Item 1A. Risk Factors
Item 1A. Risk Factors.
There are no material changes from the risk factors previously disclosed in Part I—Item 1A, “Risk Factors,” of our annual report on Form 10-K/A for the fiscal year ended December 31, 2023, filed with the SEC on July 23, 2024, and in Part II — Item 1A, “Risk Factors,” of our quarterly reports on Form 10 ‑ Q for the quarter ended March 31, 2024, filed with the SEC on May 15, 2024 and for the quarter ended June 30, 2024, filed with the SEC on August 5, 2024, except as discussed below.
We depend on a few key distributors and the loss of one or more of these distributors could have a material adverse effect on our business, financial condition and results of operations.
We cannot assure that any of our current or future distributors will not cease purchasing products from us in favor of products of other suppliers, significantly reduce orders or seek price reductions in the future, and any such event could have a material adverse effect on our revenue, profitability, and results of operations.
Furthermore, a significant portion of our revenue and accounts receivable is derived from one distributor. See Note 11 , Significant Customers and Credit Concentrations , included in this Form 10-Q for the percentages of revenues and accounts receivable attributable to this one distributor. A downturn in the industry or lower sales could materially adversely affect our business and results of operations.
We employ modeling techniques to support the valuation of our accounts receivable and to project the timing and amount of expected collections. While these models are designed to provide reliable insights, they involve certain inherent risks, especially if any assumptions or inputs prove inaccurate, incomplete, or less indicative of future outcomes than anticipated.
As part of our risk management efforts, we use a discounted cash flow model for certain accounts receivable, informed by historical trends and robust assumptions. However, should these assumptions or historical patterns deviate from actual results, there is potential for variance in the model’s accuracy, which could affect decisions based on these forecasts.
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