Analysis of Short-term (Operating) Activity Ratios
Quarterly Data
Activity ratios measure how efficiently a company performs day-to-day tasks, such us the collection of receivables and management of inventory.
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- Analysis of Long-term (Investment) Activity Ratios
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Short-term Activity Ratios (Summary)
United States Steel Corp., short-term (operating) activity ratios (quarterly data)
Based on: 10-Q (reporting date: 2023-06-30), 10-Q (reporting date: 2023-03-31), 10-K (reporting date: 2022-12-31), 10-Q (reporting date: 2022-09-30), 10-Q (reporting date: 2022-06-30), 10-Q (reporting date: 2022-03-31), 10-K (reporting date: 2021-12-31), 10-Q (reporting date: 2021-09-30), 10-Q (reporting date: 2021-06-30), 10-Q (reporting date: 2021-03-31), 10-K (reporting date: 2020-12-31), 10-Q (reporting date: 2020-09-30), 10-Q (reporting date: 2020-06-30), 10-Q (reporting date: 2020-03-31), 10-K (reporting date: 2019-12-31), 10-Q (reporting date: 2019-09-30), 10-Q (reporting date: 2019-06-30), 10-Q (reporting date: 2019-03-31), 10-K (reporting date: 2018-12-31), 10-Q (reporting date: 2018-09-30), 10-Q (reporting date: 2018-06-30), 10-Q (reporting date: 2018-03-31).
- Inventory Turnover
- The inventory turnover ratio shows moderate fluctuation over the observed periods. Starting near 5.88 in early 2019, it generally oscillates between approximately 5.4 and 7.1, reaching a peak of 7.12 in the last quarter of 2020. This suggests somewhat variable efficiency in inventory management, with no consistent long-term upward or downward trend.
- Receivables Turnover
- This ratio exhibits an overall increasing tendency with certain interruptions. It rises from 8.55 in early 2019 to a peak of 12.88 in the second quarter of 2023, reflecting improved efficiency in collecting receivables over time. Some declines are noticed in mid-2021, but the general trend points to acceleration in collection efforts.
- Payables Turnover
- The payables turnover ratio shows variability without a consistent direction. Values fluctuate mainly between 3.97 and 7.24, with the highest point in the third quarter of 2020. This indicates changing payment dynamics to suppliers, possibly influenced by cash flow management decisions or supplier terms.
- Working Capital Turnover
- The working capital turnover ratio reveals significant volatility. After increasing up to 11.58 in late 2018, it drops sharply to 3.86 by the third quarter of 2020 before rebounding toward the end of that year. The figures stabilize thereafter in a range near 5.0 to 6.0, indicating fluctuating efficiency in using working capital to generate sales.
- Average Inventory Processing Period
- The average inventory processing period tends to hover near two months with some variability. It generally remains within the 51 to 68 days range, lengthening notably in 2022 but shortening again in the first half of 2023. This reflects occasional changes in how long inventory is held before sale.
- Average Receivable Collection Period
- This period generally decreases over time, moving from over 40 days in early 2019 to below 30 days in early 2023, with a few extensions during mid-2021. The trend suggests improvement in collecting receivables more quickly, enhancing cash flows.
- Operating Cycle
- The operating cycle demonstrates a modest downward trend, moving from over 100 days in early 2019 to under 90 days in early 2023. This implies a more efficient conversion from inventory to cash, despite some fluctuations.
- Average Payables Payment Period
- The average payables payment period exhibits variability, ranging roughly from 50 to 92 days. There is a peak around late 2020 to early 2021, indicating a longer time taken to pay suppliers during that period, followed by a decline to shorter payment periods in 2023.
- Cash Conversion Cycle
- The cash conversion cycle shows improvement overall, decreasing from around 30 days in early 2019 to nearly 13 days in the first quarter of 2023. This trend reflects enhanced efficiency in managing the time between outflows and inflows of cash, contributing positively to liquidity management.
Turnover Ratios
Average No. Days
Inventory Turnover
United States Steel Corp., inventory turnover calculation (quarterly data)
| 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 | Dec 31, 2018 | Sep 30, 2018 | Jun 30, 2018 | Mar 31, 2018 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data (US$ in millions) | |||||||||||||||||||||||||||||
| Cost of sales | |||||||||||||||||||||||||||||
| Inventories | |||||||||||||||||||||||||||||
| Short-term Activity Ratio | |||||||||||||||||||||||||||||
| Inventory turnover1 | |||||||||||||||||||||||||||||
| Benchmarks | |||||||||||||||||||||||||||||
| Inventory Turnover, Competitors2 | |||||||||||||||||||||||||||||
| Freeport-McMoRan Inc. | |||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2023-06-30), 10-Q (reporting date: 2023-03-31), 10-K (reporting date: 2022-12-31), 10-Q (reporting date: 2022-09-30), 10-Q (reporting date: 2022-06-30), 10-Q (reporting date: 2022-03-31), 10-K (reporting date: 2021-12-31), 10-Q (reporting date: 2021-09-30), 10-Q (reporting date: 2021-06-30), 10-Q (reporting date: 2021-03-31), 10-K (reporting date: 2020-12-31), 10-Q (reporting date: 2020-09-30), 10-Q (reporting date: 2020-06-30), 10-Q (reporting date: 2020-03-31), 10-K (reporting date: 2019-12-31), 10-Q (reporting date: 2019-09-30), 10-Q (reporting date: 2019-06-30), 10-Q (reporting date: 2019-03-31), 10-K (reporting date: 2018-12-31), 10-Q (reporting date: 2018-09-30), 10-Q (reporting date: 2018-06-30), 10-Q (reporting date: 2018-03-31).
1 Q2 2023 Calculation
Inventory turnover
= (Cost of salesQ2 2023
+ Cost of salesQ1 2023
+ Cost of salesQ4 2022
+ Cost of salesQ3 2022)
÷ Inventories
= ( + + + )
÷ =
2 Click competitor name to see calculations.
- Cost of sales
- The cost of sales exhibited fluctuations over the analyzed periods. Initially, it increased from 2808 million US dollars in March 2018, reaching a peak of 3227 million in June 2019. Thereafter, it decreased to a low of 2274 million in June 2020. Starting mid-2020, the cost of sales began to rise sharply again, peaking at 4661 million in June 2022 before experiencing some decline towards 4161 million in June 2023. Overall, the trend indicates periods of both contraction and significant expansion in cost of sales, with marked growth in the recent years.
- Inventories
- Inventory levels generally increased over the examined periods. From 1824 million US dollars in March 2018, inventories rose steadily to 2210 million by December 2021. A notable surge occurred in 2022, with inventories peaking at 3014 million in June 2022 before retreating somewhat to about 2540 million by June 2023. This suggests a buildup of stock in 2022 and some stabilization thereafter. The inventory management appears to have adjusted to changing market conditions, balancing inventory accumulation and reduction phases.
- Inventory turnover ratio
- The inventory turnover ratio demonstrated moderate volatility but remained within a relatively narrow range throughout the periods with available data. It started at 5.88 in March 2019, fluctuating between 5.39 and 7.12 over the subsequent quarters. A notable low point occurred at 5.39 in June 2022, coinciding with peak inventory levels, which may reflect slower inventory movement during that time. Following this, the ratio improved somewhat, indicating moderately efficient inventory use overall, though with periods of lower turnover that suggest temporary inventory build-up or slower sales.
Receivables Turnover
United States Steel Corp., receivables turnover calculation (quarterly data)
| 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 | Dec 31, 2018 | Sep 30, 2018 | Jun 30, 2018 | Mar 31, 2018 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data (US$ in millions) | |||||||||||||||||||||||||||||
| Net sales | |||||||||||||||||||||||||||||
| Receivables, less allowance | |||||||||||||||||||||||||||||
| Short-term Activity Ratio | |||||||||||||||||||||||||||||
| Receivables turnover1 | |||||||||||||||||||||||||||||
| Benchmarks | |||||||||||||||||||||||||||||
| Receivables Turnover, Competitors2 | |||||||||||||||||||||||||||||
| Freeport-McMoRan Inc. | |||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2023-06-30), 10-Q (reporting date: 2023-03-31), 10-K (reporting date: 2022-12-31), 10-Q (reporting date: 2022-09-30), 10-Q (reporting date: 2022-06-30), 10-Q (reporting date: 2022-03-31), 10-K (reporting date: 2021-12-31), 10-Q (reporting date: 2021-09-30), 10-Q (reporting date: 2021-06-30), 10-Q (reporting date: 2021-03-31), 10-K (reporting date: 2020-12-31), 10-Q (reporting date: 2020-09-30), 10-Q (reporting date: 2020-06-30), 10-Q (reporting date: 2020-03-31), 10-K (reporting date: 2019-12-31), 10-Q (reporting date: 2019-09-30), 10-Q (reporting date: 2019-06-30), 10-Q (reporting date: 2019-03-31), 10-K (reporting date: 2018-12-31), 10-Q (reporting date: 2018-09-30), 10-Q (reporting date: 2018-06-30), 10-Q (reporting date: 2018-03-31).
1 Q2 2023 Calculation
Receivables turnover
= (Net salesQ2 2023
+ Net salesQ1 2023
+ Net salesQ4 2022
+ Net salesQ3 2022)
÷ Receivables, less allowance
= ( + + + )
÷ =
2 Click competitor name to see calculations.
The quarterly financial data exhibits several notable trends in revenue, receivables, and receivables turnover ratios over the analyzed periods.
- Net Sales
- The net sales show a fluctuating pattern from 2018 through mid-2020, with values ranging approximately between $2.8 billion and $3.7 billion. A significant decline is observed starting in the first quarter of 2020, reaching a low around $2.1 billion in the second quarter of 2020. From the third quarter of 2020 onwards, net sales steadily increase, peaking at nearly $6 billion in the third quarter of 2021. Subsequently, there is a gradual decline into early 2023, although the figures remain above the earlier lows of 2020.
- Receivables, Less Allowance
- Receivables generally follow a trajectory that aligns with revenue trends but with some variations. Starting around $1.6 billion in early 2018, receivables slightly decline into 2019, dropping to roughly $1.2 billion by the end of that year. The lowest receivables are recorded in mid-2020 at approximately $939 million, coinciding with the decline in net sales during the same period. From mid-2020 onward, receivables rise sharply, peaking near $2.6 billion by mid-2022, suggesting increased sales activity or longer collection periods. Afterwards, there is a contraction back to roughly $1.8 billion in mid-2023.
- Receivables Turnover Ratio
- The receivables turnover ratio, which measures how efficiently receivables are collected, shows considerable variability. Starting from values near 8.5 to 9.9 in 2018 and 2019, the ratio declines significantly into 2020, reaching a low near 6.58 in the first quarter of 2021, reflecting a slower collection pace during periods of lower sales. From 2021 onward, the ratio recovers and significantly increases, peaking at 12.88 in the second quarter of 2023, indicating improved efficiency in receivables collection relative to sales.
Overall, the data reveals a period of disruption centered around 2020, with declines in sales and receivables alongside reduced turnover efficiency, likely reflecting adverse operational or market conditions at that time. Subsequently, there is a recovery phase marked by rising sales and receivables, complemented by enhanced turnover ratios suggesting improved cash flow management and collection procedures. Despite this, some softness in net sales and receivables appears in early 2023, warranting close monitoring for potential sustained trends.
Payables Turnover
United States Steel Corp., payables turnover calculation (quarterly data)
Based on: 10-Q (reporting date: 2023-06-30), 10-Q (reporting date: 2023-03-31), 10-K (reporting date: 2022-12-31), 10-Q (reporting date: 2022-09-30), 10-Q (reporting date: 2022-06-30), 10-Q (reporting date: 2022-03-31), 10-K (reporting date: 2021-12-31), 10-Q (reporting date: 2021-09-30), 10-Q (reporting date: 2021-06-30), 10-Q (reporting date: 2021-03-31), 10-K (reporting date: 2020-12-31), 10-Q (reporting date: 2020-09-30), 10-Q (reporting date: 2020-06-30), 10-Q (reporting date: 2020-03-31), 10-K (reporting date: 2019-12-31), 10-Q (reporting date: 2019-09-30), 10-Q (reporting date: 2019-06-30), 10-Q (reporting date: 2019-03-31), 10-K (reporting date: 2018-12-31), 10-Q (reporting date: 2018-09-30), 10-Q (reporting date: 2018-06-30), 10-Q (reporting date: 2018-03-31).
1 Q2 2023 Calculation
Payables turnover
= (Cost of salesQ2 2023
+ Cost of salesQ1 2023
+ Cost of salesQ4 2022
+ Cost of salesQ3 2022)
÷ Accounts payable and other accrued liabilities
= ( + + + )
÷ =
- Cost of Sales
- The cost of sales exhibits fluctuations over the examined periods. From early 2018 through the end of 2019, there is a general downward trend from a peak of 3,227 million USD in June 2019 to 2,781 million USD in December 2019, with some intermittent increases and decreases during this timeframe. A marked decline in cost of sales is observed in 2020, reaching a low point near 2,274 million USD in June 2020, likely reflecting external economic factors affecting production or demand.
- From 2021 onward, the cost of sales demonstrates an upward trajectory, increasing sharply from 3,080 million USD in March 2021 to a peak of 4,661 million USD in June 2022. This suggests rising production costs or increased volume of sales during this period. However, in the last quarters of 2022 and into 2023, the cost of sales shows a slight moderation but remains elevated relative to earlier years, settling around 4,161 million USD in June 2023.
- Accounts Payable and Other Accrued Liabilities
- The accounts payable and other accrued liabilities have generally trended upward over the period. Starting at 2,174 million USD in March 2018, the figures increase steadily with some variability, peaking notably in June 2022 at 3,385 million USD. This increase aligns broadly with the rise in cost of sales, indicating possibly larger procurement or accrued expenses consistent with heightened business activity.
- There was a distinct dip in liabilities during mid-2020, dropping to 1,459 million USD in June 2020, which coincides with the lowest cost of sales. Post-2020, liabilities resume an upward movement, suggesting recovery or expansion in operational scale. The values towards June 2023 show a modest decrease compared to the mid-2022 peak but remain higher than earlier periods, at about 3,112 million USD.
- Payables Turnover Ratio
- The payables turnover ratio fluctuates over the periods with values ranging approximately between 3.97 and 7.24. Generally, this ratio is higher during 2020, peaking at 7.24 in September 2020, which may indicate faster payment cycles or improved management of payables during the period of reduced cost of sales and liabilities.
- Post-2020, the turnover ratio declines, stabilizing around values between 4.0 and 5.6, suggesting a return to more typical payables management pace. Notably, in 2023, the ratio remains relatively stable, between 5.27 and 5.56, indicating consistent payment practices amid higher liability and cost levels.
- Summary
- The data reflects a correlation between cost of sales and accounts payable, both of which dip in 2020—likely due to exogenous shocks—and then rise substantially through 2021 and mid-2022, before plateauing or slightly decreasing in 2023. Meanwhile, payables turnover ratio responds inversely to these trends, increasing when cost and payables decline and normalizing as those increases stabilize. This pattern suggests responsive financial management adapting to changing operational demands and external factors during the examined periods.
Working Capital Turnover
United States Steel Corp., working capital turnover calculation (quarterly data)
| 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 | Dec 31, 2018 | Sep 30, 2018 | Jun 30, 2018 | Mar 31, 2018 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data (US$ in millions) | |||||||||||||||||||||||||||||
| Current assets | |||||||||||||||||||||||||||||
| Less: Current liabilities | |||||||||||||||||||||||||||||
| Working capital | |||||||||||||||||||||||||||||
| Net sales | |||||||||||||||||||||||||||||
| Short-term Activity Ratio | |||||||||||||||||||||||||||||
| Working capital turnover1 | |||||||||||||||||||||||||||||
| Benchmarks | |||||||||||||||||||||||||||||
| Working Capital Turnover, Competitors2 | |||||||||||||||||||||||||||||
| Freeport-McMoRan Inc. | |||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2023-06-30), 10-Q (reporting date: 2023-03-31), 10-K (reporting date: 2022-12-31), 10-Q (reporting date: 2022-09-30), 10-Q (reporting date: 2022-06-30), 10-Q (reporting date: 2022-03-31), 10-K (reporting date: 2021-12-31), 10-Q (reporting date: 2021-09-30), 10-Q (reporting date: 2021-06-30), 10-Q (reporting date: 2021-03-31), 10-K (reporting date: 2020-12-31), 10-Q (reporting date: 2020-09-30), 10-Q (reporting date: 2020-06-30), 10-Q (reporting date: 2020-03-31), 10-K (reporting date: 2019-12-31), 10-Q (reporting date: 2019-09-30), 10-Q (reporting date: 2019-06-30), 10-Q (reporting date: 2019-03-31), 10-K (reporting date: 2018-12-31), 10-Q (reporting date: 2018-09-30), 10-Q (reporting date: 2018-06-30), 10-Q (reporting date: 2018-03-31).
1 Q2 2023 Calculation
Working capital turnover
= (Net salesQ2 2023
+ Net salesQ1 2023
+ Net salesQ4 2022
+ Net salesQ3 2022)
÷ Working capital
= ( + + + )
÷ =
2 Click competitor name to see calculations.
The financial data reveals several notable trends over the observed periods.
- Working Capital
- Working capital demonstrates substantial fluctuations throughout the quarters. Initially, there is a slight decline from 1,871 million USD in March 2018 to a low point of 1,188 million USD in December 2019. Subsequently, a marked recovery is observed starting in early 2020 with values rising sharply to 2,783 million USD by June 2020. The upward momentum continues strongly into 2022, peaking at 4,467 million USD in December 2022. However, this is followed by a moderate decline in early 2023, with working capital decreasing to 3,763 million USD by June 2023.
- Net Sales
- Net sales exhibit cyclical volatility with an overall upward trend over the long term. Sales began at 3,149 million USD in March 2018 and climbed to a peak of 5,964 million USD in September 2021. There are intermittent dips observed, particularly in 2020, coinciding with global economic disruptions, where sales descended to as low as 2,091 million USD in June 2020. After the peak in late 2021, net sales show some variability but remain generally elevated, with figures around 5,000 million USD in mid-2023.
- Working Capital Turnover Ratio
- The working capital turnover ratio, only available from March 2019 onwards, displays significant volatility. Starting at 8.68, the ratio climbs to a high of 11.58 in December 2019, indicating increasing efficiency in utilizing working capital to generate sales during that period. However, this efficiency markedly decreases in 2020, hitting a low close to 3.86 in June 2020, reflecting challenges in capital utilization amid lower sales. Recovery is evident through 2021, with the ratio rising again to 11.45 in June 2021. In subsequent quarters, the ratio declines and stabilizes around 5.0 to 5.7, suggesting a moderate and more consistent use of working capital relative to sales in recent periods.
Average Inventory Processing Period
United States Steel Corp., average inventory processing period calculation (quarterly data)
| 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 | Dec 31, 2018 | Sep 30, 2018 | Jun 30, 2018 | Mar 31, 2018 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data | |||||||||||||||||||||||||||||
| Inventory turnover | |||||||||||||||||||||||||||||
| Short-term Activity Ratio (no. days) | |||||||||||||||||||||||||||||
| Average inventory processing period1 | |||||||||||||||||||||||||||||
| Benchmarks (no. days) | |||||||||||||||||||||||||||||
| Average Inventory Processing Period, Competitors2 | |||||||||||||||||||||||||||||
| Freeport-McMoRan Inc. | |||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2023-06-30), 10-Q (reporting date: 2023-03-31), 10-K (reporting date: 2022-12-31), 10-Q (reporting date: 2022-09-30), 10-Q (reporting date: 2022-06-30), 10-Q (reporting date: 2022-03-31), 10-K (reporting date: 2021-12-31), 10-Q (reporting date: 2021-09-30), 10-Q (reporting date: 2021-06-30), 10-Q (reporting date: 2021-03-31), 10-K (reporting date: 2020-12-31), 10-Q (reporting date: 2020-09-30), 10-Q (reporting date: 2020-06-30), 10-Q (reporting date: 2020-03-31), 10-K (reporting date: 2019-12-31), 10-Q (reporting date: 2019-09-30), 10-Q (reporting date: 2019-06-30), 10-Q (reporting date: 2019-03-31), 10-K (reporting date: 2018-12-31), 10-Q (reporting date: 2018-09-30), 10-Q (reporting date: 2018-06-30), 10-Q (reporting date: 2018-03-31).
1 Q2 2023 Calculation
Average inventory processing period = 365 ÷ Inventory turnover
= 365 ÷ =
2 Click competitor name to see calculations.
- Inventory turnover ratio
- From the first available data in March 2019, the inventory turnover ratio shows fluctuations within a range approximately between 5.39 and 7.12. Initially, there is a gradual increase from 5.88 in March 2019 to a peak of 7.12 in December 2020. Following this peak, the ratio dips to a low of 5.39 by June 2022 but recovers subsequently to stabilize around 6.46 to 7.11 through early 2023.
- The pattern suggests periodic variations in the efficiency of inventory management, with some notable improvements towards the end of 2020 and again in early 2023, indicating potentially better turnover and inventory utilization during these periods.
- Average inventory processing period (number of days)
- This metric inversely correlates with inventory turnover and generally moves within a range of 51 to 68 days. Starting from 62 days in March 2019, it decreases to a low of 51 days at the end of 2020, aligning with the peak inventory turnover at that time.
- Subsequently, the average processing days increase towards mid-2022, peaking at around 68 days, indicating slower inventory processing during that interval. This is followed by a decline to approximately 55–57 days in early 2023, reflecting an improvement in inventory handling efficiency.
- Summary of trends
- Overall, the data reveal cyclical fluctuations in inventory management effectiveness over the observed periods. Periods of higher inventory turnover coincide with shorter inventory processing times, indicating more efficient stock management. Conversely, decreases in turnover are associated with longer processing periods, suggesting slower inventory flow.
- Recent data from early 2023 indicate a positive trend with improved turnover ratios and reduced processing periods, which may signify enhanced operational efficiency or improved demand conditions contributing to better inventory performance.
Average Receivable Collection Period
United States Steel Corp., average receivable collection period calculation (quarterly data)
| 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 | Dec 31, 2018 | Sep 30, 2018 | Jun 30, 2018 | Mar 31, 2018 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data | |||||||||||||||||||||||||||||
| Receivables turnover | |||||||||||||||||||||||||||||
| Short-term Activity Ratio (no. days) | |||||||||||||||||||||||||||||
| Average receivable collection period1 | |||||||||||||||||||||||||||||
| Benchmarks (no. days) | |||||||||||||||||||||||||||||
| Average Receivable Collection Period, Competitors2 | |||||||||||||||||||||||||||||
| Freeport-McMoRan Inc. | |||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2023-06-30), 10-Q (reporting date: 2023-03-31), 10-K (reporting date: 2022-12-31), 10-Q (reporting date: 2022-09-30), 10-Q (reporting date: 2022-06-30), 10-Q (reporting date: 2022-03-31), 10-K (reporting date: 2021-12-31), 10-Q (reporting date: 2021-09-30), 10-Q (reporting date: 2021-06-30), 10-Q (reporting date: 2021-03-31), 10-K (reporting date: 2020-12-31), 10-Q (reporting date: 2020-09-30), 10-Q (reporting date: 2020-06-30), 10-Q (reporting date: 2020-03-31), 10-K (reporting date: 2019-12-31), 10-Q (reporting date: 2019-09-30), 10-Q (reporting date: 2019-06-30), 10-Q (reporting date: 2019-03-31), 10-K (reporting date: 2018-12-31), 10-Q (reporting date: 2018-09-30), 10-Q (reporting date: 2018-06-30), 10-Q (reporting date: 2018-03-31).
1 Q2 2023 Calculation
Average receivable collection period = 365 ÷ Receivables turnover
= 365 ÷ =
2 Click competitor name to see calculations.
- Receivables Turnover Ratio
- The receivables turnover ratio demonstrates a generally fluctuating pattern over the observed periods. Starting in early 2018, data is missing but from March 2018 onwards, the ratio begins around 8.55 and gradually increases to a peak of approximately 11.43 in September 2020. Subsequently, the ratio falls to around 6.58 in March 2021, indicating a slowdown in receivables collection during this timeframe. From this low point, there is a recovery trend with ratios rising back to values around 10.98 by September 2022 and peaking at 12.88 in March 2023 before declining slightly to about 10.20 by June 2023. This behavior signals periods of improved efficiency in collecting receivables interspersed with temporary declines.
- Average Receivable Collection Period (days)
- The average collection period inversely correlates with the receivables turnover ratio, as expected. The collection period decreases from 43 days in early 2018 to a low of 32 days in September 2020, suggesting enhanced efficiency in collecting receivables during this phase. However, the period then extends significantly to 55 days by March 2021, indicating a slowdown in collections. After this peak, the collection period shortens steadily, reaching a minimum of 28 days around March 2023, which corresponds with the higher turnover ratios during the same period. It slightly increases to 33 and 36 days in the subsequent quarters, indicating a mild deceleration in collection speed.
- Summary Insight
- The data reveals cyclical fluctuations in the efficiency of receivables management. Periods of improved turnover align with shorter collection periods, reflecting quicker realization of receivables and likely better cash flow management. Conversely, intervals of declining turnover ratio and extended collection days may signal challenges in gathering receivables promptly. The most notable downturn occurred around early 2021, in which collection efficiency dropped significantly but improved progressively thereafter, reaching a relative peak in performance in early 2023. The recent slight decrease in turnover and increase in days suggests close monitoring is advisable to maintain optimal collection effectiveness.
Operating Cycle
United States Steel Corp., operating cycle calculation (quarterly data)
No. days
| 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 | Dec 31, 2018 | Sep 30, 2018 | Jun 30, 2018 | Mar 31, 2018 | ||||||||
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| Average inventory processing period | |||||||||||||||||||||||||||||
| Average receivable collection period | |||||||||||||||||||||||||||||
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| Operating cycle1 | |||||||||||||||||||||||||||||
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| Operating Cycle, Competitors2 | |||||||||||||||||||||||||||||
| Freeport-McMoRan Inc. | |||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2023-06-30), 10-Q (reporting date: 2023-03-31), 10-K (reporting date: 2022-12-31), 10-Q (reporting date: 2022-09-30), 10-Q (reporting date: 2022-06-30), 10-Q (reporting date: 2022-03-31), 10-K (reporting date: 2021-12-31), 10-Q (reporting date: 2021-09-30), 10-Q (reporting date: 2021-06-30), 10-Q (reporting date: 2021-03-31), 10-K (reporting date: 2020-12-31), 10-Q (reporting date: 2020-09-30), 10-Q (reporting date: 2020-06-30), 10-Q (reporting date: 2020-03-31), 10-K (reporting date: 2019-12-31), 10-Q (reporting date: 2019-09-30), 10-Q (reporting date: 2019-06-30), 10-Q (reporting date: 2019-03-31), 10-K (reporting date: 2018-12-31), 10-Q (reporting date: 2018-09-30), 10-Q (reporting date: 2018-06-30), 10-Q (reporting date: 2018-03-31).
1 Q2 2023 Calculation
Operating cycle = Average inventory processing period + Average receivable collection period
= + =
2 Click competitor name to see calculations.
The analysis of the quarterly financial data reveals notable trends in the company’s operational efficiency and working capital management over the observed periods.
- Average Inventory Processing Period
- The average inventory processing period remained relatively steady between 54 and 62 days from the end of 2018 through 2019, indicating a consistent pace of inventory turnover. In 2020, this period showed fluctuations, rising to 66 days in the second quarter before declining to a low of 51 days by the fourth quarter. Throughout 2021 and into early 2022, it increased again, peaking at 68 days in the second quarter of 2022, suggesting slower inventory movement during this time. Toward the end of the observation window, the inventory period decreased back to the low 50s by the first half of 2023, pointing to improved inventory management or higher turnover.
- Average Receivable Collection Period
- The receivable collection period showed a generally declining trend from the first quarter of 2019 through 2019, decreasing from 43 days to 37 days, reflecting enhanced efficiency in receivables management. However, the period lengthened significantly in 2021, peaking around 55 days in the first quarter, which may indicate slower collections or credit policy changes. Following this peak, the collection period improved, decreasing to 33 days by the first quarter of 2023, but rising slightly again toward mid-2023.
- Operating Cycle
- The operating cycle, which combines inventory and receivables periods, generally shortened from approximately 105 days in early 2019 to a low of 79 days in the first quarter of 2023. This suggests overall improved operational efficiency and working capital management. Notably, the operating cycle extended to around 119 days in the first quarter of 2021, coinciding with increases in both inventory and receivables periods, indicative of slower inventory turnover and collections during that time. The cycle then declined steadily, reflecting recovery and optimization of working capital processes.
In summary, the company experienced some volatility in inventory and receivable turnover over the periods analyzed, with a particularly challenging phase around 2021. Subsequent improvements in managing both inventory and receivables contributed to a more efficient operating cycle by early 2023, suggesting enhanced overall operational effectiveness and potentially stronger liquidity management in recent periods.
Average Payables Payment Period
United States Steel Corp., average payables payment period calculation (quarterly data)
Based on: 10-Q (reporting date: 2023-06-30), 10-Q (reporting date: 2023-03-31), 10-K (reporting date: 2022-12-31), 10-Q (reporting date: 2022-09-30), 10-Q (reporting date: 2022-06-30), 10-Q (reporting date: 2022-03-31), 10-K (reporting date: 2021-12-31), 10-Q (reporting date: 2021-09-30), 10-Q (reporting date: 2021-06-30), 10-Q (reporting date: 2021-03-31), 10-K (reporting date: 2020-12-31), 10-Q (reporting date: 2020-09-30), 10-Q (reporting date: 2020-06-30), 10-Q (reporting date: 2020-03-31), 10-K (reporting date: 2019-12-31), 10-Q (reporting date: 2019-09-30), 10-Q (reporting date: 2019-06-30), 10-Q (reporting date: 2019-03-31), 10-K (reporting date: 2018-12-31), 10-Q (reporting date: 2018-09-30), 10-Q (reporting date: 2018-06-30), 10-Q (reporting date: 2018-03-31).
1 Q2 2023 Calculation
Average payables payment period = 365 ÷ Payables turnover
= 365 ÷ =
The analysis of the payables turnover and average payables payment period over the observed quarterly periods reveals notable fluctuations indicative of changes in payment and credit management practices.
- Payables Turnover
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This ratio, which was not reported before March 31, 2018, demonstrates variability over the subsequent quarters. Starting around 4.85, the turnover generally trends upward reaching a peak of 7.24 in September 2020. This indicates an acceleration in the rate at which the company pays off its payables during this period.
After this peak, the turnover decreased sharply to 6.05 by December 2020 and then continued to decline to approximately 3.97 by June 2021, reflecting a slowdown in the payment cycle. Following this trough, the turnover ratio recovered gradually, rising again to a range near 5.3 to 5.6 in the most recent quarters of 2022 and the first half of 2023.
The fluctuation suggests that the company experienced periods of both faster and slower payables clearance, possibly in response to liquidity management strategies or supplier negotiations.
- Average Payables Payment Period
-
This metric complements the turnover ratio by indicating the average number of days the company takes to pay its suppliers. Initially recorded at 75 days in March 2018, this period decreased steadily to a low of 50 days by September 2020, coinciding with the peak in payables turnover.
Following this low point, the payment period extended significantly, peaking at 92 days in March 2021, which marks the slowest payment phase. After this, the payment period gradually shortened again, stabilizing around the mid-60 to low 70 day range in the last observed quarters.
The inverse relationship between payables turnover and payment period is consistent throughout the dataset, with shorter payment periods aligning with higher turnover and vice versa. This dynamic reflects the company's adjustments in managing its payment terms over the various quarters.
Overall, the data reveals a cycle of tightening and loosening in payables management. The company appears to have accelerated payments around 2020, likely improving supplier relationships or taking advantage of early payment discounts temporarily. Afterward, it extended payment periods possibly to conserve cash or respond to external economic pressures before stabilizing its payables management approach more recently.
Cash Conversion Cycle
United States Steel Corp., cash conversion cycle calculation (quarterly data)
No. days
Based on: 10-Q (reporting date: 2023-06-30), 10-Q (reporting date: 2023-03-31), 10-K (reporting date: 2022-12-31), 10-Q (reporting date: 2022-09-30), 10-Q (reporting date: 2022-06-30), 10-Q (reporting date: 2022-03-31), 10-K (reporting date: 2021-12-31), 10-Q (reporting date: 2021-09-30), 10-Q (reporting date: 2021-06-30), 10-Q (reporting date: 2021-03-31), 10-K (reporting date: 2020-12-31), 10-Q (reporting date: 2020-09-30), 10-Q (reporting date: 2020-06-30), 10-Q (reporting date: 2020-03-31), 10-K (reporting date: 2019-12-31), 10-Q (reporting date: 2019-09-30), 10-Q (reporting date: 2019-06-30), 10-Q (reporting date: 2019-03-31), 10-K (reporting date: 2018-12-31), 10-Q (reporting date: 2018-09-30), 10-Q (reporting date: 2018-06-30), 10-Q (reporting date: 2018-03-31).
1 Q2 2023 Calculation
Cash conversion cycle = Average inventory processing period + Average receivable collection period – Average payables payment period
= + – =
- Average Inventory Processing Period
- The average inventory processing period generally fluctuates within a range of approximately 50 to 68 days. Starting at around 62 days in early 2019, it shows some cyclical variation with peaks notably in mid-2020 and mid-2022 reaching the high 60s. Despite these fluctuations, there is a tendency towards a gradual reduction in duration by the most recent quarters, suggesting somewhat improved inventory turnover efficiency over the entire period.
- Average Receivable Collection Period
- The receivable collection period demonstrates an improvement trend from the early data points in 2019, where the period was around 43 days, declining to the high 20s and low 30s by 2023. Notable exceptions include elevated values in early to mid-2021, peaking over 50 days, indicating a temporary slowdown in collections during that period. After this peak, the trend reverses, reflecting quicker collection cycles in subsequent quarters.
- Average Payables Payment Period
- The average payables payment period shows significant volatility, especially between 2019 and 2021. It decreased from mid-70s days in early 2019 to a low near 50 days in late 2020, then sharply increased to above 90 days in early 2021, indicating a strategic extension of payment terms at that time. Following this peak, the period gradually decreased but remained somewhat elevated compared to early 2019 levels, stabilizing around the high 60s by mid-2023.
- Cash Conversion Cycle
- The cash conversion cycle exhibits variability, generally moving between roughly 13 and 38 days. The cycle shows a modest improvement over time, decreasing significantly in the latest reported quarters, with values around 13 to 24 days by 2023 compared to earlier figures near 30 or more days. This trend implies an overall enhancement in working capital management and operational efficiency, reducing the time duration between cash outflows and inflows despite some intermittent increases.