AP invoice automation insights by Medius

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Why AP Professionals Should Take a Spend Transparency Approach Towards Accounts Payable

Spend transparency is crucial to understand buying behaviors, procurement performance and to increase cost savings and reduce procurement costs. At the core of success is the AP process and the organization’s ability to capture high-quality invoice data.

The concept has been around for around twenty years. It started with the classical “spend cube” in Excel that many consulting firms used as a foundation for crafting the cost out programs they sold to CFOs and CPOs. Then it developed further with more advanced spend analytics tools and technology to help AP professionals. In the last few years, the rapid increase of automation in the invoice management process and technology such as machine learning has revolutionized how companies can capture detailed spend data even on non-PO invoices.

Gartner predicts that “By 2022, 50% of all legacy spend analysis software will be retired; replaced by artificial intelligence (AI)-powered, cloud-based solutions.” and “by 2020, natural-language generation and AI will be standard features of 90% of modern BI platforms.” 

The business case is clear as Aberdeen research shows that businesses can achieve up to a 20% savings for each new dollar of spend brought under management and maintain a 73% rate of contract compliance with spend analytics best practices in place. Ardent Partners’ research shows that with a superior level of spend visibility, the average Best-in-Class procurement organization reports higher levels of annual savings (6.3% vs. 6.1%) and 47% better contract compliance rates than the other groups in the market (66% vs. 45%). 

Many names for the same concept

Throughout the years the same basic concept has been re-packaged, re-named and re-marketed following trends around “big data”, “analytics” and “digital”. A common definition is hard to find but when Spend Matters asked Lora Cecere of AMR research (acquired by Gartner) she suggested a definition that summarizes the key components that make up the general understanding of spend analytics.  

Her definition is:  "A collection of best practices in management of 'spend' data, (i.e. data related to sourcing and procurement) leveraging software tools built around core data classification and enrichment technologies. This enables delivery of enterprise spend visibility to ensure compliance and control./…/. The benefits include the ability to analyze the impact of sourcing strategies on key categories, along with assessments of business results (savings), and measurement of KPIs (periodic review of compliance with preferred suppliers and contracts, savings realized etc.) along with ongoing control of spend for improved profitability."

In short: Spend analytics is the process of turning invoice data to meaningful insights that lead to savings. The process can be described in five main steps that are the same regardless if you use powerful technology or a spreadsheet.

Create a spend category tree. The category tree spans across geographies, cost centers, functions, and any organizational belongings or responsibilities. Within indirect spend, the purchasing categories can be created from standards such as, for example, UNSPSC. The scope of direct material varies considerably more between different industries, and thus a more tailored approach towards defining the category tree may be needed. 

Identify, extract, and import data. Extract spend data from, for example, the accounts payable ledger in the ERP system or directly from the invoice management system. The data should at least include every invoice or invoice row with associated information about the supplier, date, total, currency, account, and cost center. However, all information is good information when AP professionals want to draw insights from the data. With a best in class automated accounts payable process that capture all information on the invoice, down to the line item level, the effort in this and the two following steps in the process is considerably lower. 

Data cleansing. The third step involves cleaning, correcting, and normalizing data. For example, managing errors in the supplier list and grouping duplicates, or misspelled item or supplier names.

Categorize purchases to the category tree. Once all the data is in the same format, the fourth step is to categorize the AP data. Start the categorization on the account level, i.e., certain GL accounts can be mapped with their entire spend into certain categories. Second, analyze the suppliers. Since the category tree is constructed based on how the supply market is organized, the majority of the suppliers will fit into one or a few categories. This means that the vast majority of categorization can be done by defining which category each supplier should belong to. Be aware of the cases where the supplier needs to be divided between more categories. 

Before there is insight, there is data

Having spend visibility and using spend visibility to achieve an advantage are two different things. The value of spend analysis is not the data itself, but in the ways that the data is leveraged to inform the decisions that drive greater value and improve performance. Thus the fifth and final step is to analyze, conclude and implement actions.

There is a set of standard analysis that should be done in every category, for example, supplier pareto, contract compliance, volume bundling, and internal benchmarking between same items.  AT Kearney’s book ”The purchasing chessboard” is by many considered a go-to publication for the levers to apply depending on the insights from spend analysis. Here are actions to implement once spend transparency is achieved. 

  • Reduce maverick purchasing. Spend analysis reviles how often available contracts, negotiated for the best prices and terms are not being used. This is true, especially in the case of indirect spend.
  • Rationalize supplier base: Bundle volume to more suppliers and negotiate volume discounts.
  • Manage tail spend. Suppliers that add up to 20% of total spend is an area usually referred to as the tail spend. Capture and AP automation software play a crucial role as the analysis of the tail spend often require the use of all available information captured in invoices. Streamline, consolidate, and monitor low-value areas of spending reduce ad-hoc spend transactions, such as purchases made on low-volume items off-contract, rush orders, or special projects. 
  • Benchmark performance against market development. Are the raw material and logistics costs following the market indices? Benchmark internal performance. Are you paying the same price for different items in different geographies? 
  • Optimize working capital. This is one of the quick-wins that you can take through robust spend analysis. Identifying categories and suppliers with lower-than-policy-level payment terms provides a great starting point for freeing working capital.

If benefits are so great- why is it so difficult to achieve?

There are several reasons why spend visibility is difficult to achieve. Purchasing data often sits in disparate systems, ERP, supplier databases, procurement systems and invoice management systems. Just bringing all of it together to analyze in one place is a challenge. Hence, the market and need for specialized spend analysis tools.

With a best in class automated accounts payable process companies can ease or even bypass this challenge. The invoices you receive normally includes all the fields of information needed to get to spend transparency, so why the need for information from other systems? The answer is that you need capture software and best practices in the workflow of the invoice management process that secures that the AP department can capture all the details of the information on the invoice down to the line item level. 

However, it is not unusual that companies build the business case for AP automation on reducing invoice processing times and increase operational efficiencies. This may lead to implementing sub-optimal best practices in the AP process. One example is to capture invoice information on the header level and not on the line item level. Some time might be saved in processing the invoice, but the opportunity is lost to capture data that can later be used as leverage in the procurement process.

Spend analysis re-imagined for the future

Imagine if the invoice management process in addition to processing the invoice also classifies each purchase to purchasing categories and agreements - then the business intelligence brought by spend transparency can be a direct outcome of the AP process through standard reporting and without the need for specific spend analysis technology. 

Now, re-imagine step four above if your invoice management process utilizes templates and machine learning to classify spending on certain GL accounts and supplier IDs directly in connection to the processing of the invoice. The solution will be able to not only classify the data but also interpret it to draw out trends and recommend actions – this is the spend analysis of the future! 

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