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)
Based on: 10-Q (reporting date: 2019-07-31), 10-Q (reporting date: 2019-04-30), 10-Q (reporting date: 2019-01-31), 10-K (reporting date: 2018-10-31), 10-Q (reporting date: 2018-07-31), 10-Q (reporting date: 2018-04-30), 10-Q (reporting date: 2018-01-31), 10-K (reporting date: 2017-10-31), 10-Q (reporting date: 2017-07-31), 10-Q (reporting date: 2017-04-30), 10-Q (reporting date: 2017-01-31), 10-K (reporting date: 2016-10-31), 10-Q (reporting date: 2016-07-31), 10-Q (reporting date: 2016-04-30), 10-Q (reporting date: 2016-01-31), 10-K (reporting date: 2015-10-31), 10-Q (reporting date: 2015-07-31), 10-Q (reporting date: 2015-04-30), 10-Q (reporting date: 2015-01-31), 10-K (reporting date: 2014-10-31), 10-Q (reporting date: 2014-07-31), 10-Q (reporting date: 2014-04-30), 10-Q (reporting date: 2014-01-31).
- Inventory turnover
- Over the observed periods, inventory turnover exhibited a declining trend overall. Starting from around 14.34 times in early 2014, it decreased gradually reaching lows of approximately 7.34 to 8.89 times in 2018 and 2019. Notably, there was a brief increase during 2015 to early 2016, peaking near 16.78 times, but the long-term direction was downward indicating slower inventory movement through the year-end periods.
- Receivables turnover
- The receivables turnover ratio showed considerable variability. It remained relatively stable around 8 times in 2014 and early 2015, surged significantly to over 21 times at the beginning of 2016, and then declined to roughly 11 times by mid-2019. This early 2016 spike indicates a temporary improvement in collection efficiency, followed by a return to more stable but lower turnover levels.
- Payables turnover
- Payables turnover demonstrated a noticeable downward trend from about 6.81 times in early 2014 to around 3.2 to 3.5 times in 2018 and 2019. This suggests the company extended its payment periods to suppliers over time, as confirmed by the increasing average payables period.
- Working capital turnover
- Data for working capital turnover was limited, but notable observations include a peak near 35.77 times in mid-2015 followed by a sharp decline to about 10.77 times by the end of 2015. Later periods lack sufficient data to confirm sustained trends.
- Average inventory processing period
- The average inventory processing period tended to increase steadily, beginning near 25 days in early 2014 and reaching a peak around 50 days by late 2017. After that, it slightly decreased but remained elevated near 40 to 46 days by mid-2019. The trend signals slower inventory turnover and longer holding periods as time progressed.
- Average receivable collection period
- The collection period started around 44-47 days in 2014, decreased sharply to as low as 17 days in early 2016, then gradually increased again to stabilize around 30-34 days in 2018 and 2019. This pattern aligns with fluctuations in the receivables turnover ratio, reflecting changes in credit terms and collection efforts.
- Operating cycle
- The operating cycle showed overall growth from about 69-77 days in 2014 and 2015 to roughly 75-81 days by 2017 and 2018, indicating an increasing sum of inventory processing and receivable collection periods. This elongation suggests slower operational throughput and potential working capital constraints during the later years.
- Average payables payment period
- The payables payment period increased significantly, from roughly 54 days in early 2014 to over 100 days from 2016 onward, peaking near 114 days in late 2017. This extension indicates the company took longer to pay suppliers, possibly to manage cash flows or leverage supplier credit.
- Cash conversion cycle
- The cash conversion cycle decreased over the period, turning negative from early 2016 and remaining so through 2019. Starting near 15 days in 2014 and dropping steadily to approximately -35 days by 2019, this negative cash conversion cycle implies the company was collecting cash from customers more rapidly than it was paying its suppliers and turning over inventory, which improves liquidity and cash flow management.
Turnover Ratios
Average No. Days
Inventory Turnover
| Jul 31, 2019 | Apr 30, 2019 | Jan 31, 2019 | Oct 31, 2018 | Jul 31, 2018 | Apr 30, 2018 | Jan 31, 2018 | Oct 31, 2017 | Jul 31, 2017 | Apr 30, 2017 | Jan 31, 2017 | Oct 31, 2016 | Jul 31, 2016 | Apr 30, 2016 | Jan 31, 2016 | Oct 31, 2015 | Jul 31, 2015 | Apr 30, 2015 | Jan 31, 2015 | Oct 31, 2014 | Jul 31, 2014 | Apr 30, 2014 | Jan 31, 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data (US$ in millions) | ||||||||||||||||||||||||||||||
| Cost of revenue | ||||||||||||||||||||||||||||||
| Inventory | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio | ||||||||||||||||||||||||||||||
| Inventory turnover1 | ||||||||||||||||||||||||||||||
| Benchmarks | ||||||||||||||||||||||||||||||
| Inventory Turnover, Competitors2 | ||||||||||||||||||||||||||||||
| Apple Inc. | ||||||||||||||||||||||||||||||
| Arista Networks Inc. | ||||||||||||||||||||||||||||||
| Cisco Systems Inc. | ||||||||||||||||||||||||||||||
| Dell Technologies Inc. | ||||||||||||||||||||||||||||||
| Super Micro Computer Inc. | ||||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2019-07-31), 10-Q (reporting date: 2019-04-30), 10-Q (reporting date: 2019-01-31), 10-K (reporting date: 2018-10-31), 10-Q (reporting date: 2018-07-31), 10-Q (reporting date: 2018-04-30), 10-Q (reporting date: 2018-01-31), 10-K (reporting date: 2017-10-31), 10-Q (reporting date: 2017-07-31), 10-Q (reporting date: 2017-04-30), 10-Q (reporting date: 2017-01-31), 10-K (reporting date: 2016-10-31), 10-Q (reporting date: 2016-07-31), 10-Q (reporting date: 2016-04-30), 10-Q (reporting date: 2016-01-31), 10-K (reporting date: 2015-10-31), 10-Q (reporting date: 2015-07-31), 10-Q (reporting date: 2015-04-30), 10-Q (reporting date: 2015-01-31), 10-K (reporting date: 2014-10-31), 10-Q (reporting date: 2014-07-31), 10-Q (reporting date: 2014-04-30), 10-Q (reporting date: 2014-01-31).
1 Q3 2019 Calculation
Inventory turnover
= (Cost of revenueQ3 2019
+ Cost of revenueQ2 2019
+ Cost of revenueQ1 2019
+ Cost of revenueQ4 2018)
÷ Inventory
= ( + + + )
÷ =
2 Click competitor name to see calculations.
The analysis of the quarterly financial data reveals several notable trends and shifts over the periods observed.
- Cost of Revenue
- The cost of revenue displays an overall fluctuating trend but with important periodic shifts. From early 2014 through the fourth quarter of 2015, the cost of revenue remained relatively stable, mostly ranging between approximately $19,000 million to $21,700 million. Starting from the first quarter of 2016, a notable decrease is observed, with values roughly halving compared to previous periods, hovering around $9,300 million to $10,200 million initially. This suggests a significant change in accounting, business structure, or operational scale. From 2017 onward, there is a gradual but consistent increase in the cost of revenue again, reaching levels of about $12,100 million by the third quarter of 2019. This upward trend from 2017 to 2019 indicates growth or increased costs associated with sales.
- Inventory
- Inventory values show a general upward movement over time with some volatility. Initially, in early 2014, inventory levels stood around $6,000 million, rising gradually with peaks and troughs to approximately $6,700 million by late 2015. A sharp drop occurs in 2016, with inventory numbers nearly halving to around $3,500 million-$4,500 million. After this dip, inventory levels resume an upward trend, climbing steadily to approximately $5,700 million by the third quarter of 2019. This suggests changes in inventory management, supply chain adjustments, or operational changes that align with the cost of revenue trends.
- Inventory Turnover Ratio
- The inventory turnover ratio demonstrates a declining trend over the analyzed period. In early 2014, turnover ratios were notably high, exceeding 14.0 and peaking near 16.8 in early 2016. However, from mid-2016 onwards, there is a marked decrease in turnover ratios, falling below 9.0 and declining further to around 7.3 by late 2017. Although there is a slight recovery towards the third quarter of 2019 reaching approximately 8.4, the general trend points to slower inventory movement over time. This slowdown may indicate increasing inventory levels relative to sales or possibly challenges in inventory liquidations.
In summary, the data reflect a significant structural adjustment around early 2016 as evidenced by the halving of both cost of revenue and inventory values, which could be linked to operational changes or reporting modifications. Following this adjustment, costs and inventory levels have generally risen, while inventory turnover has declined, suggesting increased holding periods or less efficient inventory use. These patterns could have implications for cash flow management and operational efficiency going forward.
Receivables Turnover
| Jul 31, 2019 | Apr 30, 2019 | Jan 31, 2019 | Oct 31, 2018 | Jul 31, 2018 | Apr 30, 2018 | Jan 31, 2018 | Oct 31, 2017 | Jul 31, 2017 | Apr 30, 2017 | Jan 31, 2017 | Oct 31, 2016 | Jul 31, 2016 | Apr 30, 2016 | Jan 31, 2016 | Oct 31, 2015 | Jul 31, 2015 | Apr 30, 2015 | Jan 31, 2015 | Oct 31, 2014 | Jul 31, 2014 | Apr 30, 2014 | Jan 31, 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data (US$ in millions) | ||||||||||||||||||||||||||||||
| Net revenue | ||||||||||||||||||||||||||||||
| Accounts receivable, net | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio | ||||||||||||||||||||||||||||||
| Receivables turnover1 | ||||||||||||||||||||||||||||||
| Benchmarks | ||||||||||||||||||||||||||||||
| Receivables Turnover, Competitors2 | ||||||||||||||||||||||||||||||
| Apple Inc. | ||||||||||||||||||||||||||||||
| Arista Networks Inc. | ||||||||||||||||||||||||||||||
| Cisco Systems Inc. | ||||||||||||||||||||||||||||||
| Dell Technologies Inc. | ||||||||||||||||||||||||||||||
| Super Micro Computer Inc. | ||||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2019-07-31), 10-Q (reporting date: 2019-04-30), 10-Q (reporting date: 2019-01-31), 10-K (reporting date: 2018-10-31), 10-Q (reporting date: 2018-07-31), 10-Q (reporting date: 2018-04-30), 10-Q (reporting date: 2018-01-31), 10-K (reporting date: 2017-10-31), 10-Q (reporting date: 2017-07-31), 10-Q (reporting date: 2017-04-30), 10-Q (reporting date: 2017-01-31), 10-K (reporting date: 2016-10-31), 10-Q (reporting date: 2016-07-31), 10-Q (reporting date: 2016-04-30), 10-Q (reporting date: 2016-01-31), 10-K (reporting date: 2015-10-31), 10-Q (reporting date: 2015-07-31), 10-Q (reporting date: 2015-04-30), 10-Q (reporting date: 2015-01-31), 10-K (reporting date: 2014-10-31), 10-Q (reporting date: 2014-07-31), 10-Q (reporting date: 2014-04-30), 10-Q (reporting date: 2014-01-31).
1 Q3 2019 Calculation
Receivables turnover
= (Net revenueQ3 2019
+ Net revenueQ2 2019
+ Net revenueQ1 2019
+ Net revenueQ4 2018)
÷ Accounts receivable, net
= ( + + + )
÷ =
2 Click competitor name to see calculations.
The financial data reflects several key trends over the analyzed periods with respect to net revenue, accounts receivable, and receivables turnover ratio. The observations below outline the trends and dynamics observed in these financial metrics.
- Net Revenue
-
Net revenue demonstrates variability with a noticeable cyclical pattern on a quarterly basis. Initially, from early 2014 through 2015, net revenue fluctuated in the range of approximately $25 billion to $28 billion per quarter, with slight decreases observed in mid to late 2015. From early 2016, the reported revenue figures appear approximately halved, indicating a possible change in reporting methodology or accounting standards affecting revenue recognition. Despite this adjustment, revenue trends show a gradual upward trajectory from approximately $12 billion in early 2016 to around $15 billion by late 2018. This reflects incremental growth over the subsequent quarters after the reporting change.
- Accounts Receivable, Net
-
Accounts receivable values closely follow a similar structural pattern to that of net revenue, although absolute values decrease dramatically from 2016 onwards consistent with the change observed in net revenue reporting. Before 2016, accounts receivable ranged between around $12 billion and $14.5 billion. Post-2016, these figures stabilize around $3.5 billion to $5.5 billion, showing a gradual increase over the quarters, particularly from early 2017 through 2019. This suggests that while sales volumes might have adjusted, the management of receivables maintained a relatively consistent level, reflecting steady credit and collections practices.
- Receivables Turnover Ratio
-
The receivables turnover ratio exhibits significant fluctuations. During 2014 to 2015, turnover ratios generally ranged from 7.7 to 9.0, implying an average collection period consistent with industry norms. However, 2016 marks a sharp increase in turnover ratios, with values spiking to over 21 in early 2016 before gradually declining to a range between 11 and 14 over subsequent quarters. Such elevated turnover ratios indicate improved efficiency in collecting receivables or altered accounting practices, aligned with the revenue reporting changes observed. Over time, the turnover ratio stabilizes but remains somewhat lower than the peak, suggesting a return to more typical operational levels while maintaining slightly improved collection efficiency compared to pre-2016 levels.
In summary, the data reveals a structural shift in financial reporting beginning in 2016 that affects the scale of reported revenue and receivables, coupled with noteworthy improvements in receivables turnover efficiency. Following this adjustment, steady revenue growth and consistent receivables management characterize the more recent periods, indicating controlled credit risk and potentially strengthening operational performance.
Payables Turnover
| Jul 31, 2019 | Apr 30, 2019 | Jan 31, 2019 | Oct 31, 2018 | Jul 31, 2018 | Apr 30, 2018 | Jan 31, 2018 | Oct 31, 2017 | Jul 31, 2017 | Apr 30, 2017 | Jan 31, 2017 | Oct 31, 2016 | Jul 31, 2016 | Apr 30, 2016 | Jan 31, 2016 | Oct 31, 2015 | Jul 31, 2015 | Apr 30, 2015 | Jan 31, 2015 | Oct 31, 2014 | Jul 31, 2014 | Apr 30, 2014 | Jan 31, 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data (US$ in millions) | ||||||||||||||||||||||||||||||
| Cost of revenue | ||||||||||||||||||||||||||||||
| Accounts payable | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio | ||||||||||||||||||||||||||||||
| Payables turnover1 | ||||||||||||||||||||||||||||||
| Benchmarks | ||||||||||||||||||||||||||||||
| Payables Turnover, Competitors2 | ||||||||||||||||||||||||||||||
| Apple Inc. | ||||||||||||||||||||||||||||||
| Arista Networks Inc. | ||||||||||||||||||||||||||||||
| Cisco Systems Inc. | ||||||||||||||||||||||||||||||
| Dell Technologies Inc. | ||||||||||||||||||||||||||||||
| Super Micro Computer Inc. | ||||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2019-07-31), 10-Q (reporting date: 2019-04-30), 10-Q (reporting date: 2019-01-31), 10-K (reporting date: 2018-10-31), 10-Q (reporting date: 2018-07-31), 10-Q (reporting date: 2018-04-30), 10-Q (reporting date: 2018-01-31), 10-K (reporting date: 2017-10-31), 10-Q (reporting date: 2017-07-31), 10-Q (reporting date: 2017-04-30), 10-Q (reporting date: 2017-01-31), 10-K (reporting date: 2016-10-31), 10-Q (reporting date: 2016-07-31), 10-Q (reporting date: 2016-04-30), 10-Q (reporting date: 2016-01-31), 10-K (reporting date: 2015-10-31), 10-Q (reporting date: 2015-07-31), 10-Q (reporting date: 2015-04-30), 10-Q (reporting date: 2015-01-31), 10-K (reporting date: 2014-10-31), 10-Q (reporting date: 2014-07-31), 10-Q (reporting date: 2014-04-30), 10-Q (reporting date: 2014-01-31).
1 Q3 2019 Calculation
Payables turnover
= (Cost of revenueQ3 2019
+ Cost of revenueQ2 2019
+ Cost of revenueQ1 2019
+ Cost of revenueQ4 2018)
÷ Accounts payable
= ( + + + )
÷ =
2 Click competitor name to see calculations.
- Cost of Revenue
- The cost of revenue exhibits a relatively stable pattern within each year, typically rising and falling in a seasonal manner. In the initial years from 2014 through 2015, quarterly costs hovered mostly between approximately 19,000 and 21,500 million US dollars. There is a notable split in 2016, where the figures reflect half-year segments with values roughly half those of previous periods, likely indicating a change in reporting or categorization methodology.
- From 2017 onward, the cost of revenue staggers upwards, generally increasing quarter-to-quarter with some seasonality. The values grow from around 10,000 million to peaks near 12,000 to 13,000 million by late 2018 and early 2019. This trend suggests a gradual upward pressure on costs, possibly correlating to increased sales volume or rising input expenses over time.
- Accounts Payable
- Accounts payable follow a similar evolving pattern to cost of revenue but demonstrate more pronounced increments over time. Early years report values in the 12,000 to 16,000 million range, again with a clear segmentation in 2016 to half-year scales around 9,000 to 11,000 million.
- Post-2016, accounts payable increase steadily, displaying growth from approximately 10,900 million in early 2017 to over 14,600 million by mid-2019. This suggests an accumulation of outstanding liabilities linked to purchases or operational expenses, reflecting a potential increase in procurement or extended credit terms from suppliers over the years.
- Payables Turnover Ratio
- The payables turnover ratio illustrates the frequency at which accounts payable are settled during the period. The ratio declines steadily from 6.81 in early 2014 down to roughly 4.93 by late 2015, indicating slower payment cycles or increased credit durations to suppliers.
- In 2016, the ratio fluctuates with a marked decrease further to lows near 3.53-3.64, and remains at this lower level through 2017 and into 2019, generally oscillating between 3.2 and 3.5. The downward trend implies the company is taking longer to pay its suppliers compared to earlier years, possibly as a strategic decision to manage cash flow or reflecting changes in supplier agreements.
- Overall Analysis
- Over the period analyzed, both cost of revenue and accounts payable increase in nominal terms, with a notable structural change in reporting around 2016 that halves reported figures—likely reflecting a reporting adjustment rather than an actual contraction. After this adjustment, both metrics resume a rising trend.
- Simultaneously, the payables turnover ratio declines steadily, indicating lengthening payment periods to suppliers, which aligns with growing accounts payable balances. This pattern suggests a shift toward extended credit terms or more strategic working capital management, possibly to improve liquidity or accommodate larger operating scales.
- The combined trends point to increased operational scale with rising costs and liabilities, alongside a conscious extension of payables duration, which should be monitored for impact on supplier relationships and overall cash cycle efficiency.
Working Capital Turnover
| Jul 31, 2019 | Apr 30, 2019 | Jan 31, 2019 | Oct 31, 2018 | Jul 31, 2018 | Apr 30, 2018 | Jan 31, 2018 | Oct 31, 2017 | Jul 31, 2017 | Apr 30, 2017 | Jan 31, 2017 | Oct 31, 2016 | Jul 31, 2016 | Apr 30, 2016 | Jan 31, 2016 | Oct 31, 2015 | Jul 31, 2015 | Apr 30, 2015 | Jan 31, 2015 | Oct 31, 2014 | Jul 31, 2014 | Apr 30, 2014 | Jan 31, 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data (US$ in millions) | ||||||||||||||||||||||||||||||
| Current assets | ||||||||||||||||||||||||||||||
| Less: Current liabilities | ||||||||||||||||||||||||||||||
| Working capital | ||||||||||||||||||||||||||||||
| Net revenue | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio | ||||||||||||||||||||||||||||||
| Working capital turnover1 | ||||||||||||||||||||||||||||||
| Benchmarks | ||||||||||||||||||||||||||||||
| Working Capital Turnover, Competitors2 | ||||||||||||||||||||||||||||||
| Apple Inc. | ||||||||||||||||||||||||||||||
| Arista Networks Inc. | ||||||||||||||||||||||||||||||
| Cisco Systems Inc. | ||||||||||||||||||||||||||||||
| Dell Technologies Inc. | ||||||||||||||||||||||||||||||
| Super Micro Computer Inc. | ||||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2019-07-31), 10-Q (reporting date: 2019-04-30), 10-Q (reporting date: 2019-01-31), 10-K (reporting date: 2018-10-31), 10-Q (reporting date: 2018-07-31), 10-Q (reporting date: 2018-04-30), 10-Q (reporting date: 2018-01-31), 10-K (reporting date: 2017-10-31), 10-Q (reporting date: 2017-07-31), 10-Q (reporting date: 2017-04-30), 10-Q (reporting date: 2017-01-31), 10-K (reporting date: 2016-10-31), 10-Q (reporting date: 2016-07-31), 10-Q (reporting date: 2016-04-30), 10-Q (reporting date: 2016-01-31), 10-K (reporting date: 2015-10-31), 10-Q (reporting date: 2015-07-31), 10-Q (reporting date: 2015-04-30), 10-Q (reporting date: 2015-01-31), 10-K (reporting date: 2014-10-31), 10-Q (reporting date: 2014-07-31), 10-Q (reporting date: 2014-04-30), 10-Q (reporting date: 2014-01-31).
1 Q3 2019 Calculation
Working capital turnover
= (Net revenueQ3 2019
+ Net revenueQ2 2019
+ Net revenueQ1 2019
+ Net revenueQ4 2018)
÷ Working capital
= ( + + + )
÷ =
2 Click competitor name to see calculations.
The analysis of the recent quarterly financial trends reveals several notable patterns in key financial metrics. Working capital exhibited significant volatility from early 2014 through mid-2019. Initially positive, working capital sharply declined into negative territory beginning in early 2016 and remained negative through mid-2019, indicating increased short-term liabilities relative to current assets during this period. The most substantial declines were observed around early 2018 and early 2019, where working capital reached its lowest levels.
Net revenue demonstrated more stability in contrast to working capital, though it exhibited some fluctuations. From early 2014 through 2015, net revenue showed a generally mild downward trend. Starting in early 2016, the revenue figures presented a seasonal pattern with quarterly variations, but overall maintained a relatively steady range. A gradual increase in revenue is visible from early 2017 through early 2019, followed by minor decreases and rebounds. The revenue remained consistently above the 11,000 million USD level during most of this later period.
The working capital turnover ratio was only reported through the 2015 period and displayed variability. The ratio increased from approximately 15.85 at the start of 2014 to a peak near 35.77 in mid-2015, indicating improved efficiency in utilizing working capital to generate revenue. However, it sharply decreased to 10.77 by the end of 2015, suggesting a possible decline in operational efficiency or a change in working capital structure.
- Working Capital
- Experienced a pronounced shift from positive to negative values starting in early 2016, maintaining negative levels thereafter, which may signal liquidity pressures or changes in short-term asset/liability management.
- Net Revenue
- Exhibited a seasonal pattern with moderate fluctuations but generally maintained steady levels with a slight upward trend from 2017 to early 2019.
- Working Capital Turnover
- Remained variable but showed an initial improvement in efficiency up to mid-2015, followed by a notable drop by late 2015, indicating changes in the relationship between revenue generation and working capital deployment.
In summary, while revenue generation remained relatively stable with slight growth, the shift in working capital to negative territory and the associated changes in turnover ratios indicate potential challenges in short-term financial management and asset utilization efficiency in the latter years of the examined period.
Average Inventory Processing Period
| Jul 31, 2019 | Apr 30, 2019 | Jan 31, 2019 | Oct 31, 2018 | Jul 31, 2018 | Apr 30, 2018 | Jan 31, 2018 | Oct 31, 2017 | Jul 31, 2017 | Apr 30, 2017 | Jan 31, 2017 | Oct 31, 2016 | Jul 31, 2016 | Apr 30, 2016 | Jan 31, 2016 | Oct 31, 2015 | Jul 31, 2015 | Apr 30, 2015 | Jan 31, 2015 | Oct 31, 2014 | Jul 31, 2014 | Apr 30, 2014 | Jan 31, 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data | ||||||||||||||||||||||||||||||
| Inventory turnover | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio (no. days) | ||||||||||||||||||||||||||||||
| Average inventory processing period1 | ||||||||||||||||||||||||||||||
| Benchmarks (no. days) | ||||||||||||||||||||||||||||||
| Average Inventory Processing Period, Competitors2 | ||||||||||||||||||||||||||||||
| Apple Inc. | ||||||||||||||||||||||||||||||
| Arista Networks Inc. | ||||||||||||||||||||||||||||||
| Cisco Systems Inc. | ||||||||||||||||||||||||||||||
| Dell Technologies Inc. | ||||||||||||||||||||||||||||||
| Super Micro Computer Inc. | ||||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2019-07-31), 10-Q (reporting date: 2019-04-30), 10-Q (reporting date: 2019-01-31), 10-K (reporting date: 2018-10-31), 10-Q (reporting date: 2018-07-31), 10-Q (reporting date: 2018-04-30), 10-Q (reporting date: 2018-01-31), 10-K (reporting date: 2017-10-31), 10-Q (reporting date: 2017-07-31), 10-Q (reporting date: 2017-04-30), 10-Q (reporting date: 2017-01-31), 10-K (reporting date: 2016-10-31), 10-Q (reporting date: 2016-07-31), 10-Q (reporting date: 2016-04-30), 10-Q (reporting date: 2016-01-31), 10-K (reporting date: 2015-10-31), 10-Q (reporting date: 2015-07-31), 10-Q (reporting date: 2015-04-30), 10-Q (reporting date: 2015-01-31), 10-K (reporting date: 2014-10-31), 10-Q (reporting date: 2014-07-31), 10-Q (reporting date: 2014-04-30), 10-Q (reporting date: 2014-01-31).
1 Q3 2019 Calculation
Average inventory processing period = 365 ÷ Inventory turnover
= 365 ÷ =
2 Click competitor name to see calculations.
- Inventory Turnover Ratio
- The inventory turnover ratio demonstrates substantial fluctuations over the observed periods. Initially, the ratio maintains relatively high levels, ranging mostly above 12 and peaking at 16.78 in January 2016, indicating efficient inventory management during that time. However, after April 2016, there is a notable downward trend, with the ratio declining steadily to values around 7.3 to 8.9 by 2017 through 2019. This decline suggests a decrease in inventory turnover efficiency, potentially due to slower sales or higher inventory levels.
- Average Inventory Processing Period (number of days)
- The average inventory processing period inversely mirrors the turnover ratio trends. It starts at approximately 25 days in early 2014, then gradually increases to about 30 days heading into late 2015, indicating that inventory took longer to be processed. After a period of low values around 22 days in early 2016, the processing period then increases sharply to reach values in the range of 42 to 50 days between 2016 and 2018. Gradually from late 2018 to mid-2019, it somewhat decreases but remains elevated around 40 to 44 days. This lengthening period corroborates the decline in turnover ratio and indicates slower inventory movement through the system.
- Overall Trends
- The data reveals an overall shift from efficient inventory management in early years, notably up to 2016, towards a slower turnover and longer processing periods in later years. The inventory turnover ratio's peak in early 2016 represents the highest efficiency point, followed by a consistent downward trajectory. Concurrently, the average inventory processing period increases, signaling potential challenges such as overstocking or reduced demand affecting inventory levels. The stabilization of processing time slightly above 40 days in recent periods suggests a new equilibrium, albeit less optimal than in earlier years.
Average Receivable Collection Period
| Jul 31, 2019 | Apr 30, 2019 | Jan 31, 2019 | Oct 31, 2018 | Jul 31, 2018 | Apr 30, 2018 | Jan 31, 2018 | Oct 31, 2017 | Jul 31, 2017 | Apr 30, 2017 | Jan 31, 2017 | Oct 31, 2016 | Jul 31, 2016 | Apr 30, 2016 | Jan 31, 2016 | Oct 31, 2015 | Jul 31, 2015 | Apr 30, 2015 | Jan 31, 2015 | Oct 31, 2014 | Jul 31, 2014 | Apr 30, 2014 | Jan 31, 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data | ||||||||||||||||||||||||||||||
| Receivables turnover | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio (no. days) | ||||||||||||||||||||||||||||||
| Average receivable collection period1 | ||||||||||||||||||||||||||||||
| Benchmarks (no. days) | ||||||||||||||||||||||||||||||
| Average Receivable Collection Period, Competitors2 | ||||||||||||||||||||||||||||||
| Apple Inc. | ||||||||||||||||||||||||||||||
| Arista Networks Inc. | ||||||||||||||||||||||||||||||
| Cisco Systems Inc. | ||||||||||||||||||||||||||||||
| Dell Technologies Inc. | ||||||||||||||||||||||||||||||
| Super Micro Computer Inc. | ||||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2019-07-31), 10-Q (reporting date: 2019-04-30), 10-Q (reporting date: 2019-01-31), 10-K (reporting date: 2018-10-31), 10-Q (reporting date: 2018-07-31), 10-Q (reporting date: 2018-04-30), 10-Q (reporting date: 2018-01-31), 10-K (reporting date: 2017-10-31), 10-Q (reporting date: 2017-07-31), 10-Q (reporting date: 2017-04-30), 10-Q (reporting date: 2017-01-31), 10-K (reporting date: 2016-10-31), 10-Q (reporting date: 2016-07-31), 10-Q (reporting date: 2016-04-30), 10-Q (reporting date: 2016-01-31), 10-K (reporting date: 2015-10-31), 10-Q (reporting date: 2015-07-31), 10-Q (reporting date: 2015-04-30), 10-Q (reporting date: 2015-01-31), 10-K (reporting date: 2014-10-31), 10-Q (reporting date: 2014-07-31), 10-Q (reporting date: 2014-04-30), 10-Q (reporting date: 2014-01-31).
1 Q3 2019 Calculation
Average receivable collection period = 365 ÷ Receivables turnover
= 365 ÷ =
2 Click competitor name to see calculations.
- Receivables Turnover Ratio
- The receivables turnover ratio demonstrates notable fluctuations over the reported periods. Initially, from January 2014 through October 2015, the ratio ranges modestly between approximately 7.7 and 9, indicating relatively stable efficiency in collecting receivables during this timeframe. A substantial spike is observed beginning in January 2016, where the ratio peaks sharply at 21.58, followed by a gradual decline in subsequent quarters down to around 11 by the end of July 2019. This peak suggests a significant improvement in receivables collection efficiency in early 2016, but the subsequent decline indicates a trending decrease in collection speed or changes in credit policies moving forward.
- Average Receivable Collection Period
- The average collection period inversely mirrors the pattern seen in the receivables turnover ratio. Initially, the days outstanding trend relatively steady between 41 to 47 days through late 2015. In early 2016, the collection period sharply decreases to as low as 17 days, supporting the earlier observation of improved collection efficiency. Following this improvement, the days gradually increase over time, stabilizing around 30 to 34 days from 2017 through mid-2019. This suggests that while the company achieved a period of significantly faster collections, the efficiency normalized in later years at a moderately improved level compared to the initial periods.
- Overall Trends and Insights
- Overall, the data reveals a pronounced improvement in accounts receivable management beginning in early 2016, reflected by a much higher receivables turnover and a lower collection period. This change may indicate strategic adjustments in credit control or customer payment terms that enhanced cash flow timing. However, the gradual return towards lower turnover ratios and higher collection periods in later years suggests either market-driven challenges, less stringent credit policies, or a repositioning of receivables strategy. Despite this reversion, the company appears to maintain a receivables collection performance that is somewhat better than the initial earlier periods, indicating a net positive long-term adjustment in receivables management practices.
Operating Cycle
| Jul 31, 2019 | Apr 30, 2019 | Jan 31, 2019 | Oct 31, 2018 | Jul 31, 2018 | Apr 30, 2018 | Jan 31, 2018 | Oct 31, 2017 | Jul 31, 2017 | Apr 30, 2017 | Jan 31, 2017 | Oct 31, 2016 | Jul 31, 2016 | Apr 30, 2016 | Jan 31, 2016 | Oct 31, 2015 | Jul 31, 2015 | Apr 30, 2015 | Jan 31, 2015 | Oct 31, 2014 | Jul 31, 2014 | Apr 30, 2014 | Jan 31, 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data | ||||||||||||||||||||||||||||||
| Average inventory processing period | ||||||||||||||||||||||||||||||
| Average receivable collection period | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio | ||||||||||||||||||||||||||||||
| Operating cycle1 | ||||||||||||||||||||||||||||||
| Benchmarks | ||||||||||||||||||||||||||||||
| Operating Cycle, Competitors2 | ||||||||||||||||||||||||||||||
| Apple Inc. | ||||||||||||||||||||||||||||||
| Arista Networks Inc. | ||||||||||||||||||||||||||||||
| Cisco Systems Inc. | ||||||||||||||||||||||||||||||
| Dell Technologies Inc. | ||||||||||||||||||||||||||||||
| Super Micro Computer Inc. | ||||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2019-07-31), 10-Q (reporting date: 2019-04-30), 10-Q (reporting date: 2019-01-31), 10-K (reporting date: 2018-10-31), 10-Q (reporting date: 2018-07-31), 10-Q (reporting date: 2018-04-30), 10-Q (reporting date: 2018-01-31), 10-K (reporting date: 2017-10-31), 10-Q (reporting date: 2017-07-31), 10-Q (reporting date: 2017-04-30), 10-Q (reporting date: 2017-01-31), 10-K (reporting date: 2016-10-31), 10-Q (reporting date: 2016-07-31), 10-Q (reporting date: 2016-04-30), 10-Q (reporting date: 2016-01-31), 10-K (reporting date: 2015-10-31), 10-Q (reporting date: 2015-07-31), 10-Q (reporting date: 2015-04-30), 10-Q (reporting date: 2015-01-31), 10-K (reporting date: 2014-10-31), 10-Q (reporting date: 2014-07-31), 10-Q (reporting date: 2014-04-30), 10-Q (reporting date: 2014-01-31).
1 Q3 2019 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 initially increased from 25 days to 30 days during 2014 and early 2015, indicating a gradual lengthening in the time inventory is held before sale. It then exhibited notable volatility from 2016 onward, with periods reaching as high as 50 days in early 2018. From that peak, the period slightly decreased but remained elevated around 40 to 48 days through 2019. This suggests a trend of slower inventory turnover in the latter years compared to the earlier periods.
- Average Receivable Collection Period
- The receivable collection period showed a steady and moderate pattern between 41 and 47 days from 2014 through early 2015. A significant reduction occurred in 2016, with the period dropping sharply to as low as 17 days, suggesting improved efficiency in collecting receivables during that year. Subsequently, there was a gradual increase in the following quarters, stabilizing around 30 to 34 days from late 2016 through 2019. Overall, the data indicate improved collection performance in 2016 followed by stabilization at a lower level compared to the earlier years.
- Operating Cycle
- The operating cycle, combining inventory processing and receivables collection periods, began near 70 days in 2014 and early 2015, showing relative consistency. In 2016, it decreased substantially to a low of 39 days, paralleling the drop in the receivable collection period and despite fluctuations in inventory processing. From 2017 onward, the operating cycle increased again, reaching above 80 days at one point in 2017, before settling in the mid-to-high 70 days range by 2019. This pattern suggests improved operational efficiency in 2016 followed by a return to longer operating cycles in subsequent years, largely influenced by the extended inventory holding periods observed.
Average Payables Payment Period
| Jul 31, 2019 | Apr 30, 2019 | Jan 31, 2019 | Oct 31, 2018 | Jul 31, 2018 | Apr 30, 2018 | Jan 31, 2018 | Oct 31, 2017 | Jul 31, 2017 | Apr 30, 2017 | Jan 31, 2017 | Oct 31, 2016 | Jul 31, 2016 | Apr 30, 2016 | Jan 31, 2016 | Oct 31, 2015 | Jul 31, 2015 | Apr 30, 2015 | Jan 31, 2015 | Oct 31, 2014 | Jul 31, 2014 | Apr 30, 2014 | Jan 31, 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data | ||||||||||||||||||||||||||||||
| Payables turnover | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio (no. days) | ||||||||||||||||||||||||||||||
| Average payables payment period1 | ||||||||||||||||||||||||||||||
| Benchmarks (no. days) | ||||||||||||||||||||||||||||||
| Average Payables Payment Period, Competitors2 | ||||||||||||||||||||||||||||||
| Apple Inc. | ||||||||||||||||||||||||||||||
| Arista Networks Inc. | ||||||||||||||||||||||||||||||
| Cisco Systems Inc. | ||||||||||||||||||||||||||||||
| Dell Technologies Inc. | ||||||||||||||||||||||||||||||
| Super Micro Computer Inc. | ||||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2019-07-31), 10-Q (reporting date: 2019-04-30), 10-Q (reporting date: 2019-01-31), 10-K (reporting date: 2018-10-31), 10-Q (reporting date: 2018-07-31), 10-Q (reporting date: 2018-04-30), 10-Q (reporting date: 2018-01-31), 10-K (reporting date: 2017-10-31), 10-Q (reporting date: 2017-07-31), 10-Q (reporting date: 2017-04-30), 10-Q (reporting date: 2017-01-31), 10-K (reporting date: 2016-10-31), 10-Q (reporting date: 2016-07-31), 10-Q (reporting date: 2016-04-30), 10-Q (reporting date: 2016-01-31), 10-K (reporting date: 2015-10-31), 10-Q (reporting date: 2015-07-31), 10-Q (reporting date: 2015-04-30), 10-Q (reporting date: 2015-01-31), 10-K (reporting date: 2014-10-31), 10-Q (reporting date: 2014-07-31), 10-Q (reporting date: 2014-04-30), 10-Q (reporting date: 2014-01-31).
1 Q3 2019 Calculation
Average payables payment period = 365 ÷ Payables turnover
= 365 ÷ =
2 Click competitor name to see calculations.
- Payables Turnover
- The payables turnover ratio shows a declining trend from January 2014 through October 2015, decreasing from 6.81 to 4.93. This suggests that the company was taking longer to pay its suppliers during this period. A notable increase occurs in January 2016, reaching 7.52, indicating a faster payment cycle. However, after April 2016, the turnover ratio declines sharply to its lowest point of 3.53 by October 2016 and stabilizes in the range of 3.2 to 3.47 until July 2019. This prolonged lower ratio suggests a consistent lengthening of the payment period in recent years compared to the earlier period.
- Average Payables Payment Period
- Corresponding with the payables turnover, the average payables payment period increased from 54 days in January 2014 to 74 days by October 2015. This trend indicates that the company was extending its payment terms to suppliers. There is a marked reversal in January 2016 when the payment period drops significantly to 49 days, coinciding with the peak in payables turnover. After this, the payment period rises sharply again, reaching over 100 days from October 2016 onwards, peaking at 114 days in October 2017, and remaining consistently above 105 days through July 2019. This higher payment period confirms a strategy or condition of delayed supplier payments in the later years.
- Overall Observations
- The analysis reveals two distinct phases in the payment patterns: the initial phase (2014 to early 2016), where payment periods gradually lengthened but then briefly shortened in early 2016; and the second phase (late 2016 through mid-2019), characterized by significantly extended payment terms maintained consistently. These patterns suggest changes in working capital management or supplier payment policies, potentially aimed at optimizing cash flow or responding to external financial pressures during the periods of elongated payables.
Cash Conversion Cycle
| Jul 31, 2019 | Apr 30, 2019 | Jan 31, 2019 | Oct 31, 2018 | Jul 31, 2018 | Apr 30, 2018 | Jan 31, 2018 | Oct 31, 2017 | Jul 31, 2017 | Apr 30, 2017 | Jan 31, 2017 | Oct 31, 2016 | Jul 31, 2016 | Apr 30, 2016 | Jan 31, 2016 | Oct 31, 2015 | Jul 31, 2015 | Apr 30, 2015 | Jan 31, 2015 | Oct 31, 2014 | Jul 31, 2014 | Apr 30, 2014 | Jan 31, 2014 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Selected Financial Data | ||||||||||||||||||||||||||||||
| Average inventory processing period | ||||||||||||||||||||||||||||||
| Average receivable collection period | ||||||||||||||||||||||||||||||
| Average payables payment period | ||||||||||||||||||||||||||||||
| Short-term Activity Ratio | ||||||||||||||||||||||||||||||
| Cash conversion cycle1 | ||||||||||||||||||||||||||||||
| Benchmarks | ||||||||||||||||||||||||||||||
| Cash Conversion Cycle, Competitors2 | ||||||||||||||||||||||||||||||
| Apple Inc. | ||||||||||||||||||||||||||||||
| Arista Networks Inc. | ||||||||||||||||||||||||||||||
| Cisco Systems Inc. | ||||||||||||||||||||||||||||||
| Dell Technologies Inc. | ||||||||||||||||||||||||||||||
| Super Micro Computer Inc. | ||||||||||||||||||||||||||||||
Based on: 10-Q (reporting date: 2019-07-31), 10-Q (reporting date: 2019-04-30), 10-Q (reporting date: 2019-01-31), 10-K (reporting date: 2018-10-31), 10-Q (reporting date: 2018-07-31), 10-Q (reporting date: 2018-04-30), 10-Q (reporting date: 2018-01-31), 10-K (reporting date: 2017-10-31), 10-Q (reporting date: 2017-07-31), 10-Q (reporting date: 2017-04-30), 10-Q (reporting date: 2017-01-31), 10-K (reporting date: 2016-10-31), 10-Q (reporting date: 2016-07-31), 10-Q (reporting date: 2016-04-30), 10-Q (reporting date: 2016-01-31), 10-K (reporting date: 2015-10-31), 10-Q (reporting date: 2015-07-31), 10-Q (reporting date: 2015-04-30), 10-Q (reporting date: 2015-01-31), 10-K (reporting date: 2014-10-31), 10-Q (reporting date: 2014-07-31), 10-Q (reporting date: 2014-04-30), 10-Q (reporting date: 2014-01-31).
1 Q3 2019 Calculation
Cash conversion cycle = Average inventory processing period + Average receivable collection period – Average payables payment period
= + – =
2 Click competitor name to see calculations.
- Average Inventory Processing Period
- The average inventory processing period initially shows a gradual increase from 25 days to a peak of 50 days by the beginning of 2018, indicating that the company took longer to process inventory over this period. Subsequently, a moderate decline occurs, with the days reducing to around 41-44 toward mid-2019, signifying improved inventory turnover efficiency in the later periods.
- Average Receivable Collection Period
- Receivable collection periods remain relatively stable in the initial years, fluctuating between 41 to 47 days from early 2014 to late 2015. A notable sharp decrease occurs in early 2016, dropping to approximately 17 days, followed by a gradual increase to around 30-34 days through 2017 to mid-2019. This suggests a period of improved cash collections around 2016, with a slight relaxation in collection discipline afterward.
- Average Payables Payment Period
- The payables payment period exhibits a rising trend from 54 days at the start of 2014 to a high of 114 days by late 2017, indicating extended payment terms or delayed payments to suppliers. After peaking, the period fluctuates but remains elevated between 105 to 113 days through mid-2019, suggesting sustained working capital management focused on longer payment cycles.
- Cash Conversion Cycle
- The cash conversion cycle shows a significant decline over the reported period, moving from positive values (around 15 days) in early 2014 to increasingly negative figures, reaching approximately -36 days by 2019. This indicates that the company managed to accelerate the cash inflows relative to cash outflows, largely influenced by the lengthened payables period and the shortened receivables period in the mid-years, improving overall liquidity and cash efficiency.