Strategic Human Resource Management

 

Name A Case Study in Strategic Human Resource Management with System Dynamics
Modelers Andreas Größler, Alexander Zock
Client/Participant N/A
Client Type N/A

 

The Issue You Tackled The case-study company is a German service provider in the logistics industry that is the market leader in this industry and that has a quasi-monopolistic status. One of the major services the firm provides requires the availability of highly skilled operator staff. The company’s performance depends to a great extent on the timely and effective provision of this service. The intellectual and personal requirements that these operators have to meet are demanding, and the duration of their training is long. Thus, selecting and developing employees is a complicated matter resulting in a difficult and often sub-optimal workforce-planning process that has a major effect on operational performance. As a result of preferable employment conditions and the company’s excellent reputation, however, there is no principle shortage of potential employees. The workforce planning practice employed at the case-study company was to assume continuous growth of demand for the future (for instance, assuming 3% p.a. growth of demand) and, on the basis of this figure, to calculate for each geographical division the deviation between the assumed and the required operator capacity for the future. The forecasting horizon was determined by the time required to train a newly hired operator to be ready for operational services. In the case-study company, the human resource department considered this time lag to be 51 months without variation. In the past few years, the case-study company has experienced a situation of overall growth in demand for its services. The company’s managers described its long-term planning scheme over this period as sub-optimal primarily because they perceived the staff situation to be characterized by transient but prolonged periods of staff shortages, followed by periods of staff surpluses. Both situations are highly undesirable as they can result in a declining service quality, excessive workforce costs, and lower productivity. In short, the seemingly trivial problem of providing just the right number of qualified people at the right time is actually highly dynamically complex.
What You Actually Did The company decided to complement its regular planning process, which consisted of forecasting future workforce demand through spreadsheet analyses, with a modelling approach based on system dynamics. The goals that were set by the case-study company’s management for this study were

  • to conduct a structural analysis of the existing long-term workforce planning process for service operators,
  • to provide a dynamic analysis of the existing planning policies, and
  • to construct a scenario tool to improve the existing planning policies.

To meet these requirements, the company defined the following project framework:

  1. Interviews with members of the company with all the departments involved.
  2. Participative construction of a basic system dynamics model.
  3. Testing and validation of the constructed model for one service centre.
  4. Testing and validation of the model for a second service centre.
  5. Definition and evaluation of future scenarios to demonstrate the capabilities of the planning tool.
  6. Possible roll-out to all remaining service centres in Germany.
  7. This multiphase approach highlights the fact that system dynamics-based modelling frequently is much more than a back-office modelling exercise with some expert involvement. The main reason for this approach is the necessity of building intensive involvement in the organization to foster commitment and trust in such a new approach to organizational planning. The modelling project was conducted in 2008.ed in 2008.

The Results The modelling process involved several working sessions with a group of six to ten people. In the context of these sessions, several qualitative insights were gained:

  • There were data inconsistencies in the planning databases used by the service centres and the centralized planning department. These inconsistencies had never been laid bare, and only the rigorous character of the formal modelling approach led to a comparison of the databases in such detail that the problem could be identified.
  • The overall lead time between the request for new employees and those employees becoming operational was much longer than had been assumed. This insight also showed that the implemented planning horizon was actually not long enough, which also brought up questions about the adequacy of the existing forecast methods.

In the course of the sessions, most participants reported that the systemic representation of the planning process provided an integrative picture that had never before been accessible in the organization. They also said that the quality of the discussion in the sessions was very high and had led to interdepartmental dialogue that had not taken place before the project started. In addition to these qualitative insights, a number of quantitative aspects of the planning process were found to be of considerable interest. One example is the dynamic consequence of the variations in the lead times of the recruitment process. The overall lead time of this process were divided into three blocks: the recruitment lead time (12 months), the basic training (15 months) and the on-the-job training (24 months). If one assumes that these lead times are only average times and that they display some degree of variance, the analysis demonstrates that, although the average lead time is 51 months, not all of the recruited operators are fully productive after this time. Although some may finish their training earlier, quite a few take longer. Thus, in the simulation it takes 68 months until about 100% of the newly hired operators are actually available. Although this insight may be intuitive when one considers the meaning of the term “average lead-time”, the planning process did not take it into account before this simulation outcome was presented. Other quantitative insights gained in the modelling process included the influence of limited capacity at the local centre for on-the-job training, the effects of different policies for distributing trainees across the centres, and the occurrence of cyclic behaviour in workforce capacity.

 

Related Publications Supporting long-term workforce planning with a dynamic aging chain model: A case study from the service industry download
Understanding human resource flows with System Dynamics (Slideshow) download

 

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