Big data is now driving more operational choices than ever. Predictive analytics in SAP software might assist users enhance their supply chain.
Predictive analytics in the supply chain might help reduce risk and optimise inventories.
Here's how SAP's predictive analytics capabilities may improve supply chain management (SCM).
Predictive analytics today
Predictive analytics may aid businesses in several situations, including SCM.
For years, supply chain managers have used statistical models to estimate demand and refine predictive models based on various criteria. The computing capacity of modern systems has increased dramatically. These days' data mining tools can crunch more statistics and factor in more variables.
In 2014, SAP's Integrated Business Planning (IBP) product included predictive analytics features. IBP uses SAP's HANA in-memory database to provide quicker and more comprehensive real-time analytics.
The Supply Chain Control Tower warns users when supply chain circumstances deviate from established criteria. This allows SCM experts to immediately identify possible issues and modify the course as needed.
SAP predictive analytics uses
Predictive analytics can be very useful in some facets of supply chain management.
Predictive analytics may help customers enhance their supply chain management (SCM) in the following ways:
Demand forecasting. Machine learning uses algorithms to find connections, trends, and anomalies in data. It can also take into account current selling prices, weather, and marketing budgets.
Stock optimisation. Algorithms establish optimal stock levels, eliminating excess inventory and any shortages.
- Risk reduction. Predictive analytics can warn managers of weather-related supply chain bottlenecks and labour stoppages, among other variables.
Routing. In addition to distance from supply sources, traffic and weather may also be factored into predictive analytics.
Dynamic pricing. Managers can use dynamic pricing to balance demand for products with the amount of products available, as well as market conditions and income.