Discover more from Thematiks
The hidden cost of employee turnover
New research establishes a direct link between manufacturing worker churn and decreased product quality
One of the competitive challenges faced by global high-tech manufacturers is maintaining extremely high levels of product quality while continuously bringing new technologies to market. Consequently, manufacturers typically invest heavily in a variety of quality control and assurance technologies to minimize defects in production environments. Researchers have focused extensively on this issue, noting, for example, the impact that quality management systems and techniques have on product performance.
One of the issues that has not received as much attention from researchers is the impact that human capital dynamics have on product quality. Unlike service industries, where human capital is widely acknowledged to be a principal driver of quality and customer satisfaction, manufacturing workers have not generally been considered an important determinant of high-tech product quality for two main reasons. First of all, the products themselves are sophisticated enough that their ultimate quality is seen as less dependent on the human capital used in assembly. Second, the high degree of standardization and automation in high-tech manufacturing operations suggests the human factor is less critical in overall production line quality outcomes. Consequently, when looking at human capital in production, researchers have tended to focus on economic issues such as plant efficiency and productivity.
In a break from the past research trends noted above, a recent paper from Ken Moon (Wharton), Prashant Loyalka (Stanford), Patrick Bergemann (UCI Merage), and Joshua Cohen (Apple University) provides an interesting analysis of one way in which human workers affect product performance. Specifically, their research examines the impact that turnover in a manufacturing workforce has on product quality.
The authors start by noting that high-tech manufacturers typically deploy two main strategies to control quality. As shown in Figure 1(b) below, the first strategy is that "received components undergo initial testing and inspection for defects, prior to being used in product assembly." Second, "the partially assembled devices and key components undergo controlled “burn-in” usage under stressful conditions, such as elevated temperatures and voltages, in order to force defect-based early failures to manifest." As shown in Figure 1(a) below, product failures tend to occur early (due to defects) or late (due to premature wear) in the testing process, giving rise to what is known as the "bathtub curve."
Figure 1: Conceptual illustration of failure hazard rates over the lifetime of a product unit (Source: Authors)
The bathtub curve, however, does not tell the whole story, because even stringent tests cannot always detect minute defects. Consequently, the authors note, even small errors in assembly could increase field failures. For example:
The touchscreen multi-touch functionality in some mobile devices depends on the soldering connecting touch-related chips to a circuit interface. The resilience of the soldering is stressed under repeated customer usage, e.g., bending or dropping of the device. Similarly, a small imperfection in battery placement can lead to uneven power supply that degrades the screen display over extended usage.
As a result of these undetected minute defects, "even world-class assembly line design and quality control may allow some variability in the quality of manufactured units that reveals itself only after repeated use, as minute imperfections in assembly degrade a product’s performance or accelerate its wear-out." Given the impact that assembly activities can have on product performance, the authors set out to understand to what degree worker turnover is related to the kinds of errors noted above and thus to overall product quality.
For their research, the authors collaborated with a major consumer electronics producer and one of its Chinese contract manufacturers. The manufacturer provided data tracking field failures for nearly 50 million devices, as well as weekly assembly line statistics and component quality data. Specifically, the authors looked at data such as "quarterly fallout rates for six key components: the device’s battery, display, housing, circuit board, rear camera, and front camera." Additionally, they obtained "the weekly pass rates of devices at the burn-in stations of the individual assembly lines" as well as human resources staffing data to track factory worker quits occurring on specific assembly lines and weeks.
For their quality analysis, the authors examined the percentage of delivered devices that "failed in the field," i.e., devices that needed any kind of service repair or replacement. For devices produced from September 2014 through June 2015, the authors analyzed all field failures as of July 6, 2019. Importantly, "because each device has been observed in the field for at least four years post-production, such field failure rates should capture virtually all variation in product reliability that originates in the factory assembly process."
The contract manufacturer provided the human resources data required to track production line assignments and worker turnover patterns. The authors defined worker turnover as "the fraction of workers on an assembly line who quit the factory during a given week."1 The contract manufacturer’s data covered 52,214 workers during the study period, and mean and median worker turnover were 5.1% and 3.2% per week. The mean assembly line headcount was 301, and staffing was relatively steady throughout the study period.
All told, the study data covers 40 weeks of production for 44 assembly lines in China, and there were no unusual events relating to production, suppliers, or labor during the study's time period.
As shown in Figure 2 below, the authors found that the manufacturer's pay cycle has a major impact on when a worker quits. In fact, assembly line turnover averages 2.9% the week before a payweek and rises to 8.9% the week after a payweek. As Figure 2 also illustrates, there is a direct relationship between turnover levels and field failure rates.
Figure 2: Field failure rates and worker turnover rates over the manufacturer’s pay cycle (Source: Authors)
The payweek-turnover cycle has important implications. First, worker turnover "causes a net loss of experience in staffing the affected assembly lines, as relatively experienced workers exit and new workers enter." As shown by Figure 3 below, "the average level of experience is not higher among quits in post-payday weeks, but the total weekly outflow of experience is substantially greater (382 versus 171 years of experience on average)." Second, the replacement process itself "may be disruptive, requiring attention and effort from both supervisors and workers." For example, because only experienced workers can handle certain workstations, some workers must switch stations in response to turnover even as new workers are being integrated into the production line. Both factors could be expected to impact negatively the assembly line overall output quality.
Figure 3: Experience-based composition of worker exit in post-payday weeks (Source: Authors)
The authors hypothesized that if monthly post-payday turnover leads to compromised workmanship, the facility’s weekly field failure rates "should manifest a pattern resembling an electrocardiogram," i.e, the field failure rate moved in regular, monthly cycles of peaks and troughs. Figure 4 below shows the expected result for the three assembly buildings at the contract manufacturer.
Figure 4: Rates of device field failures attributed to their weeks of production during Sept. 2014 to June 2015 (Source: Authors)
The authors note that "not only does each building and assembly line suggest a cyclically peaking pattern, the assembly lines’ field failure rates co-vary to a striking degree: nearly 41% of the variation is explained by the weekly averages."
For post-payday weeks, each 1 percentage point increase in worker turnover rate is estimated to increase field failures by 0.74-0.79%. Thus, at the average rate of turnover for post-payday weeks, "worker turnover makes field failures 7.1-7.6% more common." Moreover, the authors found that workload also had an impact on quality: "a 50% increase in workload results in a 0.4-0.9% increase in the field failure rate, which when combined with the 50% expanded output amounts to a 0.55-1.34% increase in the weekly number of eventual field failures."
For non-payday weeks, worker turnover's effect on field failures is also significant: "field failures become 0.21-0.44% more common per each 1 percentage point increase in turnover." At the 5.1% average rate, "worker turnover raises field failure incidence by 1.1-2.3%, and similarly by 1.6-3.3% at the 75th percentile rate of 7.5% worker turnover."
In all, the authors found that worker turnover is detrimental to quality throughout the production calendar. As they conclude:
We find a surprisingly close relationship between product field failure rates and episodes of factory worker quits, as they rise and fall together. We find this relationship to hold consistently both (1) when some assembly lines experience greater turnover than others within the same week and (2) most strikingly when turnover periodically elevates facility-wide following paydays.
To the surprise of the manufacturer participating in the study, the effects noted by the authors are significant enough that they suggest it should take concrete steps to curtail post-payweek resignations, especially among more experienced staff.
The authors believe that there are several practical issues for manufacturers to consider as a result of their research. First of all, manufacturers may want to reconsider the conventional wisdom that the high degrees of process standardization and product sophistication in high-tech products mean that worker turnover does not materially impact product quality. Second, manufacturers could benefit from raising compensation, increasing benefits, improving working conditions, etc., to reduce worker turnover. Indeed, the authors believe that the direct relationship between high turnover and increased field failures (which in turn drive higher warranty and service costs), makes a compelling business case for increased wages. Moreover, the authors note, that even in settings where skills are less important, "firms may benefit from incorporating additional training or skills progression into their assembly designs in order to better retain workers, and scheduling best practices can also aid worker retention."
Another implication of this study is related to failure analysis for design and engineering teams, as this study's results suggest assembly worker turnover should be included in product failure analysis and warranty cost estimation. In fact, the paper makes a strong argument for directly tracing field quality issues back to workforce characteristics. As the authors note, "whereas traceability verifies the sourcing of goods to address issues such as adulteration or quality-based recalls, we use traceability to link data tracking workforce dynamics and working conditions to connected product data." Indeed, because connected products today can be tracked and traced directly by the manufacturers, firms have more opportunities than ever to connect product field performance back to in-factory workforce dynamics.
By taking a new view of the impact of worker stability on product quality, this paper provides manufacturing, quality, after-market, and quality leaders much to consider. As one of the authors noted in an interview:
A lot of turnover introduces uncertainty…. You don’t know who is going to leave when, and how that might trigger cascade effects on a production line when workers start to leave in part because their friends have left the firm. To deal with this uncertainty, you over-hire. That’s part of the analysis that we go through in the paper, and that has a very real cost.
In other words, when you start to control turnover, there are two main [results]. One is, “I raise compensation, and I keep people around longer [reducing disruption].” Your firm is no longer as much of a rotating door or turnstile. The second is, “I have less uncertainty, so I hedge less. I don’t need an excess workforce sitting around or to prepare for [production shortfalls caused by] turnover. I’m no longer putting out fires.” It becomes a much more stable and productive environment for the firm and for the workers. It looks to us like it could be a win/win.
From my experience with high-tech supply chain operations, I know that many other issues can draw managers’ attention from human capital factors when considering product quality at production sites or in after-sales and warranty modeling. This paper makes a strong case for re-assessing those priorities. Managers should look at human capital dynamics as a key variable not just of production economics but also the quality and performance of even the most sophisticated products.
Moon, Ken and Loyalka, Prashant and Bergemann, Patrick and Cohen, Joshua, The Hidden Cost of Worker Turnover: Attributing Product Reliability to the Turnover of Factory Workers (August 3, 2021). Available at SSRN: https://ssrn.com/abstract=3568792 or http://dx.doi.org/10.2139/ssrn.3568792
Interestingly, the authors note that “due to Chinese labor regulations requiring significant severance pay absent termination for specified reasons, involuntary turnover is virtually non-existent after 2-3 days of initial training.”