Who is winning in supply chain planning: Excel or Power BI?
If we go by usage, Excel dominates.
No question about it.
Spreadsheets are everywhere: trackers, pivot tables, manual dashboards.
But, if we look beyond usage, the answer might surprise you.
In this article, I will compare Excel and Power BI using practical examples, based on my real-world experience in supply chain planning.
As a heads-up, the magic happens when we combine both tools.
Forecasting
Excel:
Great for building and tweaking forecast models like moving averages or weighted blends.
For example, a planner uses Excel to apply a 3-month moving average to a stable SKU.
It’s flexible and transparent but misses patterns like seasonality or promotions unless manually adjusted.
Excel now has Python integrated. With this, advanced modeling is possible but it still requires coding skills.
Power BI:
Power BI offers built-in AI forecasting with no code.
It detects trends, seasonality, and anomalies automatically.
For instance, Power BI flags a demand spike after a competitor exits and adjusts the forecast accordingly.
It also connects with Azure ML for more advanced models.
Use both:
Build and test logic in Excel.
Visualize forecast performance and automate exception alerts in Power BI.
Real-Time Insights
Excel:
Excel relies mostly on manual data refreshes.
For example, a planner downloads weekly stock levels from the ERP (e.g. SAP S4 Hana, Netsuite) and pastes them into Excel to update inventory trackers.
Power Query can automate some of this, but scheduling updates or sharing live views across teams is limited.
Staying current depends heavily on individual discipline and process consistency. Not easy.
Power BI:
Power BI supports automated and scheduled data refreshes. This is nice because you don’t have to remember to do it manually.
It connects directly to ERP and forecasting systems to keep dashboards up to date; daily, hourly or even in real time.
For instance, a planner sees low safety stock alerts triggered automatically when inventory drops below thresholds, without manual checks.
Use both:
Use Excel for ad hoc analysis or quick checks.
Use Power BI for live dashboards and exception monitoring that drive daily decisions.
They are great for both S&OP and S&OE (sales and operations execution).
Visualization
Excel:
Good for creating charts, graphs, and pivot tables.
For example, a supply planner builds a bar chart to compare production vs. capacity by plant.
It works well for static views, but customizing or filtering visuals takes time and manual updates.
Dashboards can get cluttered and don’t scale easily across teams.
Power BI:
This is Power BI’s bread and butter. It’s designed for interactive, dynamic visuals with built-in filters and drill-downs.
For example, a planner uses a heatmap to spot which SKUs are overloading capacity next week.
Another common use is showing different levels of data aggregation in S&OP meetings; something I personally love.
Dashboards are clean, user-friendly, and refresh automatically with live data.
Use both:
Use Excel for quick visual tests or when working offline.
Use Power BI to scale dashboards, track trends, and share insights across the organization.
Excel:
Good for small to medium datasets.
For example, a planner filters a few thousand MRP rows to analyze open orders.
Power Query can handle millions of rows if loaded into the Data Model (not the worksheet); but performance still lags with complex, multi-source data.
Power BI:
Built for large, complex datasets.
For instance, a planner analyzes millions of ERP and forecast records with no lag.
It compresses data and handles advanced filtering and modeling at scale.
Use both:
Use Excel + Power Query for prep and exploration.
Use Power BI to analyze and visualize large datasets with speed, stability, and scale.
Data Integration
Excel:
Mostly manual or semi-automated.
For example, a planner downloads data from SAP and supplier files to build a tracker.
Power Query helps combine sources, but setup and refreshes still take effort.
Version control is a challenge when sharing files.
Power BI:
Built for integration.
It connects directly to SAP, SQL, and cloud tools to unify MRP, inventory, and forecast data.
Refreshes are automatic, and everyone sees the same version; no manual stitching.
Use both:
Use Excel + Power Query to connect and shape data.
Use Power BI to centralize, automate, and distribute integrated datasets for faster, aligned decisions.
Automation
Excel:
Automation is possible with macros, Power Query, or Power Automate.
But it’s often manual and user-dependent.
For example, a planner runs a macro weekly to update and email forecast reports.
Power BI:
Built for automation.
Dashboards refresh on a schedule, and alerts or workflows trigger automatically through Power Automate.
No manual steps required.
Use both:
Use Excel for light automation.
Use Power BI to scale and streamline reporting and alerts.
Complexity Management
Excel:
Works for simple models but struggles with complexity.
For example, mapping the impact of a supplier shutdown across SKUs and plants requires heavy manual tracing and linked formulas; prone to errors.
Power BI:
Handles complexity with ease.
For instance, planners visualize dependencies across products, locations, and suppliers to assess risks instantly; no manual digging.
Use both:
Use Excel to explore logic and test small-scale scenarios.
Use Power BI to manage complexity across large datasets and visualize ripple effects quickly.
Customization Capabilities
Excel:
Highly customizable with formulas, pivot tables, VBA, and now Python.
For example, a planner builds a custom safety stock calculator tailored to SKU-level rules.
However, complex workbooks can become hard to maintain or scale.
Power BI:
Customizations are model-driven and visual.
For instance, a planner builds DAX measures to calculate dynamic KPIs and custom visuals to highlight service risk.
Requires some technical skill but scales better across teams.
Use both:
Use Excel for quick, flexible modeling.
Use Power BI to create scalable, reusable analytics and dashboards.
Scenario Analysis
Excel:
Ideal for building and testing what-if scenarios manually.
For example, a planner changes demand assumptions or lead times in a supply model to see how it affects inventory levels.
It’s flexible and transparent. However, updating scenarios takes time and can’t easily be scaled or shared dynamically.
Power BI:
Supports fast, interactive scenario comparisons with slicers and parameters.
For example, a planner simulates a 10% demand surge or a production delay and instantly sees the impact on service levels, capacity, and cost.
Helps stakeholders align quickly during S&OP meetings.
Use both:
Use Excel to build and validate logic.
Use Power BI to run, visualize, and compare multiple scenarios at scale.
An alternative is to run the scenarios using machine learning and visualize the results in Power BI.
Together, Excel and Power BI offer the best of both worlds for supply chain planning: flexibility and scalability.
