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Scope AI (XCNQ:SCPE) Receivables Turnover : 0.00 (As of Mar. 2024)


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What is Scope AI Receivables Turnover?

The Receivables Turnover ratio measures the number of times a company collects its average accounts receivable balance. It is calculated as Revenue divided by average Accounts Receivable. An efficient company has a higher accounts receivable turnover ratio while an inefficient company has a lower ratio. Scope AI's Revenue for the three months ended in Mar. 2024 was C$0.00 Mil. Scope AI's average Accounts Receivable for the three months ended in Mar. 2024 was C$0.03 Mil.

Scope AI Receivables Turnover Historical Data

The historical data trend for Scope AI's Receivables Turnover can be seen below:

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

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Scope AI Receivables Turnover Chart

Scope AI Annual Data
Trend Sep21 Sep22 Sep23
Receivables Turnover
- - -

Scope AI Quarterly Data
Sep21 Dec21 Mar22 Jun22 Sep22 Dec22 Mar23 Jun23 Sep23 Dec23 Mar24
Receivables Turnover Get a 7-Day Free Trial Premium Member Only Premium Member Only Premium Member Only - - - - -

Competitive Comparison of Scope AI's Receivables Turnover

For the Software - Infrastructure subindustry, Scope AI's Receivables Turnover, along with its competitors' market caps and Receivables Turnover data, can be viewed below:

* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap. Note that "N/A" values will not show up in the chart.

Scope AI's Receivables Turnover Distribution in the Software Industry

For the Software industry and Technology sector, Scope AI's Receivables Turnover distribution charts can be found below:

* The bar in red indicates where Scope AI's Receivables Turnover falls into.


Scope AI Receivables Turnover Calculation

Receivables Turnover measures the number of times a company collects its average accounts receivable balance.

Scope AI's Receivables Turnover for the fiscal year that ended in Sep. 2023 is calculated as

Receivables Turnover (A: Sep. 2023 )
=Revenue / Average Total Inventories
=Revenue (A: Sep. 2023 ) / ((Accounts Receivable (A: Sep. 2022 ) + Accounts Receivable (A: Sep. 2023 )) / count )
=0 / ((0.021 + 0.041) / 2 )
=0 / 0.031
=N/A

Scope AI's Receivables Turnover for the quarter that ended in Mar. 2024 is calculated as

Receivables Turnover (Q: Mar. 2024 )
=Revenue / Average Total Inventories
=Revenue (Q: Mar. 2024 ) / ((Accounts Receivable (Q: Dec. 2023 ) + Accounts Receivable (Q: Mar. 2024 )) / count )
=0 / ((0.041 + 0.016) / 2 )
=0 / 0.0285
=N/A

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

Scope AI  (XCNQ:SCPE) Receivables Turnover Explanation

An efficient company has a higher accounts receivable turnover ratio while an inefficient company has a lower ratio. This metric is commonly used to compare companies within the same industry to check whether they are on par with their competitors.

Scope AI Receivables Turnover Related Terms

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Scope AI (XCNQ:SCPE) Business Description

Industry
GURUFOCUS.COM » STOCK LIST » Technology » Software » Scope AI Corp (XCNQ:SCPE) » Definitions » Receivables Turnover
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Traded in Other Exchanges
SCPCF:USA
Address
1800 - 510 West Georgia Street, Vancouver, BC, CAN, V6B 0M3
Website
https://www.scopecarboncorp.com
Scope Carbon Corp is a Canadian technology company. It develops Artificial Intelligence (AI) analytical software and intellectual property for use in analyzing data related to nature-based objects (e.g. forests, wetlands, and other areas) as it relates to carbon credit certification. The company's current business plan is to enable large volumes of object based data to be converted into digestible data that carbon credit experts and others are able to use to verify the characteristics of trees, wetlands, and other areas.