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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 description of the resource planning problem at HPES
Source: Santos et al. 2013, p. 154
In addition, according to Santos and his associates (2013), demand is non-determinate in this model. For instance, Santos et al. report that, "In particular, whether a project opportunity will be won, its starting time, and its workforce requirements (determined by the scope of work) are uncertain" (p. 154). Likewise, supply is also indeterminate in the model. In other words, "The number of employees available over the planning horizon is affected by attrition. They might also be engaged in ongoing projects for longer or shorter periods than expected" (Santos et al., p. 154). These uncertainties are not unique to HPES, of course, but they are accentuated by the number of knowledge workers that are involved as well as the complexity of the project opportunities that are involved. According to Ravesteijn and Zoet (2010, p. 2), "Knowledge workers are workers that work with intangible resources. Knowledge workers are individuals whose work effort is centered around creating, using, sharing and applying knowledge." The resource planning model refined by Santos et al. inevitably involved uncertain factors such as how many knowledge workers with the requisite expertise and tacit knowledge needed for a given project opportunity would be available at the project's launch, as well as throughout the pendency of the project's completion and implementation. The hierarchy of the job attributes used in the model developed by Santos et al. is presented in Figure 2 below.
Figure 2. Hierarchy of job attributes used in the Santos et al. model
Source: Santos et al., p. 157
The uncertainty of the availability of such knowledge workers is inextricably interrelated to their perceptions of satisfactory remuneration and benefits which inevitably differ from individual to individual and from time to time, as well as over time (Droege & Hoebler 2003; Heerwagen, Kampschroer, Powell & Loftness 2004). For example, Ghosh (2008, p. 216) emphasizes that, "The hyper-competitive business environment is experiencing an intensifying fight for knowledge workers, the key to enhancement of productivity in which rests on designing ways and means to retain key performers in the organization." The former resource planning model use by HPES had a mixed track record of success in formulating optimal labor supply and demand solutions. In one glaring case, though, this business unit's inability to respond to order backlogs cost the company nearly $120 million in sales (Houston & Goggins 2008).
Simplifying assumptions made in the development of the models
The main assumption made by Santos et al. (2013) was that all project opportunities are independent of each other; however, it is reasonable to suggest that any number of enterprise resource planning projects currently underway at HPES share some common goals and overlapping requirements that should be taken into account. Therefore, this assumption was inappropriate for providing a robust short list of solutions that could be compared for similar project opportunities to identify opportunities for resource sharing and the selection of knowledge workers with the tacit knowledge needed to coordinate these solutions across project opportunities.
More generally, the entire resource planning approach involves certain assumptions concerning its efficacy and appropriate for identifying optimal labor solutions in uncertain supply and demand situations. In this regard, Doving and Nordhaug (2010, p. 293) report that:
Resource planning comprises the strategies and routines [a company] has elaborated in order to be better prepared to analyze and develop its human resources, and depends on the extent to which it invests attention and effort in elaborating these strategies and routines. [It is] assumed that such investments increase the firm's capacity to plan deployment and development of competencies for organizational ends. (p. 293)
Simplifying these assumptions will involve a focus on identifying and quantifying the tacit knowledge that is available in the talent pool for project opportunities. In this regard, Doving and Nordhaug (2010, p. 293) add that, "The resource-based view of the firm focuses on the quality of resources owned or controlled by the firm, rather than on the firm's investment in capacity to manage such resources." Unfortunately, there remains a paucity of timely and relevant research concerning how best to identify and quantify the irreplaceable and intangible qualities of tacit knowledge in resource planning analyses. As Doving and Nordhaug (2010, p. 294) emphasize, "Both from a practical and a theoretical point-of-view there is a relative dearth of knowledge about the firm-specific preconditions enabling or encouraging the firm to invest in resource planning."
Strengths and weaknesses of the developed model
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