Tableau

The standout feature for us is without a doubt its visualization capability. Tableau excels at translating millions of rows of complex supply chain data into clear, intuitive, and actionable insights. We've built dashboards that track everything from supplier On-Time-In-Full (OTIF) performance to live inventory levels across our global warehouses. The ability for a non-technical manager to drill down from a high-level KPI on a map to a specific shipment or SKU in seconds has empowered faster, more data-driven decision-making across the board.

For pixel perfect charts and maps the product is in a league of its own. It can also be easily integrated with 3rd party products through its APIs and then be able to market on the Salesforce website

The strongest aspect of Tableau is its intuitive visualization layer combined with powerful data connectivity. The platform makes it easy to build rich dashboards that can be embedded into applications , portals and internal platforms. Key strengths include: highly interactive and visually rich dashboards, strong embedded analytics capabilities via APIs and embedding frameworks. Low-code approach for building dashboards and data models. Wide range of connectors to databases and cloud data platforms. Strong support for self-service analytics and data exploration. Ability to blend multiple data sources without heavy engineering effort. From a data science perspective , Tableau supports quick exploration and visualization of data sets , helping teams validate insights and communicate findings effectively.

While Tableau is best-in-class for descriptive (what happened) and diagnostic (why it happened) analytics, it is not an all-in-one solution for advanced predictive and prescriptive analytics. To leverage true AI/DSML for things like demand forecasting or network optimization, we've found it necessary to perform the modeling in separate environments (like Python or R) and then import the results back into Tableau for visualization. I would love to see more integrated, out-of-the-box capabilities for building and deploying these advanced predictive models directly within the platform to better support a true Digital Supply Chain Twin initiative.

The biggest problem is being able to manage the sheer content of the landscape once it as been in operation for a few years. There can be vast numbers of dashboards and datasources and its difficult to know what is linked to where. The product has also failed to adapt to Natural Language questions capability

From an enterprise perspective, Licensing and scaling costs can increase as usage grows. Advanced data science workflows may still require external tools for complex modeling. Governance and version control for dashboards can require additional processes. Embedded deployments require careful authentication and security configuration. Managing multiple data sources can become complex without standardized architecture.Additonally, while low-code features are strong for visualization, more advanced application -style workflows may require complementary platforms.