Businesses waste 6,500 working hours, or more than $170,000 per year, on inefficient purchase-to-pay (P2P) processes, according to SourceToday.
While purchase-to-pay might be a necessary part of running a business, the traditional approach—shopping quotes, placing orders, gathering invoices, and closing the books—is a time-consuming and expensive hassle.
In recent years, disruptive technology has upended the traditional purchase-to-pay process. A handful of technologies have streamlined these tasks, reduced errors, and unlocked savings for companies.
At Negotiatus, we’ve spent countless hours researching and developing technologies to revolutionize the P2P process—and we’ve seen the advantages of a variety of systems. In this piece, we explore how technologies, APIs, blockchain, big data, and machine learning have led to significant gains for procurement and finance teams.
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APIs connect purchasing applications in the purchase-to-pay process
If step one is using an automated purchasing platform, then step two is integrating that platform with other systems in your stack—e.g., your accounting software or your payments platform—through an API.
An application programming interface, or API, serves as the go-between between two systems.; it’s how two disparate applications “talk.”
Separate systems that don’t communicate with each other are inefficient. In the purchase-to-pay context, the inefficiency looks like ordering 30 different types of supplies from 30 different vendors using 30 different systems.
Say you integrate your single purchasing platform into an ERP (enterprise resource planning) or accounting system with a few lines of embedded code (the API).
Once you connect your purchasing platform directly to your ERP or accounting system, payments for approved purchases flow seamlessly between the two apps. As an example, we use a private API to connect the Negotiatus purchasing platform with Quickbooks. Our purchasing information is fed automatically into this accounting system, so there’s no need to manually move data from one system to another to reconcile purchases and close the books.
Because companies use between 10 and 100 different systems to operate, automated data transfer like this, between systems, is a must because it makes the purchasing process run smoothly for both procurement and finance.
Blockchain simplifies record-keeping
One of the biggest problems in traditional purchasing is record-keeping. Shortfalls, such as improperly coded purchases or lost employee receipts, leave the finance department scrambling to reconcile expenses and close the books. Blockchain can help.
Commonly associated with cryptocurrency, blockchain is primarily a record-keeping system that can be applied to the P2P process to code purchases accurately.
Here’s how it works: blockchain keeps records in a series of blocks. Each block consists of three elements:
- Transaction data, which is precisely what it sounds like—the important information about an actual exchange between two parties. In a purchase-to-pay scenario, transaction data includes product identifier, purchase price, quantity, and other relevant details.
- A time stamp, which is the exact moment when the transaction happens.
- A cryptographic hash, which protects the record with encryption so the record cannot be deleted, edited, or reversed.
Once a block is created, it’s added to a ledger that grows in time and cannot be altered or deleted.
A purchasing platform with blockchain-based record-keeping codes purchases when they’re made and automatically records the transaction. That record is saved to the platform ledger and is available for finance to review at any time. There’s no need to chase employees for expense reports or tear through purchasing history to correct coding errors.
Big data helps you find value in your purchasing data
While there’s no single definition for big data, it includes any technology that can extract and analyze vast amounts of data that’s often too big or too complicated for your typical software.
When applied to the P2P process, this type of technology can help businesses unlock new insights from purchasing data. Before big data hit the scene, teams were limited by a person’s ability to analyze endless rows of data in a spreadsheet. With big data, that limit doesn’t exist.
Using big data software, teams can look at all purchases, segment them in categories, and analyze where better options are available. Cozen O’Connor, a law firm with 21 offices around the globe, used big-data technology to analyze purchasing data across its offices. Within 24 hours of crunching its purchasing information, the firm identified immediate opportunities for 10% in cost reductions. Since making this analysis a regular practice, the firm has saved $5,000-$6,000 monthly on the products it purchases to run the business.
It’s difficult to find this level of savings by manually digging through invoices and spreadsheets. Big data is key to finding value—and potential savings—in your purchasing data.
Machine learning identifies purchasing insights that humans miss
Manually sifting through purchasing data to find ways to save money is a hit-or-miss for your purchasing team. It’s also time-consuming. Machine learning (ML) takes care of the process for you, digging through your purchase-to-pay data and automatically analyzing it to find purchase opportunities that your team might overlook.
Machine learning uses algorithms and other coded instructions to perform tasks it wasn’t expressly programmed to do. Similar to big data solutions, ML technology sifts through massive amounts of purchasing information for insights.
But, unlike big data, machine learning isn’t coded to perform specific tasks. Instead, it searches for patterns and opportunities that aren’t necessarily visible to the human eye. For example, maybe your vendors offer discounts toward the end of each quarter. You delay purchases to take advantage of the price drops. But ML may find that certain days of the week or certain hours of the day offer even deeper discounts.
At Negotiatus, we use machine learning to help optimize vendor selection. The network effects of compiling data across hundreds of clients allow clients (and Negotiatus) to make more informed purchasing decisions.
And not only does the technology save customers money, but it also saves valuable time. According to CEO and co-founder of Sievo Sammeli Sammalkorpi:
“No matter how much time is spent cleaning up and classifying long-tail spend [i.e., the spend outside of a contract], the complexity of millions of line items of spend goes beyond the time and resources of even the most talented Procurement teams.”
Without machine learning, a single purchasing analyst takes countless days to dig into machine-learning level of detail. ML also removes repetitive P2P tasks and frees up your purchasing team’s time so they can focus on other things.
Tech is shaping the future of the purchase-to-pay process
According to McKinsey, many CPOs implementing procurement programs supported by technology expect a 40% increase in annual savings, 30% to 50% less time spent on transactional sourcing, and a 50% reduction in value leakage. The days of manual, error-prone purchase-to-pay are nearing their end. Teams can now leverage next-generation technology to transform purchasing into a strategic advantage.
If you’re ready to disrupt your manual purchase-to-pay process, sign up for a Negotiatus demo today.