Evaluating HP Enterprise Services Resource Planning Model
This paper evaluates the quality of the resource planning optimization model developed by Santos et al. (2013) for Hewlett Packard Enterprise Services (HPES), a business segment employing more than 100,000 knowledge workers worldwide. The evaluation examines the implemented modeling approach, its primary constraints, and the simplifying assumptions embedded in the model — notably the assumption that all project opportunities are independent. The paper also analyzes the strengths and weaknesses of the developed two-phase optimization framework and assesses the sufficiency of the validation effort, concluding that while the model represents a significant improvement over HPES's prior manual approach, limitations around tacit knowledge quantification and project interdependence remain areas for further refinement.
- Introduction: Context and scope of the evaluation
- Evaluation of the Modeling Approach: Strengths and Weaknesses: HPES constraints, SOAR model limitations, demand uncertainty
- Simplifying Assumptions in Model Development: Independence assumption and resource-based view limitations
- Strengths and Weaknesses of the Developed Model: Refined model benefits and tacit knowledge gaps
- Evaluation of the Validation Process: Two-phase SDC and optimization module assessment
- Conclusion: Summary of findings and model limitations
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What makes this paper effective
- The paper applies a clear evaluative framework — separately addressing strengths, weaknesses, simplifying assumptions, and validation — which keeps complex technical content organized and readable.
- It grounds abstract modeling concepts in concrete organizational context, using specific figures such as the $120 million sales loss and the 100,000-employee workforce to illustrate real-world stakes.
- Secondary sources (Doving & Nordhaug, Ghosh, Ravesteijn & Zoet) are integrated effectively to support and extend the primary source's claims rather than simply restating them.
Key academic technique demonstrated
The paper demonstrates sustained critical analysis of a single primary source. Rather than summarizing Santos et al. (2013), the author interrogates assumptions, identifies gaps — particularly around tacit knowledge and project interdependence — and evaluates whether the validation process was sufficient. This moves the paper beyond description into genuine critique.
Structure breakdown
The paper opens with an introduction that establishes organizational context and scope. A combined strengths-and-weaknesses section evaluates the original SOAR model and its constraints. A dedicated section then examines the simplifying assumptions before a parallel section evaluates the refined model's own strengths and weaknesses. The validation process is assessed separately before a brief conclusion summarizes findings. Each section builds logically on the last, maintaining a consistent evaluative thread throughout.
Introduction
Any multinational organization with more than 100,000 knowledge workers faces 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 develop 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 the simplifying assumptions made during model development. An analysis of the respective strengths and weaknesses of the developed model is followed by an evaluation of the sufficiency of the effort used in the validation process. A summary of key research findings is provided in the conclusion.
Evaluation of the Modeling Approach: Strengths and Weaknesses
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 at the time, 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:
1. The scale and complexity of the models. Thousands of professionals with diverse service delivery roles and skills must be dynamically matched 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).
This HP subsidiary enjoys the combined totality of the tacit knowledge of more than 100,000 workers worldwide. This business segment accounted for nearly one-third of the company's total 330,000 employees (Business profile 2013). Moreover, the HPES segment was well situated to take advantage of the proliferation of computer-based networks in organizations of all types and sizes. For instance, this business segment provides: (a) enterprise information management solutions for structured and unstructured data, (b) IT management software, (c) security intelligence and 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 represent value-added opportunities for this business segment, assuming it can overcome the several weaknesses involved in the modeling process discussed below.
Although the Solution Opportunity Approval and Review (SOAR) model used by HPES provides workforce supply and demand matching, its previous resource planning methodology had the following limitations:
1. Resource managers lacked visibility of the project funnel because SOAR staffing decisions were decentralized. A resource manager could 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 did not consider the uncertainty of resource demand or treated 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 opportunities with win probabilities below 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 discretion. These last-minute decisions were costly because HPES frequently had to resort to a more expensive contingent workforce to fill gaps.
4. Matching relied primarily on management judgment or soft matching rules that resource or project managers implicitly implemented. No unified and systematic approach to performing such soft matching existed (Santos et al., p. 153).
According to Santos et al. (2013), demand is non-determinate in this model. As they report, "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: "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, but they are accentuated by the number of knowledge workers involved and the complexity of the project opportunities in question. 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 would be available at launch and throughout its completion. The hierarchy of job attributes used in the model developed by Santos et al. is presented in Figure 2 in the original study.
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 change 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 used by HPES had a mixed track record of success in formulating optimal labor supply and demand solutions. In one notable case, this business unit's inability to respond to order backlogs cost the company nearly $120 million in sales (Houston & Goggins 2008).
Simplifying Assumptions in Model Development
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 common goals and overlapping requirements that should be taken into account. This assumption was therefore inappropriate for generating a robust shortlist of solutions that could be compared across similar project opportunities to identify possibilities for resource sharing and for selecting knowledge workers with the tacit knowledge needed to coordinate solutions across projects.
More generally, the entire resource planning approach involves certain assumptions concerning its efficacy and appropriateness 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.
Simplifying these assumptions requires a focus on identifying and quantifying the tacit knowledge available in the talent pool for project opportunities. As Doving and Nordhaug (2010, p. 293) add, "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."
Conclusion
The research showed that Hewlett Packard Enterprise Services employs more than 100,000 workers around the world. The business segment is responsible for providing timely enterprise information management solutions for structured and unstructured data, IT management software, security intelligence and risk management solutions as software licenses, software-as-a-service, and hybrid or appliance deployment models. The research also showed that the manual model previously used by HPES to formulate optimal staffing solutions for project opportunities was inadequate, and that the refined model developed by Santos et al. provided a number of advantages over the former approach. Nevertheless, the research also showed that the developed model assumed each project opportunity to be unique in its goals and processes, thereby limiting the model's ability to generate optimal solutions under uncertain labor supply and demand conditions. Addressing this assumption — and better incorporating tacit knowledge into the modeling framework — represents the most significant opportunity for future improvement.
References
'Business summary' (2013) Yahoo! Finance. [online]
Doving, E & Nordhaug, O (2010, July 1) 'Investing in Human Resource Planning: An International Study,' Management Revue, vol. 21, no. 3, pp. 292–295.
Droege, SB & Hoebler, JM (2003) 'Employee turnover and tacit knowledge diffusion: A network perspective,' Journal of Managerial Issues, vol. 15, no. 1, p. 50.
Ghosh, P (2008, December) 'Retention strategies in the Indian IT industry,' Indian Journal of Economics and Business, vol. 5, no. 2, pp. 215–222.
Hassan, M & Hagen, A (2006, January 1) 'Strategic human resources as a strategic weapon for enhancing labor productivity: Empirical evidence,' Academy of Strategic Management Journal, vol. 5, p. 6.
Heerwagen, JH, Kampschroer, K, Powell, KM & Loftness, V (2004, November–December) 'Collaborative knowledge work environments,' Building Research & Information, vol. 32, no. 6, pp. 510–528.
Houston, M & Goggins, P (2008, January) 'Notes from a Centralized Office: A Renewed Interest in ERP Has School Administrators Reconsidering the Vast Business Management Systems They Abandoned a Few Short Years Ago,' Technology & Learning, vol. 28, no. 6, pp. 19–23.
Ravesteijn, P & Zoet, M (2010, July) 'A BPM-Systems Architecture That Supports Dynamic and Collaborative Processes,' Journal of International Technology and Information Management, vol. 19, no. 3, pp. 1–5.
Santos, C et al. (2013) 'HP Enterprise Services Uses Optimization for Resource Planning,' Interfaces, vol. 43, no. 2, pp. 152–169.
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