Chris WattHuman Capital Management

Article 24/16/15

Optimization models have been used for decades in the supply chain management system. These

models have reduced inventory levels, reduced overall operating costs, and maximized profits. Altering

these models to fit a human supply chain is distinctly different than these traditional uses of modeling.

The people aspect is quite different from machine parts and transportation routes. People can change,

learn, adapt, perform differently in different situation, and dynamic. These differences from parts and

pieces pose a challenge to making an effect model.

The service industry employees 75 percent of the labor force and they produce 70 percent of the

total industry output. The ability to put the right person, in the right place, at the right time is a critical

component of this industry and one that can substantially change the business success rate and

profitability. IBM has taken this task head on developing a model to aid their human capital management

practices, specifically their Integrated Technology Services department. Their annual revenues are

approximately $4 billion with its 10 service lines they offer a range of integration and support service

products. This part IBM’s business is based on human talent; the people that make up the department are

not used up and written off after they have performed a service, they are used again and again. Workload

and utilization are key components of effectiveness, people are capable of deploying more than one skill

at a time and using their skills across multiple engagements simultaneously. Modeling is further

complicated by uncertainty in staffing, allocation of multiple people, and timesharing their skills across

different engagements. To solve these complex problems IBM developed OnTheMark with the ability to

forecast engagement demand and human-talent requirements.

“We derive stochastic modeling and optimization methods to support risk-based capacity planning that

determines human-talent levels to maximize business performance. We develop an approach to predict

future talent and skill composition via stochastic modeling and optimization (control) of supply evolution.

We perform an optimal matching of multiskill talent against demand targets; we then optimize investment

decisions to address talent shortages and overages via our combined modeling and optimization

solutions.”

The model is composed of five main parts demand forecasting, risk-based capacity planning,

supply evolution and optimization, multiskill shortage and overage analysis, and skill shortage and

overage management. Together these five components determine the talent levels and investments over

time that maximizes business performance. The five components put together create a very powerful

tool, independently they are still used to support various human capital supply chain processes.

Demand forecasting is the first step in this five stage process. The components that make up the

demand forecasting are revenue to the provider, number of engagements to be delivered, required time for

starting each service delivery, and the skills required. They use historical data with machine learning

methods to estimate the skill staffing requirements. Statistical forecasting is used to estimate the demand

for each engagement. When these two are put together with the staffing template the demand forecasting

stage is complete. The demand forecasting stage feeds information to the risk based capacity planning

and supply evolution and optimization stages.

Once the outputs are figured form the demand forecast model they become inputs to the risk-

based capacity planning model. This model’s overall goal is to find the optimal solution of staffing levels

and the possible lost opportunity cost associated to deploying certain skill levels to other engagements.

There are definite trade-offs that happen when a skill (person) is deployed to a project. They are no

longer in inventory to be used for a possibly better (more profitable) project. The other side to the model

is determining the ideal staffing levels of skilled personal. Finding the optimal solution to these two

problems is this models purpose. The main decision factors in this particular model are the ideal skill

capacity targets, optimal skill capacity levels, and business investment decisions to achieve these two

levels, while maximizing business performance.

The next stage in the process is the supply evolution and optimization. People unlike parts evolve

and are dynamic, accounting for this is difficult in a model but necessary. Generally, people acquire

skills, gain efficiencies, change roles, leave the company, and obtain new employees which all need to be

accounted for. The model takes all of these factors into consideration and provides an optimal solution

for the amount of employees that should be hired and how many employees should be offered incentives

to stay past their planned retirement date. These outputs are based on expected profits if these optimal

levels are maintained. The outputs from this model are fed as inputs to the risk-based capacity planning

and multiskill shortage and overage models.

The multikill shortage and overage model accounts for the people in the organization that have

the ability to perform more than one skill or job function. The model figures the demand that is needed in

each planning period and pulls from the list of available people that meet the required skill set for each

project. The multiskill people are optimally matched to which skill is in a higher forecasted demand that

exceeds the single skill people to fill the remaining demand. This model’s output feed risk-based capacity

planning and supply evolution and optimization.

The final model, skill shortage and overage management, pulls all the other models together with

the goal of maximizing business performance over time. The risk-based capacity planning provides

human talent levels, supply evolution and optimization provides evolutionary dynamics of future skill

composition, multiskill shortage and overage analysis provides matching of human capital against the

skill capacity targets, and then this model ties them all together to provide the maximum business

performance.

To measure the success of and the on-going accuracy of such a complex model IBM had a

feedback loop integrated into the model. This allowed them to calibrate and validate predications made

from the model against actual business outcomes, which increased its accuracy in for future outputs. IBM

observed an 85-90 percent accuracy rate for pipeline revenue forecasts and a 90+ percent accuracy rate

for overall human capital demand forecasts. By applying the evolution models to determine how many

people to employee with each type of skillset were within a few percentage points of actual demand in

large companies.

IBM has been able to apply these models conjointly in many different types of applications.

Applied in a cost minimizing problem the risk-based model was able to reduce the individual loss risk

probabilities for different projects by more than 15 percent and have more than a 40 percent reduction in

expected capacity costs. Using the models in a revenue maximizing problem they were able to increase

the capacity levels for skills that are used in high margin activities across a wide range of project types.

The outputs from the model determined that the higher margin activities needed to carry a high loss risk

to that type of skill (employee) and increased revenues by 35 percent. There are many different

applications that these models have been very successful in increasing revenue or decreasing overall or

specific risks.

IBM has reviewed its quarterly performance while using these models based on previously used

approaches. The reviews have highlighted the overall effectiveness by reducing skill shortages by 10-80

percent and overages by 30-150 percent. In the first year of implementation IBM saw over an $11 million

increase in the first quarter in the United States alone in cost savings and increased revenue. Similar

results have been seen in the world-wide adoption of this form of human capital management. Human

capital management is beginning to explode in the industry, being able to forecast the type of skilled

people and how to proper apply those skills to maximize profits and reduce risk is very important to the

service industry. This is an area that will continue to develop and be deployed by many businesses.

There are many businesses that will use human capital management in the future and some were

identified in the article; nursing, insurance agents, sales force numbers, financial analysts, really any

industry that requires a skilled or trained person to perform a task for another person. When I was

reading through this article I pictured another industry that I saw an application for, restaurants and their

servers. Today there are club, frequent purchase cards, and many other ways to track the spending

patterns of customers. Why limit this to just the spending? If the memberships provided an on-line

platform to describe the characteristics that they like most in a server, than that can be applied and select

from the available serves that are available.

Each server would need to be evaluated by management to list certain characteristics;

friendliness, speed, accuracy, gender (probably illegal), and as many other personal traits as you can think

of. The servers profile is stored in a database at the restaurant, when a server clocks in their profile then

becomes available for selection. When a customer arrives with their membership card and it is scanned

when they request a table, the server that meets their criteria is assigned the table. There would need to be

some constraints that were built into the model like maximizing the number of tables any one server could

have and time in between assigning the same server to different tables, among others. If a customer came

in that didn’t have the proper information on their membership or didn’t have one than they would just be

assigned a server in the typical fashion that they have been using for years.