New Possibilities for Workforce Algorithms

Every year for the Society of Workforce Planning Professionals (SWPP) conference, I help design the Interactive Intelligence Interaction Decisions T-shirt. Sounds sort of weird, but I really love doing this. My favorite T-shirt so far said, “Plan for the Possibilities,” with a superstructure of a bridge.

For those of you who’ve never heard of Interaction Decisions, it’s a strategic planning application for long-term analysis and fine-tuning of staffing requirements for contact centers.

Let’s chat about “the possibilities” today in terms of algorithms. Contact center workforce management, planning and forecasting is a discipline built on the back of very sophisticated algorithms. These algorithms can do a lot, such as:

  • read historical volumes
  • apply different forecasting techniques
  • present the best forecast
  • estimate how many agents are needed to hit service goals
  • simulate multi-site and multi-skilled workgroups
  • schedule efficiently
  • develop hiring plans and overtime plans

In the past, there’s been significant conflict between building algorithms that were accurate and optimal, and building algorithms that were fast. You could get accurate, but not fast and vice versa. Algorithm developers have worked on a variety of techniques made possible by each technology advancement to make algorithms produce good results in a timely fashion.

Erlang-C Equation: One early attempt, circa 1917, was the old Erlang-C equation. Its purpose was to calculate a quick approximation of the number of staff required to hit service standards. As contact centers became more complicated, the Erlang approximation became less and less accurate. Surprisingly, many systems still use this fairly inaccurate approximation.

(Check out our white paper, Contact Center Planning Calculations and Methodologies: A Comparison of Erlang-C and Simulation Modeling).

Processing Power: Around 15 years ago, there was a great improvement in processing power. With the advent of multi-core processors, algorithms could be coded to allow different processors to handle different parts of problems separately. For problems that allowed a separation of its algorithms, math modelers were able to produce better solutions to complex problems in reasonable timeframes.

The Cloud: We’ve all heard the positives of moving a business to the cloud. I believe for analytic systems it’s something to get very excited about. The advent of the cloud has resulted in an even more impressive leap in processing capability. Through technologies like Amazon Web Services, software that’s been designed using a cloud architecture can access not just the four or eight computing cores available to modern desktop computers, but can turn on new processing cores as needed, and then turn them off again when the math model is finished. If constructed properly, algorithms can solve incredibly complicated or CPU-intensive problems very quickly.

But there is a rub. In order to take advantage of the new possibilities available through the cloud, you have to rewrite the algorithms in modern computer languages. The inital investment can be pretty large. I expect older workforce management systems will be too hard to upgrade and companies will choose to skip the investment.

My prediction is this: we’ll see a mix of both, making it hard for buyers to discern the best choice for them. We’ll end up with some systems in the cloud that have very accurate models, fantastically efficient schedules and hiring plans. And for those built with old technologies, the systems will be inefficient and inaccurate schedules and plans. One easy question for buyers to ask during the buying cycle is, “When were your algorithms written and coded?”

Has your company checked into the possibilities the cloud offers to improve your management systems? We’d love to hear what’s working and what’s been a challenge.

 

 

Ric Kosiba

Ric Kosiba

I joined Interactive Intelligence in August 2012 as part of the Bay Bridge Decision Technologies acquisition. I helped found that company back in 2000 and thoroughly enjoyed working with our brilliant development and operations research team, which helped us become the leading U.S. supplier of long-term forecasting and planning solutions. In my current role as vice president of the Bay Bridge Decisions Group, I’m responsible for the development and enhancement of our contact center capacity planning and analysis product line. I tripped into the call center industry about 22 years ago and can honestly say that I still love it. I hold an M.S.C.E., B.S.C.E., and Ph. D in Operations Research and Engineering from Purdue University (go Boilers!). I reside in Maryland with my wife and four children. I love being a dad and enjoy coaching kid’s football, basketball and lacrosse.