¶ … Quality of the Optimization for Resource Planning Model by C. Santos et al. (2013)
Any multinational organization with more than 100,000 knowledge workers is faced with some profound challenges in harnessing this pool of talent for a diverse set of information technology projects. The resource planning function for Hewlett Packard's Enterprise Services business segment was especially challenged in this area, prompting Santos et al. (2013) to provide a refined model that can be used to identify optimal supply and demand solutions in highly uncertain environments. This paper provides an evaluation of the quality of the implemented approach for the refined modeling method developed by Santos et al. (2013), including its strengths and weaknesses as well as simplifying assumptions that were made in the development of the model. Finally, an analysis of the respective strengths and weaknesses of the developed model is followed by an evaluation of the sufficiency of the effort that was used in the validation process. A summary of the research and important findings concerning this initiative are provided in the paper's conclusion.
Review and Evaluation
Evaluation of the quality of the implemented approach for the modeling methods
Although every resource planning situation will be unique in some fashion, the overall objective of resource planning is to develop optimal solutions for matching workforce resources with dynamic job requirements (Santos et al. 2013). As the world's largest technology company, the resource planning process used by Hewlett Packard Enterprise Services (hereinafter alternatively "HPES" or "the company") was faced with three primary constraints that affected the modeling methods needed as follows:
1. The scale and complexity of the models. Thousands of professionals with diverse service delivery roles and skills must be matched dynamically to a myriad of projects and jobs in countries worldwide.
2. The uncertainty of labor supply and demand. Most demand information comes from estimates of future project opportunities by the HP sales force. The main sources of demand uncertainty relative to a project are whether HPES will win it, when it will start, and what its associated labor needs are; on the supply side, the availability of workers is often uncertain because of attrition.
3. Matching resources and jobs must consider multiple attributes. A well-defined objective function for matching is not readily available (Santos et al. 2013, p. 153).
Strengths. This HP subsidiary enjoys the combined totality of the tacit knowledge of more than 100,000 workers worldwide. Indeed, this business segment accounts for nearly one-third of the company's total 330,000 employees (Business profile 2013). Moreover, the HPES segment is well situated to take advantage of the proliferation of computer-based networks in organizations of all types and sizes (see stock performance graph at Appendix A). For instance, this business segment provides: (a) enterprise information management solutions for structured and unstructured data, (b) IT management software, (c) security intelligence/risk management solutions as software licenses, (d) software-as-a-service, and (e) hybrid or appliance deployment models (Business summary 2013). Taken together, these products and services are all value-added opportunities for this business segment assuming that it can overcome the several weaknesses involved in the modeling process which are discussed below.
Weaknesses. Although the Solution Opportunity Approval and Review (SOAR) model used by HPES provides workforce supply and demand matching, its previous resource planning methodology has the following limitations.
1. Resource managers lack visibility of the project funnel because SOAR staffing decisions are decentralized. A resource manager can look only at the resource requirements and search supply in a specific business domain, with no communication between other domains of service.
2. SOAR either does not consider the uncertainty of resource demand or treats it in a primitive way -- HPES staffed project opportunities with a win probability over a pre-specified threshold. This approach often led to suboptimal solutions because it completely ignored the resource demand incurred by those opportunities with win probabilities that are less than the threshold.
3. Because of limitations (1) and (2), many last-minute decisions had to be made manually based only on managers' experiences and subjective discretions; these types of last-minute decisions were costly because HPES had to resort to a more expensive contingent workforce to fill gaps on a frequent basis.
4. Matching relied primarily on management judgment or soft matching rules, which resource or project managers implicitly implemented. No unified and systematic approach to performing such soft matching existed (Santos et al., p. 153).
The diagram in Figure 1 below depicts the high-level description of the resource planning problem that was confronted by Santos et al. (2013):
Figure 1. High-level...
PetCo Workforce Planning Workforce planning at Pet-Co The Importance of Workforce Planning Linking the Needs of Staffing With Workforce Planning The Importance of Workforce Planning At Pet-Co General information about Pet-Co The Business Model Analysis of Pet-Co The Pet-Co strategy HRM Considerations Company Profile in a Nutshell The Workforce Planning Model Setting the Strategic Direction Conducting a Workforce Analysis The Demand Side of WorkForce Planning The supply side of workforce planning Workforce Demand Forecast Workforce Supple Forecast Conducting Gap Analysis Develop Strategies Implement Strategies Monitoring, Evaluation and Revision Functional Considerations of
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