Instructions & User Manual

The initial panel displays two buttons: "New Bridge File" and "Open Existing Bridge File"

In the Data worksheet starting from column B, enter data types in the first row, field names in the second row, and data in all rows from the third row onwards. Column A contains auto-generated identifiers for the first three rows that need data input. Column A itself has no impact on data and analysis.

2.1 Data Type Selection

In the first row of the "Data" worksheet, use the generated dropdown menu starting from column B to select the data type. The type depends on the meaning the data needs to represent. The program will automatically analyze based on the data type.

Data types are divided into 5 categories: Dimension, Key, SumY, SumN, Result. Each field must be defined with one data type, which can be selected using the dropdown menu.

Now using our simulated supermarket data as an example, let's explain the meaning of each data type. Each type is defined as follows:

2.1.1 Dimension

Represents data dimensions such as date, time, region, and other data dimensions and facets. These fields do not participate in calculations and are only used to filter data.

2.1.2 Key

Key is a type of Dimension, and only one field can be marked as Key. The field marked as Key will serve as the minimum granularity for analysis and calculate the impact of each factor on the final result (Result) within this dimension. You can change other dimensions to Key fields for analysis at different granularities.

For example: If "Product Name" is selected as Key, the tool will calculate the impact of each variable's change on the final result (Result) within this "Product Name" dimension.

2.1.3 SumY

Data in the same column from different rows can be added together and the sum is meaningful. Whether SumY or SumN is correctly set will affect the final analysis results.

For example: Quantity of different products in each row can be summed, and the total represents the total quantity of goods. Therefore, the Type of Quantity can be set to SumY.

SumY can contain direct numerical values or use formulas to calculate values through other fields. However, formulas can only use Excel's basic arithmetic operators: addition (+), subtraction (-), multiplication (*), division (/), left parenthesis "(", and right parenthesis ")". Formula Usage Guidelines

2.1.4 SumN

Data in the same column from different rows cannot be added together, and the sum would be meaningless. Whether SumY or SumN is correctly set will affect the final analysis results.

For example: Different products have different Prices. To calculate the average Price of all products, you cannot directly add Product A's Price plus Product B's Price to get the overall Price. Therefore, the Type of Price can be set to SumN.

SumN can contain direct numerical values or use formulas to calculate values through other fields. However, formulas can only use Excel's basic arithmetic operators: addition (+), subtraction (-), multiplication (*), division (/), left parenthesis "(", and right parenthesis ")". Formula Usage Guidelines

2.1.5 Result

Only one field can be selected as Result to serve as the data result we need to analyze. The Result column must contain formulas that calculate the Result field through other variables.

For example: If the Result field contains sales, then the cell should input = Price * Quantity. Or if it's margin%, then the cell should input = profit / revenue.

The formula in Result is crucial information that the tool uses to associate variables with results and analysis logic dependencies. Please ensure that each Result cell in every row contains a formula that can calculate the Result. The formula should be concise and clear. Result will analyze the impact of each variable in the formula on the Result, while variables not in the formula are not within the analysis scope. The Result formula should contain at least one field with Data Type SumY to enable aggregated analysis of data from different rows.

Result formulas can only use Excel's basic arithmetic operators: addition (+), subtraction (-), multiplication (*), division (/), left parenthesis "(", and right parenthesis ")". Formula Usage Guidelines

2.2 Field Name Setup

In the second row of the "Data" worksheet starting from column B to the right, enter field names. The meaning of the field names should correspond to the data types in the first row. Field names cannot be duplicated.

After clicking "New Bridge File" or "Open Existing Bridge File" to open a file, blue buttons New Bridge and Refresh Bridge will appear on the panel.

The dropdown menu is divided into two areas: Baseline and Comparison

The fields in Baseline and Comparison dropdown menus contain exactly the same options. Users can filter which options serve as Baseline data and which serve as Comparison data in the dropdown menus, using these as the comparison and analysis data for both ends of the Bridge. Note: Initially, by default, all fields in the Baseline and Comparison dropdown menus are selected, so Baseline and Comparison get exactly the same dataset, resulting in a Bridge with no differences.

4.1 Baseline dropdown menu - Select the baseline data for comparison analysis. For example, we often use data from certain years as the comparison baseline, so here we select 2023.

4.2 Comparison dropdown menu - Select the Comparison data for comparison analysis. For example, here we select 2024 as the year of interest to compare with 2023.

The Bridge worksheet displays the final analysis results, including Bridge charts and Contribution Analysis tables.

5.1 Bridge Data Source - Starting from cell B3 in the upper left corner, field names and data will appear. This is the data used to generate Bridge charts.

5.2 Bridge Chart - Displays the Bridge analyzed and obtained after program execution. The blue bar on the left is Baseline, the middle shows the impact contributed by each variable, and the right bar is Comparison.

5.3 Contribution Analysis Table - Directly below the Bridge chart, a Contribution Analysis table will be displayed, showing the contribution of each Item in the Key field to overall changes under different factors.

Through the Bridge Chart and Contribution Analysis Table, you can quickly identify the main Items (SKUs) affecting changes and the factors causing changes (price? cost? or quantity mix?).

You can analyze different dimensions and different result data by modifying 2.1 Data Type Selection to choose which field is Key and which field is Result. You can also use the dropdown menus to filter which data serves as Baseline and Comparison, then click Refresh Bridge to generate updated Bridge charts and Contribution Analysis tables for analyzing actual business situations and proposing improvement suggestions.

Added Data — Records that exist in the Comparison period but did NOT exist in the Baseline period. They represent things that are entirely new, such as a newly launched product line, a newly opened store, or a newly acquired customer.

Removed Data — Records that existed in the Baseline period but NO LONGER exist in the Comparison period. They represent things that have been retired, discontinued, lost, or closed.

Because there is no "before" value for Added Data and no "after" value for Removed Data, their impact cannot be attributed to any single factor (such as price change, quantity change, or cost change). They must be isolated and reported on their own bars in the Bridge chart.

If Added Data and Removed Data are mixed into the regular price / quantity / mix variance calculations, the results become mathematically meaningless:

• A newly launched product has no Baseline price, so "price variance" is undefined.
• A discontinued product has no Comparison quantity, so "volume variance" is undefined.
• Forcing zero values into the formula would arbitrarily inflate or deflate other factors, distorting the entire bridge.

By isolating these two categories first, the remaining "like-for-like" items can be analyzed cleanly by price, volume, mix, cost, FX, and other true drivers.

The most common real-world manifestation of Added / Removed Data is the NPI / EOL product lifecycle:

• NPI — New Product Introduction: A product that just entered the market this year (Comparison) but did not exist last year (Baseline). Its entire revenue, cost, and margin are attributed to the Added Data bar.
• EOL — End of Life: A product that was sold last year (Baseline) but has been discontinued and removed from the catalog this year (Comparison). Its lost revenue and margin are attributed to the Removed Data bar.

The same pattern applies to many other business contexts:

• Retail / Store Network: Newly opened stores = Added Data; closed stores = Removed Data.
• Customer / Subscription Business: New customers acquired this period = Added Data; churned customers = Removed Data.
• Geographic Expansion: Newly entered countries / regions = Added Data; exited markets = Removed Data.
• M&A Activity: Acquired business units = Added Data; divested business units = Removed Data.
• Headcount Analysis: New hires = Added Data; departures = Removed Data.

Suppose a company is analyzing the year-over-year change in total revenue:

The program automatically detects Added and Removed records by comparing the Key columns (e.g., Product ID, Customer ID, Store ID) between the Baseline and Comparison data tables:

• A Key value that appears only in the Comparison table is treated as Added Data.
• A Key value that appears only in the Baseline table is treated as Removed Data.
• A Key value that appears in both tables is analyzed through normal factor decomposition (price, volume, cost, etc.).

No manual tagging is required — simply ensure your Key columns (such as Product Code) are consistent across both periods, and Bridge Analysis will handle NPI / EOL and similar structural changes correctly.