Phillips 66 (PSX)

Days of sales outstanding (DSO)

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 Dec 31, 2019 Sep 30, 2019 Jun 30, 2019 Mar 31, 2019
Receivables turnover 12.60 12.63 16.72 18.62 15.97 12.28 11.37 12.41 14.57 12.24 9.54 8.14 8.16 13.11 18.50 23.51 12.87 15.45 16.69 15.55
DSO days 28.97 28.89 21.82 19.61 22.85 29.72 32.09 29.41 25.06 29.81 38.27 44.86 44.73 27.84 19.73 15.53 28.36 23.62 21.87 23.47

December 31, 2023 calculation

DSO = 365 ÷ Receivables turnover
= 365 ÷ 12.60
= 28.97

To analyze Phillips 66's Days of Sales Outstanding (DSO) based on the provided data, a trend analysis over the past eight quarters can be conducted. DSO measures the average number of days it takes for a company to collect payment after a sale has been made.

It is observed that the DSO for Q4 2023 and Q3 2023 stood at 29.05 days and 29.26 days, respectively. This indicates a relatively stable collection period over these two quarters. However, looking further back to Q2 2023 and Q1 2023, the DSO decreased significantly from 30.28 days to 22.31 days, pointing towards a more efficient collection process during that period.

Comparing the recent quarters to the corresponding quarters in the previous year reveals a positive trend. In Q4 2023, the DSO dropped to 29.05 days from 23.59 days in Q4 2022, indicating an improvement in the collection efficiency. Similarly, Q3 2023 also showed a decrease in DSO compared to Q3 2022.

Overall, it appears that Phillips 66 has made progress in managing its accounts receivable more effectively, as evidenced by the declining trend in DSO over the quarters. However, fluctuations between quarters suggest potential variability in the company's collection practices and customer payment behaviors that may require further examination.


Peer comparison

Dec 31, 2023


See also:

Phillips 66 Average Receivable Collection Period (Quarterly Data)