The current advancement in and deployment of agentic AI systems has created a set of key challenges for the legal frameworks that govern their use. We cover two central components: first, the regulatory classification of agents under the EU AI Act, and second, the legal status and validity of autonomous actions within the established framework of EU contract law. We argue that the unique capacity of agents to autonomously reason, plan, and execute tasks across disparate external systems necessitates a fundamental shift in oversight toward the orchestration layer, where multi-agent interactions introduce novel risks of misalignment. While agents generally utilise general-purpose AI models, we posit that their structural complexity and cross-system permeability require them to be regulated as "AI systems" with distinct obligations under the AI Act. Consequently, our proposals highlight the need for robust accountability mechanisms to manage this heightened autonomy. On the contractual side, we advocate for a "traffic light" system of staggered task authorization based on operational risk and the creation of a statutory list of non-delegable legal acts. By implementing these measures, we provide a pragmatic pathway to ensure that the increasing autonomy of AI agents remains firmly anchored in human accountability and existing legal standards

This paper addresses the regulatory and liability implications of modifying general-purpose AI (GPAI) models under the EU AI Act and related legal frameworks. We make five principal contributions to this debate. First, the analysis maps the spectrum of technical modifications to GPAI models and proposes a detailed taxonomy of these interventions and their associated compliance burdens. Second, the discussion clarifies when exactly a modifying entity qualifies as a GPAI provider under the AI Act, which significantly alters the compliance mandate. Third, we develop a novel, hybrid legal test to distinguish substantial from insubstantial modifications that combines a compute-based threshold with consequence scanning to assess the introduction or amplification of risk. Fourth, the paper examines liability under the revised Product Liability Directive (PLD) and tort law, arguing that entities substantially modifying GPAI models become “manufacturers” under the PLD and may face liability for defects. The paper aligns the concept of “substantial modification” across both regimes for legal coherence and argues for a one-to-one mapping between “new provider” (AI Act) and “new manufacturer” (PLD). Fifth, the recommendations offer concrete governance strategies for policymakers and managers that propose a federated compliance structure, based on joint testing of base and modified models, implementation of Failure Mode and Effects Analysis and consequence scanning, a new database for GPAI models and modifications, robust documentation, and adherence to voluntary codes of practice. The framework also proposes simplified compliance options for SMEs while maintaining their liability obligations. Overall, the paper aims to map out a proportionate and risk-sensitive regulatory framework for modified GPAI models that integrates technical, legal, and wider societal considerations.

Scholars argue that artificial intelligence (AI) can generate genuine novelty and new knowledge and, in turn, that AI and computational models of cognition will replace human decision making under uncertainty. We disagree. We argue that AI's data-based prediction is different from human theory-based causal logic and reasoning. We highlight problems with the decades-old analogy between computers and minds as input-output devices, using large language models as an example. Human cognition is better conceptualized as a form of theory-based causal reasoning rather than AI's emphasis on information processing and data-based prediction. AI uses a probability-based approach to knowledge and is largely backward looking and imitative, whereas human cognition is forwardlooking and capable of generating genuine novelty. We introduce the idea of data-belief asymmetries to highlight the difference between AI and human cognition, using the example of heavier-than-air flight to illustrate our arguments. Theory-based causal reasoning provides a cognitive mechanism for humans to intervene in the world and to engage in directed experimentation to generate new data. Throughout the article, we discuss the implications of our argument for understanding the origins of novelty, new knowledge, and decision making under uncertainty.

We explore how organizations leverage algorithms to improve knowledge work in contexts where the tasks require skilled work, as distinct from routine tasks that have traditionally been the focus of academic enquiry. Drawing on a multiple-case study of four business areas in a multinational energy firm undergoing a digital transformation, we find that contrary to what the literature predicts, tasks that require skilled work can also benefit from the adoption of algorithmic solutions. To benefit, business areas engaged in two distinct pathways for transforming knowledge work. The first focuses on automating a specific task, replacing human activity with algorithms in a single task. The second involves re-engineering an entire process, whereby sequences of steps adjacent to the task at hand are redesigned on integration of an algorithm. We find that these pathways have different effects on the ability to improve knowledge work, suggesting that alignment between the task and the pathway chosen is crucial to realizing any improvement. We also find that the ability to sustain any improvement depends on the adjustment of the knowledge regime-the practices and structures that sanction knowledge. Building on these findings, we propose a general process model for the adoption of algorithmic solutions in knowledge work. In the wider context of the future of work debate, our findings challenge the prevailing notion that a task's skill requirements determine the extent to which knowledge work can be improved by algorithmic solutions. When using algorithms to improve knowledge work, there are two choices: to automate a given task or to re-engineer the entire process. Re-engineering the process tends to yield greater operational improvements. Socializing and validating the algorithm's outputs tend to increase their acceptance.

Artificial intelligence (AI) now matches or outperforms human intelligence in an astonishing array of games, tests, and other cognitive tasks that involve high-level reasoning and thinking. Many scholars argue that—due to human bias and bounded rationality—humans should (or will soon) be replaced by AI in situations involving high-level cognition and strategic decision making. We disagree. In this paper we first trace the historical origins of the idea of artificial intelligence and human cognition as a form of computation and information processing. We highlight problems with the analogy between computers and human minds as input-output devices, using large language models as an example. Human cognition—in important instances—is better conceptualized as a form of theorizing rather than data processing, prediction, or even Bayesian updating. Our argument, when it comes to cognition, is that AI's data-based prediction is different from human theory-based causal logic. We introduce the idea of belief-data (a)symmetries to highlight the difference between AI and human cognition, and use "heavier-than-air flight" as an example of our arguments. Theories provide a mechanism for identifying new data and evidence, a way of "intervening" in the world, experimenting, and problem solving. We conclude with a discussion of the implications of our arguments for strategic decision making, including the role that human-AI hybrids might play in this process.

As more and more companies adopt artificial intelligence to increase the efficiency and effectiveness of their products and services, they expose themselves to ethical crises and potentially damaging public controversy associated with its use. Despite the prevalence of AI ethical problems, most companies are strategically unprepared to respond effectively to the public. This paper aims to advance our empirical understanding of company responses to AI ethical crises by focusing on the rise and fall of facial recognition technology. Specifically, through a comparative case study of how four big technology companies responded to public outcry over their facial recognition programs, we not only demonstrated the unfolding and consequences of public controversies over this new technology, but also identified and described four major types of company responses-Deflection, Improvement, Validation, and Pre-emption. These findings pave the way for future research on the management of controversial technology and the ethics of AI.

As scholars in the field of operations management (OM), we would like to suggest that our field fell short in terms of due diligence when transitioning from statistical process control (SPC) to Six Sigma—accepting without scrutiny, building theory around, and teaching heuristics and algorithms without recognizing its underlying statistical inaccuracies. It is our view that these incorrect heuristics and algorithms have introduced bias and inefficiencies in process improvement throughout the OM field, contributing to a disconnect between OM and knowledge development in data science more generally. We call for a return to first principles and the establishment of formal conceptual definitions for the theory and methods underlying Six Sigma. We urge the OM academic community to embrace the lessons from SPC and Six Sigma so that we prioritize our due-diligence role, beginning with a requirement that all algorithms and tools be vetted before entering our curricula and case-study repertoires, especially as we move forward into an age of big data and potentially further opaque algorithms and tools. We propose that our top journals be open to research that scrutinizes methods developed in practice, so that OM will continue to be the focal field for quality assurance—even when the “product” of a process is data. The application of statistical methods to quality management has been a central theme in OM since Shewhart's seminal work at Western Electric's Hawthorne Works nearly a century ago (Shewhart, 1925; Shewhart, 1926). He studied process variation to determine “how and under what conditions observations may contribute to a rational decision to change or not a process to accomplish improvements” (W. Edwards Deming, p.i, in the foreword of the 1986 edition of Shewhart, 1931). His work fostered methods and tools to monitor, diagnose, measure, reduce, and control variation in the output of a process to increase its consistency and capability. The capability of a process with respect to a process parameter hence can be defined as the number of standard deviations (“sigmas”) of the parameter that fit between the mean for that parameter and its specification limits. If the process is not centered between the specification limits, then the process capability is set using whichever specification limit is closer to the process center. Six Sigma moves the concept of process capability from descriptive to prescriptive. At the time that Motorola's process-improvement ideas were proposed by Bill Smith in 1986 (Harry, 1994), process capability was defined in terms of specification limits set three standard deviations from the process mean (a “3σ process”). Following Shewhart's logic, a centered and normally distributed 3σ process is expected to produce 2700 parts-per-million (ppm) pieces that are more than three standard deviations from the process mean. Motorola quantified the “zero defect” philosophy at the heart of the “quality is free” argument proposed by popular and influential authors like Crosby (1979) by translating the number of standard deviations that fit between the process center and the tightest specification limit into ppm defects. Historically it was assumed that a 3σ process capability was good enough. The 2700 ppm out-of-specification pieces produced by a 3σ process capability, however, is too high in many contexts. Harry described Bill Smith as demanding a higher standard: “Bill's proposition was eloquently simple. He suggested that Motorola should require a 50% design margin for all its key product performance characteristics” (Harry, 2003, p. 1). This buffer, which adds another three standard deviations between either side of the mean and the specification limits (as illustrated in Figure 1), marked the inception of the “Six Sigma” concept. Source: Harry (2003, p. 27) Up to this point, Motorola's approach can be seen as a protocol that adds intuition and goal setting to a simple application of the theory of process capability. Had the Motorola project stopped there, a refined and stricter protocol might have seen the light of day, resulting in an incremental but eminently sensible extension to SPC as applied to high volume, repetitive-manufacturing contexts. Unfortunately, this is not what happened. Several approaches to the application of these core concepts—artifacts of the limitations of practical application at the time—are difficult to justify today. In the following subsections, we will address a few of the more notable of these legacy issues, and how they might be re-evaluated and addressed in current practice, teaching, and research. Harry and his team—tasked with implementing Smith's vision—noted two challenges to resolve. First, the sample mean is only an estimate of the population mean, so confidence intervals need to be considered. Second, the distribution of the process output risked changing over time when something changed in the process—such as loss of calibration, a tool losing its edge, or a setup error—causing a change in the process mean or standard deviation. Harry wanted to ensure that these factors would be taken into consideration when defining what was to be promised to the customer. They proposed adjustments, but their proposals were deemed to be too “technical” and “complicated” by management and so were disregarded (Harry, 2003, see p. 6ff). In short, the idea of an expanding and contracting standard deviation was found to be outside the realm of ‘common sense reasoning’ without the provision of statistical instruction. However, the idea of a ‘shift correction’ carried high appeal and inevitably promoted lively and meaningful discussion – without the prerequisite education. Therefore, those of us at Motorola involved in the initial formation of Six Sigma (1984-1985) decided to adopt and support the idea of a ‘1.5 Sigma equivalent mean shift’ as a simplistic (but effective) way to account for the underlying influence of long-term, random sampling error. Harry and colleagues thus proposed that the estimated defect level for a given process and set of specification limits should be adjusted to take into consideration the possibility that the process mean could be closer to one or the other specification limit by 1.5σ, as illustrated in Figure 2: The distribution of the process parameter remains normal, but—rather than being centered between the specification limits that are 6σ from the mean—is now 4.5σ from one specification limit (implying 3.4 ppm defects on that side) and 7.5σ from the other. Setting a process-capability goal that results in 3.4 ppm defects—a 4.5σ process-capability level—thus required reducing the standard deviation to fit six standard deviations between the true process center and the specification limits on both sides to allow room for this expected shift in the process center. Harry seems to have misinterpreted the factor of 1.5 as the allowance for the shifts in the mean of a single component, due to its being manufactured in different lots. As suggested by Bender and Gilson, it might make sense to inflate the estimator of the standard deviation of the assembly to allow for shifts in the means of individual components. It does not make sense, however, to allow for a 1.5-sigma shift in the process mean of individual components. Whether Harry's proposal of the 1.5σ mean shift came from confidence intervals around the population mean or from Bender's and Gilson's 1.5σ allowance to be used in tolerance (or both), it had important consequences: Most writings on Six Sigma interpreted the shift literally, meaning that Six Sigma was built around the expectation that the process mean would shift by 1.5σ. Standard Six Sigma reference guides like the Goal/QPC Black Belt Memory Jogger state that “on average, short-term process means tend to shift and drift by 1.5 sigmas” (GOAL/QPC, 2002, p. 45). From a SPC point of view, such a 1.5σ shift induces a fundamental contradiction. It means that the process is no longer in statistical control. A process is defined as running under statistical control with respect to a given parameter when its mean and standard deviation are not changing. If the process is operating under statistical control, then all variation is random (“common-cause variation”) rather than emerging from an assignable cause. When a process is no longer running under statistical process control because its mean has shifted, the appropriate corrective action is to eliminate the factor that caused the mean to shift so that the process is again centered. The effect of Six Sigma has been to get people thinking that it was no longer a top priority to ensure that the process was being run down the center, awarding a higher value to reducing the process standard deviation to make it possible to deliver a small number of defects even though the process was not centered. This change in thinking contradicted the generally accepted principles of SPC, yet Six Sigma gained acceptance with minimal challenge from the academic community. (…) the shift factor (1.5 sigma) does not constitute a ‘literal’ shift in the mean of the performance distribution – as many quality practitioners and process engineers falsely believe or try to postulate through uniformed speculation and conjecture. It is only here that Harry fully explained the source of the idea that the true process mean could lie 1.5σ from the estimated mean in either direction. It is not that the process mean shifts, but that decision makers should take into consideration confidence intervals in estimating it from process data. Harry suggested that a reasonable confidence interval would be around 1.5σ. If the estimated process mean is centered between the specification limits with six standard deviations on each side, then the true process mean could vary by 1.5σ. By promising the customer 3.4 ppm defects on one side—rather than 1 part per billion on either side—the supplier would fulfill the promised quality even if the true process mean was different from the estimate. Harry and his colleagues at Motorola clearly set in motion a framework that led users to conclude that process centrality no longer mattered. But, the damage would have been largely mitigated had the academic OM community scrutinized the Six Sigma methodology more closely. In fact, with the notable exception of some attempts to define the underlying theory of Six Sigma (de Treville et al., 2008; Schroeder et al., 2008) and identify its underlying structures and mechanisms (Anand et al., 2010; Braunscheidel et al., 2011; Linderman et al., 2003; Linderman et al., 2006), we as a field failed to question the core assumptions of Six Sigma and rather adopted it at face value in our syllabi. We would like to suggest that an archaic understanding of SPC created an environment in which confusion kept formal conceptual definitions from being fully used to maintain order and discipline in thinking. Impenetrable SPC algorithms that were largely developed in the 1950s continue to be used and taught without questioning, which establishes acceptance of concepts that are not understood. This acceptance without understanding gives rise to an environment in which mystique triumphs over the first principles that peer review is designed to tease out. Next, we explore initial ideas about how to overcome this archaic understanding of process capability and SPC. The ability to infer whether a process is running under statistical control with respect to a specific parameter remains as useful today as it was when Walter Shewhart developed the statistical control chart (Shewhart, 1926) nearly a century ago. SPC charts help a decision maker decide whether to intervene in an ongoing process. Intervening in a process that is running correctly tends to increase variability, but failing to intervene in a process that is running incorrectly is costly. Shewhart proposed that samples of measurements of a process parameter be collected from the process to assess whether it is more likely that the process is running normally, or, if the mean or standard deviation have changed, warranting intervention. Each sample mean (e.g., X ¯ ) is evaluated to see whether it is within three standard deviations of the estimated process mean. The standard deviation is the estimated population standard deviation divided by the square root of the sample size (n). The population mean and standard deviation are estimated from process data (typically at least 20 samples) at a time when the process appears to be running under control. The population mean ( μ ) is estimated as the mean of the sample means (e.g., X ¯ ¯ ). The generally prescribed method for estimating the population standard deviation (σ) was developed before calculators were readily available and so needed to be done using a slide rule. Patnaik (1950) expanded the works of Tippett (1925) and showed that the average range ( R ¯ ) of a set of samples was an unbiased (although noisy) estimator of the standard deviation, which made estimating σ feasible under these conditions. The estimate of the sample standard deviation was then calculated using R ¯ over the set of samples from the period during which the process was perceived as running under statistical control. Patnaik (1950) calculated the factor (denoted “d2”) used to transform R ¯ into an unbiased estimate of σ for a given n. Doing this for n = 5, for example, entails dividing R ¯ by 2.326. This value is then divided by the square root of n to estimate the standard deviation of the distribution of sample means. X ¯ SPC charts are drawn with the center line at X ¯ ¯ and control limits three standard deviations above and below it. It is common to denote 3 d 2 √ n as “A2” so that the control limits of an SPC chart are denoted as X ¯ ¯ ± A 2 R ¯ . If a given X ¯ is outside of these control limits, it is highly likely that the process is no longer running under statistical control. More than seven decades later, we in the field of OM continue to teach SPC using R ¯ with Patnaik's table of factors as the approach of choice for estimating σ. The SPC tables providing Patnaik's factors were first published by the American Society for Testing and Materials in 1951 (ASTM, 1951). It was none other than Walter Shewhart who chaired this Committee in the 1930s, while W. Edwards Deming was a member for many years during the 1950s when the SPC standards were set and published. These SPC standards remain in common use and are typically provided in OM textbooks (e.g., Cachon & Terwiesch, 2013, p. 204; Jacobs & Chase, 2024 , p. 381; Krajewski & Ritzman, 2002, p. 294; Nahmias & Olsen, 2015, p. 842), as well as textbooks on statistical quality control (e.g., Montgomery, 2019). While some adjustments and derivatives have been proposed over the years (e.g., Nelson, 1984; Western Electric Company, 1956; Westgard, 2009), the logic of how we define the boundaries of our control charts has remained unquestioned and unchanged. The essential step of estimating σ is thus carried out using factors that are presented without explanation and typically without reference. Patnaik and Tippett are not referenced as a source of these factors in any of the standard OM textbooks that we considered. Without these mysterious factors, the obvious way to estimate σ would be to use a spreadsheet function on the data collected from the process, recalling that it was taken from a process assumed to be functioning normally, with no assignable causes changing its μ or σ. The standard deviation of the data making up those samples will be unbiased, like that estimated using R ¯ , albeit considerably less noisy. Such an exercise would be well understood by average business students. The number of standard deviations to be used in setting the control limits could then be determined based on the nature of the tradeoff between failing to intervene when something has changed (Type I error) and intervening in a stable process (Type II error). In some cases, an X ¯ more than 1.5σ from X ¯ ¯ would be of concern. In other cases, one or more X ¯ s would need to be more than 3σ from X ¯ ¯ to elicit concern. Thus, a heuristic to estimate σ, designed long before the widespread availability of computers, continues to be taught unquestioningly as the correct way to do this estimation. Students who rightfully ask for an explanation of the mysterious factors are told that they should be taken on faith. Students, professors, and professionals lack a clear understanding of how σ was calculated. This has created an environment where one loses the ability to rely on statistical understanding, instead carrying out calculations using the factors based on blind faith. A student who has a strong understanding of standard deviations and normal distributions will be perplexed, assuming that there is some mysterious extra insight that comes from using the factors rather than a regular estimation of σ from the sample data. Other students who have not yet mastered the idea of the standard deviation in this context will be completely lost. If, however, σ is calculated directly, learning about SPC charts can contribute to general understanding of the normal distribution and the use of a standard deviation. It is time to relegate this mystical artifact to history so that prospective users of SPC can do the obvious calculations. We suggest that this blind-faith approach to estimating σ created an environment in which the 1.5σ mean shift of Six Sigma went easily unquestioned. Thus far, our discussion has focused on the use of descriptive tactics to inform prescription. What of prediction? Processes are defined as running outside of statistical control when one or more sample means are outside of control limits drawn during a time when the process was perceived to be running normally (i.e., appeared to be running under statistical control) or otherwise demonstrate non-random patterns suggestive of special cause. But, what if the apparent lack of special cause was instead a mixture of several causes? As we replace these outdated techniques with the standard tools of data science that are now available, direct (non-heuristic) estimation of variation makes possible an objective assessment of trends in sample data. Simple, serial correlational regressive tactics would suffice to flag such non-random patterns, and more sophisticated multivariate approaches are available—assuming that we clearly understand what we are looking for rather than following recipes that we do not understand on blind faith. As we put these antiquated techniques to the side, we make space for SPC to benefit from state-of-the-art statistical capabilities. Our intent is not to deny that implementing Six Sigma has had a positive impact in many companies over the past decades, nor do we wish to discredit the tried-and-tested heuristics that underpin SPC. We seek to draw attention to and rectify the persistent failure of our field to use the peer-review process to clarify Six Sigma's statistical claims, and to update process capability and SPC to allow decision-makers to use these tools with full comprehension. Peer-reviewed research is intended to eliminate this combination of misunderstanding and mystique, facilitating the transformation of interesting ideas emerging from the world of practice into a solid increase in knowledge. Before peer review can function, it is necessary for top journals to be open to submissions whose contribution is this type of due diligence. Back in the mid-1990s when Six Sigma emerged to great excitement, top OM journals were not perceived as being open to this kind of submission. Suri and de Treville (1986)—a conceptual article published in JOM—gives an idea of the kind of research contribution that could open the way to the fact checking that we are calling for. In the early 1980s, the idea of “rocks and stream” became popular: The claim was that as inventory was removed from a system, the resulting line stoppages would cause learning. Suri and de Treville explored in detail what happens between two workstations as intermediate inventory is reduced. Learning can occur when one workstation blocks or starves another, but such blockage and starvation can also result in a failure to learn. This academic exploration contributed to a more nuanced understanding of the relationship between inventory reduction and learning. Along similar lines, we suggest that JOM should be open to relatively technical, conceptual submissions that permit an in-depth exploration of a phenomenon that has emerged from practice to great enthusiasm. Allowing these SPC and Six-Sigma methods to stand unchallenged and unchanged has caused confusion and kept OM theory and tools from evolving to be able to address today's data-rich environment. Rather than analyzing small samples of manually collected data from a production line, modern statistical quality monitoring systems produce high frequency, real-time data that is automatically captured in digital form. By linking SPC and process capability to sound statistical principles, we become able to evaluate systems like internet of things (IoT) and real-time location and sensing using OM thinking and tools. Effective use of advanced tools like machine learning requires a solid statistical foundation in a framework that has undergone peer review, not opaque algorithms and arcane heuristics. We call for the OM community to insist that process capability and SPC be returned to sound statistical roots and formal conceptual definitions, recognizing Six Sigma as a protocol from the world of practice that prompted important discussions, but whose underlying assumptions are fundamentally flawed. This return is essential if we are to remain relevant to what is now happening in the world of quality assurance and data science more generally. We further call for JOM to be a journal that welcomes the manuscripts that bring ideas from practice to peer review, thus replacing blind faith in magic sauces with solid increases in knowledge. Suzanne de Treville, Tyson R. Browning, Matthias Holweg, and Rachna Shah.

Recent advances in algorithmic technologies have led many to suggest that these could also be used to improve knowledge work, while others highlight the potential negative learning consequences in such contexts. In this paper, we study how algorithmic technologies are used to improve knowledge work in four areas of a multinational engineering firm. Our findings show that organisations engage in two fundamental pathways of transforming knowledge work: altering a specific task or reengineering an entire process. We also found that reengineering the process induces a necessary alteration of the knowledge regime in order to improve, or at least ensure, the quality of the constructed knowledge. In practice, this means that the ability to improve knowledge work with algorithmic technologies is primarily a function of the motivation, interpretations and means available, and not of the required skills to perform a task, as present studies commonly suggest. In the wider context of a digital transformation, it thus is possible to also automate tasks that require creativity and social interaction, provided the degree of novelty remains low.

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Additive manufacturing (AM) has widely demonstrated its ability to economically produce parts at low volumes. Attention is now shifting to higher volume applications, such as mass customization and the manufacture of standard parts. In these contexts, production losses due to process variability and inefficient machine use are common concerns for the application of AM. The Overall Equipment Effectiveness (OEE) metric is a tool widely used in traditional manufacturing to assess the effective use of production capacity. Previous studies seeking to apply OEE to AM are limited in scope and have neglected the above sources of inefficiency. This article seeks to address this gap in two ways. First, we present a framework for measuring OEE within AM operations; and systematically map the ‘six production losses’ to the AM workflow to codify our understanding of the main sources of inefficiency. Second, we conduct a simulation study investigating how the AM operations approach, product variety and lead time requirements affect the OEE of an AM process. Our findings demonstrate that – with some conceptual adaptation – the OEE metric can indeed be used in the context of AM. Furthermore, we identify the approach to AM operation as a major determinant of performance in terms of the OEE achieved. We conclude with a set of managerial insights on how to apply the OEE metric to AM processes in practice.

The phenomenon of the Toyota Production System (TPS) and the term “Lean” have received much attention from researchers and especially practitioners over the past 40 years. As scholarly perspectives on these topics continue to evolve, we invited Wally Hopp and Mark Spearman to contribute an essay to the JOM Forum that became “The Lenses of Lean,” and we invited several other prominent authors affiliated with Lean to react to that article and share their perspectives on Lean. We are delighted to have received four such contributions, which we have assembled here with the hope of furthering this important conversation (Note: For consistency of exposition, we have capitalized the term “Lean” throughout these commentaries when it is used in the phenomenological sense). —Tyson Browning and Suzanne de Treville

Avoiding overloading the healthcare system remains a central issue during the COVID-19 pandemic. The logic of preventing such overload situations is intuitive since the level and quality of critical care is a function of the available capacity to provide it. Where this capacity is no longer available due to a surge in admissions, patient outcomes will invariably deteriorate in the long run, which ultimately leads to disproportionate mortality. In this paper, we study the three worst affected regions in Italy, the Netherlands, and Germany during the first COVID-19 wave in the spring of 2020. We report on quantitative analyses that show how mortality rises non-linearly as the proportion of COVID-19 patients in the ICU increases. We identify changes to the patient-staff ratio, increasing exhaustion and infection levels amongst staff, as well as equipment shortages as likely causes driving this rise in mortality. We explore these findings further with interviews of key stakeholders in the respective healthcare systems. Our results demonstrate that the common approach of managing COVID-19 surges by stretching ICU capacity in hotspot regions may be detrimental to patient outcomes. Instead, we posit that transferring patients proactively out of developing hotspots to less affected regions, well before high ICU workload situations emerge, will improve patient outcomes.

Digital technologies, such as advanced analytics, autonomous vehicles or the Internet of Things, are often touted as means to substantially improve operations. While this potential has been frequently highlighted and evidenced from single case applications, we still lack a deeper theoretical understanding of the underlying mechanisms how digital technologies can support process improvement in general, and lean practices more specifically. In this paper, we use a qualitative study based on focus group design to understand how manufacturing and supply chain management professionals perceive the potential of digital technologies in support of lean practices. We identify eight digital waste reduction mechanisms that illustrate how digital technologies can support lean practices. These include a cluster of mechanisms that augment operational execution in terms of speed and precision of execution, as well as flexibility in space and time. Furthermore, we identify a second cluster of mechanisms that augment decision-making through visibility, feedback, engagement, and prevention. In terms of managerial implications, our findings provide firms with a structured approach how to identify those digital technologies that can most effectively support their respective process improvement activities.

Hospitals increasingly adopt standardized policies as a way to improve the efficiency of health care delivery. One key policy has been to reduce a patient's length of stay, which is commonly perceived as an effective means of improving patient outcome, as well as reducing the cost per procedure. We put this notion to the empirical test by using a database of 183,712,784 medical records of patients in the English NHS between 1998 and 2012, studying the effects of the NHS's policy of decreasing length of stay for hernia patients. While we found it to be an effective way of reducing the cost per procedure, on aggregate, we also found that it increases the risk of readmission and of death for vulnerable and elderly patients, unduly increasing the long-term failure costs of the operation for these patient groups. Based on our findings, we propose a differentiated policy to selectively decrease length of stay, which we estimate could save up to US$565 per nonemergency hernia procedure (19.97% reduction in the cost per procedure). We outline the implications of our findings for medical practice and discuss the wider theoretical contributions to the wider standardization-customization debate in health care operations management.

The digitalization of intra- and inter-organizational processes offers significant opportunity for research in the field of operations and supply chain management (OSCM). This essay summarizes the contributions of the special issue articles, highlighting their focus on additive manufacturing and the encapsulation of design and production information in a digital artifact. We conceptualize the digital artifact as containing the digital genes of the associated physical object. Digital encapsulation thus involves the integration of product design information with additional information on how that design is to be translated into a physical object, delivered to the customer, and used. Building on insights from the special issue articles, we identify three pathways by which digital encapsulation affects OSCM practice, as well as theory elaboration and extension. First, digital encapsulation allows each unique digitally encapsulated artifact to be acted on independently by OSCM systems. Second, digital encapsulation enables the redistribution of activities across organizational and geographic landscapes. Third, digital encapsulation facilitates interactivity of the digital artifact with external environment inputs. We conclude with a number of directions for future research.

Hospitals increasingly adopt standardized policies as a way to improve the efficiency of health care delivery. One key policy has been to reduce a patient's length of stay, which is commonly perceived as an effective means of improving patient outcome, as well as reducing the cost per procedure. We put this notion to the empirical test by using a database of 183,712,784 medical records of patients in the English NHS between 1998 and 2012, studying the effects of the NHS's policy of decreasing length of stay for hernia patients. While we found it to be an effective way of reducing the cost per procedure, on aggregate, we also found that it increases the risk of readmission and of death for vulnerable and elderly patients, unduly increasing the long‐term failure costs of the operation for these patient groups. Based on our findings, we propose a differentiated policy to selectively decrease length of stay, which we estimate could save up to US$565 per nonemergency hernia procedure (19.97% reduction in the cost per procedure). We outline the implications of our findings for medical practice and discuss the wider theoretical contributions to the wider standardization‐customization debate in health care operations management.