Workforce Obsolescence

Workforce ObsolescenceThe departure of detailed skills, i.e., the disappearance of non-replaceable fixforce, is a problem faced by galore(postnominal) product sectors tasked with computer backuping(a) fine dodges. This problem is common for disposals that must man long time the DMSMS-type obsolescence problems for hardw ar, softw atomic number 18 and materials discussed in the other(a) chapters of this book.For some products, the disadvant get on with of player skills and set out can be apologise by simply adjusting hiring rates and instituting training of younger workers, however, in other sectors fellowship can be very difficult to replace. This chapter focuses on the passing of critical skills that are either non-replaceable or take prohibitively long times to reconstitute.13.1 Defining Workforce ObsolescenceMismatches between the skills possessed by the workforce and the skills needed by employers create a number of issues that complicate the long-term manufactu ring and sustainment of strategys. These mismatches start been classified into the following three general categories skills obsolescence, skill shortage, and critical skills loss.Skills obsolescence (also referred to as homophileity capital obsolescence) describes situations in which workers lack the skills needed to either become employed or remain employed, (De Grip and Van Loo, 2002). This often includes the segment of the workforce that have skills, barely those skills are obsolete requiring retraining of the worker. Where obsolete skills refer to skills that are no longer needed.Skill shortage describes situations where there are insufficient operable skill competences to fill the needs of an organization, e.g., (Green et al., 1998). Skills shortage articulates the need to identify, train and retain the workforce to fill legitimate and expected future skill needs. Skills shortage has many causes including the speed of technology advancement, e.g., (Duan et al., 2002), t raining and pedagogy gaps (Skinner et al., 2004), and can also be the result of an organizations inability or failure to protect its core skill competencies all everywhere long periods of time or during economic downturns (Melymuka, 2002).Critical skills loss is relevant to this book and is the focus of this chapter. Critical skills loss refers to the loss of skills that either cannot be replaced or require a prohibitively long time to reconstitute, (Sandborn and Prabhakar, 2015). In this object lesson reconstitution of the skills may require many years if possible at all. Critical skills loss is a special case of organizational forgetting, i.e., the loss of knowledge gained through learning-by-doing. Organizational forgetting can be ca employ by labor turnover, periods of inactivity, and/or failure of an organization to institutionalize tacit knowledge (Brsanko et al., 2010). Critical skills loss is a permanent and involuntary form of organizational forgetting that may be unr ecoverable. Critical skills loss (in the context of legacy trunk put up) is the result of long-term (20+ years) of workforce attrition where highly- ingenious workers retire without a sufficient number of younger workers to learn their skills and take their place.1 Critical skills loss is not necessarily the result of poor preparedness or lack of foresight (and although activity is light, it is not nonexistent) rather it is an inevitable outcome of the organizations dependence on a highly-specialized highly-critical skill set for which there is small, but non-zero, demand, (Sandborn and Prabhakar, 2015). It should be stressed that critical skills loss is a long-term phenomenon it occurs gradually over 20+ years, i.e., over the span of several generations of management coupled with mergers, acquisitions, and product line changes, critical skills often diffuse and eventually disappear.In the context of this book, the salient issue that defines workforce obsolescence for legacy m ission-, infrastructure-, and safety-critical outlines is critical skills loss.13.2 How Critical Skills Loss Impacts Systems and Where it Comes FromCritical skills loss is rarely a problem in high-volume low-skill manufacturing applications, e.g., assembly-line workers. For these applications, an divert workforce n primordial always exists or can be readily constructed through training programs. However, managing human skills obsolescence is becoming a significant problem for organizations tasked with supporting legacy systems. These support organizations need to be able to understand, forecast and manage a highly-specialized workforce with potentially irreplaceable skill sets.The system support and management challenges created by the loss of critical human skills have been reported in many industry sectors including healthcare (Waldman, 2004), thermonuclear power (Nuclear Workforce Planning, 2008), aerospace (Testimony of Elliot Pulham, 2002), and other enterprises(Leib old(a) and Voelpel, 2002). In the IT industry, the shortage of mainframe application programmers undergo in legacy applications is very problematic, (Goodridge and McGee, 2002) and (Hilson, 2001) in this case the necessary skills are no longer being taught because demand has dropped and younger workers interests are elsewhere. The loss of critical skills is about troublesome for organizations that must provide long-term support for legacy systems. For example, for defense systems, the loss of critical skills is potentially devastating Even a 1-year stay put in funding for CVN-76 aircraft carrier will result in the loss of critical skills which will take up to 5 years to reconstitute through untried hires and training. A longer delay could cause a permanent loss in the skills necessary to maintain our carrier force. (Congressional Record, 1994).The causes of critical skills loss include education and training declines (e.g., universities no longer educate engineers in the programming la nguages that are used in many legacy systems, (S fling, 2013) younger workers may perceive that sealed occupations are in decline, e.g., nuclear power (Nuclear Workforce Planning, 2008) and are therefore discouraged from entering them similarly younger workers may perceive certain occupations as not cutting-edge and therefore not enter them (Ahrens et al., 1995) (Adolph, 1996) younger workers may leave jobs supporting legacy systems to pursue other positions that appear to be to a greater extent lucrative and exciting ( mannequin 13-2 in Section 13.3.3 shows an exit age distribution for a legacy control system) the shrinkage of feeder occupations, e.g., historically the U.S. Navy has provided highly-skilled workers to the nuclear power industry (Nuclear Workforce Planning, 2008) older workers protecting their jobs by not passing knowledge along to younger workers, e.g., (Andolek, 2011) and fundamental differences between young and old workers regarding job perceptions (i.e., socia l and cultural influences) (Goodridge and McGee, 2002).13.3 Quantifying the Impact of Critical Skills LossCritical skills loss invasions the sustainment of mission-, infrastructure- and safety-critical systems. As the human capital that possesses the skills to support a system shrinks, the time that the system is down (non-operational) when the system requires support will increase. Downtime increases lead to increased business fall apart time, which results in a loss of revenue for manufacturing systems. Increases in downtime in the transportation, defense and service industries decreases system availability, which can lead to a loss of revenue, safety compromises, property damage, and loss of life (e.g., emergency vehicle unavailability).In this section, we briefly review the applicability of some existing manikins to quantifying the trespass of critical skills loss and then describe one border approach that estimates the financial impact of the problem.13.3.1 Existing Approa chesNearly all of the existing modeling and quantitative treatments address the problem of skills obsolescence, which is a different problem than the critical skills loss problem communicate in this chapter. Most skills obsolescence treatments assume that workers skills become outdated or are otherwise no longer useful, possibly as a result of mechanisation and other advances in technology. These works focus on the mitigation of skill decay in a workforce over time. The only existing work applicable to critical skills loss focuses on knowledge preservation, i.e., the capture of non-replenishable knowledge, (Joe and Yoong, 2004) (Hailey and Hailey). Some applicable work has also been done on retirement wave planning (Friel, 2002) however, this work focuses on head forecast rather than skill content.The modeling performed by Bohlander and Snell (2010) addresses a situation that is similar to critical skills loss, however, worker attrition and the be associated the unavailability of the workers is not considered. In Bordoloi (1999), a model for different skill level workers that enter and exit a company is developed the model takes into history the rate at which the company gains and loses workers. However, the model in(Bordoloi, 1999) does not estimate workers experience as a function of time and therefore does not determine the impact of critical skills loss on supporting systems. In the planning model developed by Huang et al.(2009) the goal is the determination of an ideal hiring rate using differing worker skill levels. While this model uses workforce simulation and determines the ideal hiring rate, the model does not take into account the costs incurred by the unavailability of workers.The basis for some workforce planning models is the physical sum of people employed, (Holt, 2011). However, the model developed by Holt, however, does not consider the aging of individual workers over time. There are models that have some applicability to critical skills loss in the sustentation workforce planning literature, e.g., (Koochaki et al., 2013) (Martorell et al., 2010) (Ait-Kaki, et al. 2011) and (Ahire et al., 2000). These models focus on optimizing fear scheduling and resource allocation. alimony policies have the goal of maximizing plant or process line availability plot con before long minimizing cost through the timely presence (and appropriate skill set) of guardianship workers. Koochaki et al., 2013) points out that maintenance workers are usually highly skilled and therefore difficult to recruit and that the efficient and effective use of a scarce maintenance workforce is very important. The model in (Koochaki et al., 2013) addresses the impact of limited maintenance workers (i.e., maintenance resource constraints) on the grouping of maintenance activities while comparing age-based backup man and condition-based maintenance (CBM). In (Ahire et al., 2000), the makespan (which is the total aloofness of the schedule) is minimi zed for a groups of preventive maintenance tasks constrained by workforce availability. Other text file treat the influence of CBM on maintenance scheduling and workforce planning, for examples look at (Koochaki et al., 2013) and the references contained therein. In general these references focus on the determination of the optimum size maintenance workforce.13.3.2 Modeling Human Skills LossA detailed model for the loss of non-replinishable maintenance resources has been developed in (Sandborn and Prabhakar, 2015) and (Sandborn and Williams, 2016). The technical development of the model is briefly summarized here, see (Sandborn and Prabhakar, 2015) and (Sandborn and Williams, 2016) for to a greater extent detail. The model uses historical workforce data to forecast the size and experience of the workforce jackpot as a function of time. The workforce experience family is then used to determine the cost of supporting (sustaining) a system as a function of time. The model was cre ated to address the questions what will todays skills pool look like in the future? and what impact will the future skills pool have on the organizations ability to continue to support the system?A key assumption in this model is that sufficient experience exists today to adequately support the system, and we wish to forecast the future workforce skills pools experience recounting to todays skills pool. The model has four primary inputs a current age distribution (fC), a hiring age distribution (fH), an exit age distribution (fL) and the hiring rate (H). take for granted a stationary analysis, the distribution of exit ages (fL) and the distribution of hiring ages (fH) and are constant. This does not mean that the same number of people are hired each year, but rather that the hired peoples ages are always distributed equivalently. The same assumption is made for fL. The distribution of current ages (fC) is used as an initial condition.To assess workforce pool size and experience ov er time, we must project the experience of the workers in the pool into the future. This projection starts with the initial conditions in the pool and accounts for age related loss and subsequent hiring. The level of experience within the skills pool changes over time and can be determined from 1) the novel hires added to the skills pool 2) the attrition (loss) rate of skilled workers and 3) the varying skill levels of the workers in the pool and how those skill levels (experience) increase as workers remain in the pool.The net frequency of people in the pool of age a during year i relative to year 0 is given by,(13-1)where, i is the number of years from the start of the analysis, a is age, and Hi is the fraction of new hires per year (fraction of the pool size at the start of the analysis period i = 0). The first term in the brackets in comparability 13-1 is the current workforce pool size (relative to year 0), the second term in the brackets in comparability 13-1 is the number of new hires (relative to year 0), and the multiplier accounts for the retention rate. Note, Equation 13-1 assumes that the hiring rate, Hi is the same for all ages, a.The initial condition for the model is that the fraction of people of age a relative to year 0 in year 0 is given by,. The cumulative net frequency of people in the skills pool, NNET, in year i is determined by summing Ni(a) over all the ages (y = youngest to r = retirement), (13-2) calculative the size of the workforce pool (head count) over time is necessary but not sufficient to capture an organizations future ability to support a system because workers have different levels of experience. Because of the varying experience, not all workers provide an equivalent level of value to the support of the system. In this model, experience is defined as the length of time that a worker has spent in a particular position. The cumulative experience in the workforce pool in year i, Ei, is work out using,(13-3)where, RE and IE map age to the experience measured in years (RE and IE are determined using a parametric model from actual data). Note, while experience has the units of time, Ei, which is used in this model, represents the cumulative experience relative to the initial condition.The time to perform maintenance in year i is found from the cumulative experience using,(13-4)where, is the time to perform a maintenance activity with a skills pool having E0 experience at i = 0. In Equation 13-4 the time demand to perform maintenance increases as experience decreases due to the following factors 1) less-experienced workers require more time to perform maintenance (learning curve effects), and/or 2) if the pool of workers capable of performing the needful maintenance task shrinks, appropriate workers may not be available at every site and may have to travel from a different location, which takes time.The most significant impact of the loss of critical human skills for legacy systems is the ability to pe rform system support (corrective maintenance) in a timely manner. Corrective maintenance costs consist of spare parts, labor, downtime, overhead, consumables/handling, and equipment/facilities. When a corrective maintenance event occurs, the cost of performing the required maintenance action is,(13-5)where is the fraction of the maintenance events of severity level j that result in a business interrupt, is the cost of replacement parts (if replacement parts are needed) in year i,is the cost of labor (per unit time) in year i (with appropriate overhead applied), and is the cost of business interrupt (per unit time) in year i. , and are assumed to be discounted using an appropriate discount rate.13.3.3 Example System Support Case StudyA detailed case memorise was previously published in (Sandborn and Prabhakar, 2015) and (Sandborn and Williams, 2016). In this section we only provide a a couple of(prenominal) highlights from that case study. The case study considered the support of a legacy control system for a chemical product manufacturing company (the system was originally developed and deployed in the 1970s) and has over 2000 instances (plants) installed and currently operating and supported worldwide. In this case, because the process line availability is very important, unscheduled downtime cannot be tolerated.The model overviewed in Section 13.3.2 requires three distribution inputs the current age distribution (fC), the distribution of hiring age (fH) and the distribution of exit age (fL). Two of these distribution inputs are readily available from organizations field data the hiring age (fH) and a current age distribution (fC), Figure 13-1. The current age distribution (in Figure 13-1b) has a mode of 55 years, which is very close to the early retirement age in the organization, thereby demonstrating the issue that this chapter is focused on.The exit age distribution (fL) shown in Figure 13-2 for this case study was synthesized using the distributions for fH(a) and fC(a) in Figure 13-1 along with the assumption of a stationary process. Figure 13-2 is a bathtub curve. It indicates that workers either exit early or exit late (but few exit between ages 45 and 60. The younger workers exit because they are changing jobs within the company. The company modeled in this case study, has had difficulty retaining young workers (engineers) to support the legacy system. The younger engineers have a tendency to relocate to other job opportunities within the company that they perceive as having better long-term life prospects. Above age 60 the workers are retiring. Figure 13-2 supports the critical skills loss observation made in Section 13.2 that younger workers leave legacy system support jobs (presumably for other positions).The number of workers (pool size) is shown in Figures 13-1 and 13-2, but the experience contained within the pool is not reflected in these distributions. To get from pool size to the workforce pool experience, the mapp ing from age to applicable experience is needed. The parameters for the mapping function in Equation 13-3 were generated from the years of experience (on the control system) and the years of service to the company.The net pool size (number of workers) over time as a fraction of the pool size in 2010, NNET, is shown in Figure 13-3a. Figure 13-3b shows the experience relative to 2010, and Figure 13-3c shows the average age of the workers in the pool. The results in Figure 13-3 assume no hiring, H = 0. Figures 13-3a and 13-3b indicate that although a 10% drop in head count occurs in the first 6 years, the experience remains approximately constant (existing workers are gaining enough on-the-job experience to offset the drop in head count). After 2016, the experience drops as the oldest and most experienced workers leave and are not being sufficiently replenished.Assuming that the lost skills are replenishable (they are not for the real company treated in this case study), we can estimat e what the future hiring rate, Hi, would have to be to preserve the initial level of experience, E0, in the skills pool. Equation 13-1 is used to determine the annual hiring rate, Hi, that is required to replenish the cumulative experience lost as a result of attrition and retirement. Figure 13-4 shows results for hiring rate, Hi, relative to the initial pool size P0as a function of the number of years from the start of the analysis.Figure 13-4 shows that no hiring is required in the first five years (we are not allowing hiring to drop below 0, a hiring rate below 0 would reflect a layoff situation). A hiring rate of over 6% is required commencement in 2017 for 9 years and then settles to 2-5% for all the years thereafter. When H is greater than zero in (4), the hiring rate is applied to the entire hiring age distribution, fH. The required hiring rate solved for in Figure 13-4 accounts for both the time required for new workers to learn the skills necessary to support the system an d the exit age distribution in Figure 13-1.Figure 13-5 shows the annual cost of supporting the legacy control system through year 2040 (all 2000+ instances of the system are costed here). The cost modeling is performed using a stochastic discrete-event simulator that samples time-to-failure distributions for the components of the control system to come up maintenance events (determining the maintenance event dates and the components that need replacement). Subsystem-specific (and severity category specific2) failure distributions are sampled to obtain failure dates for the system. At each maintenance event, maintenance resources are drawn and a cost is estimated using Equation 13-5. Most of the maintenance events do not result in business interrupt time because they only impact one of the two parallel control systems and = 0, however, a small fraction (the most severe events) result in dual control system failures where 0. The risk of dual failures and the resulting business int errupt is captured by the differing severity categories. The specific data associated with the system count, the subsystem/severity category reliabilities, and the cost of business interrupt time is proprietary to the customer and therefore not included here.For this case study, was determined to be 0.54, convey that when the number of people in the pool drops below 54% of the number that are in the pool initially (in 2010), the extra maintenance time penalisation (modeled by (15)) is applied.Figure 13-5 shows two support cost results. The results demonstrate that there is minimal effect of skills loss prior to 2030. In year 2028 existing liveliness buys of spares parts (hardware) start to run out resulting in the cost step between 2028 and 2030. We obtain the lower curve in Figure 13-5 when there is no skills loss, Ei/E0 = 1 for all i in Equation 13-4. In this case there is still an annual cost increase caused by part obsolescence that is extenuate via lifetime buys of parts (th ese buys commit significant capital to the pre-purchase of spare parts and long-term holding costs). The higher cost curve in Figure 13-5 is the case where no replenishment of lost skills is possible (H = 0), this is close to reality for the company considered in this case study.13.4 DiscussionWorkforce planning means ensuring that you have the castigate number of people, with the right skills sets, in the right jobs, at the right time. This chapter presents a model that enables workforce planning in cases where the workforce is non-replenishable. The model developed estimates both the number of skilled employees (workforce pool size) and the cumulative experience in the workforce pool. This information is used to determine the resources available to maintain a system as a function of time. Cumulative experience dictates the time (and the resultant cost) required for workers to perform the maintenance activities necessary to support the system. Because of the prohibitively large co st of legacy system replacement, these systems are rarely replaced unless a catastrophic failure occurs or their support costs become impractical. The model can potentially be used by companies to support the development of business cases for system replacement, see (Sandborn and Prabhakar, 2015).Numerous important assumptions were made in the development of the model. In the event presented here, we assume that years on the job is the only way workers can gain experience. We have not accounted for methods that could be used to accelerate the rate at which workers become more experienced, e.g., capturing older workers knowledge in knowledge bases 29,30 could accelerate experience. We have performed a discrete-time analysis because the input data that was available to us only exists annually. A continuous-time solution could also be developed, but one must be careful to match the model to the form of the input data.There are several verificatory consequences of the loss of critical skills that we have not addressed, and which would be challenging to quantify in terms of cost. The workers that are maintaining systems (particularly engineers) are likely to be performing other beneficial tasks in addition to corrective maintenance. Besides corrective maintenance, they may also perform preventative maintenance, projects intended to upgrade the reliability and/or military operation of the system, and knowledge transfer activities. As workforce resources decrease, it is reasonable to assume that all tasks, except corrective maintenance, would decrease. Even if sufficient resources remain available for corrective maintenance tasks, an inability to perform the other tasks that the engineers might do results in a loss of maintenance efficiency improvements, system reliability improvements that could decrease future maintenance requirements, and system performance. Further, if the job satisfaction amongst the engineers that are forced to only perform maintenance decre ases then their retention may be negatively impacted.There are other factors that may modify the case study presented herein. These factors include location (culture certainly impacts the likelihood that highly-skilled workers remain in system support jobs), gender, the product sector, etcetera These effects could be analyzed with the presented in this chapter model if sufficient data existed.1 For many types of legacy systems, 5 or more years of on-the-job experience may be required to become competent.2 The level of maintenance required (which dictates the maintenance resources required) and the degree of business interrupt associated with the maintenance event are governed by the severity categories. See From (Sandborn and Williams, 2016) for details.