The Factor-F: A Measure of the Human-Power Resource Quality to Diagnose Segment and Predict the Workforce
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The Factor-F is introduced as a non-dimensional variable that helps to diagnose, segment, and predict workforce quality to enhance engagement and retain talent while addressing skills mismatches. Using data analytics, it evaluates workforce productivity and creativity, with distinct metrics for high and low performers based on their performance curves. The findings suggest tailored training and strategic management can enhance organizational performance by effectively aligning human resources with business objectives.
The Factor-F: A Measure of the Human-Power Resource Quality to Diagnose Segment and Predict the Workforce
- 1. The Factor-F: A Measure of the Human-Power Resource Quality, to Diagnose, Segment, and Predict the Workforce; for the Optimization that locates the Right Workforce in the Right Environment, Delivering Profitable Business Results By Peter Anyebe Information technology and specifically the computer, has made it possible to manage large sets of data than ever before. And big data has made it a breeze, to keep track of customer preferences for the sake of segmentation and targeted sales efforts. Moreover computer aided designs, CAD reduce time to the market, with new products that satisfy customer preferences. But both these sectors are linked, and have to be driven by a third, production, as well as the support services. In general, data and analytics are mere tools in the hands of the workforce. So that workforce engagement and retention issues, as well as skills mismatch and shortages especially for specific industries and many in-demand job roles, have remained cogs in the wheel of the huge business growth that could characterize the increasing use of data and analytics. This requires that the same developments, especially predictive analytics, be applied to the management of human resources, with specific emphasis on talent. The factor-F is therefore presented as a non-dimensional variable constant, nvc that enables as follows: Diagnoses of workforce, to optimize same, for workforce engagement and retention as well as the correction of skills mismatch and shortages Segmentation of workforce into those that function within, and those that function outside the box; to distinguish the leaders of industry from the others Prediction of workforce, to put labor on the shelf, according to the capacity to make return on investment, ROI The associated metrics are trim and include mainly, creativity, tp productivity, fs thought pattern, f0 and behavior pattern, Po. The segmentation is achieved by the evaluation on the curve, for the others; and on the gradient, for the leaders. The data and time series graphs attached show that the methodology rejects mixed multitudes, for purity of population. The normal shape of the time series ought to look like the one insert-left, for grading on the curve; and insert -right, for grading on the gradient. The distinction is important, for diagnoses. First, there Agape Consultants 8, De-Bangler Street Gboko, Nigeria +234(0)703-430-2486 anyebepeter@yahoo.com tp Po f0 fs Po fs f0 tp Grade on Curve Grade on Gradient
- 2. is segmentation between the section of the workforce that functions within, and those that function outside the box. And then two, 2 other possibilities could be observed as follows, according to whether the curve deeps at the beginning or at the end: When the curve deeps at the beginning, it is productivity, fs that would be the problem When it deeps at the end, the trouble would be with creativity, tp Creativity and productivity are the anchors on which the behavior pattern, Po and the thought pattern, f0 are hinged. They supply the energy, in the form of motivation, to drive thought and therefore behavior. Then in a loop of continuity, given motivation, the appropriate attitude would be shaped, to drive the acquisition of the required abilities. A sample of the graphs that demonstrate the diagnoses is presented below, along with the data from which they have been derived. Chart 1: Graphs of Workforce Segmentation at High PerformanceBHI = 1.4707, OrgC = 1.2363 tp Po f0 fs tpPo f0 fs tp Po f0 fs Curve is Normal Creativity is Defective Productivity is Defective
- 3. At optimum performance as evident in bill of health index, BHI = 1.4707 and organizational culture, OrgC = 1.2363, when the workforce is evaluated on the gradient, the top performers return a time series graph that is normal, as exemplified by the first, 1 st two, 2 graphs of the time series at the top. For the rest of the workforce, the graphs are wrinkled, to indicate misfit. But for evaluations on the curve the situation is reversed, so that it is the top performers who become misfits in the system, as evident on the time series graphs at the bottom. In both cases, the histograms are statistically normal. Notice that with the high fliers, the deep of the curve is at the end, to signify trouble with creativity, tp. With the others, the deep is at the beginning, for productivity, fs troubles. At low performance as evident in bill of health index, BHI = 40.8397 and organizational culture, OrgC = 0.0864, when the workforce is evaluated on the curve, all of the workforce return a time series graph that is normal, as exemplified above. They are at home!!! But for evaluations on the gradient the situation is reversed, so that the graphs are wrinkled, to indicate misfit. In both cases however, the histograms are statistically normal. In this case, the deep of the curve for everybody is at the beginning, which means that all the subjects have productivity problems. This is to be expected, since the low performance is a general case. This would be a situation in which the leadership lacks the capacity to creatively derive the procedures that are required for optimum performance in the organization, which lack of capacity has been identified in the earlier diagnoses with the high performers. For business growth therefore, a team would be required to derive the procedures, and then to train the others to adopt same. Training would have been effective when productive behavior is Chart 2: Graphs of Workforce Segmentation at Low PerformanceBHI = 40.8397, OrgC = 0.0864
- 4. observed, to perform at task by the standard procedure. To derive the standard procedures require creative thought, when the waves-duality principle is adopted. There are four, 4 expressions of the factor-F. The factor-FN predicts productivity as the capacity to reduce phenomena into the four, 4 strategic objectives, for Nu → 4. Fb predicts Soul, FM is the measure of faith, from which FQ is derived as the measure of human-power resource quality, HpRQ. Then given FM and FQ, the factor-FN would be determined, to complete the circle! For corroboration, the soul factor-S predicts the appraised return on investment, ROIApp for ROIApp = S. Confirm these predictions on Table-1, with the first, 1st two, 2 subjects who are high fliers, and on Table-2, for the others. Pc Rn /n ScR ScT f0b RtN C EB Nu FN Fb FM FQ fs tp f0 Po F S ROIp 1.64 2.28 2.13 0.51 0.5 0.5 0.54 1.71 3.41 3.88 1.06 1.71 0.97 1.15 0.83 0.68 0.86 0.93 1.06 0.44 0.44 1.58 2.16 2.05 0.49 0.57 0.52 0.56 1.67 3.4 3.94 1.05 1.67 1.04 1.15 0.8 0.69 0.86 0.93 1.05 0.45 0.46 1.65 1.35 1.36 0.48 1.27 0.92 0.89 1.43 3.05 2.14 1.24 1.09 1.43 1.23 0.52 0.9 0.8 0.9 1.08 0.81 0.96 1.34 1.09 1.56 0.88 1.13 0.98 0.98 1.6 3.37 3.05 1.12 1.02 1.51 1.23 0.51 0.92 0.79 0.9 1.11 0.91 1.02 1.18 1.09 1.36 1.02 1.2 0.97 0.97 1.36 3.46 3.26 1.1 1.03 1.56 1.24 0.51 0.92 0.79 0.9 1.12 0.89 0.98 1.69 2.61 2.06 0.15 1.33 0.42 0.5 1.51 3.68 3.52 1.08 1.88 1.77 1.24 0.5 0.93 0.79 0.89 1.19 0.39 0.4 1.66 3.57 1.26 0.09 1.34 0.25 0.39 1.44 3.19 2.56 1.17 2.38 1.44 1.23 0.52 0.9 0.8 0.9 1.08 0.25 0.27 1.4 2.39 1.19 0.18 1.35 0.42 0.53 1.27 3.4 3.16 1.11 1.9 1.52 1.23 0.51 0.92 0.79 0.9 1.11 0.38 0.41 2.65 3.1 1.89 0.16 1.08 0.51 0.45 1.23 3.36 4.66 1.02 1.7 1.18 1.18 0.63 0.81 0.84 0.92 1.0 0.44 0.4 1.32 2.2 1.1 0.22 1.37 0.45 0.56 1.22 3.41 3.23 1.1 1.82 1.51 1.23 0.51 0.92 0.79 0.9 1.11 0.4 0.44 1.8 2.6 2.35 0.15 1.36 0.45 0.5 1.83 3.79 3.76 1.06 1.82 1.84 1.24 0.5 0.93 0.79 0.89 1.22 0.41 0.41 1.95 2.89 2.55 0.13 1.34 0.42 0.47 1.95 3.77 3.69 1.07 1.9 1.82 1.24 0.5 0.93 0.79 0.89 1.21 0.38 0.38 2.85 2.74 2.81 0.79 0.28 0.63 0.58 2.62 2.86 1.54 1.41 1.47 0.63 1.24 0.5 0.93 0.79 0.89 1.13 0.53 0.64 2.33 3.66 3.08 0.09 1.3 0.35 0.4 2.23 3.72 3.52 1.08 2.07 1.81 1.24 0.5 0.93 0.79 0.89 1.21 0.33 0.32 2.6 4.7 3.7 0.06 1.3 0.26 0.33 2.44 3.65 3.09 1.11 2.34 1.85 1.24 0.5 0.94 0.79 0.89 1.22 0.26 0.25 3.64 6.29 4.89 0.05 1.26 0.25 0.28 3.22 3.66 3.28 1.1 2.39 1.8 1.24 0.5 0.93 0.79 0.89 1.2 0.24 0.22 5.37 7.29 6.03 0.06 1.21 0.34 0.28 4.52 3.72 4.19 1.04 2.1 1.67 1.24 0.5 0.93 0.79 0.89 1.16 0.32 0.26 BHI = 1.4707 OrgC = 1.2363 Table 1: Workforce Segmentation at High Performance Pc Rn /n ScR ScT f0b RtN C EB Nu FN Fb FM FQ fs tp f0 Po F S ROIp 1.64 2.28 2.13 2.08 0.11 0.5 0.45 1.71 0.13 3.88 1.06 1.71 0.58 1.24 0.5 0.93 0.79 0.89 1.18 0.44 0.37 1.58 2.16 2.05 1.98 0.13 0.52 0.47 1.67 0.13 3.94 1.05 1.67 0.6 1.24 0.5 0.93 0.79 0.89 1.16 0.45 0.38 1.65 1.35 1.36 1.96 0.28 0.92 0.74 1.43 0.13 2.14 1.24 1.09 0.45 1.27 0.46 0.98 0.76 0.88 1.32 0.81 0.8 1.34 1.09 1.56 3.6 0.25 0.98 0.82 1.6 0.12 3.05 1.12 1.02 0.64 1.24 0.51 0.92 0.79 0.9 1.13 0.91 0.85 1.18 1.09 1.36 4.17 0.27 0.97 0.81 1.36 0.13 3.26 1.1 1.03 0.63 1.24 0.5 0.93 0.79 0.89 1.13 0.89 0.82 1.69 2.61 2.06 0.63 0.3 0.42 0.42 1.51 0.12 3.52 1.08 1.88 0.74 1.21 0.54 0.89 0.81 0.9 1.06 0.39 0.33 1.66 3.57 1.26 0.35 0.3 0.25 0.33 1.44 0.14 2.56 1.17 2.38 0.4 1.32 0.41 1.05 0.73 0.86 1.38 0.25 0.23 1.4 2.39 1.19 0.75 0.3 0.42 0.44 1.27 0.14 3.16 1.11 1.9 0.47 1.26 0.47 0.97 0.77 0.88 1.3 0.38 0.34 2.65 3.1 1.89 0.65 0.24 0.51 0.38 1.23 0.16 4.66 1.02 1.7 0.34 1.44 0.32 1.21 0.65 0.82 1.42 0.44 0.34 1.32 2.2 1.1 0.88 0.31 0.45 0.47 1.22 0.14 3.23 1.1 1.82 0.45 1.28 0.45 0.99 0.76 0.88 1.33 0.4 0.36 1.8 2.6 2.35 0.62 0.3 0.45 0.42 1.83 0.12 3.76 1.06 1.82 0.79 1.2 0.58 0.85 0.82 0.91 1.03 0.41 0.34 1.95 2.89 2.55 0.53 0.3 0.42 0.39 1.95 0.12 3.69 1.07 1.9 0.79 1.2 0.58 0.85 0.82 0.91 1.03 0.38 0.32 2.85 2.74 2.81 3.2 0.06 0.63 0.48 2.62 0.12 1.54 1.41 1.47 0.42 1.3 0.43 1.02 0.74 0.87 1.36 0.53 0.53 2.33 3.66 3.08 0.38 0.29 0.35 0.33 2.23 0.12 3.52 1.08 2.07 0.8 1.19 0.58 0.84 0.82 0.91 1.02 0.33 0.27 2.6 4.7 3.7 0.26 0.29 0.26 0.28 2.44 0.11 3.09 1.11 2.34 0.84 1.18 0.62 0.81 0.84 0.92 1.0 0.26 0.21 3.64 6.29 4.89 0.2 0.28 0.25 0.24 3.22 0.11 3.28 1.1 2.39 0.82 1.19 0.6 0.83 0.83 0.92 1.01 0.24 0.19 5.37 7.29 6.03 0.24 0.27 0.34 0.23 4.52 0.12 4.19 1.04 2.1 0.73 1.22 0.54 0.89 0.81 0.9 1.06 0.32 0.22 BHI = 40.8397 OrgC = 0.0864 Table 2: Workforce Segmentation at Low Performance
- 5. The derivation of the model human is presented, to operationalize the variable constant, F: The model human would therefore be described as follows: Given: L = A Fn : The Duality Model L1 = A1 F2 : The Creativity Model L2 = 1/A2 F2 : The Relativity Model A = LF2 : The Normality Model Then: The analyses presented in this work are done on four, 4 models that include the identity kit, Id-K, the bill of health index, BHI the appraisal model, AppM and the self- containment model, ScTM all of which will soon be available on the web. Other papers that give more details on the concepts presented in this document include: 1. Relativity 2. Dimensions 3. Normality 4. Duality 5. The Natural Order L = Positivity A = Negativity F = Relativity F2 = The Absolute Mathematical Model of Humans An equation of the form: L = ∂1 1 /A L = ∂2F A = ∂3F ∂3 = 1 /∂2 ∂1 = LA: from 1 = F2 : from 2, 3, and 4 For: L = 1 /A F2 QED: Substituting in 1 Recall the four, 4 fundamental principles that govern the universe F = Faith: Intellect L = Love: Emotion A = Attitude: Will A1 = Appreciation A2 = Evaluation F2 = Characterization Fn = Procedurization L1 = Conceptualization L2 = Actualization L = 1/A2 F2 , A1 = 1/A2 Fn = 1/ F2 L1 = L2 Empirically, the factor-F is evaluated as, F = ScT + PfI. The derivation of PfI is presented below: 6. The F-Scale 7. The Identity Kit 8. The Business Case 9. The Path to Soul 10. What is Man 11. The Agape Constant, F