Resources

By Karen Elliott, CEO/Cofounder, Cinareo AI is starting to show up more in workforce management and capacity planning. As new capabilities emerge, it is clear that the industry is exploring how these technologies can enhance planning workflows in practical ways. This is an exciting development. At the same time, capacity planning sits in a unique category of decision-making, one where precision, transparency, and repeatability are essential. Planning models inform real-world

Small changes rarely break a plan. It is how they accumulate and surface that creates the real problem. The plan did not fail overnight. It stopped holding quietly. Nothing obvious broke. Forecasts were still within range. Staffing looked reasonable. Service levels had not collapsed. On paper, everything still appeared under control. But something had shifted. A small change in demand. A slight increase in handling time. A hiring delay that did not seem urgent at the time. In

Capacity planning often looks solid on paper, yet breaks down when real-world pressures are applied. As complexity increases, timelines compress, and executive scrutiny intensifies, many organizations find their planning outputs no longer inspire confidence or hold up when decisions matter most. This session brings together executive and practitioner perspectives to examine why capacity planning struggles in practice and how experienced leaders respond. Rather than focusing o

A practical discussion on why workforce plans fail in execution and how standardizing processes brings control and predictability back to operations. What You'll Learn Even the best workforce plans can fail in execution. Forecasts are accurate, spreadsheets look sound, yet service levels still slip and teams end up firefighting. In this webinar, Cinareo’s Mark Alpern and industry advisor Daniel Piper explore why this happens and how standardized workforce management pract

By Mark Alpern, COO, Cinareo. This article reflects patterns we see repeatedly when reviewing real-world staffing models across contact center environments. In many of these environments, the largest source of staffing error is not forecast accuracy, but how abandonment and retries are treated, or ignored, in the underlying model. Despite decades of technological change in contact center operations, Erlang-based staffing models remain the de facto standard for determining f

By Karen Elliott, CEO, Cinareo. In capacity planning software, complexity has become a proxy for credibility. It seems like more exposed variables and more buttons and functions sends the message: if it’s complicated, it must be accurate. After years of building a planning system, I’ve come to believe the opposite. Complexity is the easiest thing to promote. Clarity is the hard part. And when complexity becomes the interface, planning slows, trust erodes, and decision-making