Cash Management Benchmarks You Should be Hitting - The Ultimate Guide

Introduction

Getting cash management right is table stakes for companies of all sizes and industries. In this guide, we’re going to explore key benchmarks Treasury teams should be striving for (comprehensive cheat sheet below). We’ll do this by asking ourselves fundamental questions that cash management operations seek to answer. The answers to these questions may vary by company size and industry, so we will consider these nuances as well.

Is your Treasury team hitting these benchmarks? Let’s find out.

Question: How quickly can we confirm cash balances?

Liquidity decisions are only as good as the data latency. If cash balances are confirmed manually, then borrowing, investing and funding decisions become reactive instead of strategic.

From a risk mitigation perspective, delayed visibility leads to increased overdraft risk, risk of trapping cash and even missing opportunities for intercompany funding.

Companies with near real-time visibility consistently demonstrate stronger governance, audit trails and fraud detection.

Key metrics

• Cash visibility (% global cash visible daily)
• Time to daily cash position (# hours to produce cash position)
• Cash position refresh frequency (# times per week)

What does good look like?

For the rest of this article, we’re going to assume that “large cap” companies are companies with >$5bn in annual revenue. We’ll assume that “mid-market” refers to companies with $500m-$5bn in annual revenue and <$500m for smaller companies.

Large companies should expect to have > 90% of global cash visible on a daily basis. It should be closer to 100%, but acknowledging there are always exceptions for companies spread globally without a Treasury presence in every single country/region. The mid-market has a little more leeway to support flexibility, however should still have upwards of 75-80% of global cash visible every single day.

Smaller companies also need to keep a close eye on their cash, but it is not always feasible to access and manage every bank account daily and they can lag larger companies when it comes to daily cash visibility.

Daily cash preparation would ideally be prepared over morning coffee, leaving the rest of the day for more strategic initiatives. Large companies should expect to have their daily cash position highly automated and take a couple of hours or less to prepare. Mid-market companies should still be striving for 2 hours as well, however a range of 2-4 hours is more reasonable factoring in limited resources and less visibility into daily cash.

Depending on the complexity of a small company’s banking footprint (which we’ll explore a little later), this could take an entire day, especially if the process is highly manual and without dedicated resources.

Most companies should be striving toward a daily cash position refresh frequency. Smaller companies often don’t have that luxury and prepare on a less frequent cadence e.g. weekly or on an ad hoc basis.

As we’ll see, the industry that these companies operate in have a material impact on their cash management operations.

For capital intensive industries, such as manufacturing, telecom and energy, you can expect these metrics to slide (downward) due to the complexity of operations. For example multi-system reconciliations will slow down the ability to prepare a daily cash balance.

When we think about less capital intensive industries, such as technology and professional services organizations, you should expect to see an upward trend in these metrics. Operations are simpler, by comparison, and a more centralized approach promotes visibility and speed. By leveraging API-driven integrations these companies are achieving close to real-time daily cash positioning.

Question: How reliably can we forecast cash?

Forecast accuracy determines whether the Treasury team is acting as a planner or firefighter. Poor forecasts lead to expensive short-term borrowing and idle cash buffers.

The ability to allocate capital relies on the ability to forecast accurately. Key events such as M&A, debt repayment and share buy-backs all rely on confidence in the forecast.

It provides insights into operational maturity across other areas of the business e.g. Accounts Receivable and Accounts Payable. You could argue that it is as much of a broader Finance KPI as it is a Treasury one in this sense.

Key metrics

• Forecast accuracy (30-90 day variance)
• Forecast frequency (# updates per week/month)
• Forecast inputs (% automated forecast inputs)

What does good look like?

Large companies should expect to have the vast majority of their forecast inputs automated (> 75%) and be preparing their forecast updates on a rolling weekly basis, if not daily. With this level of automation and frequent oversight, there should be a low tolerance for forecast variance. When considering a 30-90 day forecast, these larger companies should be managing variances within ± 3-5% i.e. ~3% for a 30-day forecast and ~5% for a 90-day forecast.

Mid-size companies should be striving for the same margin for error, although forecast inputs may not be as highly automated and forecasts typically prepared on a weekly basis. With that, companies can expect to see a little more variance in their forecast, within ± 5-8%. High performing teams are forecasting within a 5% variance on their 30-day forecast.

Companies preparing their forecast less frequently and with fewer automated inputs should expect higher levels of volatility in their forecast. Smaller companies will be more impacted by a customer payment that doesn’t clear when expected. This too leads to larger variances. With that, a more reasonable expectation for smaller companies is a variance of ± 8-15%. While that may seem high, it reflects the reality of a lack of automation in forecast inputs and infrequent preparation of the forecast.

Manual forecast inputs are common for more capital intensive industries - there are a lot of moving parts. Volatile project spend is also a factor that reduces the precision of forecasts. 

The story looks a little different for our technology and professional services companies. Forecast inputs are automated through tight ERP and TMS integrations, supporting continuous forecasting models vs manual preparation. With the generally predictable inflows, these companies should be striving for the ±3-5% variance range we discussed for large companies and high performing teams are able to consistently forecast within a 3% variance on their 30-day forecast.

Question: How much cash is sitting idle?

Idle cash can impact earnings. Even modest optimization of idle cash can generate a meaningful yield without increasing liquidity risk.

Excess cash balances often mask inefficiencies built into cash operations e.g. fragmented accounts and weak cash pooling structures.

Leadership is increasingly viewing idle cash as a return metric and not just a liquidity one, especially for companies holding onto significant cash balances. 

Key metrics

• Idle cash ratio (% idle cash vs total cash)
• Investment deployment lag (# days to invest surplus cash)
• Days cash on hand (average # days cash on hand)

What does good look like?

Large companies are keeping their idle cash balances down by taking advantage of things like advanced cash pooling mechanisms, automated cash sweeps and active investment programs. Large companies...

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