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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Short-term Activity Ratios (Summary)
Steel Dynamics Inc., short-term (operating) activity ratios (quarterly data)
Based on: 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), 10-K (reporting date: 2017-12-31), 10-Q (reporting date: 2017-09-30), 10-Q (reporting date: 2017-06-30), 10-Q (reporting date: 2017-03-31).
Analysis of the quarterly financial data reveals several observable trends and shifts in operational efficiency metrics over the periods presented.
- Inventory Turnover
- The inventory turnover ratio shows a general decline from early 2017 through late 2021, dropping from approximately 5.24 to a low near 3.69 before recovering slightly toward the end of the dataset, reaching around 4.7. This suggests the company has been turning over its inventory less frequently over time, potentially indicating slower sales or increased inventory levels, though the slight rebound towards the later periods may reflect some operational improvement or adjustments in inventory management.
- Receivables Turnover
- The receivables turnover ratio exhibits fluctuations with a moderate downward trend from about 10.98 down to roughly 8.34, with some intermittent recoveries. This decline points to a lengthening in the time customers take to pay, indicating a slight deterioration in collection efficiency or more lenient credit terms. Fluctuations may reflect variability in customer payment behavior or credit policies.
- Payables Turnover
- This ratio declines noticeably from around 16.26 to lows near 7.71, then slightly improves to about 13.61 by the end of the period. The decline denotes slower payment to suppliers over time, which could suggest extended credit terms or cash flow management measures aimed at conserving liquidity. The partial recovery toward the end indicates a shift back to somewhat quicker payments.
- Working Capital Turnover
- The working capital turnover ratio remains relatively stable, fluctuating between approximately 3.2 and 4.04 throughout the periods. Stability here suggests consistent efficiency in using working capital to generate sales, despite changes in other operational cycle components.
- Average Inventory Processing Period
- The average inventory processing period increases steadily from about 70 days to a peak near 99 days before improving to 78 days by the end of the dataset. This increase aligns with the declining inventory turnover, reinforcing that inventory is staying on hand longer, which could impact holding costs and working capital requirements. The improvement toward the end may be linked to inventory management adjustments.
- Average Receivable Collection Period
- This metric shows a general lengthening trend, rising from roughly 33 days to a peak around 47 days, followed by some reduction to 37 days. The longer collection period correlates with the decrease in receivables turnover, suggesting a growing need to manage credit risk and collections more effectively.
- Operating Cycle
- The operating cycle, which combines inventory processing and receivable collection periods, increases significantly from about 103 days to over 135 days, peaking at 139 days. This reflects an extension in the time taken to convert raw materials into cash, highlighting operational delays or slower turnover of assets during the period. The slight reduction towards the end suggests some efficiency gains.
- Average Payables Payment Period
- The average payables payment period shows an upward trend from about 22 days to a high near 47 days before decreasing somewhat to 27 days. A longer payment period can indicate improved cash flow management, deferring outflows, but may also affect supplier relations. The eventual decrease could signal a strategic decision to normalize supplier payments.
- Cash Conversion Cycle
- The cash conversion cycle exhibits a general increase from around 81 days to over 100 days at its peak, then decreases to 88 days toward the later periods. This trend demonstrates a lengthening in the time between cash outflows for purchases and inflows from sales, representing a potential challenge in liquidity management during the peak periods, followed by some operational recovery.
Turnover Ratios
Average No. Days
Inventory Turnover
Steel Dynamics Inc., inventory turnover calculation (quarterly data)
| 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 | Dec 31, 2017 | Sep 30, 2017 | Jun 30, 2017 | Mar 31, 2017 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data (US$ in thousands) | ||||||||||||||||||||||||||||||
| Costs of goods sold | ||||||||||||||||||||||||||||||
| Inventories | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio | ||||||||||||||||||||||||||||||
| Inventory turnover1 | ||||||||||||||||||||||||||||||
| Benchmarks | ||||||||||||||||||||||||||||||
| Inventory Turnover, Competitors2 | ||||||||||||||||||||||||||||||
| Freeport-McMoRan Inc. | ||||||||||||||||||||||||||||||
Based on: 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), 10-K (reporting date: 2017-12-31), 10-Q (reporting date: 2017-09-30), 10-Q (reporting date: 2017-06-30), 10-Q (reporting date: 2017-03-31).
1 Q3 2022 Calculation
Inventory turnover
= (Costs of goods soldQ3 2022
+ Costs of goods soldQ2 2022
+ Costs of goods soldQ1 2022
+ Costs of goods soldQ4 2021)
÷ Inventories
= ( + + + )
÷ =
2 Click competitor name to see calculations.
- Costs of goods sold
- The cost of goods sold shows an overall upward trend across the examined quarters, starting at approximately 1.90 billion US dollars in Q1 2017 and reaching a peak close to 4.33 billion US dollars by Q2 2022. There is some quarterly variability, with occasional declines, notably in the last quarters of 2019 and the first half of 2020, likely reflective of broader economic or operational challenges during that period. From early 2021 onwards, the COGS increases markedly, highlighting either higher sales volumes, increased input costs, or changes in production scale.
- Inventories
- Inventory levels also exhibit a generally increasing trend over the period, starting at around 1.36 billion US dollars in Q1 2017 and climbing to over 3.37 billion US dollars by Q3 2022. The increase in inventory is relatively steady, although there are phases of deceleration and slight reductions, such as seen towards the end of 2019 and mid-2020. The acceleration of inventory accumulation is particularly pronounced from mid-2020 onwards, which could indicate stockpiling in anticipation of higher demand or supply chain considerations.
- Inventory turnover
- Inventory turnover ratios are available from Q3 2017 and demonstrate a declining trend from an initial 5.24 times turning over to lower levels around 4.7 times by Q3 2022. This decline suggests that inventory is being sold less frequently relative to stock on hand. The turnover ratio decrease is gradual but consistent, especially notable from 2019 forward, which may reflect either slower sales velocities, increasing inventory levels, or a combination of both factors.
- Summary of trends and insights
- The data reflect steady growth in the scale of operations as indicated by rising costs of goods sold and inventory balances. However, the declining inventory turnover ratio points to a relative slowdown in how quickly inventory is converted to sales. This trend warrants attention as it may impact working capital efficiency and liquidity. The period around 2019 to 2020 shows some fluctuations in both costs and inventories, likely correlating with changing market conditions. The subsequent strong increase in both metrics from 2021 onwards suggests an expansion phase but could also indicate potential inventory build-up that may require management focus to avoid obsolescence or excessive capital lock-up.
Receivables Turnover
Steel Dynamics Inc., receivables turnover calculation (quarterly data)
| 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 | Dec 31, 2017 | Sep 30, 2017 | Jun 30, 2017 | Mar 31, 2017 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data (US$ in thousands) | ||||||||||||||||||||||||||||||
| Net sales | ||||||||||||||||||||||||||||||
| Accounts receivable, net | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio | ||||||||||||||||||||||||||||||
| Receivables turnover1 | ||||||||||||||||||||||||||||||
| Benchmarks | ||||||||||||||||||||||||||||||
| Receivables Turnover, Competitors2 | ||||||||||||||||||||||||||||||
| Freeport-McMoRan Inc. | ||||||||||||||||||||||||||||||
Based on: 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), 10-K (reporting date: 2017-12-31), 10-Q (reporting date: 2017-09-30), 10-Q (reporting date: 2017-06-30), 10-Q (reporting date: 2017-03-31).
1 Q3 2022 Calculation
Receivables turnover
= (Net salesQ3 2022
+ Net salesQ2 2022
+ Net salesQ1 2022
+ Net salesQ4 2021)
÷ Accounts receivable, net
= ( + + + )
÷ =
2 Click competitor name to see calculations.
The financial data indicate several notable trends across the reported periods. Net sales demonstrate an overall upward trajectory with some fluctuations. Starting at approximately 2.37 billion US dollars in the first quarter of 2017, net sales increased to peak around 6.21 billion in the second quarter of 2022, reflecting substantial growth over the period, particularly evident from early 2021 onward. There are periods of decline, especially in mid-2020 where net sales fell to approximately 2.09 billion, possibly reflecting external challenges during that timeframe.
Accounts receivable, net also display a generally increasing trend in value, moving from roughly 883 million in the first quarter of 2017 to over 2.33 billion by the third quarter of 2022. This growth aligns with increasing sales volumes but also suggests a rising investment in credit extended to customers. Notably, there are fluctuations with some quarters showing reductions, such as the decrease in the fourth quarter of 2019 and the middle of 2020, which may coincide with attempts to manage credit risk or changes in payment cycles.
Receivables turnover ratios offer insight into the efficiency of accounts receivable management. The ratio exhibits variability but generally centers around a range between approximately 7.74 and 12.39. Higher turnover ratios in several quarters (e.g., Q1 2018 with 11.33 and Q1 2020 with over 12.39) indicate relatively efficient collection periods, whereas lower ratios observed in later periods (e.g., 7.74 in mid-2021) suggest a slowdown in collections or longer credit terms granted. The decline and moderate rebound in subsequent periods imply changing credit policies or customer payment behaviors, coinciding with the broader sales and receivables trends.
- Net Sales
- Exhibit strong overall growth over the five-year span, with some quarterly declines, most prominently mid-2020. This growth reflects rising demand or price adjustments in the core business environment.
- Accounts Receivable, Net
- Increase parallels sales growth but with signs of intensified credit exposure, marked by notable rises during high-sales periods and occasional reductions indicative of receivable management efforts.
- Receivables Turnover
- Shows fluctuations suggesting shifts in credit collection efficiency. Peaks suggest efficient collection processes, while troughs may point to extended credit terms or delayed payments. Recent years show a moderate decline relative to earlier periods.
Overall, the data reveal an expanding business footprint with increasing revenues accompanied by proportional growth in accounts receivable. However, slight deterioration in receivables turnover in recent years warrants attention to credit management practices to sustain cash flow and reduce credit risk exposure.
Payables Turnover
Steel Dynamics Inc., payables turnover calculation (quarterly data)
Based on: 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), 10-K (reporting date: 2017-12-31), 10-Q (reporting date: 2017-09-30), 10-Q (reporting date: 2017-06-30), 10-Q (reporting date: 2017-03-31).
1 Q3 2022 Calculation
Payables turnover
= (Costs of goods soldQ3 2022
+ Costs of goods soldQ2 2022
+ Costs of goods soldQ1 2022
+ Costs of goods soldQ4 2021)
÷ Accounts payable
= ( + + + )
÷ =
- Costs of goods sold
- The cost of goods sold exhibits a general upward trend over the analyzed quarters. Beginning at approximately 1,896 million USD in Q1 2017, there is a gradual increase, with some fluctuations, reaching peaks above 3,548 million USD by Q4 2021. The data shows a pronounced rise from Q1 2021 onward, indicating increasing production or procurement costs. Despite some periodic decreases within quarters such as Q1 to Q4 of 2019, the long-term trajectory points toward rising costs.
- Accounts payable
- Accounts payable reflect significant volatility but also a generally increasing pattern over time. Starting near 531 million USD in Q1 2017, levels fluctuate in early periods but start to rise steadily from 2020 onwards, reaching over 1,385 million USD by Q3 2022. This increase in payables corresponds to the rise in costs of goods sold, suggesting a possible extension of payment terms or increased purchase volumes on credit.
- Payables turnover ratio
- The payables turnover ratio shows a declining trend from Q1 2017 through much of 2021, falling from roughly 16.26 to a low near 7.71 by Q1 2021. A declining ratio typically suggests slower payment to suppliers. However, after this low point, the ratio gradually rebounds towards 13.61 by Q3 2022, indicating somewhat faster payment cycles or improved management of payables in the later periods analyzed.
- Overall insights
- The increasing costs of goods sold combined with rising accounts payable suggests growing operational scale or inflationary pressures in procurement. The initially declining payables turnover ratio implies lengthening payment terms or slower payments, which may have improved in the most recent quarters. These patterns reveal a dynamic working capital management environment, with notable changes around 2020 and beyond, possibly influenced by broader economic conditions impacting supply chains and cash management.
Working Capital Turnover
Steel Dynamics Inc., working capital turnover calculation (quarterly data)
| 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 | Dec 31, 2017 | Sep 30, 2017 | Jun 30, 2017 | Mar 31, 2017 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data (US$ in thousands) | ||||||||||||||||||||||||||||||
| 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: 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), 10-K (reporting date: 2017-12-31), 10-Q (reporting date: 2017-09-30), 10-Q (reporting date: 2017-06-30), 10-Q (reporting date: 2017-03-31).
1 Q3 2022 Calculation
Working capital turnover
= (Net salesQ3 2022
+ Net salesQ2 2022
+ Net salesQ1 2022
+ Net salesQ4 2021)
÷ Working capital
= ( + + + )
÷ =
2 Click competitor name to see calculations.
The analysis of the quarterly financial data reveals several notable trends in working capital, net sales, and working capital turnover over the observed periods.
- Working Capital
-
The working capital exhibits a generally upward trajectory from March 2017 through September 2022. Starting at approximately 2.36 billion US dollars, it increased steadily with some fluctuations, reaching a peak of about 5.68 billion US dollars in September 2022. There are occasional decreases or slower growth periods, such as between March 2020 and September 2020 where working capital declined from around 3.11 billion to 2.68 billion US dollars, likely reflecting external impacts during that timeframe. Despite these, the overall trend is one of growth, particularly strong after March 2021, indicating a substantial increase in current asset base relative to current liabilities.
- Net Sales
-
Net sales exhibit moderate volatility and a general ascending trend over the entire period. Net sales started around 2.37 billion US dollars in March 2017, with some variability quarter to quarter. A notable rise appears beginning in mid-2018, peaking dramatically from March 2021 onwards, reaching a maximum close to 6.21 billion US dollars in June 2022 before a slight downturn in September 2022. These fluctuations suggest cyclical demand or market conditions affecting sales volumes or pricing. The marked sales growth after early 2021 indicates improved market positioning or operational success.
- Working Capital Turnover Ratio
-
Working capital turnover, calculated as net sales divided by working capital, shows consistent values from December 2017 onward. Ratios typically range between approximately 3.2 and 4.0, representing the efficiency with which working capital generates sales. There is slight variability, with the lowest turnover around 3.2 observed at several points post-2019 and peaks approaching or exceeding 4.0 at times, such as in June 2022. The relatively stable ratio amid rising working capital and net sales suggests proportional growth in both metrics. This implies that expansions in working capital did not lead to diminished efficiency in sales generation, reflecting balanced operational scaling.
In summary, the financial data portrays a company with increasing working capital and net sales over the long term, punctuated by short-term fluctuations possibly aligned with broader economic or industry-specific events. The working capital turnover remains relatively stable, indicating that growth in resources supporting operations has been matched by an increase in sales output, maintaining operational effectiveness.
Average Inventory Processing Period
Steel Dynamics Inc., average inventory processing period calculation (quarterly data)
| 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 | Dec 31, 2017 | Sep 30, 2017 | Jun 30, 2017 | Mar 31, 2017 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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: 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), 10-K (reporting date: 2017-12-31), 10-Q (reporting date: 2017-09-30), 10-Q (reporting date: 2017-06-30), 10-Q (reporting date: 2017-03-31).
1 Q3 2022 Calculation
Average inventory processing period = 365 ÷ Inventory turnover
= 365 ÷ =
2 Click competitor name to see calculations.
- Inventory Turnover Ratio
- Between March 2018 and September 2019, the inventory turnover ratio exhibited a generally stable pattern, fluctuating slightly around a range of approximately 4.8 to 5.35. This indicates a consistent efficiency in converting inventory to sales during this period.
- Starting in December 2019, a declining trend is observed. The ratio decreased from around 5.29 to 5.0 by March 2020, followed by a more marked drop in the subsequent quarters, reaching its lowest point at 3.69 by March 2022. This decline suggests a reduced efficiency in inventory turnover, possibly reflecting slower sales or excess inventory levels.
- There is a moderate recovery in turnover noted in the latter part of 2022, with the ratio improving from 4.01 in June to 4.7 by September 2022, indicating some restoration of operational efficiency towards the end of the observed period.
- Average Inventory Processing Period
- The average inventory processing period initially hovered around the low 70s in days from March 2018 through December 2019, implying a stable cycle time for inventory turnover.
- From early 2020 onwards, there was a noticeable increase in processing days, peaking at 99 days by March 2022. This extension in processing time corresponds inversely to the decline in inventory turnover ratio, highlighting slower movement of inventory and potential challenges in inventory management or demand fluctuations.
- Subsequent to the peak, the processing period shortened moderately to 78 days by September 2022, consistent with the partial recovery observed in the turnover ratio, suggesting improving inventory handling efficiency.
- Overall Insights
- The data demonstrates a period of stable inventory management followed by a phase of deterioration in turnover efficiency and an increase in inventory processing time, particularly apparent from 2020 through early 2022. This may reflect external market challenges or internal operational difficulties impacting inventory movement.
- The latter part of 2022 shows signs of operational improvement, with turnaround in key inventory metrics possibly signaling strategic adjustments or favorable market conditions aiding efficiency recovery.
Average Receivable Collection Period
Steel Dynamics Inc., average receivable collection period calculation (quarterly data)
| 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 | Dec 31, 2017 | Sep 30, 2017 | Jun 30, 2017 | Mar 31, 2017 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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: 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), 10-K (reporting date: 2017-12-31), 10-Q (reporting date: 2017-09-30), 10-Q (reporting date: 2017-06-30), 10-Q (reporting date: 2017-03-31).
1 Q3 2022 Calculation
Average receivable collection period = 365 ÷ Receivables turnover
= 365 ÷ =
2 Click competitor name to see calculations.
- Receivables turnover
- The receivables turnover ratio exhibits fluctuations over the analyzed quarters. It shows relatively strong performance starting from a ratio around 11 in early 2017, with a gradual decline to under 8 by mid-2021. Subsequently, there is a mild recovery towards the end of the period with the ratio rising again close to 9.75. This indicates variability in the efficiency of the company’s credit and collection policies, with some periods reflecting slower collection relative to sales.
- Average receivable collection period
- The average receivable collection period generally moves inversely to the receivables turnover. It starts at a shorter duration of 33 days in early 2017, increases to over 40 days during parts of 2018, and peaks at 47 days around mid-2021. After this peak, the collection period shortens slightly to approximately 37 days by the latter quarters. The lengthening of collection days suggests a temporary deterioration in the speed of collecting receivables, which may impact cash flow. The recent improvement indicates some recovery in this metric.
- Overall analysis
- The trends in both ratios suggest periods of both efficient and less efficient credit management. The declining receivables turnover and increasing collection period through mid-2021 may imply challenges in receivables management or changing customer payment behaviors. The mild improvement toward the end of the period may reflect adjustments in credit policies or market conditions leading to better accounts receivable management.
Operating Cycle
Steel Dynamics Inc., operating cycle calculation (quarterly data)
No. days
| 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 | Dec 31, 2017 | Sep 30, 2017 | Jun 30, 2017 | Mar 31, 2017 | ||||||||
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| Operating Cycle, Competitors2 | ||||||||||||||||||||||||||||||
| Freeport-McMoRan Inc. | ||||||||||||||||||||||||||||||
Based on: 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), 10-K (reporting date: 2017-12-31), 10-Q (reporting date: 2017-09-30), 10-Q (reporting date: 2017-06-30), 10-Q (reporting date: 2017-03-31).
1 Q3 2022 Calculation
Operating cycle = Average inventory processing period + Average receivable collection period
= + =
2 Click competitor name to see calculations.
- Average Inventory Processing Period
- The average inventory processing period shows a general upward trend over the analyzed quarters. Starting around 70 days in early 2018, it remained relatively stable through 2019, fluctuating mostly between 68 and 73 days. However, from 2020 onward, the period increased notably, reaching a peak of 99 days in the first quarter of 2022 before declining slightly to 78 days by the third quarter of 2022. This indicates a lengthening of the time inventory is held before processing over this timeframe, with a considerable extension after 2019.
- Average Receivable Collection Period
- The receivable collection period demonstrates some variability across the quarters. Initially around 33-41 days from early 2018 through late 2018, it showed a declining trend towards lower 30s by the end of 2019. A marked increase is observed during 2021, with extended collection periods peaking at approximately 47 days mid-year. Toward the latter part of 2021 and into 2022, the period slightly reduced but remained elevated relative to earlier years, fluctuating between 37 and 44 days. This suggests increasing delays or extended terms in receivable collections, particularly during and after 2020.
- Operating Cycle
- The operating cycle, representing the combined duration inventory and receivables remain outstanding, follows an increasing trajectory with some fluctuations. From about 103 days through 2018, it displayed modest decreases and increases, stabilizing near 98-105 days until 2019. Post-2019, the operating cycle escalated sharply, reaching a high of 139 days by mid-2021, indicating a significant lengthening in the overall cash conversion period. Subsequent quarters show slight decreases but remain elevated above pre-2020 levels, illustrating extended timeframes for managing both inventory and receivables.
- Summary of Trends
- Overall, the analysis reflects an elongation of key operational efficiency metrics over the periods. Both inventory processing and receivable collection times have increased particularly after 2019, contributing to a longer operating cycle. These trends may point to challenges in inventory turnover and customer payment collections during recent years, likely impacting working capital management and liquidity considerations. The moderation in the last reported quarter suggests possible initial improvements or adjustments being implemented to address these operational delays.
Average Payables Payment Period
Steel Dynamics Inc., average payables payment period calculation (quarterly data)
Based on: 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), 10-K (reporting date: 2017-12-31), 10-Q (reporting date: 2017-09-30), 10-Q (reporting date: 2017-06-30), 10-Q (reporting date: 2017-03-31).
1 Q3 2022 Calculation
Average payables payment period = 365 ÷ Payables turnover
= 365 ÷ =
- Payables Turnover Ratio Trends
- The payables turnover ratio exhibits a declining trend over the entire span from early 2017 through late 2022. Starting from relatively higher ratios around 16.26 in early 2017 quarters, this metric gradually decreased, reaching mid-to-low values near 7.71 by the first quarter of 2021. Subsequently, a slight recovery is observed, with the ratio modestly increasing to 13.61 by the third quarter of 2022. The initial decline suggests a slowing pace of accounts payable settlements during the period, which later shows some improvement towards the end.
- Average Payables Payment Period Trends
- Conversely, the average payables payment period demonstrates an increasing trend over the same timeframe. Beginning with values near 22 days in early 2017, this period extends to a peak of 47 days in the first quarter of 2021, indicating a lengthening in the time taken to settle payables. After this peak, the payment period decreases gradually, moving towards 27 days by the third quarter of 2022. This pattern mirrors the inverse relationship with the payables turnover ratio, reflecting changes in payment practices.
- Interpretation of Correlated Metrics
- The inverse relationship between the payables turnover ratio and the average payment period is consistent, as would be expected. A reduction in turnover ratio aligns with an increase in the payment period, indicating that payables are settled more slowly over time initially. The improvement in payables turnover and reduction in days payable in the later periods suggest a shift towards faster payment to suppliers.
- Overall Assessment
- The data signals a strategic variation in payment behavior over the observed quarters. The initial slowing of payables turnover combined with lengthening payment periods may suggest attempts to manage cash flows by delaying payments. The subsequent recovery in turnover rates and shortening of payment periods could indicate a normalization or a deliberate effort to improve supplier relationships or credit terms.
Cash Conversion Cycle
Steel Dynamics Inc., cash conversion cycle calculation (quarterly data)
No. days
Based on: 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), 10-K (reporting date: 2017-12-31), 10-Q (reporting date: 2017-09-30), 10-Q (reporting date: 2017-06-30), 10-Q (reporting date: 2017-03-31).
1 Q3 2022 Calculation
Cash conversion cycle = Average inventory processing period + Average receivable collection period – Average payables payment period
= + – =
The financial data reveals trends in inventory, receivables, payables, and the overall cash conversion cycle across multiple quarters from 2017 through 2022.
- Average Inventory Processing Period
- This period began at 70 days in early 2017 and showed a gradual increase through subsequent years. Notable fluctuations occurred in 2020 and 2021, where the inventory period rose significantly, peaking at 99 days in the first quarter of 2022. After this peak, there was a slight decline, ending at 78 days by the third quarter of 2022. The data indicates a trend toward longer inventory holding times over the analyzed period, suggesting potential challenges in inventory management or shifts in operational strategy.
- Average Receivable Collection Period
- The receivable collection period demonstrated variability but generally ranged between 29 and 47 days throughout the time horizon. Early years displayed a relatively stable range in the low 30s to low 40s. However, during 2020 and 2021, collection days increased notably, reaching a maximum of 47 days in the first quarter of 2021. By the third quarter of 2022, the period decreased to 37 days, closer to earlier levels. This pattern may indicate changes in credit policies, customer payment behavior, or external economic factors influencing collections.
- Average Payables Payment Period
- The payable payment period started at 22 days and experienced an upward trajectory until reaching a peak of 47 days in the first quarter of 2021. Subsequently, the period tended to decline, falling to 27 days by the third quarter of 2022. This increase followed by a decrease could reflect shifts in supplier payment terms, liquidity management practices, or bargaining power dynamics with vendors over time.
- Cash Conversion Cycle
- The cash conversion cycle (CCC), a key liquidity and operational efficiency metric, increased from around 81 days in early 2017 to a peak exceeding 102 days in the first half of 2022. This rising trend was primarily driven by extended inventory and receivables periods, partly offset by the changing payables period. The CCC's increase suggests that capital is tied up longer in working capital components, potentially impacting cash flow. Notably, after peaking in mid-2022, the CCC showed a slight improvement by the third quarter of 2022.
In summary, the company exhibited a general lengthening of the operating cycle over the analyzed period, with inventory and receivables collection times increasing significantly, especially around 2020–2021. Meanwhile, payables were managed with increasing delays until early 2021 but shortened thereafter. These dynamics reflect evolving operational and financial management conditions, which may warrant further investigation to optimize working capital efficiency.