Hospitals are some of the most complex operations environments in the world, writes The Wall Street Journal (Sept. 29, 2026). Thousands of interdependent processes—clinical workflows, monitoring systems, documentation, staffing, and technology—must function with precision. When any part of that system becomes noisy, inefficient, or overloaded, the consequences can be serious. A striking example comes from an unlikely OM case study: hospital alarm fatigue.
In modern care units, monitors and pumps generate constant beeps signaling everything from minor fluctuations to life‑threatening deterioration. For patients, the noise disrupts sleep and recovery. For nurses, the sheer volume of alarms creates cognitive overload, making it harder to distinguish true danger from routine variation. In OM terms, this is a quality‑control failure caused by excessive false positives, poor signal design, and misaligned workflow triggers.
Researchers are now applying OM thinking to redesign the alarm ecosystem.
Reducing Non‑Actionable Variation Stanford Medicine introduced time delays and conditional triggers to filter out brief, self‑correcting changes in vital signs. This is classic process‑control logic: eliminate noise so workers can focus on meaningful signals. The results were dramatic—alarm volume dropped by up to 82% without compromising safety. Nurses reported better responsiveness, demonstrating how reducing process clutter improves human performance.
Human‑Centered Design for Quality Another team is redesigning alarms using principles from music cognition. Instead of harsh electronic tones, alarms use recognizable “auditory icons” and musical cues to convey urgency and type of problem. This is OM’s human‑factors engineering at work: improving quality by making information more intuitive, reducing worker stress, and speeding decision‑making.
Predictive Systems to Prevent Failure The most powerful OM innovation may be the machine‑learning tool Concern, which analyzes patterns in nursing documentation to detect deterioration before vital signs trigger alarms. It provides a quiet, color‑coded risk indicator rather than another disruptive alert. In a 60,000‑patient trial, units using Concern saw lower mortality and shorter stays. This is proactive capacity and risk management, shifting from reaction to prevention.
The OM Lesson Alarm fatigue isn’t just a clinical issue—it’s an operations problem. Smarter triggers, better signal design, and predictive analytics show how OM principles can transform safety, reduce waste, and improve outcomes in high‑stakes environments.
Classroom Discussion Questions
- How should hospitals balance the need for constant patient monitoring with the risks of alarm fatigue?
- What lessons from hospital alarm redesign—such as smarter triggers, better sound design, or early‑warning systems—could be applied to other industries that rely heavily on alerts?
AI is rapidly shifting from digital decision‑support to hands‑on operational control. As AI systems move into the physical world, they are reshaping core OM functions—especially inventory management, quality assurance, and maintenance. 
Jersey Mike’s claimed first place, with an ACSI score of 84 out of 100, edging Chick-fil-A’s 83. The shift marks the first time in over a decade that a new chain has led the ACSI’s quick-service restaurant (QSR) category. ASCI credited Jersey Mike’s with maintaining high customer satisfaction while rapidly expanding its restaurant footprint, writing :”Jersey Mike’s success is consistent with their business performance, including rapid unit growth, strong customer demand, and a model designed around throughput and off-premise convenience from high digital pickup usage.”
That’s the new reality for many manufacturers facing a stubborn obstacle: the ever-widening gap between data and decisive action. Now, a new class of digital entities is changing that equation. AI agents powered by decision intelligence are beginning to sense, reason and act across the manufacturing ecosystem, cutting decision latency from minutes to milliseconds.
The solution, developed by the company in collaboration with Google Cloud, uses computer vision and the Gemini platform to support quality inspectors in distribution centers.
In our latest podcast, Barry Render interviews John Dyer, a well‑known speaker, consultant, and expert in continuous improvement, and the author of 
Prof. Howard Weiss, retired from Temple U., illustrates his wide range of interests.
In this chapter, we have suggested that building quality into a process and its people is difficult. In the old days, inspection was the main form of quality control. But inspection may not catch all the errors, and it may be expensive. To indicate just how difficult inspections can be, ask your students to turn to the OM in Action box on page 234, called “Inspecting the Boeing 787”.
Prof. Howard Weiss, retired from Temple U., shares his thoughts monthly.
Ford just recalled 850,000 pickup trucks and SUVs because of a potential fuel-pump failure. A bad fuel pump could result in the engine stalling while a driver is operating the vehicle.
A shadow factory is what Boeing executives call a production line where engineers and mechanics work on fixing, maintaining or updating aircraft instead of building new ones. They exist for the company’s two-bestselling models, the 737 MAX and 787 Dreamliner.
There’s been enough drama in the past year to impact U.S. airlines quality rankings. An Alaska Airlines blowout grounded dozens of planes. There was a failed JetBlue-Spirit merger and Spirit’s bankruptcy. A summer tech outage crippled Delta. Southwest Airlines faced investor pressure and said it’s switching to assigned seating. All while planes remained packed and air traffic congested.
Professor Howard Weiss shares his thoughts about a variety of unusual OM topics with us monthly.
The reason a company might purposely list ingredients that are not in its products is that it may be concerned about cross-contamination in a bakery plant and wants to ensure it will not be legally responsible in the event of cross contamination. In other words, rather than trying to introduce quality control procedures to prevent cross-contamination in its plant, the company is willing to be untruthful when listing ingredients to minimize the chance and or cost of a law suit.