Video content for Research and Evidence and Practice

This interactive sales dashboard is designed in Excel for B2C type of Businesses like Dmart, Walmart, Amazon, Shops & Supermarkets, etc. using Slicers, Pivot Tables & Pivot Chart.

Dashboard Overview

1. Sales dashboard ==> basically, it is designed for the B2C type of business. like Dmart, Walmart, Amazon, Shops & supermarkets, etc.
2. Slices ==> slices are used to drill down the data, on the basis of yearly, monthly, by sales type, and by mode of payment.
3. Total Sales/Total Profits ==> here is, the total sales, total profit, and profit percentage these all are combined into a monthly format and we can hide or unhide it to view it as individually or comparative.
4. Product Visual ==> the visual indicates product-wise sales for the selected period. Only 10 products are visualized at a glance, and you can scroll up & down to view other products in the list.
5. Daily Sales ==> It shows day-wise sales. (Area Chart)
6. Sales Type/Payment Mode ==> It shows sales percentage contribution based on the type of selling and mode of payment.
7. Top Product & Category ==> this is for the top-selling product and product category.
8. Category ==> the final one is the category-wise sales contribution.

Datasheets Overview

1. The dataset has the master data sheet or you can call it a catalog. It is added in the table form.
2. The first column is the product ID the list of items in this column is unique.
3. Then we have the product column instead of these two columns, we can manage with only one also but I kept it separate because sometimes product names can be the same, but some parameters will be different, like price, supplier, etc.
4. The next column is the category column, which is the product category. like cosmetics, foods, drinks, electronics, etc.
5. Then we have 4th column which is the unit of measure (UOM) you can update it also, based on the products you have.
6. And the last two columns are buying price and selling price, which means unit purchasing price and unit selling price.

Input Sheet

The first column is the date of Selling. The second column is the product ID. The third column is quantity. The fourth column is sales types, like direct selling, are purchased by a wholesaler or ordered online. The fifth column is a mode of payment, which is online or in cash. You can update these two as per requirements. The last one is a discount percentage. if you want to offer any discount, you can add it here.

Analysis Sheet: where all backend calculations are performed.

So, basically these are the four sheets mentioned above with different tasks.

However, a sales dashboard enables organizations to visualize their real-time sales data and boost productivity.

A dashboard is a very useful tool that brings together all the data in the forms of charts, graphs, statistics and many more visualizations which lead to data-driven and decision making.

Questions & Answers

1. What percentage of profit ratio of sales are displayed in the year 2021 and year 2022? ==> Total profit ratio of sales in the year 2021 is 19% with large sales of PRODUCT42, whereas profit ratio of sales for 2022 is 22% with large sales of PRODUCT30.
2. Which is the top product that have large number of sales in year 2021-2022? ==> The top product in the year 2021 is PRODUCT42 with the total sales of $12,798 whereas in the year 2022 the top product is PRODUCT30 with the total sales of $13,888.
3. In Area Chart which product is highly sold on 28th April 2022? ==> The large number of sales on 28th April 2022 is for PRODUCT14 with a 24% of profit ratio.
4. What is the sales type and payment mode present? ==> The sale type and payment modes show the sales percentage contribution based on the type of selling and mode of payment. Here, the sale types are Direct Sales with 52%, Online Sales with 33% and Wholesaler with 15%. Also, the payment modes are Online mode and Cash equally distributed with 50%.
5. In which month the direct sales are highest in the year 2022? ==> The highest direct sales can be easily identified which is designed by monthly format and it’s the November month where direct sales are highest with 28% as compared with other months.
6. Which payment mode is highly received in the year 2021 and year 2022? ==> The payments received in the year 2021 are the cash payments with 52% as compared with online transactions which are 48%. Also, the cash payment highly received is in the month of March, July and October with direct sales of 42%, Online with 45% and wholesaler with 13% with large sales of PRODUCT24. ==> The payments received in the year 2022 are the Online payments with 52% as compared with cash payments which are 48%. Also, the online payment highly received is in the month of Jan, Sept and December with direct sales of 45%, Online with 37% and whole...

Full Excel model providing life-cycle impacts of food and drink products. Contains all original inventory data and mid-point impact data, remodelling assumptions, and final standardised results. Requires Microsoft Excel 2007 or later to use.

This spreadsheet is intended as an example to demonstrate how the Metropolis Hastings algorithm can be implemented within microsoft Excel to undertake Bayesian inference.

If you are considering programming the Metropolis Hastings algorithm in another language/modelling package this example may be useful for you.

This example uses a very simple state transition model (with 3 states) and uses data observations of persons in State B and persons moving to Stage C.

This structure and approach can be extended to a larger more complex model and with more parameters and datasets.

This spreadsheet may be a useful illustration of the process of the MH algorithm for those considering programming this algorithm in another package.

Warning! This example is intended as a rough guide to the process only. For further details consult a statistics reference.

Referencing

The author has used a similar approach to calibrate a natural history model for colorectal cancer. The methods are published here:

Whyte S, Walsh C, Chilcott J. Bayesian Calibration of a Natural History Model with Application to a Population Model for Colorectal Cancer. Medical Decision Making 2011;31:625-641.

http://www.ncbi.nlm.nih.gov/pubmed/21127321

Spreadsheet used to calculated hydrograph recession parameters (Minimum, Most Probable Value, and Maximum) for the Stochastic Empirical Loading Dilution Model (SELDM) . The spreadsheet was used in conjunction with the SELDM simulations used in the publication: Stonewall, A.J., and Granato, G.E., 2018, Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey Scientific Investigations Report 2019-5053, 116 p., https://doi.org/10.3133/sir20195053

Microsoft Excel spreadsheet of model coefficient estimates and summary statistics.

Spreadsheet used to calculate Highway Site characteristics (Drainage area, slope and impervious fraction) for the Stochastic Empirical Loading Dilution Model (SELDM) . The spreadsheet was used in conjunction with the SELDM simulations used in the publication: Stonewall, A.J., and Granato, G.E., 2018, Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey Scientific Investigations Report 2019-5053, 116 p., https://doi.org/10.3133/sir20195053.

The process of determining time-dependent transmission rate coefficient and calculating the number of total infections by COVID-19 in the United States and related results have been described in the attached word and excel files.

In the beginning, the case was just data for a company that did not indicate any useful information that would help decision-makers. In this case, I had to ask questions that could help extract and explore information that would help decision-makers improve and evaluate performance. But before that, I did some operations in the data to help me to analyze it accurately: 1- Understand the data. 2- Clean the data “By power query”. 3- insert some calculation and columns like “COGS” cost of goods sold by power query. 4- Modeling the data and adding some measures and other columns to help me in analysis. Then I asked these questions: To Enhance Customer Loyalty What is the most used ship mode by our customer? Who are our top 5 customers in terms of sales and order frequency? To monitor our strength and weak points Which segment of clients generates the most sales? Which city has the most sales value? Which state generates the most sales value? Performance measurement What are the top performing product categories in terms of sales and profit? What is the most profitable product that we sell? What is the lowest profitable product that we sell? Customer Experience On Average how long does it take the orders to reach our clients? Based on each Shipping Mode

Then started extracting her summaries and answers from the pivot tables and designing the data graphics in a dashboard for easy communication and reading of the information as well. And after completing these operations, I made some calculations related to the KPI to calculate the extent to which sales officials achieved and the extent to which they achieved the target.

Excel spreadsheet containing, in separate sheets, the underlying numerical data of the findings.

Context

I am showcasing the financial commissions model on Kaggle. On Excel we can utilize IF statements to chart rates that reward workers based on quotas. By compiling sales on a large or small scale we can easily derive the necessary compensation for workers.

Content

The first sheet uses simple IF statements to derive a commission payment for different rates. The Sales company exceeded their quota of $95,000.00, and reached $99,343.00, which is a 104.6% return on investment.

On sheet 2 there is a detailed breakdown of individual employee rates and their deserved commission. The difference in sheet 2 is the use of nested IF statements, which can get much more complex if not catalogued properly.

Acknowledgements

There are two guides on YouTube which I credit heavily for these models here are the links: https://www.youtube.com/watch?v=bkrSVS7-CYo&list=PLQnuraB9JKXdUlDVZtcfG2_sO_uL_XyMm&index=4 https://www.youtube.com/watch?v=0Ahqr6Xdkos&list=PLQnuraB9JKXdUlDVZtcfG2_sO_uL_XyMm&index=12

Inspiration

Thanks for reading, and enjoy!

Our new SEIR epidemic model built from the l-i AIR model [1] has similar terms to the conventional SEIR epidemic model [2]. We have uploaded an instruction file for describing how to write a calculation program in Excel for calculating the model variables S, E, I, R and y.

REFERENCES [1] Liu, X. A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan. Results Phys 20, 103712 (2021). [2] Liu, X. Analytical Solution of a New SEIR Model Based on Latent Period-Infectious Period Chronological Order. medRxiv, https://doi.org/10.1101/2021.12.14.21267812, 2021.2012.2014.21267812 (2021).

Abstract

The Survey on Interest Rate Controls 2020 was conducted as a World Bank Group study on interest rate controls (IRCs) in lending and deposit markets around the world. The study aims to identify the different types of formal (or de jure) controls, the countries that apply then, how they implement them, and the reasons for doing so. The objective of the study is to advance knowledge on this topic by providing an evidence base for investigating the impact of IRCs on economic outcomes.

The survey investigates present IRCs in each surveyed country, the reasons why they have been applied, the framework and resources associated with their application and the details as to their level and functioning. The focus is on legal forms of control (i.e. codified into law) as opposed to de facto controls. The new database on interest rate controls, a popular form of financial repression is based on a survey of 108 countries, representing 88 percent of global gross domestic product. The interest rate controls presented in this dataset were in effect in 2019.

Geographic coverage

Global Survey, covering 108 countries, representing 88 percent of global GDP.

Analysis unit

Regulation at the national level.

Universe

Banking supervisors and Local Banking Associations.

Kind of data

Sample survey data [ssd]

Mode of data collection

Mail Questionnaire [mail]

Research instrument

Bank supervisors and banking associations were provided with a standard excel file with five parts. The survey was structured in five parts, each placed in a different excel sheet. Part A: Introduction. Countries with no IRCs in place were asked to only answer this sheet and leave the rest blank. Part B: Presented the definitions of controls, institutions, products and additional aspects that will be covered in the survey. Part C: Introduced a set of qualitative questions to describe the IRCs in place. Part D: Displayed a set of tables to quantitatively describe the IRCs in place. Part E: Laid out the final set of questions, covering sanctions and control mechanisms that support the IRCs' enforcement. The questionnaire is provided in the Documentation section in pdf and excel.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey (USGS) have evaluated projections of future droughts for south Florida based on climate model output from the Multivariate Adaptive Constructed Analogs (MACA) downscaled climate dataset from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The MACA dataset includes both Representative Concentration Pathways 4.5 and 8.5 (RCP4.5 and RCP8.5). A Microsoft Excel workbook is provided which tabulates model drought-evaluation statistics for the period 2056-95 based on drought characteristics derived from climate models downscaled by the MACA method assuming historical-standard stomatal resistance. Model drought-evaluation statistics based on 6-mo. and 12-mo. averaged balance anomaly timeseries are provided for four regions: (1) the entire South Florida Water Management District (SFWMD), (2) the Lower West Coast (LWC) water supply region, (3) the Lower East Coast (LEC) water supply region, and (4) ...

This revised version of our earlier mixing model incorporates additional complexity including:1. Waning immunity.2. Differential risk and infectivity by prior immune experience.3. Effects of hybrid immunity.4. Boosting.5. Diminished vaccine efficacy to reflect immune evasion with Omicron SARS-CoV-2 variants.

Excel Spreadsheet showing rock shape data for interior, margin, and exterior of Homestead hollow, Mars. Excel Triplot model used for some rock shape calculations. Original versions of all figures in paper

Excel spreadsheet tool that can be used to produce predicted costs for large pipe relining job, based on the project's final regression model.

This spreadsheet model calculates the net income for irrigated agricultural production. The model is designed to evaluate the economics of deficit irrigation (irrigation at less than the amount required to produce maximum yield). The spreadsheet first models the water production function for a crop, then uses that relationship along with crop price and production costs to calculate net income and the irrigation amount that maximizes net income. This spreadsheet is similar to another posted at Ag Data Commons: "Economic Model of Deficit Irrigation" (http://dx.doi.org/10.15482/USDA.ADC/1504421). That model was designed primarily to evaluate deficit irrigation as a means to compare revenue with reduced water consumption to income gained by transferring the saved water. The model includes two common scenarios: 1) irrigation water supply is adequate but expensive, and 2) irrigation water supply is inadequate to fully irrigate the available land. In the first scenario, net income is maximized when the marginal costs of production, including water, is equal to the marginal revenue. In the second scenario, net income is maximized when the value of the water is maximized by selecting the portion of the land that should be irrigated. In the second scenario, the value and costs of the un-irrigated land are included. The first worksheet of the spreadsheet describes the relationships used in each worksheet and the input parameters required. Additional worksheets calculate the water production function, the irrigation water production function, and the net income for each of the two scenarios. The worksheets allow the user to input the various biophysical and economic parameters relevant to their conditions and allows evaluating various parameter combinations. Each worksheet contains graphs to visualize the results. Resources in this dataset:Resource Title: Economic Model of Deficit Irrigation II (spreadsheet). File Name: WPF Econ Model V2 Mod.xlsxResource Description: Spreadsheet contains 5 worksheets. The first worksheet describes the relationships in the remaining worksheets and the parameters required by the model.Resource Software Recommended: Microsoft Excel 365 (may work on earlier versions),url: https://www.microsoft.com/en-us/microsoft-365/get-started-with-office-2019 Resource Title: Description of the Model. File Name: DataDictionary.pdfResource Description: Description of the model and input parameters.Resource Software Recommended: Adobe Reader,url: https://get.adobe.com/reader/otherversions/

An Excel document that produces Nash Equilibrium and expected payoffs for strategic form, grim trigger, and Bayesian model games. You can edit the payoff cells and some of the play columns. The play columns will update for you automatically for the Grim Trigger sheet.

Project Priority Matrix (Dynamic Excel Tool)

Overview

This dataset provides a dynamic Excel model for prioritizing projects based on Feasibility, Impact, and Size.
It visualizes project data on a Bubble Chart that updates automatically when new projects are added.

Use this tool to make data-driven prioritization decisions by identifying which projects are most feasible and high-impact.

Goal

Organizations often struggle to compare multiple initiatives objectively.
This matrix helps teams quickly determine which projects to pursue first by visualizing:

• Feasibility → How achievable a project is
• Impact → The potential benefit or value it delivers
• Size → The level of effort or resources required

How It Works

1. Each project is rated on a 1–10 scale for:
   • Feasibility
   • Impact
   • Size
2. The Excel file uses a Bubble Chart:
   • X-axis: Feasibility
   • Y-axis: Impact
   • Bubble size: Project Size
3. The chart automatically updates when new projects or scores are added.

Example (partial data):

Interpretation Guide

Excel Features

• Dynamic Bubble Chart (updates with new data)
• Named Ranges for auto-expanding data
• Optional Conditional Formatting
• Data Validation for consistent scoring

How to Use

1. Download and open Project_Priority_Matrix.xlsx.
2. Go to the Data sheet.
3. Add your project names and scores (1–10).
4. Watch the chart update instantly to reflect your data.

You can use this for: - Portfolio management
- Product or feature prioritization
- Strategy planning workshops

File Information

• File: Project_Priority_Matrix.xlsx
• Format: Excel (.xlsx)
• Version: 1.0
• Last Updated: October 2025

License

Free for personal and organizational use.
Attribution is appreciated if you share or adapt this file.

Author: [Asjad]
Contact: [m.asjad2000@gmail.com]
Compatible With: Microsoft Excel 2019+ / Office 365