Daikibo - Deloitte Data Analytics Simulation

Date: Aug 24, 2026
Tags: Operations Human Resource Conditional Classification Tableau Excel and Google Sheet

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Dashboard: Daikibo - Deloitte Data Analytics Simulation

Executive Summary

Built and interactive Tableau dashboard to track Daikibo factory devices' downtime which causes interruptions on the production line. Additionally, applied forensic algorithm in Excel to audit employee compensation for salary equality. Completed a two-part Data Analytics simulation by Deloitte (via Forage).

Task A. Factory Downtime Analysis

Context
Daikibo Industry used Internet-of-Things to track health data of the machines in their factories. They needed help to identify the biggest problems causing interruptions in their production lines.

Business Task
Identify which factory locations machines break the most and what machines broke most often in those factories.

Execution and Methodology
To get the total downtime for each machines, I created a calculated field Unhealthy on Tableau.

Unhealthy Calculated Field (Tableau)

Each occurrence of unhealthy will get 10 based on the fact that machine health is recorded every 10 minutes.

IF [Status] = "unhealthy"
	THEN 10
	ELSE 0
END

Calculated field: Unhealthy

And to get the overall downtime for each factory, I created Factory Downtime sheet where column has Factory and SUM(Unhealthy) for row.

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For total downtime per device type, I placed the Device Type on the column and SUM(Unhealthy) for the rows. I then added a filter for factory, so users can select specific factories and the bar chart will dynamically be updated.
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Key Findings

Daikibo Seiko Factory experienced the highest total device downtime (480 minutes). Every single minute of this downtime was caused by the LaserWelder device.

Task B. Salary Equity Audit

Context
Daikibo Industry received internal complaints about gender inequality on the salary of the employees. The forensics tech team developed an algorithm and needs help to apply it on the employee compensation table.

Business Task
Give Daikibo Industry a clear picture of the problem that will help guide them develop an equal pay structure for their employees.

Execution and Methodology
To find out if the equality class of the Daikibo employees, I followed the algorithm developed by the forensic tech team in writing the excel formula.

Equality Class Formula (Excel)

Algorithm:

Fair (+-10)
Unfair (<-10 AND >10)
Highly Discriminative (<-20 AND >20)

Since the range of values are the same, where one side is negative and the other is positive, I decided to use absolute value ABS() and use IF() to match the lowest value first (Fair), then the middle value (Unfair), and defaulted on Highly Discriminative if the values did not match the first two conditions.

=IF(ABS(C2)<=10, "Fair", IF(ABS(C2)<=20, "Unfair", "Highly Discriminative"))

Equality Class Formula

Equality class formula was then applied on column D.

	A	B	C	D
1	Factory	Job Role	Equality Score	Equality Class
2	Daikibo Factory Meiyo	VP	-28	Highly Discriminative
3	Daikibo Factory Meiyo	Sr. Engineer	-5	Fair
4	Daikibo Factory Seiko	VP	-19	Unfair
...	...	...	...	...

Sample data from Equality table

Key Findings

Daikibo Meiyo Factory has the most employees classified under "Highly Discriminative" (4) and "Unfair" (3) across all sites. In contrast, Daikibo Berlin has zero employees categorized as "Highly Discriminative" and only 3 under "Unfair".