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How AI is Transforming ERP Systems

Real-world applications for artificial intelligence and machine learning have been a long time coming.  ERP vendors have been integrating advanced analytics into their software for some time now, using predictive modeling to optimize equipment maintenance, improve fraud detection, and enhance our understanding of risk.

Now, with the advent of generative AI and advances in cloud analytics, we’re seeing innovation on a much broader scale. AI is going mainstream. It’s showing up virtually everywhere, including your ERP system. Software vendors and their customers are using AI to augment core ERP functionality, automating processes, predicting trends, and enabling data-driven decisions.

Let’s explore some of the practical real-world use cases that have emerged for AI in the ERP domain.


Sales & Customer Service

Enterprise software vendors have long touted the benefits of a so-called “360° view of the customer.” Originally, this entailed collecting and storing enough information to enable a high-touch, personalized experience. Knowing each client’s preferences, for example, the staff at a high-end hotel could stock the minibar with a patron’s favorite products prior to check-in.

In the age of AI, however, that concept of a 360° view takes on an entirely new meaning. AI has the capacity to ingest and analyze vast amounts of information, identifying patterns that might serve to enhance the customer experience much further. That translates into a far more accurate and relevant preference engine, helping companies to upsell and more effectively service their customers.

Amazon’s preference engine, for example, uses a technique called collaborative filtering to identify the products a customer is most likely to buy. Amazon’s algorithm focuses on correlations between the individual customer and other customers with similar purchase histories. But the same principle can be applied using correlations among other data points. Banks and telcos, for example, look for certain life events that indicate potential buying triggers. High school or college graduation, retirement, or relocation to an upscale neighborhood can all suggest that certain types of buying decisions may be imminent.

From a sales and marketing perspective, therefore, predictive analytics have powerful potential to drive new revenue growth by fueling highly relevant and timely personalized offers. Yet there is a strong customer satisfaction angle to this kind of personalization as well. Research by Twilio indicates that 66% of consumers will abandon a brand if their experience isn’t personalized.

Generative AI is playing an increasingly important role in these kinds of personalized interactions as well. AI-powered chatbots and virtual assistants are providing intelligent customer service interactions. Even where a live agent is involved, generative AI can play a supporting role, increasing the relevance and effectiveness of the interaction by offering prompts that zero in on a client’s specific needs. They can also learn from past interactions to improve their responses over time.

ERP vendors are incorporating these technologies into their core product stack. Late last year, for example, Oracle rolled out a host of generative AI tools, including CX GenAI tools designed to help customer service reps and field-service agents get prompt, relevant answers to their questions. SAP has responded with a generative AI bot called Joule that will offer assistance across a range of functions, including customer service and HR.


Machine Vision

Machine vision combines 2D or 3D imaging with artificial intelligence to provide automated inspection and analysis for process control, robot guidance, and quality assurance.

Machine vision is widely used to inspect products. AI can zero in on factors that could indicate defects, using algorithms to refine its ability to identify misalignments, variances, or surface flaws that might be too subtle for the human eye to catch. Advanced machine vision systems use 3D imaging to measure the dimensions of precision components.

In automated manufacturing environments, machine vision is used to guide robots in tasks such as welding, material handling, and assembly. Cameras and sensors provide real-time data to robots, allowing them to adjust their actions for precision work.

Leading edge innovators are finding new ways to use machine vision technology to their advantage. Australia’s Costa Group, for example, is using mechanical pollinators with machine vision to increase crop yields in their indoor farming operations. P&G is using machine vision for real-time quality control for diapers and feminine pads, – products for which precise, high-speed assembly is essential.


Forecasting, Planning, & Scheduling

Big-box retailers like Wal-Mart and lean manufacturing pioneers like Toyota have led the way in refining and streamlining the flow of goods from source to consumer.

Wal-Mart, for example, has developed a centralized hub for demand forecasting, based on the premise that it’s possible to predict the future by analyzing the past. If meteorologists know that a tropical storm is headed for the gulf coast, Wal-Mart’s inventory planners know that they need to move food, water, batteries, flashlights, and generators to affected areas in advance of the event.

That may be an obvious example, but the principle remains the same. If AI algorithms can identify correlations between the demand for products and services and external events and conditions, it can inform supply chain managers’ decisions and improve logistical efficiencies.

Consider how a single social media post from a prominent celebrity might influence the demand for a pair of sneakers, sunglasses, or other fashion accessory. Using SAP’s sentiment analysis tools with Twitter or similar social media platforms, demand planners can get virtual real-time feedback on market acceptance for a new product.

Again, much of AI’s power lies in its ability to ingest vast amounts of information, discover correlations, and make predictions based on those insights.

Equipment Maintenance

After-the-fact repairs cost an estimated 40% more than predictive maintenance. Unexpected breakdowns mean downtime, potentially affecting an entire production line. Several years ago, ERP vendors began to build predictive maintenance applications into their products. By analyzing machine performance and identifying factors that indicate needed maintenance, manufacturing teams can avoid unexpected emergencies and keep production lines running smoothly.

Predictive maintenance applications require IoT-enabled equipment that offer real-time feedback on utilization rates, performance metrics, and machine diagnostics. SAP, Oracle, Infor, and numerous other ERP vendors have incorporated predictive maintenance into their applications.

Nevertheless, the use of predictive maintenance by manufacturers has only recently edged above 50%. This underscores an important point about AI applications in general: it is incumbent upon business and technology leaders to identify the use cases that offer the greatest value, and to proactively pursue those initiatives that stand to benefit their organizations.

The integration of AI with ERP systems signifies a paradigm shift in how businesses manage their operations and interact with customers. Real-world use cases like these aren’t just enhancing efficiency and accuracy in processes like sales, customer service, quality assurance, planning, and maintenance; they are also opening new avenues for innovation and value creation. As companies like Oracle and SAP continue to integrate advanced AI tools into their ERP solutions, the potential for transformative change is immense.

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