Are We Wasting Human Talent Today?

In the modern era of global competition and rapid technological change, the efficient use of human effort has become as crucial as the conservation of natural resources. We often recognize and measure the waste of material resources—energy losses, unused raw materials, or idle equipment—but the less visible waste of human labor and talent is equally significant. Every day, countless tasks in businesses and industries are performed with methods that are ill-directed or inefficient, leading to lost productivity. These inefficiencies, though not as tangible as a pile of wasted material, accumulate to a great loss in potential output and prosperity.

Around the turn of the 20th century, President Theodore Roosevelt identified this issue as the problem of “national efficiency,” suggesting that just as we conserve our forests and fuel, we must also strive to eliminate the waste of human effort. This observation is even more pertinent today. Organizations large and small continue to seek capable people—from executives to front-line workers—but often they look for individuals who somehow already possess exceptional skill or drive. Too little attention is given to systematically training and organizing ordinary employees to achieve excellence. In the past, it was often said that “leaders (or captains of industry) are born, not made.” The prevailing assumption was that if you could hire the right people, they would find the best methods themselves. In the future, however, we recognize that even a naturally talented person cannot outperform a well-designed system that enables a team of average individuals to work effectively together. In the past the man was first; in the future the system must be first. This does not diminish the importance of capable individuals—on the contrary, a good system develops great individuals by enabling them to reach their full potential. Under systematic management, the best workers and leaders emerge more surely and rapidly than ever before.

This text has been written with three primary aims:

To illustrate the great loss society suffers due to inefficient work methods – using simple, concrete examples drawn from real workplaces, we will show how much productivity is currently being lost in daily operations through outdated, unscientific practices.
To convince the reader that the remedy for this inefficiency lies in systematic, scientific management – rather than blaming individuals or hoping for extraordinary talent, the solution is to change the way work is managed and organized. We will argue that improvements in management systems, not just harder work by employees, are the key to higher efficiency.
To demonstrate that the best management is a true science, resting upon clearly defined principles – we will outline the fundamental principles of scientific management and show, through modern analogies, how they constitute a science of work. Just as engineering or medicine relies on analysis and data, effective management of work can be developed into an evidence-based discipline.

By modernizing Frederick Winslow Taylor’s original Principles of Scientific Management for today’s contexts, we aim to preserve its core message: that maximum prosperity for both employer and employee can be achieved only through a complete rethinking of how work is done, using scientific methods. We will follow the original structure of Taylor’s work, first examining the problem and fundamental concepts (Chapter 1), then laying out the principles and practical examples of the scientific approach to management (Chapter 2). Throughout, we maintain the formal and technical tone of the original, while updating the language and examples to reflect contemporary industries such as warehouse logistics, software development, and advanced manufacturing.

Chapter 1: Fundamentals of Scientific Management

The Objective of Maximum Prosperity for All

The fundamental objective of scientific management is to secure the maximum prosperity for both the employer and the employee. This principle is as true today as it was a century ago. Maximum prosperity for an employer means not simply short-term profit, but the development of every part of the business to its highest potential efficiency, yielding sustainable growth and competitiveness. For the employee, maximum prosperity means not just higher wages in one pay period, but the opportunity to develop their capabilities to the fullest extent and to earn higher income and job satisfaction in the long term. These two interests—of management and of labor—may seem at odds under traditional thinking, but under scientific management they are closely intertwined. The true aim of good management is to make workers more productive in a way that increases their earnings and improves their working conditions, while simultaneously lowering the real cost of production for the company. When done correctly, higher productivity enlarges the pie so that both sides gain: the company can produce goods or services more cheaply and in greater quantity (leading to higher profits and potentially expanding market share) and the workers can earn more pay and experience less drudgery for each unit of output.

It is important to emphasize that “maximum prosperity” is not achieved by driving workers to exhaustion or by extracting more output at the expense of employee well-being. On the contrary, it is achieved by finding better ways of working so that each task is done with less wasted effort. The goal is a situation in which each worker is doing the highest grade of work for which their abilities are suited, in the most efficient way, and earning pay that reflects this greater output. In such a scenario, the employer’s unit costs fall and the business prospers, while the worker’s skill and productivity rise and they take home higher wages. The experience of countless firms in the last century has shown that this win-win outcome is possible: for example, a classic study showed that through scientifically designed methods, laborers handling heavy materials were able to quadruple their daily output (from about 12.5 tons to 47 tons per day) while receiving a 60% increase in wages, clearly benefiting both the company and the workers (Henry Noll - Wikipedia). This fundamental harmony of interests is the bedrock of scientific management.

However, under the management practices common until recently (and still prevalent in many organizations), this harmony is rarely realized. Instead, we often find a deep-seated conflict between the objectives of employees and managers. Workers tend to believe that if they work too fast or improve productivity too much, they will soon hurt their own interests (perhaps by making some of their peers redundant or by causing their piece rates or wages to be cut). Managers, on the other hand, may assume that workers will always do as little as possible unless coerced or incentivized by supervision and pay schemes. These attitudes lead to a vicious cycle: workers deliberately restrict their output, and managers respond with pressure or distrust, reinforcing the conflict. Breaking out of this unproductive cycle requires a complete change in mindset on both sides – a theme we will return to as the “mental revolution” needed for scientific management.

The Inefficiency of Rule-of-Thumb Methods

To understand why a new approach to management is needed, we must first recognize how inefficient typical work methods are when left to tradition, habit, or “rule of thumb.” By “rule-of-thumb” methods, we mean the common practice of doing work based on informal knowledge, personal experience, or guesswork, rather than through careful analysis or data. Before the advent of scientific management, almost every task in industry was performed in the way that individual workers or supervisors thought best, often relying on handed-down routines that had never been critically examined. Each worker developed their own approach to their job over time. In a machine shop, for example, one lathe operator might decide by feel and past experience how fast to run his machine and how deep a cut to take, while another operator a few yards away might use a completely different method. In a warehouse, one forklift driver might have a personal habit of how to organize pallets for loading, whereas another driver does it differently. Each person operates by rules of thumb—“I always do it this way because it usually works.” While this accumulated practical knowledge has value, it often varies greatly in effectiveness and is rarely optimal.

The result of rule-of-thumb methods is gross variation and generally low efficiency. Observers have found that in many trades, the average worker’s output is far below what is actually possible under better methods. In some cases, a task that one person completes in an hour might be completed in half the time by another person who has unconsciously hit upon a better method—or could perhaps be done in 20 minutes if the best method were developed and taught. This is not because one person is necessarily working harder, but because one method can be inherently more efficient than another. Without a scientific approach to discover these best methods, most workplaces suffer a consistent loss of potential performance.

A related problem in traditional management is the reliance on workers’ “initiative” alone to drive productivity. Under the old system, it was assumed that if you offer a worker some incentive (say, a piece-rate pay per unit produced, or the threat of dismissal for not meeting a minimum output), they will use their initiative and skill to find a way to produce as much as possible. In practice, however, it turned out to be extremely rare to get workers’ true initiative for sustained periods. There were several reasons for this, which were astutely observed even a century ago and remain relevant today. The habit of deliberately taking it easy on the job, known historically as “soldiering” (or “loafing”), was widespread under rule-of-thumb management. Workers would often work at a slow, easy pace—far slower than their real capability—because the environment gave them no reason to work faster and several reasons not to work faster.

Through careful study and interaction with workers, three primary causes of this intentional slow working were identified:

Fear of reducing employment opportunities: Many workers believed that if each person worked faster and output per person greatly increased, it would result in fewer jobs (because fewer workers would be needed). This “fallacy of unemployment through productivity” made workers intentionally limit their output. For example, if a factory of 100 workers doubled its productivity, some workers assumed half of them might be laid off. Such fears were not unfounded in the past, because managers did sometimes respond to increased output by reducing the workforce. Workers therefore concluded that by “soldiering” (working slower than they could), they were protecting their jobs and their fellow workers’ jobs. This belief often persists today whenever employees resist productivity-improving changes like automation or new workflows, worrying that these will make their roles redundant.
Flawed wage systems and management practices: Under many traditional pay systems, especially piece-rate or quota-based pay, workers discovered that if they did increase their output, management would often respond by lowering the piece rate or raising the required quota, thus nullifying the workers’ extra effort. There was a long history of such rate-cutting: a worker might work very hard and produce, say, 30% more units in a day, only to find the next week that the payment per unit was cut so that his total pay remained the same as before. Understandably, workers learned that “no good deed goes unpunished” and that it was against their interest to ever let the boss know how fast the work could be done. In other cases, when paid by the day or hour with no incentive for extra output, workers saw no reward for working at a high pace—indeed, working faster might just cause them to “work themselves out of a job” sooner or make it appear that fewer workers were needed in the department. Additionally, most managers of the old school gave little guidance or help to the workers in how to work better—their job was merely to demand output, not to enable it. In such an environment, workers understandably settled to a comfortable minimal pace that avoided drawing negative attention or burnout.
Natural human tendency to take it easy: Beyond the rational fear or strategic reasons for holding back output, there is also a natural inclination in most people to avoid unnecessary effort. If left without guidance or motivation, the “path of least resistance” is to work at a slower, more leisurely pace. People are not naturally inclined to drive themselves to maximum performance at all times—especially if they see others around them also coasting. In an unstructured environment, a new worker who might start with energy and hustle will soon observe that his peers are working at a slower rate and that there is no immediate benefit to outworking them; indeed, he may even face peer pressure not to “over-do it,” since faster work by one person could raise expectations for everyone. Over time, the group norms encourage each individual to slow down to what is informally considered a “fair day’s work,” which is often well below their true capacity.

These three factors—misaligned incentives, job security fears, and natural relaxation—combined to make soldiering a nearly universal phenomenon under traditional management. Taylor observed (and modern studies confirm) that in almost every workplace, whether it was a factory, a construction site, or even an office, the average worker might be working at only 30–50% of their potential efficiency due to these reasons. This represents a huge productivity loss that seems “baked into” the old way of managing. It is important to note that this was not usually because workers were lazy or unskilled as individuals, but because the system around them virtually encouraged low productivity. Under those conditions, any smart worker acting in their own interest would do exactly what others do: not exert themselves beyond the accepted norm.

The Need for a Scientific Approach

If we accept that a vast amount of human labor is being wasted each day due to suboptimal methods and motivations, the next question is: What can be done about it? The answer proposed by scientific management is that the remedy lies in changing the system of management itself, rather than expecting a change in human nature or a windfall of exceptional people. In other words, instead of searching for or demanding “the one perfect worker” or trying to force workers to go against their incentives, we change the rules of the game so that ordinary people, following a good system, can and will produce extraordinary results.

Scientific management calls for replacing rule-of-thumb work methods with methods based on systematic study. It is, at its heart, the application of the scientific method to the world of work. Just as a scientist conducts experiments to discover the laws of nature, management must study work through observation, data gathering, and analysis to discover the best ways of executing each task. This includes precise time and motion studies to break down how work is done, experiments with different methods or tools, and measurement of results. By doing this, management can determine, for example, the most efficient sequence of assembly for a product, the optimal speed and feed for a cutting machine, or the fastest way for a warehouse picker to fulfill an order. Once these “laws of work” are discovered, they can be used to standardize the best practice and teach it to all workers performing that job.

Under scientific management, knowledge and initiative shift in part from the worker to the manager. In the traditional system, managers often left all the practical know-how to the workers – each worker had to figure out the best way to do his job, and if he was skilled and experienced, he might develop a pretty good method (though likely still improvable). The manager’s role was merely to set goals or quotas and perhaps discipline those who didn’t meet them. Under the new system, managers take on a much more involved role: it becomes the manager’s job to understand the work in detail, often better than the workers themselves, by studying it scientifically. Managers (often with the help of engineers or trained analysts) gather all the informal knowledge that used to reside solely with seasoned workers, codify it into rules and formulas, and then use it to guide and train employees. In essence, management engineers the process of work, instead of leaving it entirely to worker initiative. This does not mean the worker becomes a mindless automaton—rather, the worker’s valuable experience is captured and improved upon, and they are then given the benefit of the best knowledge on how to do their job, freeing them from guesswork.

By scientifically designing work, we address the second and third causes of soldiering (flawed practices and natural easiness) directly: the work is structured so well that it is actually easier (or at least no harder) for the worker to achieve a high productivity than it was to operate slowly under the old method. For example, if analysis shows that a certain task can be done best with a specialized tool or a better workflow, management provides that tool and re-organizes the workflow. The worker no longer has to struggle with inappropriate tools or methods; they perform fewer unnecessary motions and encounter less frustration, which means less actual effort is needed to accomplish much more output. In many cases, properly designed methods also reduce the physical strain on workers – counterintuitively, working smarter can be less tiring even though more gets done, because wasted motions and pointless tasks that drain energy are eliminated (Frederick Winslow Taylor: Hero of Scientific Management | QAD Blog).

However, removing those barriers alone is not enough; we must also tackle the first cause of soldiering: the workers’ fear that higher productivity will harm them. This is where a complete change in the wage system and in the labor-management relationship comes in. Scientific management insists that management must provide proper incentives and a new level of cooperation with workers such that the workers trust that working faster or better will truly benefit them and not backfire. In practical terms, this means:

Paying a significantly higher wage to workers who achieve the higher productivity standards determined by the scientific study. This higher pay is not a token gesture but a substantial increase that makes it very clear to workers that they are better off under the new system. In our earlier example of the heavy laborers, the selected worker was offered a 60% raise in pay for nearly quadrupling his output. In modern settings, this could mean implementing bonus systems, profit-sharing, or performance pay that do not cut rates opportunistically, but share a fair portion of the gains with the employees.
Ensuring job security and fairness as productivity rises. Management must dispel the fear that increasing an individual’s productivity will simply lead to layoffs or an unattainable raising of quotas. Under scientific management, the typical result of increased efficiency is that the company can lower costs and prices, leading to higher demand, or the company can expand to take on more work, so that additional output finds a market or use. Management should handle these transitions humanely and communicate clearly that the goal is expansion, not reduction. In a modern context, this might involve retraining workers for new roles if their old role becomes too efficient.
Building a cooperative relationship and mutual trust. The ethos of scientific management requires what Taylor termed a “mental revolution” in both managers and workers. Both parties must shift from an adversarial, distrustful mindset to a collaborative one. Managers must come to genuinely see workers as partners in improving the business, not as cogs to be driven harder. Workers must come to see management not as an enemy or exploiter, but as a guide and supporter that will help them earn more and work better. Each side must abandon the old habit of trying to deceive or outwit the other and instead align on the common goal of higher output and prosperity.

In summary, the fundamental problem in traditional work settings is inefficiency born of unscientific methods and mutual distrust, and the solution lies in a new kind of management that uses science to determine the best methods, coupled with a new relationship with workers based on incentive and cooperation. Scientific management is not just a set of techniques; it is a holistic philosophy that redefines the roles of managers and workers. Managers assume the responsibility of planning, analyzing, and teaching – in short, of knowing what is best – while workers accept the responsibility of performing according to these best methods with full earnest effort, backed by the confidence that doing so will benefit them as much as it benefits the company.

With these fundamentals in mind, we can proceed to lay out the core principles of scientific management in a formal way, and then explore examples of how these principles can be applied in modern workplaces to achieve dramatic improvements. The remainder of this text will show that by following these principles, an organization can transform its productivity and reach the ideal of maximum prosperity for all members.

Chapter 2: The Principles of Scientific Management

Overview and Key Questions

When the ideas of scientific management are introduced, people naturally have several important questions in mind. First, what exactly are the principles of this new management approach, and how do they differ from traditional practices? Second, what evidence is there that these principles actually work in practice, especially in modern industries? Third, what are the consequences of applying these principles for the workforce – will workers accept them, and how are workers affected in terms of skill requirements, compensation, and satisfaction? In this chapter, we address these questions by first outlining the core principles of scientific management and then providing contemporary examples that demonstrate these principles in action. Through these examples, we will see not only the effectiveness of the approach but also the way it changes the roles of both managers and workers.

It is worth noting that the principles of scientific management, as originally formulated, are universal in their applicability. Taylor emphasized that these principles apply “to all kinds of human activities, from our simplest individual acts to the work of great corporations.” In modern terms, whether you are managing a team of software engineers, a hospital staff, a sales force, or an automated factory, the underlying principles can be adapted to improve efficiency and cooperation. The specific techniques will of course differ – one would not use a stopwatch study on a creative design process in the same way as on an assembly line – but the philosophy of using data and analysis to find better methods, and aligning incentives to those methods remains broadly relevant. Many modern management approaches, such as Lean manufacturing and Agile software development, owe a debt to these basic principles, focusing on eliminating waste and continuous improvement in a very similar spirit.

The Four Principles of Scientific Management

Develop a science for each element of work, replacing old rule-of-thumb methods.
The first principle is to study each job in detail and determine the one best way to perform it. Instead of allowing each worker to approach the task in their own ad hoc manner, management discovers and codifies the most efficient methods. This involves gathering data, using tools like time-and-motion studies, analyzing different approaches, and then establishing standard procedures. Everything from the proper tools and materials to the sequence of steps and the optimal timing is determined scientifically. In a modern context, this could mean using analytics software to study how tasks flow through an office or how users interact with a software application, and then redesigning the process for maximum efficiency. It could involve industrial engineers determining the best arrangement of machines on a factory floor. The output of this step is a clear set of instructions or a “standard operating procedure” for workers to follow that is far more effective than what they could invent on their own. Importantly, this “science of work” must be continually updated as new technologies and techniques emerge.
Scientifically select, train, and develop each worker.
The second principle is about people. Under traditional management, workers often chose their own trade or were assigned arbitrarily, and then they mostly learned on the job by shadowing others or through trial and error. Scientific management turns this into a much more careful and proactive process. Management should study the strengths, weaknesses, and potential of each employee and assign them to work for which they are well-suited. This means using evidence (such as aptitude tests, physical capability assessments, or skill evaluations) to place people in roles where they can excel. Once assigned, the organization doesn’t assume the worker is already expert; instead, it provides scientific training to teach the worker the exact best way to do the job as determined in the first principle. Continuous development is emphasized – workers are not only trained once, but over time their skills are built up, and they are helped to advance to higher levels of proficiency.
Heartily cooperate with the workers to ensure all work is done according to the devised science.
Merely devising a scientific method and training workers is not enough; there must be ongoing cooperation between management and workers to actually carry out the work in the prescribed way. This third principle recognizes that even a great system on paper fails if it is not executed in practice, and execution requires goodwill and constant coordination. Management must work alongside workers, almost as coaches or partners, to make sure the workers are using the methods correctly and to help them whenever issues arise. Workers, in turn, are expected to follow the methods and standards that have been set, rather than reverting to their old way or improvising. This compliance comes because the workers have been convinced (through proper training and incentive) that the prescribed method is truly better for them.
Divide the work and responsibility almost equally between management and workers, with each doing the tasks best suited to them.
The fourth principle formalizes a new division of labor. In the past, a worker often had to do both the planning and the executing of their work. Under scientific management, many planning and administrative tasks are shifted to managers and specialists, so that workers can concentrate on execution. Management takes on all aspects of work for which it is better suited than the workers, such as analyzing data, planning schedules, devising techniques, and monitoring quality. Workers are then free to focus on the actual production, using their training to follow the methods. If a worker fails to meet the output targets, it is as much a management failure (of planning, training, or support) as it is a worker’s failure.

These four principles form the philosophical framework of scientific management. To recap succinctly:

Science, not guesswork – Every job is based on laws and facts gleaned from study, not on traditional habits.
Harmony, not discord – Managers and workers actively cooperate.
Training and development, not passive workforce – Workers are chosen and taught to be their best.
Division of responsibility – Planning and execution responsibilities are separated so that each is carried out expertly.

Practical Examples in Modern Workplaces

Optimizing Physical Labor – From Pig Iron to Warehouse Logistics:

One of the original case studies of scientific management involved loading pig iron (heavy iron ingots) at Bethlehem Steel in the late 1890s. The task was simple but physically demanding: lift 92-pound pieces and carry them up an inclined plank onto a freight car, repeatedly, all day. Initially, each worker was loading about 12.5 tons per day. By applying scientific management, the team discovered that a first-class handler, using the right method, could load 47 tons per day – nearly four times as much. The method to achieve this was not “work four times harder”; it was about working smarter and with the proper rest periods. Through experiment, they created a precise schedule of work and rest, and they scientifically selected a suitable worker who was offered higher pay for cooperating with the new system. The worker achieved the target daily, earned a 60% higher wage, and reportedly finished each day tired but not overstrained.

Now consider a modern parallel in a warehouse logistics context. Instead of pig iron, workers might handle 40-pound boxes of products. By default, each loader might develop their own way of carrying or take breaks arbitrarily, leading to modest throughput. Applying scientific management, methods engineers would develop a science for the task—experimenting with loads, introducing equipment like handcarts or lifts, observing rest intervals, and optimizing layout. They would select and train workers who can handle that load efficiently, offer proper incentives, and cooperate closely so that each worker follows the prescribed method. As a result, overall productivity might more than double, and workers could receive higher pay without feeling overly tired, because the process is designed to minimize wasted effort.

Optimizing Task Efficiency – From Shoveling Coal to Modern Tooling and Workflow Improvements:

Another early example was the “science of shoveling” at Bethlehem Steel, where different materials (coal, iron ore, etc.) varied in density. By designing shovels so that each fully loaded scoop weighed about 21 pounds—optimal for sustained effort—productivity soared. A worker could shovel more tons per day with less fatigue, and the company could reassign surplus workers elsewhere while paying the shovelers higher wages.

Translated to modern work, consider data processing in an office. Each clerk doing data entry might have a personal method, potentially wasting time. A scientific approach would analyze the task, identify bottlenecks (like switching windows or re-typing information), and provide better tools or procedures (such as automation scripts, more ergonomic software interfaces, or standard keyboard shortcuts). With training and cooperation, each clerk might process twice as many forms per hour. The company gains efficiency, and clerks appreciate an easier, faster process—and potentially higher pay tied to performance.

Application to Knowledge Work – Software Development and Beyond:

While scientific management is often associated with manual labor, its principles can also enhance knowledge work like software development. Rather than each developer using their own “rule-of-thumb” coding style or deployment process, the team can study workflow data, adopt best practices such as continuous integration and automated testing, and train developers to use them. Management cooperates by providing the right tools (build servers, code review guidelines) and removing roadblocks, while developers focus on writing code that follows the new standards. Dividing responsibility between planning (architecture, tool setup) and execution (coding, testing) means everyone does what they do best. The result is higher quality software delivered faster, with fewer late-night fixes and less overall stress.

Conclusion: The Lasting Legacy of Scientific Management

We have preserved the original structure and intent of The Principles of Scientific Management while updating its language and examples to resonate with today’s world. Yet, despite the changes in context—whether it’s pig iron and shovels or software code and warehouses—the major themes remain constant. Work can be studied and improved systematically. Cooperation between those who manage and those who do the work is not only possible; it is the most effective route to success. And when done correctly, the gains of efficiency can be shared so that everyone involved is better off.

To summarize the journey: We began by recognizing the vast inefficiencies that persist in most human activities and the conventional mistrust that pits worker output against worker interest. We then saw that by substituting science for tradition, cooperation for individualism, and equitable prosperity for zero-sum thinking, we can dramatically increase productivity. We enumerated four guiding principles that any organization can follow to implement this approach. Finally, we examined how these principles play out in practical scenarios today, showing that scientific management is not an outdated concept but rather the foundation of many modern best practices in operations and project management.

For the contemporary reader, the takeaway is clear: the highest achievements in efficiency and excellence come when you treat management as a science and workers as partners. Whether you are leading a team of AI researchers or running a chain of restaurants, you ignore these principles at your peril. If you rely on guesswork, if you leave your people unguided or untrained, or if you pit your interests against theirs, you will inevitably waste potential. But if you embrace the mindset of systematic improvement and mutual gain, there is virtually no limit to the advancements you can make.

The world has changed greatly since 1911, but the Principles of Scientific Management have proven enduring. They underlie methodologies like Six Sigma, Agile, Lean, and many data-driven management techniques today. Every time a company analyzes data to refine a process or a manager determines fair bonuses for productivity, Taylor’s influence is visible. Of course, we have also learned to be cautious and humane in applying these ideas—treating workers with respect and ensuring that increases in productivity do not come at the cost of employee burnout. The goal is not to turn people into machines; it is to use our human ingenuity to make work more effective and more fulfilling. Ideally, repetitive drudgery is minimized, creative thinking is welcomed, and the fruits of improved efficiency are broadly shared.

In conclusion, scientific management in its modern form offers a path to sustainable excellence: a business that continually improves its processes through knowledge and cooperation, and a workforce that grows in capability and prosperity as a result. It requires, as it did in Taylor’s time, strong leadership and vision from management to implement, and open-mindedness and trust from labor to embrace. When those are in place, the results speak for themselves – higher output, lower costs, better wages, and a more positive workplace. This modernized treatise reaffirms that the principles articulated over a century ago are not relics of history, but guiding lights for any organization that seeks efficiency with humanity, and profit with fairness. By adhering to these principles, we prepare ourselves to meet the challenges of today’s complex world of work with a proven foundation that is both rational and respectful of the human element. In the final analysis, the true principle of scientific management is the science of mutual success.

科学的管理原理——现代社会中持久而有效的管理之道。