Introduction

The Order Analyzer is the central components of the data analysis suite and serves as the most critical module in the toolkit. It ingests raw customer order profile data and delivers a comprehensive analysis, segmented by date and customer type. The output includes color-coded Excel files and a suite of visual graphs that highlight order trends over time.

This module integrates with secondary script, which is dedicated to generating weekly order profile visualizations. These visuals include automatic detection of the customer’s peak week, making it easier to identify high-demand periods and plan accordingly.

By default, Order Analyzer 9 is configured to process both e-commerce and retail order files together. However, this behaviour is fully customizable with the Python script, allowing users to tailor the analysis to specific data structures or customer channels.

Running the Code

When the Order Analyzer script is launched, it first prompts the user to decide whether to separate the data analysis by unique dates, It is recommended to select ‘Y’(yes) to ensure that the resulting Excel outputs and visualizations are structured and meaningful, with performance metrics grouped by day or week.

Following this, the tool requires the user to define a series of key input columns from the source dataset. These include:

·      Date: The field representing the order date.

·      Customer Type: such as B2B, B2C, ECOM or RETAIL

·      Order line: The unique order line identifier

·      Quantity Sold: The number of items ordered

·      SKU ID: The identifier for the stock keeping unit

Once these inputs are provided, the user is asked to specify the number of working hours per day for the site. This value is used to calculate hourly throughput, which is a critical metric for capacity planning and shift scheduling.

The script then offers the option to separate B2B and B2C orders, based on the values in the Customer Type Column. Selecting ‘Y’ enables more granular analysis, distinguishing between wholesale and direct-to-customer order patters.

Finally, the tool prompts the user to enable a nested function, which should also be set to ‘Y’ by default. This function calculates a weighted average across dates and appends it as a new row labelled with the suffix “1”. This aggregate provides a useful benchmark for comparing daily performance against the overall average.

Once all required inputs have been entered, the calculation process begins. This phase is computationally intensive and may take anywhere from 5 to 10 minutes, depending on the size of the dataset and the performance of the user’s machine.

During this time, the tool processes, groups, and analyses all relevant order data to generate meaningful outputs. Users are advised to remain patient while the script runs, as the workload can be significant, especially for large or multi-week datasets. Proper filtering and limiting of data beforehand (e.g. by date range or customer type) can help optimize performance and reduce runtime.

Export Files

Once the processing is complete, the first visual output is a bar chart displaying total order volumes by week. Each bar represents a week’s total order count, with the exact number of orders displayed above each column for clarity.

To enhance visual analysis, the script automatically identifies and highlights the top three peak weeks:

·      Peak week (highest volume) is marked in red

·      Second peak in amber

·      Third peak in purple

Additionally, the chart includes a color-coded breakdown of B2B and B2C orders within each weekly total:

·      B2B orders are shaded in yellow

·      B2C orders in blue

This visualization provides a quick and intuitive overview of weekly order fluctuations, peak activity periods, and customer-type contributions. It’s especially useful for capacity planning, labour forecasting, and identifying seasonal trends.

The second bar chart presents the total number of order lines per week, offering a more granular view of order complexity and fulfilment workload. Like the previous graph, it employs the same colour coded scheme for peak periods.

Displaying the order lines as opposed to just total orders gives deeper insight into operational demands, particularly in terms of picking and packing complexity. A single order may contain multiple lines, especially in B2B scenarios. So this chart helps in identifying weeks where workload intensity may have spiked, even if the number of total orders remained stable.

The final chart illustrates the total number of items sold per week, again following the same colour coded peak highlight patterns.

This graph provides a clear view of weekly throughput, reflecting the actual volume of items that needed to be picked, packed, and shipped. It is particularly useful for understanding fulfilment intensity and for cross-checking whether peaks in order counts align with peaks in item volume, which can reveal differences in order size or customer behaviour across weeks.

The primary Excel export generated by the tool is titled KOROUEI-ANALYSIS.xlsx, which serves as the most detailed and critical output for operational review. This file provides a comprehensive breakdown of the order profile, segmented by individual dates.

To enhance readability and usability, the data is automatically colour coded by date, allowing users to easily distinguish between days and track fluctuations in performance, order patterns, and SKU activity. This formatting significantly streamlines manual review and supports further custom analysis, whether in Excel or as part of a broader reporting process.

Another Excel file generated by the tool is the Week_Info.xlsx, which provides a mapping of individual dates to their corresponding week numbers. This file is particularly useful for validating the weekly groupings used in the visual bar chart outputs and ensures transparency in how the time-based aggregation was performed.

By referencing this file, users can confidently trace each data point in the graphs back to its original date, making it easier to align analytical outputs with operational records or customer order logs.