How to Predict the Future: Workforce Planning Research Paper. Alexandra Chauran. Workforce Planning. Dr. David A. Ross.

Transcription

1 How to Predict the Future: Workforce Planning Research Paper By Alexandra Chauran Workforce Planning Dr. David A. Ross July 17 th, 2013 Valdosta State University 1

2 Introduction Workforce planning hinges on a practice which is essentially fortune telling. Before one can implement an effective workforce planning strategy or even collect data, one must at least speculate about the future of the organization in question. The more precise and accurate the forecast, the more effectively one can mitigate any gaps between the present situation, as measured with data, and the elusive future one sees. A frequent first step, the SWOT analysis of Strengths, Weaknesses, Opportunities and Threats is half observation and half prediction (IPMA 2002, 20). There are many things one has to predict about the future for adequate workforce planning. One must analyse competencies that will be needed in the future in order to make comparisons with present competencies (IPMA 2002, vi). One must also develop an attrition forecast in order to figure out how fast employees are leaving and why (IPMA 2002, 24). Linked to this is the more important point of forming an overarching vision, mission and strategy for the organization (IPMA 2002, 11). In addition to predicting the future for the organization, the future must be predicted for the economy and the world at large (Sinclair 2004, 2). Things like unemployment, aging generations retiring and global phenomenon will exert an environmental influence on the organization. There are several basic ways to predict the future, some more obvious than others. For example, one must notice and carefully observe trends, since they may have a lot to say about the future (IPMA 2002, 4). Whether the organization is on the rise or is headed for disaster, trends and change are inevitable. 2

3 And, of course, one must predict the results of proactive strategies or remediation, for example recruiting, training and development, and succession planning (IPMA 2002, 13). Over the course of this paper, I will guide you through the methods of prediction which have been used over the past few decades leading up to the most recent research on the matter. My essential question is: What is the future of predicting the future in workforce planning? Past to Current Research Studies I ll begin with the oldest study, A Composite Forecasting Methodology for Manpower Planning utilizing Objective and Subjective Criteria (Drandell 1975). Statistics is an older method that is still used for workforce planning. The next study from this era to analyse is An Industry-Occupational Forecast Incorporating Tecchnological Change for Local/Regional Manpower Planning (Iacobelli & Muczyk 1976). This marked a continuation of the scare that machines would replace people in the workforce, a consideration that is still relevant today. Moving forward three decades, the focus is on acknowledging statistical error. We ll first look at Using Warehouse Workforce Flexibility to Offset Forecast Errors (Sanders & Ritzman 2004). Then, Stay In Front of the Talent Curve (Ruse 2008), which goes further to reject the idea of ever having sufficient data and focuses instead on the concept of human capital, or competency based planning. In The Long View (Hansen, F. 2008), long-term goals for organizations are re-evaluated as a concept. In Talent Planning for the Times, (Frauenheim 2009), workforce planning software is evaluated as a tool. 3

4 As for current studies, we ll bring things up to date with Where Have All The Workers Gone? Exploring Public Sector Workforce Planning (Colley 2010), which again points to the lack of accurate predictive data as a barrier to workforce planning. Supporting Long-Term Workforce Planning with a Dynamic Aging Chain Model: A Case Study from the Service Industry (Größler & Zock 2010), shows how our systems theory approach causes further error, as delays in recruiting, training or promoting employees cause further delays in same down the road. Finally, Personnel Precision (Frauenheim 2011), claims that the data isn t the problem, rather it is our analysis that is wrong. Review of the Current Literature Pertaining to Workforce Planning Beginning with statistics, we face the obvious problems of deviation in trends. Using exponential smoothing is one statistical approach to solving that problem (Drandell 1975, 511). Of course, smoothing out the jumpy lines does not predict future wild fluctuations, but it can predict the standard error, which can help an organization prepare for the worst or for the best. Comparing statistics of supply and demand must take into account new technology that placates a demand for workers (Iacobelli & Muczyk 1976, 126). There will never be a way to eliminate error. Predicting the future is not like a space race of technology in which one victor will emerge with an invention of ideal performance. Rather, organizations will have to find ways to deal with imperfect predictions, such as separating and compartmentalizing processes (Sanders & Ritzman 2004). This study, in particular, brings our attention to greater flexibility in the workforce that will be needed in the future, brought about by cross-training, part-time employees and employees that wear many hats depending on what is needed. This is expanded further by unpacking the idea of competencies when planning, rather than just focusing on number of employees. 4

5 Competencies are based on roles and objectives, so roles must be carefully analysed to weigh the pros and cons of splitting or combining roles (Ruse 2008, 39). Our long-term goals as organizations may also need to be re-imagined in order for workforce planning to work without accurate data. Insufficient attention to the future is now being identified as a liability (Hansen 2008, 6). For example, how does accountability fit in with predictions that are often inaccurate without scaring away good administrators? As the use of workforce planning software increases in order to save money and resources in human resources, it can actually distract from the problem of making broader goals by making that inaccurate data the center of attention. (Frauenheim 2009, 1). Sadly, the lack of accurate forecasting data has consequences that are now measurable, including a lack of appreciation for data collection and associated changes in direction of the organization and limitations places on human resource staffing (Colley 2010, 202). Could predicting the future become a thing of the past? Natural ebbing and flowing of errors and mistakes in workforce staffing have been observed, so just as things are starting to get better, a chain of problems can make workforce planning seem ineffective at best Größler, A., & Zock, A. 2010, 829). The future of statistical analysis of workforce planning is not over, however. Innovative new approaches are still being investigated, especially in the realm of risk assessment. Although using data to lull oneself into a false sense of security is never good, data can still be used to be alerted to red flags of danger. Probability can be applied on multiple levels, from that of the organization all the way down to individual employees (Frauenheim 2011, 2). Overall, the most important part of workforce planning is turning strategies into action, and that is unlikely to be done without data to back it up. Perhaps we 5

6 can t prove our best hopes for the future, but we can try at least to steer clear of organizational disaster. Analysis, Synthesis & Inference Before predicting the future, one must define the relevant aspects of the future to be investigated. For an organization, the future might be defined as a vision or mission statement, individual goals and objectives, a strategic plan or core competencies (MT 2013, 2). Narrowing down priorities is key. Approaching the projections of current employee populations into the future can be done in several ways: A workload approach, a workforce approach or a competencies approach (IPMA 2002, 16-17). Within the competency approach, there are still more levels to analyse, such as organizational, leadership, functional, occupational, individual and team competencies (NV 2005, 11). Gap analysis before an action plan is vital, and this is the part in which projected numbers can be compared with present data (IPMA 2002, 20-21). Analysis of supply and demand, for example, is all about predicting the future. When demand is analysed, important information about the next three to five years must be known such as how changes will effect organizational structure and design, amount and type of work to be done, skills and numbers in the workforce, ratios of supervisors to employees and contractors or volunteers, new technology and innovations and more (USGS 2013, 8). Unfortunately, there are problems with predicting the future that can never be eliminated. When looking forward, we will always fall prey to wishful thinking. For 6

7 example, when analysing trends it can be only too easy to ignore negative trends or to imagine limitless increase on trends of interest (Karoly, Panis & Constantijn 2004, 3). Conclusion Ironically, we have no way to accurately predict the future of prediction. It is said that workforce planning is not just predicting the future, but building a long-term context for short-term decision making (Fairfax 2003, 5). Paradoxically, the trend in workforce planning forecasts seems to be split between the positivist approach that serves public administration so well and a more theoretical and broad-minded approach. Imagine workforce planning to be like weather prediction for an analogy. One might not be able to predict exactly how many inches of rain will fall, but one can still make a decision as to whether or not it is a good idea to have a picnic. The real future of workforce planning will have to strike a happy medium between searching for data about possible organizational problems and planning for the best picnic the entire workforce can imagine. Bibliography Colley, L., & Price, R Where Have All The Workers Gone? Exploring Public Sector Workforce Planning. Australian Journal Of Public Administration, 69(2), doi: /j x County of Fairfax, Virginia Strategic Workforce Planning. Presented at Valdosta State University, Summer,

8 Drandell, M A Composite Forecasting Methodology for Manpower Planning Utilizing Objective and Subjective Criteria. Academy Of Management Journal, 18(3), doi: / Emmerichs, Robert M., Marcum, Cheryl Y., Robbert, Albert A An Operational Process for Workforce Planning. Santa Monica: RAND Corporation. Frauenheim, E Talent planning for the times. Workforce Management, 88(11), Frauenheim, E Personnel Precision. Workforce Management, 90(3), Größler, A., & Zock, A Supporting long-term workforce planning with a dynamic aging chain model: A case study from the service industry. Human Resource Management, 49(5), Guinn, Kenny C State of Nevada Workforce Planning Manual. Carson City: Department of Personnel. Hansen, F The Long View. Workforce Management, 87(7), Iacobelli, J. L., & Muczyk, J. P An Industry-Occupational Forecast Incorporating Technological Change for Local/Regional Manpower Planning. Academy Of Management Review, 1(2), doi: /amr International Personnel Management Association IPMA Workforce Planning Resource Guide for Public Sector Human Resource Professionals. Alexandra: Presented at Valdosta State University, Summer

9 Karoly, Lynn A. & Panis, Constantijn W. A The 21 st Century at Work: Forces Shaping the Future Workforce and Workplace in the United States. Santa Monica: RAND Coporation. Ruse, D. E Stay In Front of the Talent Curve. Research Technology Management, 51(6), Sanders, N. R., & Ritzman, L. P Using Warehouse Workforce Flexibility to Offset Forecast Errors. Journal Of Business Logistics, 25(2), Sinclair, Alice History of Workforce Planning. Extracted from: Workforce Planning: A Literature Review. Presented at Valdosta State University, Summer State of Montana Workforce Planning Model; or Who Will Fill Their Shoes? Presented at Valdosta State University, Summer, Strategic Initiative Team United States Geological Survey Workforce Planning Desk Guide. Presented at Valdosta State University, Summer,