NXP Semiconductors NV (NXPI)

Inventory turnover

		Dec 31, 2024	Sep 30, 2024	Jun 30, 2024	Mar 31, 2024	Dec 31, 2023	Sep 30, 2023	Jun 30, 2023	Mar 31, 2023	Dec 31, 2022	Sep 30, 2022	Jun 30, 2022	Mar 31, 2022	Dec 31, 2021	Sep 30, 2021	Jun 30, 2021	Mar 31, 2021	Dec 31, 2020	Sep 30, 2020	Jun 30, 2020	Mar 31, 2020
Cost of revenue (ttm)	US$ in thousands	5,610,000	5,725,000	5,781,000	5,832,000	5,788,000	5,661,000	5,670,000	5,682,000	5,688,000	5,599,000	5,399,000	5,143,000	4,996,000	4,883,000	4,782,000	4,565,000	4,377,000	4,250,000	4,152,000	4,274,000
Inventory	US$ in thousands	2,356,000	2,234,000	2,148,000	2,102,000	2,134,000	2,140,000	2,107,000	1,977,000	1,782,000	1,581,000	1,462,000	1,311,000	1,189,000	1,173,000	1,116,000	1,056,000	1,030,000	1,064,000	1,228,000	1,227,000
Inventory turnover		2.38	2.56	2.69	2.77	2.71	2.65	2.69	2.87	3.19	3.54	3.69	3.92	4.20	4.16	4.28	4.32	4.25	3.99	3.38	3.48

December 31, 2024 calculation

Inventory turnover = Cost of revenue (ttm) ÷ Inventory
= $5,610,000K ÷ $2,356,000K
= 2.38

The inventory turnover ratio for NXP Semiconductors NV has been showing a decreasing trend over the past few years. It stood at 3.48 on March 31, 2020, which means the company's inventory was turned over 3.48 times during that period. The ratio decreased slightly to 3.38 on June 30, 2020, but then started to increase steadily, reaching a peak of 4.32 on March 31, 2021.

Subsequently, the inventory turnover ratio has been fluctuating, but overall showing a downward trend. As of December 31, 2024, the ratio was 2.38, indicating a decrease in the efficiency of converting inventory into sales.

A declining inventory turnover ratio can suggest inefficiencies in managing inventory levels, which could lead to higher carrying costs and potential obsolescence risks. It may also indicate slower sales or issues with demand forecasting and inventory control.

It is important for NXP Semiconductors NV to closely monitor its inventory turnover ratio and consider implementing strategies to improve inventory management practices, such as optimizing procurement processes, reducing excess inventory, and enhancing demand forecasting accuracy.