Finance and HR Connecting the Dots for Workforce Optimization

Updated: Jul 5, 2018


During a time when many financial services companies have gone out of business, a mid-size financial services company based in the western U.S. is not only stable, but is growing as a result of targeted acquisitions and organic growth. The company has a deep history in the western U.S. and its core strategy is to expand its brand and operations beyond its home state. Each market that it has looked to expand banking operations into has been selected with careful research and well-executed strategy. Due to the success of this strategy, management asked whether the same strategy could be scientifically applied to its workforce to create a location optimization strategy in support of long-term growth, reduced risk and controlled workforce costs. Management asked the following additional questions regarding the workforce prior to moving forward:


Workforce Optimization Questions:

  • Considering the cost of labor, historical trends and projected future labor and economic conditions, how do we optimize our workforce?

  • Since the majority of the workforce is in one state, what is the business continuity risk?

  • If economic conditions change, then can we stress-test locations so we know which geographies are optimal under many different economic scenarios?

  • Since the business is expanding to other U.S. markets, is it advantageous to expand the workforce to other markets?

  • What is the best market for different critical skill sets and how do we determine a best workforce market?

  • Besides cost, what else should be considered for a location optimization?

Businesses tend to have difficulties measuring the workforce and its impact on the organization and this financial services company was no exception. HR and finance leadership understood that workforce analytics and workforce planning methods could not only help management make this type of strategic decision about the workforce, but they could also apply those methods to other critical business decisions as well.


Answering the Questions: To answer management’s questions, clearly a wide variety of variables needed to be addressed which would require deep external markets data across locations as well as extensive data on the current workforce, none of which was easily available.


The first priority was to define workforce optimization. The company was considering not only expanding business into different markets, but also the expansion of products and services. Where should the workforce be expanded and what markets might have the right skills and talent to optimize the workforce both currently and post-expansion? The question was potentially solvable by a combination of analytics and workforce planning, but where to start?


As the organization began looking at potential markets for expansion and optimization, several areas of focus were immediately identified. Real Estate, Tax, and HR savings were the initial focus. Consultants were hired to look at Real Estate and Tax savings first, with labor and HR savings as secondary factors.

Once HR reviewed the initial findings, it was determined that many significant human capital considerations such as turnover, talent supply, costs, and potential savings were either excluded or incomplete. This meant that workforce risks and challenges, the kind that can wreck any expansion or relocation project, had not been well considered.


To address the missing pieces the company engaged Human Capital Management Institute (HCMI), to quantify the impact a “Location-Optimization Project” would have on the workforce. HCMI is specialized in workforce analytics and planning, predicting and quantifying in dollars, the impact of workforce changes to an organization.


About Analytics and Workforce Planning: While still an emerging discipline in HR, workforce analytics and workforce planning are rapidly becoming critical workforce intelligence partners that bridge the gap between HR and finance. Analytics is simply the study of the workforce from the past to the present and using that historical data to make predictions about the organization. Typically, comprehensive talent management data about each employee and contingent worker in the organization is needed. Once aggregated and integrated, the data enables powerful predictive analytics and workforce planning. Ultimately, the two work together closely as the better a company understands its workforce through analytics, the better it can project and model the future workforce.

When done properly, workforce planning has the ability to model scenarios that can give companies the ability to test and quantify the impact that various decisions have on the workforce. Best-case, worst-case, and most-likely-case scenarios can be modeled to give a range of impacts based on various decisions. Different variables impacting the workforce can modeled together to give insight on risks and opportunities. Typical variables include headcount, turnover, total cost of workforce (TCOW), future talent needs (demand) and current-to-projected workforce growth or change (supply). Advanced workforce planning can also quantify workforce productivity, workforce growth rate, internal mobility, inflation, skills, and more.


While the opportunity to control costs was important, cost was secondary to selecting optimal locations with the necessary skills to support its future growth and new financial services products. Over time, a strategy to relocate some part of its core operations to position for future growth with cost savings began to emerge. In addition to cost savings in a lower-cost market, the strategy would create a healthy redundancy to protect against potential business disruptions in case of disaster.


Normally, the decision to relocate some or all of a company’s business to another location lies with the finance, real estate, and tax departments. Failing to factor the workforce into the equation beyond just rough labor costs is a deeply flawed approach. In leaving out issues like the talent supply, wage inflation, turnover, duplicate overhead and time to full productivity, such an analysis excludes the factors most likely to wipe out any cost savings and delay future growth and even create higher company costs due to inefficiencies.


After assessing the results to date, HCMI identified a significant number of variables (21 total) that should be included in a scenario model, showing how the different variables interacted and worked together to enable the company to model different scenarios around workforce, economic and location factors to optimize their workforce for growth and productivity while controlling or reducing costs. The following selected sample variables were recommended/included as part of the analysis:

  • Workforce productivity/time-to-full productivity impact

  • Supply of labor, concentration of talent, skill level of talent

  • Recruiting, on-boarding, and training costs

  • Total cost of turnover

  • Severance, relocation, and duplicate overhead costs

  • Salary inflation and benefits rates by market

  • Scenario modeling (best case, worst case, most-likely case)

  • Talent war risks

Internal Data & Structure Challenges: As is often the case, data availability was a challenge. A project of this scope requires a significant number of internal data points in form compensation, workforce history, tenure, demographics, internal mobility, turnover, recruiting, performance, engagement, training and benefits data. Further, the analysis needed to leverage job descriptions to compare different job groups and roles across divisions/locations requiring a layered job family classification structure that did not exist at the time. The data needed to be filterable by unit and cost center structure, facility and location. In addition to the typical data cleanup for consistency, a method structure had to be created to group and classify similar versus disparate jobs including obscure job titles with no consistency between departments. The company had a high employee-to-job-title ratio (i.e., many of its job titles had only one incumbent). This complexity added a significant challenge to mapping and comparing jobs to external generic job titles used in the broad market. Ultimately, jobs were placed into standard job families/categories, which immediately improved the company’s level of data organization.


To do a comprehensive workforce cost analysis, a typical market-to-market compensation comparison would not work. Instead, a model was created to simultaneously compare company actual average salaries in existing markets to external market workforce salary and benefit costs and then to project or predict future costs up to ten years into the future using each geographic region’s unique economic trend history and future projections. For example, some historically low-cost geographic markets had experienced far-above-average salary and benefit cost increases due to companies moving into the market while traditionally high-labor-cost geographic markets had seen no cost increases or even salary cost decreases in recent years. Surprisingly, the analysis revealed that the company was actually paying less than its local markets, which meant that the bar to look at other locations for optimization was even higher than originally thought.


In conducting a deeper-dive analysis, the company’s internal workforce data was integrated with external market data to assess key risk factors, and opportunities. In this case, factors such as future salary increases, performance, engagement, employee tenure, and other factors were analyzed to identify critical roles and build predictive models for turnover and internal career growth to see what the impact of new locations would have on the current and likely future workforce. Any unwanted turnover, especially in high performers or critical roles would have created significant lost productivity costs as well as higher overall costs for HR and the workforce due to recruiting, training and other costs.


In the workforce planning phase, the specific number and types of positions needed in different future scenarios were modeled to identify what the company might need to achieve its business goals and optimize the workforce for risk, talent supply and cost. To accomplish this, HCMI conducted research on a large number of potential markets focusing on key job groups and roles such as operations and IT job roles to assess each geographic region on historical to current talent supply and the pipeline for future entry-level talent from local universities and vocational institutions.


From this research, a workforce planning model was created to compare multiple scenarios including a current state or do-nothing scenario using a wide variety of labor, economic and geographic variables to predict the future workforce and its impact on the company by location. Specifically, the model was designed to predict not just labor supply and costs, but to specifically identify which roles to recruit for, what type of experience to look for, how much to pay, and what positions would have the most impact to the company in the future and should therefore be prioritized.


The beauty of creating an actual workforce planning model was that a large number of factors/variables could be adjusted, and many different scenarios studied, each with different assumptions, to see what worked best and what didn’t work at all. For example, going into one market dominated by a single entrenched competitor created a high probability of sparking a talent war meaning the entrenched competitor could respond to a loss of current talent by increasing wages and stealing talent back from the new entrant company. This type of situation could be disastrous as it could potentially wipe out any labor savings, increase turnover and negatively impact business growth.


In the end, specific scenario models were identified for the single best location to target for growth (overall optimization winner) as well as models that optimized individual variables such as cost savings, available talent supply or lowest talent risk. One scenario that management focused on was analysis by job group to understand which positions had the biggest savings opportunities versus others which would not result in any savings.


While no model can predict with 100% accuracy exactly what will happen in the future, by creating a model and scenarios with different variables, the company was able to determine a manageable range of possible outcomes that ultimately helped management make the correct decisions.

At the end of the analysis the HCMI workforce planning model actually answered several questions beyond the original scope including:

  • Would the market selected be able to support future growth?

  • Does the market have the right skills – were they readily available to support business growth?

  • How easy would it be to recruit the people needed in that market?

  • Was there high risk of starting a talent war upon entering that market?

Upon completion of the analyses, the team combined both cost and supply variables to create an overall index that was utilized to identify two optimal alternative geographic markets –neither of which was the absolute cheapest of the many locations included in the analysis..


The company’s decision as a result of the project has identified optimal locations that are the best to support future growth while at the same time achieving an estimated 10-year cost savings of $70 million value (calculated using a net present value methodology).


Going beyond traditional relocation analysis that just focused on real estate and tax savings with limited consideration to overall wage rates and instead using a combination of analytics and workforce planning was the key to the successful outcome for this company.


While it is important to examine real estate costs and tax implications carefully, the real determinant of success in any labor-intensive service business is the workforce. It isn’t possible to get a full picture of what the total impact will be without taking the company’s greatest and most costly asset (its workforce) into consideration.