VentureBeat presents: AI Unleashed - An exclusive executive event for enterprise data leaders. Network and learn with industry peers. Learn More
The tech world is filled with three-letter acronyms that ebb and flow like tides. The buzz phrase ‘enterprise resource planning,’ (ERP), first appeared on PowerPoint slides in the 1990s. It was the latest manifestation at the time of a long drive that began in the 1950s and 60s to use computers to manage corporate resources effectively and profitably.
The acronym seemed to disappear for a few decades after that. Not because the work went away, but just because other topics elbowed ERP out of the limelight. That’s changing, though, and today, the letters are starting to appear once again. For instance, Findability, an artificial intelligence (AI) company, announced that they were branding their new product with the name “ERP-Max” instead of a name that conjures up ideas of machine learning.
ERP software is often associated with managing the supply chain for manufacturing companies, and this part of the economy has been under increasing amounts of pressure. The challenge of delivering what customers expect when they expect it in the face of the pandemic and the war in Ukraine has become a global challenge.
The current version of ERP is up to face these steep challenges. While the name and the goal are the same as they were in the last century, the technology has improved. Throughout the years, the ERP companies have been folding in the best improvements to build something that is modern, service-oriented and completely integrated with every company’s stack.
An exclusive invite-only evening of insights and networking, designed for senior enterprise executives overseeing data stacks and strategies.
Here are eight reasons why the new version is more sophisticated and more able than ever:
AI is now widely available
Many of the companies addressing these tools are actively using artificial intelligence algorithms to smooth the workflow and take over some of the decision-making from humans. Some are making predictions to help the supply chain managers plan. Others are using AI to detect anomalies that should be fixed. All of them are taking advantage of the power that AI brings to automate steps and help humans manage large, elaborate mechanisms.
SAP, for example, has integrated AI algorithms into many places in its ERP system. In the source-to-pay part of the pipeline, for instance, SAP has been using AI algorithms to grade suppliers and help companies identify the best quality sources.
Oracle is also adding AI capabilities to various parts of the ERP solutions. Their financial tools, for example, can create dynamic pricing for each customer with a model that tracks past behavior.
Microsoft’s AI tools are now found folded into many of their different tools, from spellcheckers in the text editors to nodes in any ERP application. Dynamics 365, one of their major reporting tools, can use AI algorithms to look for fraud.
Data is more integrated than ever
All of these enterprise tools tout their ability to communicate with both old and new code running either in local legacy systems or across the internet in some third-party cloud. They speak both older formats like XML and modern ones like JSON. They communicate well with APIs and databases throughout the cloud. All of this makes it easier for ERP and ERP-like solutions to gather the data needed to make sound decisions.
Zoho, for instance, boasts that their custom ERP platform comes with “over 600 out-of-the box third-party app integrations” that can absorb data from other systems.
In addition, Amazon’s Web Services (AWS) maintains a marketplace where software companies like Acumatica can sell their services to run inside Amazon’s cloud. These ERP solutions often use the generic AWS services like S3 to simplify integration.
Often, both sides support the integration. Microsoft’s Power Automate can pull in data directly from SAP. At the same time, SAP actively works with its customers to help them move data across their gateway into SAP. This back-and-forth is common throughout the marketplace, and many pairs support data movement in both directions.
Back office processing is more automated than ever
Many of the companies are using good AI to automate the tasks that were once dominated by paper. Good optical character recognition and machine vision algorithms can read in paper invoices to help accounting or check a driver’s license to help a bank satisfy the Know Your Customer regulations. As these tasks become digitized, it’s easier for ERP software to create good aggregated insights into the enterprise.
Inifix, for instance, is helping hospitals process authorization from health insurance prior to the treatment. Their bots track requests and flag unusual issues for human review.
In another example, RPATech helps banks process new customer applications with a mixture of optical character recognition before verifying the data with third-party databases.
Low-code and no-code work
Many modern platforms like to push their ability for flexible programming. The words “no-code” and “low-ode” may be marketing hype, but the drag-and-drop interfaces make it possible for nonprogrammers to do a good job building working flowcharts that control how data is gathered and work is accomplished. Even if the promise is too big half of the time and the companies have to call the programmers out of desperation, the developers can still use many of the low-code features to speed their work too.
Quixy, a supplier of business process management software, says their platform lets companies implement ERP by creating “applications that can be built on purely visual, intuitive interfaces with no need for programming experience.”
Microsoft’s Dynamics 365 platform for business management can also be customized with a variety of options, including their low code option called “Power Apps”.
Some clever companies have developed tools that turn the power of AI and machine learning to understanding just what’s going on inside the complex collection of old and new code that keeps it running. Some call these extra tools “process mining” because the term suggests how the algorithms can watch the data flowing across the network for patterns that can be used to improve everything. These tools can make it simpler to create an ERP application and also ensure that the ERP applications are closely tracking the working environment.
IBM, for example, touts the ability of its Cloud Pak for Business Automation’s ability to reach inside legacy data flows and identify new opportunities for increasing sales, streamline workflows, and identify opportunities for automation.
One of the metaphors that’s appearing more and more frequently in the world of enterprise software is the idea of a “digital twin.” It’s more of a goal or a vision than a technology, but it captures the desire for the ERP systems to accurately track everything that is happening inside the enterprise. Sometimes the digital twin technology focuses on particular products or markets and other times it’s the organization itself. Teams use the word in different ways.
In the past, many companies were happy just to have the computer systems track invoices and shipments. Now, they can use more elaborate models to watch all the steps along the manufacturing and sales process. The idea of a digital twin is still far from being perfectly realized, but it illustrates the ambition of some CIOs.
SAP, for example, offers software that creates digital twins of products to help at each stage during the development process. These digital versions track the various parts and follow how they’re eventually assembled into finished products.
Ansys’s product development tools create a digital twin for simulation and experimentation during the development. They believe all the digital iteration during development increases the final quality by helping teams anticipate problems before production.
Predictions are more accurate than ever
The AIs are taking on more roles and controlling more of the decisions made along the chain. It’s not that they’re more integrated, but they’re often more trustworthy. Well-trained AIs can take on more and more of the decisions in moments where the data is often clear. At other times, the AI can act as a pre-filter or an advisor for the humans who make the final call.
Findability AI, for instance, touts the predictive ability of its artificial intelligence routines when the data and the circumstances are right.
““ERP-Max has an accuracy rate of over 95% forecasted predictions and is a cost-effective game-changer for the global ERP market,” says Anand Mahurkar, founder and CEO of Findability Sciences. “We believe that our solution will soon emerge to be indispensable to businesses that use ERP systems.”
There are more acronyms than ever
Over the years, the software industry has created a burgeoning number of useful categories of enterprise software, from robotic process automation (RPA) to business process management (BPM) to customer data platforms (CDP). ERP is a cousin to these three acronyms and several others too. There are differences between all of them, but the goal is the same: use the power of automation to simplify the workflow for the people in the trenches. Then use good reporting and pattern matching to help the managers spot bottlenecks and backups.
The sales teams and the IT departments can argue which acronym is the best fit, but the result is the same. Good organizations are deploying these tools to mediate the flow of information and work. This is producing more agile and efficient manufacturing and services. Customers are benefiting, whether it’s called ERP or something else.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.