HRM and SD

  • 398 views
Uploaded on

A presentation that talks about using system dynamics in an HR management context

A presentation that talks about using system dynamics in an HR management context

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
398
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
1
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Supporting Long-term Workforce Planning with a Dynamic Aging Chain Model: A Case Study from the Service Industry with two more recent additions Andreas Größler
  • 2. The case company: air traffic control © spiegel-online.de
  • 3. Long-term personnel capacity planning as a crucial success factorQuote Eurocontrol:• “Manpower Planning (MP) is necessary to meet the strategic objective: ‘the provision of the right number of staff, with the right qualification, at the right time and in the right place to meet business requirements’”.
  • 4. Goals of modelling and simulation project• Conduct a structural analysis of the existing long- term personnel planning process for air traffic controllers;• provide a dynamic analysis of the existing planning policies;• construct a scenario-tool to improve the existing planning policies as well as the established risk management approach accompanying the existing processes.
  • 5. A simple system dynamics model of capacity planning AIR TRAFFIC requirements #ATCOs needed #ATCOs at #ATCOs recruiting training graduating operative leavingrecruitment time training time productive time
  • 6. Some results from simple model AIR TRAFFIC requirements) #ATCOs operative 6,000 6,000 4,500 4,500 ATCOATCO 3,000 3,000 1,500 1,500 0 0 2006 2010 2014 2018 2022 2026 2030 2034 2006 2010 2014 2018 2022 2026 2030 2034 Time (Year) Time (Year) #ATCOs needed 600 450 No Cycle With Cycle ATCO 300 150 0 2006 2010 2014 2018 2022 2026 2030 2034 Time (Year) Independent from the scenario used, there are variations in some key variables over time that are not easy to understand.
  • 7. Time lag through training process Varies for each trainee Varies for (Ø 6 Month) Identical for each trainee all trainees (Ø 24 Month) Varies for each trainee (15 Month) (Ø 6 Month) t=0 t=6 t = 12 t = 27 t = 51Start of Signing of Start of End of End of OJTprocess contract training training, start of OJT
  • 8. Resulting delay behaviour: average is longer than they think OJT 24 Months OJT 18 Months 100 100 75 75 personperson 50 50 25 25 0 0 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Time (Month) Time (Month) t≈68 t≈58 Ordered ATCO Signed ATCO ATCO in IT ATCO in OJT ATCO
  • 9. Results from client‘s perspective• A more detailed planning paradigm can be implemented (group level instead of centre level);• the personnel planning cycle can be repeated several times a year instead of only going through the process once a year;• the risk management can be complemented by some quantitative scenarios that are provided almost in real- time;• intensified communication between all stakeholders;• the new scenario tool can act as a learning platform for the case company as it integrates the experience and perspective of several departments.
  • 10. Addition 1: Chains of entities…
  • 11. A general issue resulting from the caseThere is some confusion about the different types of supplylines (quotes from HRM review process):“The firm is a logistics service provider and is a service firm. Theissues at this firm are similar to issues faced by a service firm.Service firms, similar to a logistics provider do not have a physicalproduct and definition of inventory is very different.”“Supply chain in service firms are different and have their ownspecific issues. References relate to service chain issues in aservice firm…”
  • 12. Structural similarity of the three types of chains Physical goods supply line Work in Finished Material progress goods purchasing fabricating assembling shipping Service supply line Final Proposal Draft report accepting drafting finalizing delivering Personnel supply line Newly In Fully hired starting training finalizing productive hiring leaving training trainingBe aware of the ethical issue  qualitative vs. quantitative individualism (Simmel)
  • 13. Prototypical behaviour of chains 20 15 1 1entities 10 2 2 3 3 5 3 2 2 3 1 0 3 2 3 1 123123 23 23 22 2 12 13 1 1 1 21 31 31 3133 0 5 10 15 20 25 30 35 40 45 50 time steps 1st stock 1 1 1 1 1 1 1 1 1 1 2nd stock 2 2 2 2 2 2 2 2 2 3rd stock 3 3 3 3 3 3 3 3 3
  • 14. Perceived differences and structural similarity• Perceived differences of the three types of chains, in particular regarding – Utilization of “production” capacities – Premature outflow from the chain – Divisibility of entities• Because of structural similarity, differences are mainly caused by – Inappropriate mix of supply line elements with attributes of these elements (“co-flow”) – The three types of supply lines regularly are located at different organisational levels• “Strategic architecture” (Warren, 2007)
  • 15. Addition 2: Forms of delays…
  • 16. Female professors task• The analytics of a gender quota• Dutch university: balance number of male and female professors• Participants have shown gross mis-estimations (Bleijenbergh et al. 2011)• Influence of political loadedness of task?
  • 17. Discrete vs. continuous delays = two experimental groups input system responsex x to t* t to t* t
  • 18. Task structure in system dynamics notation Ini Male Male professors hiring male profs leaving male profspercentage female profs avg time at hirings necessary Ini Female university Female hiring female profs professors leaving female profs
  • 19. Estimations do not differ between experimental groups 27% 32% outside bounds (% < 50 or > 100) wrong estimate (diff. > 5 years) correct estimate (diff. <= 5 years) 41%No statistical differences between experimental groups for valuesof estimations  participants do not differentiate between discreteand continuous delays.
  • 20. Average error between experimental groups differs a lot, though 9 8 7 6 5 4 years 3 2 1 0 discrete continuousSignificant statistical differences between groups for goodness of estimations(estimations compared to “true” solutions derived from respective simulation model – discretevs. continuous delay version).