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Mantra for Innovative Project Management

Piyush JainSenior Delivery Manager

Infosys Limited

Effective Talent Management

Predictive Model for Skill Based Forecasting

By

Piyush Jain, Senior Delivery Manager, Infosys Limited

Vinay Prabhu, Delivery Manager, Infosys Limited

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ContentsAbstract............................................................................................................................................................................ 3

Introduction....................................................................................................................................................................... 3

Context to the paper......................................................................................................................................................... 3

Typical Talent Forecasting Model..................................................................................................................................... 4

Shortcomings of the Typical Talent Forecasting Model....................................................................................................4

Our approach to forecasting talent needs......................................................................................................................... 5

Talent skill repository.................................................................................................................................................... 5

Skill based Talent Forecasting Model............................................................................................................................... 5

Input Parameters (for each skill)................................................................................................................................... 6

Derived Parameters (for each skill)............................................................................................................................... 7

Working of the Model........................................................................................................................................................ 7

Observations..................................................................................................................................................................... 8

Assumptions & Scope for further development.................................................................................................................9

Conclusion........................................................................................................................................................................ 9

References....................................................................................................................................................................... 9

Acknowledgements........................................................................................................................................................... 9

About the Authors........................................................................................................................................................... 10

Note: All data shown in this paper is simulated. Actual data has not been used due to confidentiality reasons.

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Abstract

The world of today is a fast place. Patience is no more a virtue but a bane. Longevity is now measured in quarters and

not years. Clients are scrambling to appease customers by trying to release new products, new software versions,

more features, software upgrades, release patches and so on almost on a quarterly basis. The pace is relentless and

its consequences are being felt across project management functions.

One function that organizations have to rapidly focus is the talent management function. As project timelines get

crunched and clients demand higher productivity with fast ramp ups, there is constant flux in terms of people demands

for staffing project engagements. Availability of right people with the right skills at the right time is often the difference

between project success and failure. In light of this it is extremely important for IT services companies to ensure they

have a good model for talent forecasting leading to right sourcing and optimal utilization.

Almost all companies have some model or the other that is used for forecasting talent demand leading to talent

acquisition. However, most models tend to focus only on future demands to arrive at absolute acquisition numbers. In

our opinion this is a sub optimal model. In this paper we present a skill based talent forecasting model that would help

predict the skill utilizations more accurately. We believe this model would assist talent managers in managing the talent

pools more efficiently, thus optimizing their talent acquisition costs and ensuring optimum utilization levels.

Introduction

Talent Management has always been a key function for any enterprise. For a human resource intensive industry like IT

services, its importance is magnified many times more. Talent Management refers to the anticipation of required

human capital for an organization and the planning to meet those needs. It is the science of using strategic HR to

improve business value and to make it possible for companies to reach their goals. Everything done to recruit, retain,

develop, reward and make people perform forms a part of talent management as well as strategic workforce

planning[1].

The challenges in the current business scenario have precipitated the need for strategic and innovative approaches in

talent management for the purpose of achieving business objectives and gaining competitive advantage.The cycle of

workforce planning includes filling resource requests, analysing resource utilization, forecasting capacity, managing

and identifying the human resources to fill that capacity, and then restarting the cycle[2].

The scope of this paper is limited to the forecasting aspect of workforce planning. Through this paper we will explore

how skill plays a crucial role in forecasting of talent requirement. We take the opportunity to present a predictive model

that considers skill attributes for talent forecasting and how that would help in ensuring the right focus on optimal talent

utilization and better talent sourcing strategies.

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Context to the paper

The paper focuses on our experience of deploying skill based forecasting method for workforce planning. We take this

opportunity to share how bringing in detailed skill view in talent forecasting led to better clarity and purpose in

workforce planning leading to higher efficiencies in talent development and deployment.

Each organization has their own method/approach to talent forecasting and it is dependent on the context surrounding

their business. Our attempt here is not to dictate a particular model or methodology. The purpose of this paper is solely

to bring the focus on why skill view is important in talent requirement forecasting and how by doing that you can

achieve more desirable results both in sourcing and in utilization.

Typical Talent Forecasting Model

At a broad level traditional forecasting model for talent requirements focuses on future demands and current attrition

levels to determine the shortfall in talent needs. The demands do encapsulate the skill requirements under them, but

the focus is more on the overall number of talents required to bridge the attrition shortfall and also address the growth

needs projected as demands.

The table below provides a view of one such typical model.

Legend Parameter FormulaCurrent

Q

A Current Total Talent Strength   10000

B Current Utilization (%)**   79

B1 Implies no. of people on production work B1 = A*B/100 7900

C QoQ expected growth (%)***   2

C1Implies expected people in production in next

quarter C1=B1 + (B1*C/100) 8058

D Target utilization % for next quarter   80

E Projected Total Talent Strength by next quarter E=C1/D * 100 10073

F Current Attrition %   3.5

F1 Implies talent shortfall due to attrition F1=A*F/100 350

G Gross Talent Shortfall for the quarter G=F1+(E-A) 423

H Projected Trainees to join in the quarter   125

INet Talent Shortfall for the quarter that needs to be sourced I=G-H 298

Green Cells indicate input parameters to the model. Amber Cells indicate derived values based on input parameters

** Utilization is defined as people who are on production projects being billed for their services.

*** growth is assumed to be linear in terms of number of people billed in production

Data shown in above model is simulatedTable 1 – Traditional Talent Forecasting Model

The said model relies on inputs like current and expected utilization level, current attrition level and projected liner

growth in terms of manpower growth to arrive at the overall talent requirements

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Shortcomings of the Typical Talent Forecasting Model

The typical model for forecasting as shown above is good for overall projections. It relies on the macro level inputs

around utilization, attrition and growth projections to forecast the net talent requirements. However, it is laced with

shortcomings that can lead to sub-optimal results in getting the right talent.

The foremost shortcoming of the typical model is that it subsumes the skill view under the macro level growth

projections. This skill view is mostly based on skill requirements captured in the form of talent demands. Demands can

be for incremental growth in existing programs, attrition replacement or for completely new programs. However, it goes

without saying that talent demands tend to be rather liberal in terms of requirements. What is not explicitly captured

and known in this model is the current utilization of these skills, the impact of ramp downs if any on the utilization and

how that impacts the availability of the skill pool to meet the projected demands. Going only by the demand view and

ignoring the utilization view can lead to a skewed view of skill requirements, which can lead to mismatch between what

was required and what got sourced and thus affecting the effectiveness of the overall talent utilization.

Hence, while we totally agree that a typical forecasting model gives a good view on the overall projected talent

requirements, it needs to be supplemented by a model that explicitly captures the skill based utilization view, leading to

better workforce planning and more accurate skill based talent projections.

Our approach to forecasting talent needs

We have considered a unit / division of the organization as the base for explaining the skill based forecasting model.

This is not to say that it is restricted only in its application for a unit / division. The application of the model whether for a

unit / division or the entire organization is entirely a prerogative of how the organization approaches workforce planning

and management. Organizations that choose to de-centralize workforce planning and management can have this

model adopted and adapted at each unit / division level.

We would also like to state that the frequency of forecast we have assumed here to be on a quarterly basis, where in

the skill requirements of a said quarter are done one quarter in advance. This approach is mainly adopted keeping the

sourcing and recruitment timelines in mind. If an organization follows half yearly or annual forecasting cycle, then the

model can be suitably adapted for the same.

Before we get into the details of the model, the functioning of this model is dependent on having a robust system that

captures the skill details for each employee in the organization and follows the discipline of keeping it upto date at all

times.

Talent skill repository

A central repository that captures the skill details for every employee in the organization.

As any organization that deals with a wide variety of skills, the system designed needs to provide flexibility in

categorizing the skills according to domains, technology, lifecycle stages and management levels. This needs to be

overlaid with experience levels categorized in terms of proficiency metrics.

The accuracy of the forecast for a skill is completely dependent on integrity and accuracy of data in the system at all

times.

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Skill based Talent Forecasting Model

The skill based talent forecasting model is a predictive modelbuilt around utilization views of each skill set at the start of

current quarter and its translation to projection of skill requirements for next quarter. The utilization view of skill set

taken at the start of the current quarter is considered the most reliable view considering it represents the actual position

of utilization at the end of previous quarter which has not been affected by production / non production movements in

the current quarter. Attempting to use the model with skill utilization data captured any other time in the current quarter

can lead to erroneous results due to daily variations in production / non production status of the skill sets. Hence, we

recommend freezing the snapshot of utilization data taken at the end of previous quarter and use that as a baseline for

forecasting skill requirements for the next quarter.

[Illustration – If current quarter is Q2, then consider the skill utilization snapshot view taken at the end of Q1 for forecasting skill

requirements for Q3]

Following is a template of skill based forecasting model.

Table 2 – Skill Based Talent Forecasting Template

The template is built around a view of skills that you want to forecast for talent requirements. Using the skill

classification defined in the skill repository for employees, you create each row for a particular skill set. The model can

have as many skill rows as you wish depending on the focus you want to give to skillsets that have high utilization, you

see high demand and therefore you need to forecast against them. As a suggestion, we advise having the model focus

on top 10-15 skill sets basis of their demand and utilization. The model requires certain parameters to be input for it to

derive the forecast numbers for each skill set.

In the model template above, cells marked in Green are for the input parameters and cells marked in Amber are

derived from the input parameters.

Input Parameters (for each skill)

Current Actual Production Head Count

o Implies total current people who are doing production work and are being billed for their services

Current Actual Total Head Count

o Implies total number of people having this particular skill set.

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Current allotted trainees

o Implies the number of trainees allotted for the unit and expected to join in current quarter

Expected lateral joins

o Implies the laterals who have accepted offers and likely to join in the current quarter

Projected Attrition

o Implies number of people on notice period

Overall Growth % for next quarter

o Implies the expected growth through linear increase in manpower in the next quarter

Desired Skill Utilization % for the quarter

o Implies the target skill utilization you want to maintain for the particular skill set for the current quarter.

This is dependent on the demand that you are seeing for the said skill set to get deployed in the

current quarter.

Derived Parameters (for each skill)

Utilization %

o Implies utilization snapshot of the skill as on end of previous quarter.

Derived as C = A/B * 100

Expected Total Head Count for the current quarter

o Implies total HC derived for the particular skill set after adding the talent additions (trainees + laterals)

and minus the projected attrition numbers

Derived as G= B + D + E - F

Projected HC x% growth

o Implies the anticipated head count in production for the projected x% growth in manpower terms

Derived as H = A+(A*N/100)

Skill Util % at x% growth

o Implies the projected utilization of the particular skill set based on the projected increase in production

head count

Derived as I = H / G * 100

Projected HC required for next quarter

o Implies the projected head count for the particular skill set. This is based on two factors. First is the

increase in production head count that you anticipate for the skill set based on the growth % projection

provided and secondly the desired utilization you want to maintain for this skill set so as to not over

leverage the talent pool for this skill set.

Derived as K = H / J * 100

Net Shortfall / Excess

o Implies the net forecast in either excess or shortfall for the particular skill set

Derived as L = K - G

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Working of the Model

Now let us look at the working of the model using simulated data (actual data has not been used due to confidentiality reasons)

Shown below is the model template populated with simulated data. We have kept the data same as that used for

typical model (shown in Table 1)to explain how this model provides a more realistic view on talent requirements that the

unit needs to forecast and plan for the next quarter.

Table 3 – Skill Based Talent Forecast Model with simulated dataAs indicated above, once the input data is provided for each skill in the amber cells, the model goes through various

intermediate calculations to finally arrive at the forecasted numbers for the said skills in Col L.

Some key aspects about the working of the model are as follows:-

1. Utilization level for each skill is captured separately and it provides a view on which skills are being heavily

utilized implying higher demand and which skills are exhibiting lower demand and thus, lower utilization

2. Projected joiners + attrition for each skill provides a clearer view on the available current talent pool for the

said skill

3. Projected skill utilizationconsidering growth numbers (as given in col I)provides a view on how the skill

utilization pattern would look like if continue at the same rate of deployment for the said skill. This is an

important decision making point fordeterminingthe desired utilization level for the said skill, which would

help to determine the precise numbers needed to maintain an adequate supply for the said skill

4. The net forecast requirements for the skill (col L)can either be a shortfall in number or excess depending

on their current and future utilization levels. What this implies is that the skills that have a shortfall need to

be sourced to improve their supply, whereas skills that have excess need to be focused on generating

demand for improving their utilization.

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5. By ignoring the skills that have an excess number, one can tweak the numbers in col M (with the projected

shortfall / excess requirements (in col N) as the basis) to decide on the number of new joiners for each of

the skills.

Observations

This model has been effectively used over multiple quarters and fine-tuned during the course of its usage. Some of the

benefits and observations are:

By tracking the utilization at a skill-level, the average and maximum utilization levels for a particular skill

(for the last 6 months) gave an indication of the utilization levels that the Unit can possibly operate on.

People with skills that were not in demand were cross-trained to meet the demand of over-utilized skills.

This helped train the people on time and achieve the business demands of the Unit.

Certain niche skill sets that were found to be on the excess side, were brought to the focus of the sales

team to help them work on generating business demands suiting the said skill sets, thus, improving their

net utilization.

With focused hiring, the time taken to put a new joinee into Production reduced by a substantial margin.

Assumptions & Scope for further development

The skill based talent forecasting model presented in this paper provides a better and more accurate view on workforce

planning and talent requirements. Albeit, the model presented assumes that the lineargrowth % in terms of manpower

requirements applies uniformly across all skill levels, we plan to address this in the next version of the model.The

model going forward should also consider the experience levels of the talent and the location aspects of the

availability/demand.

We strongly believe that through this extension the model can really provide a near accurate view on skill forecasting

that can go a long way in the workforce planning for the future.

Conclusion

The Skill-based Forecasting Model aligns the Talent Management strategies with the business goals of the Unit. As

companies increase the focus on improving productivity and efficiency, it will be of paramount importance to provide

the people with the right skills at the right time and the right place. To ensure that this is possible Workforce Planning

through the Skill-based Forecasting Model will play an important role.

References

[1] Carpenter, Mason, Talya Bauer, and Berrin Erdogan. Management and Organizational Behaviour. 1. 1.

FlatworldKnowledge, 409. Print.

[2] Rudolf Melik. "Rise of the Project Workforce, Chapter 9: Workforce Planning". PM Hut. Retrieved July 9, 2010.

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Acknowledgements

The authors would like to sincerely thank Mr. Nagabhushana Samaga for his invaluable inputs, Ms. Anju Chawla

Takkar for doing a thorough proof reading and review of the paper and Mr. Manohar Atreya for his guidance and

encouragement.

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About the Authors

Piyush Jain is a Senior Delivery Manager with Engineering Services at Infosys Limited, Bangalore. As part of his

current role, he heads the engineering business for a large telecom client and additionally holds the Unit PMO

responsibilities. Prior to this, he was the head of Talent management function at engineering services.

He has 20+ years of industry experience most of which is in the software engineering and Telecommunication space.

He has been instrumental in incubating and establishing large offshore development centers for engineering clients

across multiple geographic locations.

He is a certified PMP and has published and presented papers in the prestigious PMI conferences, forums and other

technology journals.

He is a graduate in computer engineering from S.V. NIT, Surat(formerly R.E.C Surat), Gujarat. He is a sports

enthusiast and an avid reader with specific interest in current affairs and how it impacts business paradigms.

Vinay Prabhu works as a Delivery Manager in Engineering Services group of Infosys Limited, Hyderabad. He manages

the delivery of Engineering R&D programs for global clients in the Healthcare and Life Sciences segment. He has

managed complex Product Engineering programs for clients across geographies and industrial segments.

He is a graduate in Computer Engineering from M.S.R.I.T, Bangalore and has about 18 years of experience in

delivering projects in the Product Engineering space.

He is passionate about technology and people engagement related activities. He is an avid reader, an F1 enthusiast,

loves quizzing and spending his free time with family and friends.

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