Carlson School's Supply Chain Department on Decision-Making that Improves Organizational Performance

At the heart of behavioral operations is understanding the interaction of human behaviors and operations systems and processes.

Recently published Carlson School research on behavioral operations looks closer at how recalls were done in the medical industry and whether the performance of contestants participating in innovation contests improved the more times they participated. Examining these issues is important because as a discipline, supply chain and operations management must be committed to improving operational decision-making related to processes, technologies, and people, both within organizations and across organizational and country boundaries, in order to enhance the performance of organizations and the supply chains they’re a part of.

“A common assumption underlying operational decision-making is that individuals and organizations making decisions are rational, and strive to be optimal in their process of decision-making,” says Professor Kingshuk Sinha. “This is a questionable assumption. Often, individuals and organizations are irrational, and do not strive for optimality in their process of decision-making.”

Medical device recall decision-making

One of the ways in which this was explored was through research conducted by Associate Professor Rachna Shah and Professor Karen Donohue. Their recent publication, “The Decision to Recall: A Behavioral Investigation in the Medical Device Industry,” examines what influences the decision to recall a medical device.

For many managers, recalling a product can be one of the most important decisions they make in their careers. And yet, the FDA does not clearly specify how a manager should integrate the multiple—and potentially conflicting—criteria influencing this decision within the medical device industry. This leaves managers within this industry drawing more on individual judgment to arrive at their recall decision, making it an important industry to study from a behavioral perspective.

As Donohue explains, “our research team developed an experiment where actual managers within the medical device industry were given different product scenarios to evaluate and determine whether a recall was warranted. Findings from the experiment revealed that managers are often influenced by information that is not really pertinent to evaluating whether or not the product is defective.”

One of the interesting results was that managers appear to hesitate to recall a product until the cause of the potential defect is clearly understood, even though such delay could increase patient risk. Managers were also more reluctant to recall a potentially defective product if the defect could be observed by a physician before being used by a patient.

“When a product defect is detectable, managers are less likely to recall the product,” says Shah. “Instead, they rely on the physician-customer as the ‘final quality inspector’ to screen out defects and catch the mistake before it could harm the patient.”

“The extent of these behavioral tendencies was surprising to our industry partners and pushed them to think through ways to counter-act this behavior in the future,” says Donohue.

Research conducted by Sinha also explored a complementary set of issues in his paper “Product Recall Decisions in Medical Device Supply Chains: A Big Data Analytic Approach to Evaluating Judgment Bias.”

In his research, Sinha used machine learning methods to analyze over three million data points on 1,348 devices, across 108 firms over a 10-year period. He found that when it was difficult to assess the severity of an issue—there was a high noise-to-signal ratio—it tended to cloud the judgment of the manager leading to an under-reaction. When a product recall seemed severe, there tended to be an over-reaction biased because managers became more risk averse.

“What is particularly noteworthy with medical device recalls that receive widespread media attention is that the recall decisions could have been made sooner,” says Sinha. “There is also anecdotal evidence of medical device recalls made by firms that indicate that recalls were knee-jerk decisions, made too quickly and not necessary. In other words, recall decisions are often fraught with human judgment biases of under-reaction or over-reaction. We identify conditions related to the situated context of managers that are associated with an under-reaction or over-reaction likelihood.”

This study is consequential for firms and government regulatory agencies, as it sheds light on how recall decisions can be made correctly and in a timely manner, says Sinha.

“Given the behavioral nuances of medical device recall decisions, and that recalls are disruptive and exemplify among the most consequential downside risks in managing healthcare supply chain and operations, make studies on recalls a compelling and impactful line of inquiry in behavioral operations,” he says.

Upstream and downstream experience in innovation contests

Associate Professor Anant Mishra also worked on key research at the intersection of behavioral operations and innovation management with his paper “Beyond Related Experience: Upstream vs. Downstream in Innovation Contest Platforms with Interdependent Problem Domains.”

The paper examines how individuals accumulate experience on innovation contest platforms. On such platforms, complex problems are typically broken down into smaller problems that are attempted by multiple individuals.

Mishra analyzed data obtained from TopCoder, a leading platform for software development contests, from its launch in 2001 to September 2013.

Through reviewing this data, he found that it highlighted the importance of diverse experiences for participants on innovation platforms, which is contrary to the notion of “hyper-specialization” on online platforms that has been emphasized in previous research.

“By participating regularly, individuals are sharpening their skills,” Mishra says. “That being said, beyond their inherent creativity and problem solving abilities, individuals who participate regularly in contests on a particular platform also develop a better understanding of how solutions are judged, who they are likely to compete against, and what contests they should select to participate on the platform.”

Another key finding is that although contestants who participated on contests on the same platform learn from their prior experience and perform better, the benefits of such experience arise only when it is in problem domains that are downstream and related to the current problem.

The research bridges a gap in previous studies on innovation contests, which have focused on individual problem solving in a specific problem domain without considering how individuals accumulate experience across various problem domains on innovation contest platforms.