Manual Typing is Expensive: The TCO of Invoice Data Capture (Part 2)

Every company receives bills and invoices every day... and manual retyping is still the go-to solution for most companies. But as the number of invoices and complexity of the process grows, the costs start to grow as well. In the first article in this series, we have seen that automating the data entry makes a dramatic impact on the costs. Now, it is time to learn why – and exactly how much manual invoice data extraction costs Accounts Payable departments.

Meet the main metrics: Keystrokes and FTE Load

How much an invoice is going to cost is now really a function of two things – how much do you pay for the process itself, and how much effort does your workforce spend on operating it. Let's think about the latter for a minute. What does "data entry" actually mean in practice? It's simple – typing stuff! It may sound naive at first, but we have found that "how much you type" is a great proxy to the effort you spend on the data entry task. Let's measure the number of keystrokes: the number of times a human operator needs to input information, using a keyboard, mouse or other means of physical input. For example, retyping 123456789 takes 9 keystrokes, meanwhile validating an already extracted number 123456789 can take just a single mouse click.

Manual data entry - keystrokes, invoice data extraction

Next, let's think about the keystroke speed of the operator. In practice, the typing speed we have measured for a Rossum client when entering field values from scratch was 78 kpm (keystrokes per minute).

Do you feel that's too slow? Let's assume a base keystroke speed of 250 kpm for continuous typing. But the snag is that invoice data entry is very much not continuous typing. Instead, every individual invoice field comes with a fixed time overhead – to move to it in the user interface, locate it on the page, take in its value first, etc. The 250-to-78 kpm difference corresponds to 3.8 seconds per field of fixed overhead. This is realistic: when going "maximum speed", it would be difficult to jump field to field with less than 2 seconds per field. When bogged down in such routine and repetitive tasks all day, every day, we can assume twice the time is reasonable, before the error rate spikes.

We are very lucky that our model company has such a simple data entry case – we did not even consider searching internal or external databases, communicating with other departments and resolving discrepancies.

The Full Time Equivalent (FTE) Load is the effort of one employee as if working full time on data entry tasks. For example, if 9 employees each work on data entry one third of the time, you would have a data entry FTE Load of 3.

The key contributing factor to FTE Load in a company is the number of keystrokes necessary to process every invoice field on each document, combined with the keystroke speed of the operator. We will assume the fixed 78 kpm speed in our analysis, even though the speed of the system and the user interface quality is doubtlessly going to be an advantage for modern tools. We are further assuming 75% time efficiency for a back-office worker in our model company.

Not all of the nominal time spent will ever be productive time, and out of 8 working hours, at least 2 hours will be spent on inevitable overhead – from coffee and stretching to workplace discussions and administrative agenda. (Congratulations if your team beats that efficiency! But are you really sure that it does?)

The FTE Load then obviously directly influences the direct cost of the process – how much you spend on your staff doing data entry is simply the FTE Load times the employee wage.

The cost structure of AP data entry

When it comes to assessing costs of manual retyping of the data, it is easy to fall into the trap of taking only direct costs into account. Let's simply multiply the time our employees spent on manually entering the data by their wage, and voila! But the structure of costs is more complex. Let's take a look at how it works at our model company.

Total cost of ownership, invoice data extraction - direct, indirect, hidden

The actual costs

In the Total Cost of Ownership, we need to consider not just direct costs, but also indirect and hidden costs. Let's take a look at what the costs are for our model company in case of manual invoice data extraction so we can measure the efficiency of invoice data capture.

  • Direct costs are the fully-loaded costs of the employees of the AP department, in terms of the FTE Load, which ultimately stems from keystroke count.
  • Indirect costs stem from additional effort associated with the process when it goes awry. They are associated with problem-solving such as identifying and correcting data entry errors or other associated issues, such as duplicate payments. This is significant – 12.5% of invoices require some type of re-working (as estimated by the IOFM, Institute of Finance & Management) when handled manually.
  • Hidden (intangible) costs talk about everything else besides staff effort that is lost. They include penalties for late payments, loss of bonus for early payments, cash flow issues, vendor issue escalations, vendor rotation due to poor communication, employee rotation due to an inefficient process etc.

Manual invoice data capture

Manual data capture, invoice data extraction

Manual data entry is a fancy name for retyping all invoice data, field by field. Manual data extraction is still prevalent in Accounts Payable – in 2017, Billentis has pegged it at a staggering 90% of cases! We have discussed this topic in our AI benefits whitepaper.

Direct effort spent: 111 seconds per invoice

The manual process is certainly heavy on human effort, totaling 105 keystrokes per invoice. That's simply 15 fields times 7 keystrokes per field (6 characters on average, plus one keystroke to confirm and move to the next field). At 78 kpm, an invoice is completed in 81 seconds on average.

However, a manual process means that every invoice must be handled manually as a whole rather than presented automatically by a system – either in its physical form, or opened and arranged on the screen if scanned. This easily takes an additional 30 seconds per invoice in practice, making the total a neat 111 seconds. That makes the actual speed 32 invoices per hour. At 75% time efficiency, 1 direct FTE Load is 3,840 invoices a month.

3,840 invoices a month per employee may sound like a staggering amount in a manual process, but achievable in extremely efficient operations. It just goes to show how great a case for manual entry we have built in our model company when other analysts report that the average can be as low as 1000 invoice per month per FTE.

But let's not forget that hand in hand with direct FTE Load also comes indirect load – we will rely on the 12.5% rework rate figure from above. Conservatively, we would assume that such rework is going to take only three times longer than entering an invoice from scratch.

Sometimes, an error is corrected quickly – as soon as it is communicated, the document is looked up, then re-synchronized across systems and so on. But occasionally, the additional time to fix an erroneous invoice easily reaches 30 minutes (communication with other departments, vendors, banks…) and of course, in extreme cases, can take hours or even days, when more employees and external contractors are concerned.

Out of the 30,000 invoices a month received by our model company, The Corp, Inc., that means 3,750 invoices reworked over 5.55 minutes each. That's an extra 2.9 FTEs dedicated to rework!

TCO of manual invoice data extraction: Average cost per invoice is $2.03

We are close to determining the TCO of manual invoice data extraction. In terms of direct FTE Load, processing 30,000 invoices requires 7.8 FTEs. Indirect load represents an additional 2.9 FTEs. That's 10.7 FTE Loads in total, with a likely team structure setup being 8 junior FTEs and 2.7 manager FTEs (including the Head of AP, involved particularly in corrections). The costs for managers are 2.7 x $96,000, for typists 8 x $48,000, totaling $643,200 per year for both direct and indirect costs combined.

The hidden cost is the trickiest to estimate, its structure varies market by market and also in terms of invoice sizes. Since it is influenced by the process' speed and accuracy, we think about it in relation to the indirect cost and lean towards the hidden cost being roughly half of the indirect cost portion (27.2%) on average. That would make it 13.6% of the total effort cost, or an additional $87,475.

Total cost: $730,675 per year for 360,000 invoices. The resulting cost per invoice is $2.03. It is important to add that the efficiency of manual setup decreases in time because of rising costs of human labor and complexities of the invoices and validation processes.

As we explained, our fictional company extracts only a limited amount of data, furthermore only numeric! If a company retypes more data fields, including tables and description of goods, for example, the price will rise accordingly, and very dramatically (the number of data fields may move by an order of magnitude). This number can reach, according to Sterling Commerce, a staggering $12-30 for processing, depending on the complexity of the process. Some resources put the price per invoice in manual processing as low as $1 but they may follow a different TCO methodology.

Manual data extraction, invoice data extraction

We have calculated the TCO of manual invoice data extraction for our model company which is setting the bar for invoice data capture. We have discovered that this manual method is not only quite expensive but also presents a big strain on the morale of the typists -- nobody likes to spend time correcting errors stemming from one erroneously typed number.

Finally, we will compare the manual process to automation processes next time -- the template-based on-premise OCR, and cognitive cloud OCR methods. Stay tuned!

If you want to analyze your own use-case, book a call with our automation expert here.

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