Improving Capacity Planning - Why Models are Important!

Ric Kosiba

Your Seminar Leader

Ric Kosiba is vice president and founder of the Decisions Group at Genesys. He founded the company, Bay Bridge, in 2000 that was acquired in August of 2012.

Ric has been involved in the call center industry 20+ years and enjoys working with contact center analysts and helping companies maximize profitability through math modeling. He holds a B.S.C.E., M.S.C.E., and Ph.D. in Operations Research and Engineering from Purdue University (go Boilers!).

Ric is responsible for the development and enhancement of our contact center capacity planning and analysis product line. He thoroughly enjoys working with our brilliant development and operations research team, which helped Decisions become the leading U.S. supplier of long-term forecasting and planning solutions.

Ric resides in Maryland with his wife and four children. Ric loves being a dad and enjoys coaching his kid’s football, basketball and lacrosse.

What we are discussing today

◦ What is capacity planning?

◦ Types of models and what do they do

◦ How to judge whether good or not

◦ Why it is important: what do we get if we have good models

◦ What happens to our operation when we automate

Genesys confidential and proprietary information.

Unauthorized disclosure is prohibited. 3

Ensure the operation is nimble and efficient in reacting to day-of changes within the environment

Ensure the right number of agents are trained and available to meet the seasonal and changing demand.

Traditional WFM Systems Strategic Planning

✓ Scheduling✓ Shift Bids✓Adherence/Conformance✓Real-time Management✓ Etc.

✓Hiring Plans✓Budgets✓ Establishing Optimal Goals✓What-if Analysis✓ Etc.

Ensure that the right numbers of agents are available at the right time to service our customers.

Workforce Optimization

An observation

◦ Most workforce management software systems do

not determine how many agents are required to

show up to work each week, nor do they develop an

operational plan to ensure that the company delivers

the required number of agents to the operation.

◦ Workforce management systems serve to best

manage the number of agents that you already have.

Our strategic or operational plan is only place where we try to manage the seasonality of the operation

What is the Strategic/Capacity/Long Term Planning Problem?

Determine the center resource plan that maintains the (appropriate)

service standard at least cost over time.

This is not a simple problem to solve

(Hence, the mad scientist.)

Q: Why is this hard? A: There is seasonality and variability in call centers!

Call Volumes!

Handle Time!

Sick Time!

Attrition!

…also learning curves, wage rates, training requirement, by center & staff group

AND! Our (Analytic) Life is Getting Harder…

On An Inbound

Call

Arranging An

Appointment

At A Branch

Office

Answering

Emails

Processing

Fax Reports

In Three Chat

Sessions

Making an

Outbound Call

The math required to understand the relationship between staff levels and the resulting service is… difficult

What are the steps to strategic planning?

Strategic planning systems work to take you from forecast to budget. They help to:

ForecastStaff & Capacity

Plan OptimizationBudget

Requirements

Simulation

Predictive Model

“Predict the future”

(Covered in our last webinar)

Descriptive Model

“What happens to my operation in this scenario?”

Prescriptive Model

“Find me the best resource decision”

Variable Labor Model

“Cost it all out”

What data do we need?

◦ ACD/Email/Back-office management system

• How are my agents skilled and what are appropriate roll ups?

• What is the historic performance (by interval): Contacts, service performance, abandons, work hours

per interval, handle times

• Where do calls go?

◦ Workforce management:

• Shrinkage (note schedules not necessary)

• Skilling: Universal agent?, silo’d?, skilled by proficiency?

◦ Finance/payroll: Costs and possibly revenues (eg. base rates, training rate,

cost to recruit, telecom,…)

◦ Customer experience system/report (CustEx scores by location)

◦ HR/Training: How long to recruit, train and transition a rep?

11

Staff Available Vs. Service Level

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0.00 100.00 200.00 300.00 400.00 500.00 600.00 700.00 800.00 900.00

Staff Available (FTE's)

Ser

vic

e L

evel

Descriptive Modeling: We want to convert staff into service. Does data analyses work?

This is real-world data

plotting staffing against

service level. Why is it so

ugly?

Let’s look at call center data:

Each data point represents a different economies of scale, different handle times, different agent efficiency, different schedule efficiency and adherence, different distributions of behavior…

Descriptive Modeling: The Erlang Equations

It would be nice to use a simple (!) equation to determine the relationship between staff and service. There are several approaches:

• Erlang C: the most prevalent, because it is generic and simple. Assume no abandons ever. Works well if you have no abandons. You don’t.

• Erlang A: Assumes an exponential patience curve. Works well if your contact center’s customers exhibit an exponential patience curve. Not good if not.

• Erlang X: Assumes a hyper- exponential patience curve. Works well if your contact center’s customers exhibit a hyper-exponential patience curve. Not good if not.

• Assumed Occupancy (workload) calculation. Works well if you can forecast your occupancy accurately. You can’t.

None of these approaches help model a multi-skill or multi-channel contact center environment.

Descriptive Modeling: Discrete-Event SimulationWhen the QB passes:• Is he being rushed?• Is he running?• Is it a short pass?• A long pass?• Is the receiver covered?• Does the receiver have good

hands?

What are the odds that the pass will be completed? Or Intercepted? Or dropped?

Descriptive Modeling: Simulation is used because it is accurate

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

SER

VIC

E LE

VEL

(%

)

DAY #

Service Level Comparison Simulation vs. Erlang-C vs. Actuals (Weekly Summary)

SL Actual SL Sim SL Erlang

Call Type

Simulation Service Level Prediction Erlang-C Service Level Prediction

Avg. Err Avg. Abs. Err

Std. Dev. Abs.

ErrAvg. Err Avg. Abs. Err

Std.

Dev.

Abs.

Err

Loans 0.59% 0.78% 0.65% 22.42% 22.42% 4.61%

Member Services 0.24% 1.19% 1.20% 25.97% 25.97% 5.56%

Preferred Services -1.27% 2.21% 1.75% 24.11% 24.11% 4.67%

Retail 0.86% 2.66% 1.39% 3.36% 4.03% 4.85%

Credit Card 0.31% 1.01% 1.08% 9.99% 9.99% 3.41%

Auto Insurance 1.20% 2.45% 2.27% -3.46% 3.46% 1.59%

Average 0.32% 1.72% 1.39% 13.73% 15.00% 4.12%

Tip: Validation of your analytic process breeds confidence in both your analyses, and you! Make validation a regular part of your planning meetings

Descriptive Modeling: One of the goals is to develop accurate requirements (the other is to develop accurate performance what-ifs)

Our goal: developing an accurate weekly requirement.

This tells the operation how many agents are needed

each week to hit the service objective

Service: ASA, SL, Abandon, Occupancy, Cost, Revenues

Descriptive Modeling: With a simulation model, you can change one performance driver to see performance expected!

With simulation, you can change anything and see resulting service (and vice versa). Accurately.

Prescriptive Modeling: Determine your hiring, overtime, controllable shrinkage plan!

Once we have an accurate requirement, we

need to determine the plan- and this is key to the

whole endeavor– that uses those tools available

to us (hiring, OT, controllable shrink) so the

weekly over/under is almost zero!

If so, we are “hugging the requirement curve”!

Prescriptive Modeling: How Most Folks Develop Staff and Controllable Shrinkage Plans

1. Develop a weekly requirement, a weekly

expected staff level (e.g. assuming attrition),

and the difference between available and

required (over/under chart)

2. Many planners assume all centers are the

same (shrinkage, attrition, propensity to hire,

learning curves)

3. “Eyeball” the chart and manually do the best

job possible hiring, planning vacation, training,

etc…

(a type of conjecture)

Prescriptive Modeling: Manual hiring plans are tough to do well by hand

Mathematical technologies, such as integer programming will ensure that plans include just-in-time hiring and an optimal balance between hiring and overtime. These technologies will determine hiring solutions that determine the most efficient centers, understanding the differences associated with each center’s seasonal performance, handle times, attrition, sick time, etc…

Tip: Invest in automating and optimizing the creation of hiring /extra-time /undertime/ controllable shrinkage plans

Cool What-Ifs: What service level goals should the business use?

At different times, under different economies of scale and revenues per call, different service goals are optimal!

Optimal service standards!

55% within 20 seconds

70% within 20 seconds

Sensitivity analysis helps answer

what-ifs, like “What service level

goal can we afford?”

Tip: Evaluate your

service standards

often– use sensitivity

analyses!

Cool What-Ifs: For service functions (not sales) this trade-off is very interesting!

If we plan well, what happens?

◦ We have an easier workforce management process.

We have the right number of agents show up to

work Monday morning!

◦ Our service is more consistent, and our operation

smoother. We get fewer service hiccups

◦ Our costs are lower, and yet we still hit our service

goals (typically with a 5-8% improvement in

efficiency) (Precise, well run machine!)

The key to improving your planning is to improve your planning technology

Three criteria: Your planning process has to be fast, optimal, and accurate

Accurate:• You must be able to prove

(validate) that your planning models are accurate

• Hint: Erlang and occupancy forecast methodologies are typically not very accurate (they overstaff)

• Another hint: consider• Simulation is accurate if

validated

Optimal:• Your methodology has to

deliver just-in-time hiring plans

• Your methodology has to be able to find the optimal balance between overtime, undertime, and hiring, while meeting all your constraints

• Hint: You cannot do this by hand

• Another hint: optimization modeling, such as integer programming will save your company money!

Fast:• There is no reason a

planning scenario- from forecast to capacity plan to budget cannot be completed in 10 minutes

• Hint: Automate it all!

What is Decisions?

◦ Decisions is a long-term contact center strategic planning and what-if analysis system.

◦ Because it is fast and accurate:• Perform risk and sensitivity analysis of your contact center

• Evaluate center what-ifs: investments, consolidation, and growth opportunities

◦ Decisions complements traditional workforce management software by focusing on strategic decision making and long-term planning

Genesys confidential and proprietary information.

Unauthorized disclosure is prohibited. 24

ForecastStaff & Capacity

Plan OptimizationBudget

Requirements

Simulation

Thank you, and (please) sign up for the rest of the webinar series!

Ric Kosiba, Vice President, Genesys Decisions

ric.kosiba@genesys.com

317-957-1109 – office

410-224-9883 - mobile