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EEAMI (XTAE:EEAM-M) Receivables Turnover : 0.00 (As of Dec. 2024)


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What is EEAMI 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. EEAMI's Revenue for the six months ended in Dec. 2024 was ₪0.00 Mil. EEAMI's average Accounts Receivable for the six months ended in Dec. 2024 was ₪0.00 Mil.


EEAMI Receivables Turnover Historical Data

The historical data trend for EEAMI'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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EEAMI Receivables Turnover Chart

EEAMI Annual Data
Trend Dec19 Dec20 Dec21 Dec22 Dec23 Dec24
Receivables Turnover
Get a 7-Day Free Trial 1.78 2.69 2.74 3.85 5.39

EEAMI Semi-Annual Data
Dec19 Jun20 Dec20 Jun21 Dec21 Jun22 Dec22 Jun23 Dec23 Jun24 Dec24
Receivables Turnover Get a 7-Day Free Trial Premium Member Only Premium Member Only Premium Member Only 1.81 1.20 7.02 5.39 -

Competitive Comparison of EEAMI's Receivables Turnover

For the Solar subindustry, EEAMI'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.


EEAMI's Receivables Turnover Distribution in the Semiconductors Industry

For the Semiconductors industry and Technology sector, EEAMI's Receivables Turnover distribution charts can be found below:

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


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EEAMI Receivables Turnover Calculation

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

EEAMI's Receivables Turnover for the fiscal year that ended in Dec. 2024 is calculated as

Receivables Turnover (A: Dec. 2024 )
=Revenue / Average Total Inventories
=Revenue (A: Dec. 2024 ) / ((Accounts Receivable (A: Dec. 2023 ) + Accounts Receivable (A: Dec. 2024 )) / count )
=3.689 / ((0.684 + 0) / 1 )
=3.689 / 0.684
=5.39

EEAMI's Receivables Turnover for the quarter that ended in Dec. 2024 is calculated as

Receivables Turnover (Q: Dec. 2024 )
=Revenue / Average Total Inventories
=Revenue (Q: Dec. 2024 ) / ((Accounts Receivable (Q: Jun. 2024 ) + Accounts Receivable (Q: Dec. 2024 )) / count )
=0 / ((0 + 0) / 1 )
=0 / 0
=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.


EEAMI  (XTAE:EEAM-M) 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.


EEAMI Receivables Turnover Related Terms

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EEAMI Business Description

Traded in Other Exchanges
N/A
Address
4 HaBarzel Street, Ramat HaHayal, Tel Aviv, ISR, 6971008
EEAMI Ltd, Formerly Ecoppia Scientific Ltd offers cloud-based, connected platform and a suite of robotic solutions for smart management of solar plants. The company's robots clean solar panels globally every night, with no water, no electricity, and no human intervention. The robotic systems are cloud-based, utilizing information processing for an optimal and continuous solar electricity generation.

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