1. The optimisation of rosters to meet public demand
Presentation to the Criminal Justice SIG
18 November 2015 David Wrigley

2. Structure Scope of work Summary Results Benefits Lessons learned
Questions

3. The Client Public sector organisation operating 24 / 7 Multiple sites
Workload is driven by public activity outside of client control, varying moment by moment
Staff come from several groups, each with own skills base and contractual terms
Moving towards unification of contracts and cross-training

4. Study requirement
Recommending approaches/methods for optimising the rosters …
… taking account of staff wellbeing, operational efficiency, flexibility/robustness to disruption, customer service
Putting forward options for software tools to aid rostering which may include automating part, or all of the rostering process
Demonstrating compliance with working rules and regulations ….. including types of contract and availability for duties

5. Study tasks Scoping study Software trial May 2014 – October 2014
December 2014 – September 2015

6. Scoping study Three major sites selected as case studies
Interviews with staff, managers, rostering teams, unions
Developed measures of merit of a roster
Brief review of off-the-shelf software
Review of roster optimisation methods
Workshop with senior managers and rostering staff

7. Summary findings from Scoping Study
Some staff are exhausted by current rosters
Proposed set of Measures of Merit of a roster
Baseline performance
Meta heuristics is the most appropriate optimisation technique
Software is available off-the-shelf
Use MoM as benefit function for optimisation
Demonstration of example software tool
Outline for new rostering approach

8. Software Trial One site Using existing time management software
Catalogue of rostering rules, principles and constraints
Data collection and cleansing
Exploration of software and dry runs
Trials against live rostering using new process
Use MoM derived in Scoping Study phase to compare rosters

9. Working with a Black Box
Cannot be certain what it is actually doing
Technical contact with supplier …
Dry runs ….mislead myself about its capabilities and workings
A lot of time spent trying to understand it and control it
“It does what it says on the tin”

10. Rostering targets
Manual rostering against minimum staffing target per shift

11. What we were trying to achieve
Effectiveness: reduce under-coverage
Efficiency: reduce over-coverage

12. Metrics Efficiency – over coverage Effectiveness – under coverage
Work life balance
Compliance with HSE Guidelines for rosters including night shifts
Plus an overall Figure of Merit combining these

13. Rostering against minimum staffing target per shift
Rosters assessed against minimum staffing requirement per shift

14. Against time varying staffing requirement
Rosters assessed against time varying staffing requirement
Manual rostering against minimum staffing target per shift
Automated rostering against time varying staff requirement

15. Automated shift design
Shifts generated automatically given wide degree of flexibility in shift length and start time

16. Adjusted standard shifts
Rosters assessed against time varying staffing requirement

17. Potential benefits Improved efficiency of staff allocation
Reduced staff effort to construct rosters
Improved staff well being through improved work life balance
Faster response to changing circumstances

18. Rostering is difficult
Once you build in a complex set of contractual rules, the working time directive, legislation, trade union agreements and various formal and informal arrangement ….
…it is often difficult to find a staff member to fill a shift
Automation is the best way to tackle this …
… but there is no guarantee of a perfect solution

19. Automation is possible and beneficial
They said it couldn’t be done
We tried it and it worked
And we got better results against the selected metrics on all counts
This was the simple problem: one duty at one location
Confident that it will work even better for multiple duties at multiple locations

20. What would I do differently …
Ask the obvious (?) questions, even though we all think we know the answer
Push the question of data transfer more strongly and get a definitive answer before starting work
Which means: doing the risk analysis more rigorously
And: try a different software approach as a comparator

21. Questions?