A traditional ERP system stores business data and is a database. It is also a deterministic system - it computes and processes data according to predetermined business logic and provides a consistent output for a given set of input data.
For example, an ERP system with standard accounting functions ("modules") allows you to obtain balance sheets, profit and loss (income) statements, and cash flow reports, etc., as well as use stored records for data analysis; or the system has a typical inventory function, which can be used for inventory analysis through sales, purchase and inventory records. This is a deterministic ERP system that does not tolerate errors; as long as the input data remains unchanged, the same data results can be obtained at any time. It's hard to imagine a company's financial reporting with inaccurate data and information. Such a system has no randomness in mathematical calculations.
Until recently, with the ability to leverage artificial intelligence and machine learning techniques (AL/ML), we could provide predictive capabilities to ERP systems. Typical uses are forecasting your product sales figures, stock levels or customer preferences etc. Such a predictive system is stochastic*.
Unlike deterministic systems, stochastic systems do not always generate the same output for a given input. This means that some degree of randomness in the forecast results is unavoidable. However, when such a hybrid ERP system further accumulates business data, the predictive capabilities of its embedded AI/ML will increase.
We foresee more business leaders equipping their ERP systems with ML/AI capabilities, such hybrid ERP systems (deterministic and stochastic) providing real-time and online forecasts to support better business decisions and timely action.
Well, you may say, such a hybrid ERP system is an AI system.
If you want to know more how to upgrade your ERP system, contact us to explore more possibilities.
*Strictly speaking, most AI/ML algorithms use deterministic functions, but hybrid ERP systems generate randomness through a continuous stochastic training process.