Minitab Statistical Software vs. Shiny

Minitab Statistical Software is used from development batches to commercial batches to identify that process is in state of control through various charts and graphs. we can optimised our process by continuous monitoring of product trends. its is less suited where large number of data need to handle and evaluate..

Incentivized

Shiny is well suited where an organisation is looking to empower their analysts to minimise time spent on repetitive analysis by deploying repeatable analytical pipelines, but also looking for them to add greater value to the organisation by utilising more advanced analytical techniques. Ideally it is well suited where IT are on board and supportive of some of the more advanced features such as deploying R Shiny dashboards.

Incentivized

• Life cycle product verification and control
• Cpk value calculation and control
• Historical data evaluation and decision making based on that data.

Incentivized

• Data tables are appealing to look at.
• Enables us to create trend indexes in an effective way.
• Easy to integrate with the rest of my R syntax.

Incentivized

• This to be make financially affordable
• Direct feeding of data provision need to provided
• It must be update to handle large data.

Incentivized

• Easier ways to connect to data sources
• Better access control for different roles in the organization
• Video material that allows a better learning experience

Incentivized

I would give 10 out of 10 rating due to its functionality and easy to use interface. Its included wide range of tools like graph, chart, time study graph. There is predefined inbuilt templet available for easy to use and understand process well. there is availability of vast training materials enhances our experience.

Incentivized

- Faster response working with a large amount of data. - R Studio connection and flexibility. - Scenarios modelling.

Incentivized

• We could control our process and its continuous monitoring that reduce process failure chances.
• It helpful in database decision making process like rejection or acceptance of batch.
• Process is improved and productivity increased.

Incentivized

• We saw a good involvement to researchers when showing their models in shiny.
• We can have a quicker review from the user when the model is in production.
• False positives can be found easily and they help the retraining of the model.

Incentivized