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Excerpted from There’s Got to Be a Better Way: How to Deliver Results and Get Rid of the Stuff That Gets in the Way of Real Work by Nelson P. Repenning and Donald C. Kieffer. Copyright © 2025. Available from Basic Venture, an imprint of Hachette Book Group, Inc.
A molecular biologist by training, Sheila Dodge worked for the world’s leading genomics research center, the Broad Institute of Harvard and MIT located in Cambridge, Massachusetts. Established in 2004 as a Harvard-MIT research collaboration, the institute was an outgrowth of the Human Genome Project. Its main goal was the genetic analysis of diseases, including cancer and schizophrenia, that did not always respond to conventional treatment. By comparing the DNA sequences of people who had the disease to those who did not, the institute hoped to identify the genetic anomalies that were responsible. As her time at MIT’s executive MBA program was coming to a close, Sheila was promoted from managing one area to overseeing and integrating the three separate groups responsible for data production into a single, cohesive organization.
“We are trying to understand what causes disease in people,” Sheila explained to us. “We spend a lot of time and effort building up a reserve of genetic data, and we use those data to try to elucidate what’s going on in the human body. Our goal is to transform how medicine happens: How doctors interact with patients, how they understand which drugs to give people, and how they prevent and treat disease.” She knew the importance of the lab to Broad’s mission of improving public health.
Beyond being responsible for extracting DNA data from tens of thousands of samples, Sheila also had to make sure the lab stayed on the leading edge of technology. Gene sequencing is one of the world’s fastest changing industries, with cost reductions far outpacing those experienced in other technologies like semiconductors. The work involved sophisticated processes, highly precise equipment focused on teasing out the DNA from those samples, and then analyzing that DNA at a rapid pace and immense scale. The data the lab produced supported active research focused on saving lives. Sheila understood the importance of the work and the criticality of both maintaining quality and keeping up with demand.
But there was a problem. Broad had been so successful in popularizing research based on genetic data that it now had more demand for its services than it could satisfy. Everyone in the lab was overwhelmed. Sheila could see that the workflow was erratic, and the costs in people’s time and the institute’s money were too high. Most of the people in the lab worked nights and weekends to try to keep up. Many were burning out. Sheila herself was at her wit’s end. She wondered how she could keep holding the lab together and still have a life. There were also hallway whispers suggesting that the lab’s work should be outsourced since they couldn’t keep up with the demands from the Broad’s research scientists.
She knew they had to improve the workflow and with a new MBA from MIT and a promotion, she felt pressure to make progress. Her final presentation brilliantly described the problems she faced and offered a compelling vision for what the lab could be. She just didn’t quite know how to get from here to there.
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We — [authors] Nelson and Don — had been solving problems like this for over a decade, but we didn’t know anything about a high tech, biomedical lab that produced peta-bytes (a petabyte is 1 million gigabytes) of DNA data. Nonetheless, the problems Sheila described sounded all too familiar. Good work design principles aren’t about the type of work or the industry, they are about taking full advantage of an organization’s available brainpower.
We immediately sensed that the lab’s problems were rooted in the inability to get the work flowing, especially across administrative boundaries, a problem we had seen at Harley [Davidson] and many other companies. Sheila’s team worked hard to move work through the lab, but each effort, while getting an individual sample closer to completion, created more congestion and more chaos, ultimately hurting the lab’s overall performance. We suspected that a few simple changes could reduce the gridlock and get the work moving, and, in doing so, both satisfy their growing demand and get people home at a reasonable hour.
As Sheila and her team implemented the principles and approach of dynamic work design (listed below) dramatic shifts followed.
Solve the Right Problem: Formulate a problem statement isolated from any possible diagnoses
Structure for Discovery: Confirm everyone working knows why they are doing it, how it’s going, and is engaged in the process of improving it
Connect the Human Chain: Ensure the right information is transferred from one person to the next
Regulate for Flow: Allow new tasks to enter the system only when there is available capacity, thereby guaranteeing that work is always moving
Visualize the Work: Create a good visual management system to show the status and location of each piece of work
They quickly improved turnaround times by more than 80% and quadrupled their capacity, gains that allowed them to process many more samples and get the results to the customer faster and more reliably. The pace of technology development also improved by over three-fold.
The gains in these two areas not only enabled the lab to get ahead of demand for the first time in several years, but they also created space to develop innovative new products and services. Thanks to their efforts, they returned to their industry-leading position. Later, when COVID hit, in the space of just a few months, they transformed themselves into one of the most efficient testing labs in the country. When students see this example, they often ask if dynamic work design will work in their particular industry. As we sometimes joke, the stuff we teach only works in organizations that have people in them. We are confident that it will work in your industry too.
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