanda and Browne

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