Artificial Intelligence

Creating business efficiencies

AI-powered analytics and data to improve efficiency and product quality

With the introduction of digital and embracing new message streams from digital, a new approach to making sense of all this data is needed. Artificial Intelligence and Machine Learning (the engine behind AI) gives computers the ability to make sense of and learn from data to perform specific tasks without manual interference. It provides analysis and insights to users, addressing the large amounts of structured and unstructured business and industry data that companies increasingly need to consider as part of their decision-making process.

By integrating artificial intelligence technology with SYSPRO enterprise resource planning (ERP) manufacturers can leverage AI technology to identify potential downtime and accidents by analyzing the sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs.


AI in manufacturing market is expected to be valued at USD 1.1 billion in 2020 and is likely to reach USD 16.7 billion by 2026; it is expected to grow at a CAGR of 57.2% during the forecast period.
How can manufacturing use AI integrated with ERP
From significant cuts in unplanned downtime to better designed products, AI can help the business create efficiencies in the following business areas:
AI can help your procurement teams be more efficient. Suppose you are creating a purchase order and need to order new laptops for 10 new staff members starting on 1 December. As you capture the order, it would be helpful if an AI system told you that historically the provider you have chosen won’t provide laptops in December and recommended that you pick another supplier. Previously, you would have had to pull a report and do intense manual searching to see if the company had previously supplied laptops during the required period.
One of the most important aspects of any business is the ability to manage cash flow. Imagine if an accounts receivable aging schedule which helps a company see if its customers are paying on time could predict and send an alert when a customer is likely to go from 30 to 60 days in the second quarter. A CFO could then plan the organization’s cash flow and prepare for it by managing purchases so that suppliers could be paid on time.
In the customer service arena, the role of AI is to provide technology that learns continuously and enables insights that can enrich the customer relationship, something that current customer service technology does not do. The combination of AI, a company’s internal data and human service agents will let customer service teams go further in the customer relationship and therefore improve productivity, output and customer satisfaction. AI can assist with trend and anomaly detection. Instead of running reports and manually checking for unusual changes, AI would scan the history of customer transactions in the background, detect the norm for each customer and then provide an alert to an account manager if an anomalous transaction is recorded.
AI can help with up-selling and cross-selling. When a sales order is being captured, an AI system can recommend other items that the customer might like. For example, people who buy bikes also buy helmets. The salesperson can then make the suggestion to the customer, potentially creating an additional sale.
Chatbots (or virtual customer assistants) are the main AI-based applications used in business for customer interaction and are becoming more widely adopted and growing in importance. They act on behalf of humans to enable customers to get answers to questions and perform tasks for internal staff. Chatbots can address a large number of customer enquiries and reduce customer wait time by handling simple issues quickly and at any time, allowing customer agents to concentrate on more complex tasks. Chatbot service agents can provide customers with a consistent 24/7 service.

Use historical data to predict the future. Examples from a seasonal perspective we can predict when a customer might purchase a particular product. Or use historical data to predict when a customer might default on payments based on past behavior.

Anomaly detection

This is where our AI engine listens to operator activity. This is typically used to capture fraudulent transactions or even incorrect key strokes. As an example, Joe Blogs ordinarily captures 10 sales orders with an average value of $7000. On one day Joe Blogs captures a sales order for $100000. The AI engine will flag this transaction as an anomaly for someone in the organization who can determine whether this is valid or fraudulent.

Computer vision

Used currently by the Autonomous car to detect objects. With SYSPRO ERP, this capability can assist with health and safety regulations on the factory floor, for quality assurance. For example, using facial recognition to detect who is authorized to use machines or forklifts; or taking pictures of products as they come off the production line to provide real-time quality checks.

Additional practical applications of AI and Machine learning with SYSPRO include the following:
SYSPRO understands that the use and benefits of these disruptive technologies must be introduced in a manner that companies can make use of them out-of-the-box. SYSPRO’s practical application of ML and AI provides an improved user experience and enables organizations to take advantage of these new technologies to become more efficient:
  • The underlying machine learning and artificial intelligence engine is available for both cloud or on-premise deployments.
  • A ML designer is made available so non-technical users can design their own AI models simply.
  • AI models can be attached anywhere in SYSPPRO’s web based interface, , using our flexible cards infrastructure, binding a user UI and data context to an AI model; these cards become the ‘mouth-pieces’ of ML/AI, surfacing insights, predictions or detecting anomalies directly to the users processing transactions, drawing their attention to aspects they may not have been aware of.
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