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Product reliability is a key concern for manufacturers. We examine worker turnover as a significant but underrecognized determinant of product reliability. Our study collects and integrates (1) data reporting factory worker staffing and turnover from within a major consumer electronics producer’s supply chain and (2) traceable data reporting the component quality and field failures—that is, replacements and repairs—of nearly 50 million consumer mobile devices over four years of customer usage. Devices are individually traced back to the factory conditions and staffing, down to the assembly line–week, under which they were produced. Despite the manufacturer’s extensive quality control efforts, including stringent testing, each percentage point increase in the weekly rate of workers quitting from an assembly line (its weekly worker turnover) is found to increase field failures by 0.74%–0.79%. In the high-turnover weeks following paydays, eventual field failures are strikingly 10.2% more common than for devices produced during the lowest turnover weeks immediately before paydays. In other weeks, the assembly lines experiencing higher turnover produce an estimated 2%–3% more field failures on average. The associated costs amount to hundreds of millions of U.S. dollars. We demonstrate that staffing and retaining a stable factory workforce critically underlies product reliability and showcase the value of traceability coupled with connected workplace and product data in supply chain operations. This paper was accepted by Charles Corbett, operations management.

We explore marketplace design in the context of a business-to-business platform specializing in liquidation auctions. Even when the platform’s aggregate levels of supply and demand remain fixed, we establish that the platform’s ability to use its design levers to manage the availability of supply over time yields significant value. We study two such levers, each using the platform’s availability of supply as a means to incentivize participation from buyers who decide strategically when/how often to participate. First, the platform’s listing policy sets the ending times of incoming auctions (hence, the frequency of market clearing). Exploiting a natural experiment, we illustrate that consolidating auctions’ ending times to certain weekdays increases the platform’s revenues by 7.3% mainly by inducing a higher level of bidder participation. The second lever is a recommendation system that can be used to reveal information about real-time market thickness to potential bidders. The optimization of these levers highlights a novel trade-off. Namely, when the platform consolidates auctions’ ending times, more bidders may participate in the marketplace (demand-side competition); but ultimately auctions for substitutable goods cannibalize one another (supply-side competition). To optimize these design decisions, we estimate a structural model that endogenizes bidders’ dynamic behavior, that is, their decisions on whether/how often to participate in the marketplace and how much to bid. We find that appropriately designing a recommendation system yields an additional revenue increase (on top of the benefits obtained by optimizing the platform’s listing policy) by reducing supply-side cannibalization and altering the composition of participating bidders. This paper was accepted by Vishal Gaur, operations management.