Webinar on call center WFM:Man versus machine:

the human factor in WFMApril 17, 2020

Ger Koole, PhD

The topic

• Small call centers forecast and schedule by hand• When scale increases manual scheduling becomes impossible:

automation• Human touch is lost and employee satisfaction decreases• Can we avoid this?• Is there really a dichotomy: man versus machine?

• First: current state of AI/machine learning• Then: consequences for WFM

Man versus machine

What men are good at:• Decision making in complex

environmentsWhat computers are good at:• High-dimensional decision

making in simple environments

Complexity environment

Dim

ensio

nalit

y

tic-tac-toe

chess

go

imagerecognition

car driving

Examples

Dog or muffin?Easy for humans – hard for computers

Where is Waldo?Easy for computers – hard for men

How about AI?

• Computer doing “difficult” human tasks• Considerable success, especially when environment/rules are clear • But: methods are different, AI-enabled programs “think” different

than humans• Machine learning = non-linear regression using lots of data• Artificial neural networks are different from real neurons!• Humans analogs (“learning”, ”AI”, etc.) are misleading

WFM

Rules in WFM are highly complicated• Labor rules• Personal preferences of agents• Fairness between agents

How to schedule when size (= dimension) increases without losing human touch?

Complexity environment

Dim

ensio

nalit

y

tic-tac-toe

chess

go

imagerecognition

car driving

WFM in small center

WFM in big center

Human touch

What is a “human touch”?• Respect for/understanding

of personal preferences• Understandable

decisions/forecasts• Too difficult for computers

Solution:• symbiosis of man &

machine

costs

WFM goals

Easy for computers & humans

servicelevel

Easy for computers, hard for humans

agentsatisfaction

Hard for computers, easy for humans

Symbiosis

the new common way… … but the role of the computer gets bigger

Symbiosis in WFM

• For symbiosis outcomes of tooling should be understandable

• Easy analysis of alternatives: No decision making but decision support

FORECAST FORECAST +REASON

WHAT IF I CHANGE THE SHIFT OF AGENT X?

The future of WFM

• Automated machine learning: No configuration needed – tooling learns by itself• Autodetect of outliers and holiday effects• Autodetect of preferences of agents – by rating of schedules• WFM tool is emphatic rational agent

• Learning more rules

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rule

rule

rule

rule

rule rulerulerule rule

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rule

rule

rule

rule

rule

The future of WFM

• The Gig economy?• Self-employed people working short intervals from home• Lower costs – or hidden costs?• How to match demand & supply? Financial incentives to work at right time• Parallel to airline pricing – based on demand & supply forecasting

• Self-scheduling?• Works for back-office tasks• Or really good decision support

Takeaways

• From symbiosis to “one button scheduling”• Taking all rules into account• Showing consequences of decisions• Explainable forecasts

• No black boxes – explainable AI• The future: empathic WFM tooling

Next week

• Friday April 24, 16:00 AMS time• Subject & speaker: to be determined• Suggestions: koole@ccmath.com

• Questions? Chat or voice