Which statistical process control solutions offer advanced predictive analytics capabilities?

Statistical Process Control (SPC) solutions with advanced predictive analytics capabilities include Minitab, renowned for its predictive modeling tools like regression analysis and time series forecasting, aiding in identifying trends and patterns in data. JMP offers predictive modeling and machine learning features, empowering users to forecast outcomes and optimize processes. SAS with its SPC module integrates predictive analytics for real-time monitoring and predictive maintenance. Quality Companion by Minitab provides predictive tools for risk assessment and process improvement. These solutions combine SPC principles with advanced analytics, enabling businesses to anticipate future trends, make data-driven decisions.

For accurate working of models/ processing over time, monitoring regularly is essential. SPC is method to effectively monitor data using charts, KPI (key performance metrics) relevant to the process at end. If any limits are breached there are automated controls to raise flags. Any issues in visuals also help to raise issues through manual intervention.

Many modern SPC solutions will have some predictive capability. Excel (particularly SPC for Excel) may be useful for smaller datasets or for when you want to analyze a sample of a larger dataset. Oracle NetSuite offers phenomenal support for predictive modeling. In fact, many ERP solutions may be appropriate for your business needs. It is also possible to utilize statistical software packages in languages such as Python and R for prediction. However, the set-up for this can be time consuming for your organization's needs.

Machine learning a form of artificial intelligence learns from the data and helps in keeping KPI (key performance indicators) dynamic based on data patterns. It can also help in identifying any early warning indicators by learning automatically without human intervention. While earlier SPC are created based on specific algorithms, machine learning can incorporate multiple algorithms and adjust the usage as per the data patterns and help in solving issues with large unstructured data.

In most of the applications real time monitoring is highly essential. For example in trading models, impact of change of market strategy on sales etc. As any error can quickly propagate and increase quickly over time. Monitoring solutions should be run continuously as new data becomes Available and raise automated red flags in case of any KPI getting breached or if any of the early warning signs are triggered. It will help us to improve the model in real time and evaluate effectiveness of any strategy change.

Visualization helps in uncovering issues before they occur by identifying trends in the prediction and actuals. Businesses use lot of interactive tools such as Tableau, Qliksense to make interactive charts , tables to see continuous trends. Time range, KPI , any performance indicators can be modified easily by the user to see impact of any change In the model. It is easy to see future predictions to gauge impact of the model. Future predictions can be easily compared with business Strategy numbers. Models or business strategy can be adjusted easily to incorporate any changes as desired by the business.