Quality of the Optimization for Resource Planning

¶ … 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 Strengths. One of the major strengths of the developed model is its contribution to filling the gap between short-term personnel scheduling and long-term strategic workforce planning in a hierarchical modeling framework (Santos et al. 2013). In addition, the resource planning model developing by Santos et al.

"fits nicely into the HPES hierarchical workforce optimization framework, serving as a bridge between higher-level labor strategy optimization and lower-level scheduling optimization" (165). Moreover, the developed model "offers functionalities superior to those reported in the literature, and the workforce planning tools available in the market for this class of project staffing problems" (Santos et al., p. 165). The implementation of the resource planning solution developed by Santos et al. is depicted in Figure 3 below. Figure 3. Implementation of the resource planning solution at HPES Source: Santos et al., p. 162 Weaknesses.

Any attempt to accurately model a complex system is fraught with opportunities to overlook important variables that might play a role in effecting change in non-determinate analytical environments. More importantly, perhaps, identifying optimal matches between supply and demand for a given project is complicated in the extreme by an inability to quantify or operationalize the salient tacit knowledge that exists in the pool of available talent. In this regard, Santos et al.

emphasize that this weaknesses can have an inordinately large impact depending on the nature of the project that is involved. For instance, these authorities advise that, "Projects can have importance beyond their financial impact. A small project for a strategic customer could be more important than one that generates higher profits, because of the future profit opportunities. Much of the information relevant to the objective function is tacit and exists only in managers' minds" (Santos et al., p. 153).

The fact that tacit information only exist in the minds of practitioners, including managers but knowledge workers as well, means that tapping this nebulous but valuable resource requires a resource planning model this is sufficiently robust to identify the specific knowledge worker that are most suitable for project opportunities.

Therefore, it is possible, but still difficult, to include this dimension in a resource planning model by evaluating the types of human resource retention strategies that are used by HPES at a given point in time in formulating optimal labor supply and demand solutions (Hassan & Hagen 2006). These variables would include, but would not be limited to, the following: Which knowledge workers are targeted for retention.

Knowledge workers who are identified as mission-critical may be offered lucrative compensation packages and schemes, which may have long-term positive repercussions on the organization's performance. Those who are critical to the organization in the short run would be those whose supply would be lesser in the short run or those who would be critical to any project that necessitates completion in the short run. They would thus be the celebrities. Retention of such critical talents would necessitate offers which would shine bright in the short run.

Why knowledge workers are being retained. When an organization loses a critical knowledge worker, there is corresponding negative impact on innovation and major delays in delivery of services to customers, consequently affecting the profitability of the organization. The retention of key knowledge workers by offering competitive pay packages and other incentives, rather than letting them leave, is integral to sustaining their leadership and growth in the marketplace. How HPES is retaining knowledge workers.

An effective retention strategy requires the acceptance of the reality that movement of knowledge workers is influenced by the organization as well as by the market. (adapted from Ghosh 2008, p. 217) Evaluation of sufficiency of the effort put into the validation process The validation process could be improved in several ways to deal with optimization of resource planning under uncertain conditions. At present, the validation of optimal supply and demand solutions requires making a tradeoff between modeling capability and solution efficiency (Santos et al. 2013).

Because HPES faces a large-scale RP problem under uncertainty and must solve it frequently, Santos et al. did not choose an approach that attempts to address uncertainty and randomness in one model for all periods; rather, these researchers employed a two-phase approach that initially constructs a buffer capacity for the organization's workforce (this is termed "the planned bench") in order to make reliable estimates of the amount of requisite resources with known attributes by taking into account the uncertainty of labor demand and supply.

This buffer capacity is subsequently entered into the optimization model to generate salient resources planning alternatives (Santos et al. 2013). The modeling approach developed by Santos et al. (2013) is comprised of two discrete modules: (a) a preprocessing module, supply and demand consolidation (SDC), and.