Automation Process Quotes

When you repeat a behavior over time, your brain learns to automate the process. It's more energy efficient to automatically do something than to manually weigh your options and decide to act the same way every time. When you make a decision very quickly, it is probably from habit, even if you think you're actively deciding. In a way, you made the decision a while ago.

As a new entrepreneur, you're probably gonna have to hustle hard to get things going at first. But as the business grows and becomes more established, that unrefined hustle should be replaced by automated profit-producing processes and systems. Hustle is good as a temporary mode of operating, but it's unsustainable long term and unprofitable long term.

1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

However, one intriguing shift that suggests there are limits to automation was the recent decision by Toyota to systematically put working humans back into the manufacturing process. In quality and manufacturing on a mass scale, Toyota has been a global leader in automation technologies based on the corporate philosophy of kaizen (Japanese for “good change”) or continuous improvement. After pushing its automation processes toward lights-out manufacturing, the company realized that automated factories do not improve themselves. Once Toyota had extraordinary craftsmen that were known as Kami-sama, or “gods” who had the ability to make anything, according to Toyota president Akio Toyoda.49 The craftsmen also had the human ability to act creatively and thus improve the manufacturing process. Now, to add flexibility and creativity back into their factories, Toyota chose to restore a hundred “manual-intensive” workspaces.

He was thinking about automated teller machines. The term was aged and burdened by its own historical memory. It worked at cross-purposes, unable to escape the inferences of fuddled human personnel and jerky moving parts. The term was part of the process that the device was meant to replace. It was anti-futuristic, so cumbrous and mechanical that even the acronym seemed dated.

We’ve now established three things. First, we don’t need willpower when we don’t desire to do something, and it isn’t a thing some of us have in excess and some of us don’t have at all. It’s a cognitive function, like deciding what to eat or solving a math equation or remembering your dad’s birthday. Willpower is also a limited resource; we have more of it at the beginning of the day and lose it throughout the day as we use it to write emails or not eat cookies. When you automate some decisions or processes (through forming habits), you free up more brain power. Second, for us to make and change a habit, we need a cue, a routine, and a reward, and enough repetition must occur for the process to move from something we have to think about consciously (“I need to brush my teeth,” “I don’t want to drink wine”) to something we do naturally, automatically. Third, throughout the day, we must manage our energy so that we don’t blow out and end up in the place of no return—a hyperaroused state where the only thing that can bring us down is a glass (or a bottle) of wine. Maybe

It is useful for companies to look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees.

A Checklist is an Externalized, predefined Standard Operating Procedure for completing a specific task. Creating a Checklist is enormously valuable for two reasons. First, Checklisting will help you define a System for a process that hasn’t yet been formalized—once the Checklist has been created, it’s easier to see how to improve or Automate the system. Second, using Checklists as a normal part of working can help ensure that you don’t forget to handle important steps that are easily overlooked when things get busy.

Always wait until the end of designing a process—after you have questioned all the requirements and deleted unnecessary parts—before you introduce automation.

The more automated our experience becomes, the less involved we are in the art of living.

chickens are grown to standardized sizes so as to make them compatible with automated slaughtering and processing.

You may not be able to automate the whole process, but you can make the first action mindless. Make it easy to start and the rest will follow.

I began looking for these four: Smart. It doesn’t mean high IQ (although that’s great), it means disposed toward learning. If there’s a best practice anywhere, adopt it. We want to turn as much as possible into a routine so we can focus on the few things that require human intelligence and creativity. A good interview question for this is: “Tell me about the last significant thing you learned about how to do your job better.” Or you might ask a candidate: “What’s something that you’ve automated? What’s a process you’ve had to tear down at a company?” Humble. I don’t mean meek or unambitious, I mean being humble in the way that Steph Curry is humble. If you’re humble, people want you to succeed. If you’re selfish, they want you to fail. It also gives you the capacity for self-awareness, so you can actually learn and be smart. Humility is foundational like that. It is also essential for the kind of collaboration we want at Slack. Hardworking. It does not mean long hours. You can go home and take care of your family, but when you’re here, you’re disciplined, professional, and focused. You should also be competitive, determined, resourceful, resilient, and gritty. Take this job as an opportunity to do the best work of your life. Collaborative. It’s not submissive, not deferential—in fact it’s kind of the opposite. In our culture, being collaborative means providing leadership from everywhere. I’m taking responsibility for the health of this meeting. If there’s a lack of trust, I’m going to address that. If the goals are unclear, I’m going to deal with that. We’re all interested in getting better and everyone should take responsibility for that. If everyone’s collaborative in that sense, the responsibility for team performance is shared. Collaborative people know that success is limited by the worst performers, so they are either going to elevate them or have a serious conversation. This one is easy to corroborate with references, and in an interview you can ask, “Tell me about a situation in your last company where something was substandard and you helped to fix it.

Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

The optimists claim that through the course of evolution the nervous system has become adept at “chunking” bits of information so that processing capacity is constantly expanded. Simple functions like adding a column of numbers or driving a car grow to be automated, leaving the mind free to deal with more data. We

Automated testing is the process of writing a program that tests another program. Writing tests is a bit more work than testing manually, but once you’ve done it, you gain a kind of superpower: it takes you only a few seconds to verify that your program still behaves properly in all the situations you wrote tests for.

Whenever you face a problem repeatedly, your brain begins to automate the process of solving it. Your habits are just a series of automatic solutions that solve the problems and stresses you face regularly. As behavioral scientist Jason Hreha writes, “Habits are, simply, reliable solutions to recurring problems in our environment.

A use case is a description of the way that an automated system is used. It specifies the input to be provided by the user, the output to be returned to the user, and the processing steps involved in producing that output. A use case describes application-specific business rules as opposed to the Critical Business Rules within the Entities.

So, when should you think about automating a process? The simplest answer is, “When you have to do it a second time.” The third time you do something, it should be done using an automated process. This fine-grained incremental approach rapidly creates a system for automating the repeated parts of your development, build, test, and deployment process.

Every change that is made to an application’s configuration, source code, environment, or data, triggers the creation of a new instance of the pipeline. One of the first steps in the pipeline is to create binaries and installers. The rest of the pipeline runs a series of tests on the binaries to prove that they can be released. Each test that the release candidate passes gives us more confidence that this particular combination of binary code, configuration information, environment, and data will work. If the release candidate passes all the tests, it can be released. The deployment pipeline has its foundations in the process of continuous integration and is in essence the principle of continuous integration taken to its logical conclusion. The aim of the deployment pipeline is threefold. First, it makes every part of the process of building, deploying, testing, and releasing software visible to everybody involved, aiding collaboration. Second, it improves feedback so that problems are identified, and so resolved, as early in the process as possible. Finally, it enables teams to deploy and release any version of their software to any environment at will through a fully automated process.

1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics. Everything else is a recommendation

On one weekend, they marched through the factory painting marks on machinery to be jettisoned. “We put a hole in the side of the building just to remove all that equipment,” Musk says. The experience became a lesson that would become part of Musk’s production algorithm. Always wait until the end of designing a process—after you have questioned all the requirements and deleted unnecessary parts—before you introduce automation.

Kensi Gounden, Highly innovative new technologies can be both disruptive and transformative, but technology adoption can also be incremental, such as simply automating a manual process. So introducing business technology innovations, either incremental or step-change, may embrace increasing online connectivity across the business, strategic technology acquisition and use or using time-saving technologies to improve internal communication.

I am the person who spends a great deal of time on the intricacies of interstellar communication through jumpgates—a process, Your Excellency, which is so very automated and regular that my time is absolutely most valuably spent on informing you about it. Would you like to come into a conference room?” One Cyclamen was amazingly obsequious, and in such a way that Eight Antidote felt more flattered than annoyed. He wished he could learn that skill.

advances in AI are poised to drive dramatic productivity increases and perhaps eventually full automation. Radiologists, for example, are trained to interpret the images that result from various medical scans. Image processing and recognition technology is advancing rapidly and may soon be able to usurp the radiologist’s traditional role. Software can already recognize people in photos posted on Facebook and even help identify potential terrorists in airports.

The coming wave of automation will move way beyond the factory or public infrastructure and into our very biological processes such as aging and even giving birth. Used as we are to the gradual societal shifts brought about by previous change waves, often allowing decades to adjust and respond, I ask if we as a tribe are ready to abdicate our human sovereignty to the faceless forces of technology? Are you ready for the biggest loss of free will and individual human control in history?

Whenever you face a problem repeatedly, your brain begins to automate the process of solving it. Your habits are just a series of automatic solutions that solve the problems and stresses you face regularly. As behavioral scientist Jason Hreha writes, “Habits are, simply, reliable solutions to recurring problems in our environment.” As habits are created, the level of activity in the brain decreases. You learn to lock in on the cues that predict success and tune out everything else. When

The process of decision making and projecting a future in which one future among many can be selected depends less and less on human will. We may call it the paradox of the decider: as the circulation of information becomes faster and more complex, the time available for the elaboration of relevant information becomes shorter. The more space taken by the available information, the less time there is for understanding and conscious choice. This is why the interdependence between data and decisions is more and more embedded in infomachinery, in technolinguistic interfaces. This is why the execution of the program is entrusted to automated procedures that human operators can neither change nor ignore. The machine pretends to be neutral, purely mathematical, but we know that its procedures are only the technical reification of social interests: profit, accumulation, competition—these are the criteria underlying the automatic procedures embedded in the machine. Human volition is reduced to a procedural pretense.

As black-box technologies become more widespread, there have been no shortage of demands for increased transparency. In 2016 the European Union's General Data Protection Regulation included in its stipulations the "right to an explanation," declaring that citizens have a right to know the reason behind the automated decisions that involve them. While no similar measure exists in the United States, the tech industry has become more amenable to paying lip service to "transparency" and "explainability," if only to build consumer trust. Some companies claim they have developed methods that work in reverse to suss out data points that may have triggered the machine's decisions—though these explanations are at best intelligent guesses. (Sam Ritchie, a former software engineer at Stripe, prefers the term "narratives," since the explanations are not a step-by-step breakdown of the algorithm's decision-making process but a hypothesis about reasoning tactics it may have used.) In some cases the explanations come from an entirely different system trained to generate responses that are meant to account convincingly, in semantic terms, for decisions the original machine made, when in truth the two systems are entirely autonomous and unrelated. These misleading explanations end up merely contributing another layer of opacity. "The problem is now exacerbated," writes the critic Kathrin Passig, "because even the existence of a lack of explanation is concealed.

2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

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While poorhouses have been physically demolished, their legacy remains alive and well in the automated decision-making systems that encage and entrap today's poor. For all their high-tech polish, our modern systems of poverty management - automated decision-making, data mining, and predictive analysis - retain a remarkable kinship with the poorhouses of the past. Our new digital tools spring from punitive, moralistic views of poverty and create a system of high-tech containment and investigation. The digital poorhouse deters the poor from accessing public resources; polices their labor, spending, sexuality, and parenting; tries to predict their future behavior; and punishes and criminalizes those who do not comply with its dictates. In the process, it creates ever-finer moral distinctions between the 'deserving' and 'undeserving' poor, categorizations that rationalize our national failure to care for one another.

Work must be refused and reduced, building our synthetic freedom in the process.136 As we have set out in this chapter, achieving this will require the realisation of four minimal demands: 1.Full automation 2.The reduction of the working week 3.The provision of a basic income 4.The diminishment of the work ethic While each of these proposals can be taken as an individual goal in itself, their real power is expressed when they are advanced as an integrated programme. This is not a simple, marginal reform, but an entirely new hegemonic formation to compete against the neoliberal and social democratic options. The demand for full automation amplifies the possibility of reducing the working week and heightens the need for a universal basic income. A reduction in the working week helps produce a sustainable economy and leverage class power. And a universal basic income amplifies the potential to reduce the working week and expand class power.

Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics.

The specific nature of capitalist commodity production shows itself in the fact that it is not simply a labour process in which products are created by the elements of production M and L. Rather the capitalistic form of commodity production is constructed dualistically – it is simultaneously a labour process for the creation of products and a valorisation process. The elements of production M and L figure not only in their natural form, but at the same time as values c and v respectively. They are used for the production of a sum of values, w, and indeed only on condition that over and above the used up value magnitudes c and v there is a surplus s (that is, s = w - c + v). The capitalist expansion of production, or accumulation of capital, is defined by the fact that the expansion of M relative to L occurs on the basis of the law of value; it takes the specific form of a constantly expanding capital c relative to the sum of wages v, such that both components of capital are necessarily valorised. It follows that the reproduction process can only be continued and expanded further if the advanced, constantly growing capital c + v can secure a profit, s. The problem can then be defined as follows – is a process of this sort possible in the long run?”[68]

One of those was Gary Bradski, an expert in machine vision at Intel Labs in Santa Clara. The company was the world’s largest chipmaker and had developed a manufacturing strategy called “copy exact,” a way of developing next-generation manufacturing techniques to make ever-smaller chips. Intel would develop a new technology at a prototype facility and then export that process to wherever it planned to produce the denser chips in volume. It was a system that required discipline, and Bradski was a bit of a “Wild Duck”—a term that IBM originally used to describe employees who refused to fly in formation—compared to typical engineers in Intel’s regimented semiconductor manufacturing culture. A refugee from the high-flying finance world of “quants” on the East Coast, Bradski arrived at Intel in 1996 and was forced to spend a year doing boring grunt work, like developing an image-processing software library for factory automation applications. After paying his dues, he was moved to the chipmaker’s research laboratory and started researching interesting projects. Bradski had grown up in Palo Alto before leaving to study physics and artificial intelligence at Berkeley and Boston University. He returned because he had been bitten by the Silicon Valley entrepreneurial bug.

five commandments: 1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience.

If we’re not careful, the automation of mental labor, by changing the nature and focus of intellectual endeavor, may end up eroding one of the foundations of culture itself: our desire to understand the world. Predictive algorithms may be supernaturally skilled at discovering correlations, but they’re indifferent to the underlying causes of traits and phenomena. Yet it’s the deciphering of causation—the meticulous untangling of how and why things work the way they do—that extends the reach of human understanding and ultimately gives meaning to our search for knowledge. If we come to see automated calculations of probability as sufficient for our professional and social purposes, we risk losing or at least weakening our desire and motivation to seek explanations, to venture down the circuitous paths that lead toward wisdom and wonder. Why bother, if a computer can spit out “the answer” in a millisecond or two? In his 1947 essay “Rationalism in Politics,” the British philosopher Michael Oakeshott provided a vivid description of the modern rationalist: “His mind has no atmosphere, no changes of season and temperature; his intellectual processes, so far as possible, are insulated from all external influence and go on in the void.” The rationalist has no concern for culture or history; he neither cultivates nor displays a personal perspective. His thinking is notable only for “the rapidity with which he reduces the tangle and variety of experience” into “a formula.”54 Oakeshott’s words also provide us with a perfect description of computer intelligence: eminently practical and productive and entirely lacking in curiosity,

Gadgetry will continue to relieve mankind of tedious jobs. Kitchen units will be devised that will prepare ‘automeals,’ heating water and converting it to coffee; toasting bread; frying, poaching or scrambling eggs, grilling bacon, and so on. Breakfasts will be ‘ordered’ the night before to be ready by a specified hour the next morning. Communications will become sight-sound and you will see as well as hear the person you telephone. The screen can be used not only to see the people you call but also for studying documents and photographs and reading passages from books. Synchronous satellites, hovering in space will make it possible for you to direct-dial any spot on earth, including the weather stations in Antarctica. [M]en will continue to withdraw from nature in order to create an environment that will suit them better. By 2014, electroluminescent panels will be in common use. Ceilings and walls will glow softly, and in a variety of colors that will change at the touch of a push button. Robots will neither be common nor very good in 2014, but they will be in existence. The appliances of 2014 will have no electric cords, of course, for they will be powered by long- lived batteries running on radioisotopes. “[H]ighways … in the more advanced sections of the world will have passed their peak in 2014; there will be increasing emphasis on transportation that makes the least possible contact with the surface. There will be aircraft, of course, but even ground travel will increasingly take to the air a foot or two off the ground. [V]ehicles with ‘Robot-brains’ … can be set for particular destinations … that will then proceed there without interference by the slow reflexes of a human driver. [W]all screens will have replaced the ordinary set; but transparent cubes will be making their appearance in which three-dimensional viewing will be possible. [T]he world population will be 6,500,000,000 and the population of the United States will be 350,000,000. All earth will be a single choked Manhattan by A.D. 2450 and society will collapse long before that! There will, therefore, be a worldwide propaganda drive in favor of birth control by rational and humane methods and, by 2014, it will undoubtedly have taken serious effect. Ordinary agriculture will keep up with great difficulty and there will be ‘farms’ turning to the more efficient micro-organisms. Processed yeast and algae products will be available in a variety of flavors. The world of A.D. 2014 will have few routine jobs that cannot be done better by some machine than by any human being. Mankind will therefore have become largely a race of machine tenders. Schools will have to be oriented in this direction…. All the high-school students will be taught the fundamentals of computer technology will become proficient in binary arithmetic and will be trained to perfection in the use of the computer languages that will have developed out of those like the contemporary “Fortran". [M]ankind will suffer badly from the disease of boredom, a disease spreading more widely each year and growing in intensity. This will have serious mental, emotional and sociological consequences, and I dare say that psychiatry will be far and away the most important medical specialty in 2014. [T]he most glorious single word in the vocabulary will have become work! in our a society of enforced leisure.

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I became a broken record on the algorithm,” Musk says. “But I think it’s helpful to say it to an annoying degree.” It had five commandments: 1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb. 2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough. 3. Simplify and optimize. This should come after step two. A common mistake is to simplify and optimize a part or a process that should not exist. 4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted. 5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out. The algorithm was sometimes accompanied by a few corollaries, among them: All technical managers must have hands-on experience. For example, managers of software teams must spend at least 20% of their time coding. Solar roof managers must spend time on the roofs doing installations. Otherwise, they are like a cavalry leader who can’t ride a horse or a general who can’t use a sword. Comradery is dangerous. It makes it hard for people to challenge each other’s work. There is a tendency to not want to throw a colleague under the bus. That needs to be avoided. It’s OK to be wrong. Just don’t be confident and wrong. Never ask your troops to do something you’re not willing to do. Whenever there are problems to solve, don’t just meet with your managers. Do a skip level, where you meet with the level right below your managers. When hiring, look for people with the right attitude. Skills can be taught. Attitude changes require a brain transplant. A maniacal sense of urgency is our operating principle. The only rules are the ones dictated by the laws of physics. Everything else is a recommendation.

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The future of the labor market as we know it is contingent on many different things. It is by and large an iterative process in which we may only know the true outcome by repeated evaluations and followups of each implemented change and/or innovation. Therefore, securing a system of necessary checks-and-balances will be of paramount importance to ensure a successful digital transformation.

That is, no changes should be able to be made to production unless they have been committed to version control, validated by the standard build and test process, and then deployed through an automated process triggered through a deployment pipeline.

asked them two key questions. With your current data and processes, what do you need help with? That helped identify inefficiencies and automations that we could put in place to deliver those quick wins. I also asked a broader question: what are you not able to do that we can help with? This helped identify new, additional data sources or functionality not in place that formed our more longer-term plans.

For a well-defined, standard, and stable process involving hand-offs between people and systems, it is preferable to use a smart workflow platform. Such platforms offer pre-developed modules. These are ready-to-use automation programs customized by industry and by business function (e.g., onboarding of clients in retail banking). In addition, they are modular. For example, a module might include a form for client data collection, and another module might support an approval workflow. In addition, these modules can be linked to external systems and databases using connectors, such as application programming interfaces (APIs), which enable resilient data connectivity. Hence, with smart workflows, there is no need to develop bespoke internal and external data bridges. This integration creates a system with high resiliency and integrity. In addition, the standardization by industry and function of these platforms, combined with the low-code functionality, helps to accelerate the implementation.

There are no ready-to-use modules with RPA. Most of the development is bespoke, and all process flows need to be built almost from scratch. The connections also need to be constructed. This results in a more flexible design and implementation of the programs developed, which can fit with more specific business requirements. The key advantage of RPA is that it allows the creation of automation programs that can involve legacy systems (e.g., those which can’t use APIs) or address non-standard requirements (e.g., onboarding of clients for a broker insurance company under Singapore regulations). However, with RPA, the lack of native integration amongst the components has weaknesses. For example, it involves less robustness, weaker data integrity, and lower resilience to process changes. If one part of an RPA program fails, the whole end-to-end process is stopped. As an outcome, based on our experience, the leading practice is to use low-code and smart workflow platforms as a foundation of the overall automation platform. In contrast, RPA is used for any integration of the overall platform with legacy systems or for automation of bespoke processes.

To explain how a dApp works, we’ll use an example from the company Etherisc, which created a dApp for flight insurance to a well-known Ethereum conference. This flight insurance was purchased by 31 of the attendees.23 Figure 5.1 shows a simplified diagram. Using Ethereum, developers can mimic insurance pools with strings of conditional transactions. Open sourcing this process and running it on top of Ethereum’s world computer allows everyday investors to put their capital in an insurance pool to earn returns from the purchasers of insurance premiums that are looking for coverage from certain events. Everyone trusts the system because it runs in the open and is automated by code.

Bitcoin is not a smart network. Bitcoin is a dumb network. It really is a dumb network. It is a dumb transaction-processing network. It’s a dumb network for verifying a very simple scripting language. It doesn’t offer a complete range of financial services and products. It doesn’t have automation and incredible features built in. Bitcoin is simply a dumb network, and that is one of its strongest and most important features. When you design networks, when you architect network systems, one of the most fundamental choices is this: do you make a dumb network that supports smart devices, or do you make a smart network that supports dumb devices? 5.1.1. The Smart Network - Phones The phone network was a very smart network. The telephone at the end of that network was a very dumb device. If you had a pulse-dialing phone, that thing had maybe four electronic components inside it. It was basically a switch on a wire with a speaker attached to it. You could dial by flicking the hook up and down fast enough. 
The phone was a dumb device; it had no intelligence whatsoever. Everything the phone network did was in the network. Caller ID was a network feature. Call waiting was a network feature. And if you wanted to make the experience better, you had to upgrade the network but you didn’t need to upgrade the device. That was a critical design decision because, at that time, the belief was that smart networks were better because you could deliver these incredible services just by upgrading the network for everyone. There is one small disadvantage with smart networks. They have to be upgraded from the center out. And that means innovation occurs at the center, by one player, and requires permission. As a result of smart network design, innovation only happens when a feature is needed by all of the subscribers of the network, when it is compelling enough to disrupt the function of the entire network to upgrade it. 5.1.2. The Dumb Network - Internet The internet is a dumb network. It’s dumb as rocks. All it can do is move data from point A to point B. It doesn’t know what that data is. It can’t tell the difference between a Skype call and a web page. It doesn’t know if the device on the end is a desktop computer or a mobile phone, a vacuum cleaner, a refrigerator, or a car. It doesn’t know if that device is powerful or not. If it can do multimedia or not. It doesn’t know, it doesn’t care. In order to run a new application or innovate on a dumb network, all you have to do is add innovation at the edge. Because a dumb network can support smart devices, you don’t need to change anything in the network. If you push intelligence to the edge of the network, an application that only has five users can be implemented so long as those five users upgrade their devices to implement that application. The dumb network will transport their data because it doesn’t know the difference and it doesn’t care. 5.1.3. Bitcoin’s Dumb Network Bitcoin is a dumb network supporting really smart devices, and that is an incredibly powerful concept because bitcoin pushes all of the intelligence to the edge. It doesn’t care if the bitcoin address is the address of a multimillionaire, the address of a central bank, the address of a smart contract, the address of a device, or the address of a human. It doesn’t know. It doesn’t care if the transaction is carrying lots of money or not much money at all. It doesn’t care if the address is in Kuala Lumpur or downtown New York. It doesn’t know, it doesn’t care. It moves money from one address to another based on a simple locking script. And that means that if you want to build a new application on top of bitcoin, you can upgrade the

IA not only makes transaction processes more efficient, but it also generates log files for every action, creating transparency and ease of compliance. Machine learning leverages the digital information created by these programs to recognize predictive patterns and project trends.

4. When to use RPA versus smart workflow platforms? These technologies can be used interchangeably. The leading practice is to use smart workflow and low-code platforms as a foundation of the overall automation platform. RPA is used when IA needs to be integrated with legacy systems or automation of bespoke processes.

As an outcome, based on our experience, the leading practice is to use low-code and smart workflow platforms as a foundation of the overall automation platform. In contrast, RPA is used for any integration of the overall platform with legacy systems or for automation of bespoke processes.

Deep learning techniques are used when data features are numerous and there is a lack of domain understanding to identify and understand them. This data is typically complex, such as images, videos, or voice recordings. For example, an image contains an indefinite amount of data features (e.g., points, edges, shapes, or objects), and some are relevant to the problem being solved, but others are not. Deep learning is used to solve problems such as image classification, natural language processing, and speech recognition.

Another common issue is the lack of interdisciplinarity in the transformation. In our experience, the highest impact is the result of multi-lever end-to-end process automation – not small, siloed implementations, focused on one single technology lever. To achieve this, management should advocate for getting the right talents from across the different parts of an organization to work together (e.g., data scientists, developers, business analysts). Interdisciplinarity is also about avoiding limiting the transformation to the implementation of one single technology lever (e.g., RPA), and about implementing IA on end-to-end processes instead of only a few process tasks. By combining talents and technology levers and targeting end-to-end processes, the organization will create synergies, build economies of scale, and remove potential bottlenecks. Organizations failing to achieve this are not able to scale their IA transformation.

Maintaining an automated process is estimated to be about 20% of the initial implementation cost per year.

Think big, start small, and scale fast”: Initiate the transformation with the definition of a multi-year, company-wide vision, roadmap, and business case. These plans need to be flexible and adaptable. Then, “start small” with the implementation of a pilot, and take the time to learn from this first experience. Finally, implement the broader scope in stages to manage the risks. Gradually increase the speed and scale of the transformation, and as a result, generate high impact. “IA is a business transformation, not a technology project”: The perspective of business benefits should guide the transformation. This transformation involves not only technology, but more importantly, people – with change management, and retraining – and processes – with redesigns. “IA is a journey, not a destination”: IA is not a one-off exercise; it is a never-ending transformation journey. It continually brings additional benefits to the organization by applying evolving concepts, methods, and technologies. Hence, building teams with the right skills to guide the company in this transformation is critical. “Infusing IA into the culture of the company”: Implementing IA with siloed, isolated teams does not work. Automation needs to be infused into the company. Change management, education, empowerment, and incentivization of everyone in the company is vital. Every employee should know what IA is and what its benefits are, and be empowered and incentivized to identify use cases and build automation.

The long tail of lower-value automation opportunities should involve less focus on the redesign. The value will be captured through the systematic use of the lean methodology to improve the processes incrementally (e.g., simplify, reduce the number of actions).

More fundamentally, productivity gains from automation may always be somewhat limited, especially compared to the introduction of new products and tasks that transform the production process, such as those in the early Ford factories. Automation is about substituting cheaper machines or algorithms for human labor, and reducing production costs by 10 or even 20 percent in a few tasks will have relatively small consequences for TFP or the efficiency of the production process. In contrast, introducing new technologies, such as electrification, novel designs, or new production tasks, has been at the root of transformative TFP gains throughout much of the twentieth century.

The sixth wave will no doubt affect many industries, processes, and occupations, but many will remain largely untouched, at least in terms of automation: education, healthcare, sports and law-making for example. Artificial intelligence won’t replace doctors in the near future, but it will help them make better diagnoses and treatment decisions.

Willpower and brain capacity. Most of us are confused about what willpower really is. We tend to think some people have it in spades and that others like those with chemical and behavioral addictions are lacking in it. That's exactly how I saw myself as a person with no self-control or willpower which was not at all true. While impulse control was indeed a skill I had to hone. For instance through meditation, and mindfulness - staying present with feelings and reactions. Willpower, as in repression or inhibiting a desire. It isn’t a skill. It's a finite cognitive function known as inhibition. To understand a little bit more how willpower or inhibition works, a few pieces of information will help. First, willpower is one of five functions delegated to the prefrontal cortex or PFC. The other four functions are decision making, understanding, memorizing, and recalling. Second, it's important to know that the brain requires a crapload of energy from the body. It accounts for about 2% of our body mass and consumes about 20% of our energy. Most of our brain functions are automatic and don't require conscious processing. Like the beating of your heart, or a habit like driving a car. These automatic processes don't burn up metabolic resources. The PFC on the other hand requires a massive amount of energy or glucose to work. The same way you need energy to run a mile you need energy to make decisions or memorize facts. And this energy is not inexhaustible. We wake up every day with only so much gas in our tank to fuel our PFC. And we burn through it fairly quickly. What this means for willpower is that 1) it's a finite resource with only so much of it available to us each day and 2) it's a resource shared with other functions. Every time you solve a problem, make a decision, memorize a fact, remember something, or try not to do something, like eat that second cookie, or check your Instagram for the 14th time, you are draining your willpower reserves. Trying harder doesn't work when you've got nothing left in you to feel the effort. The thing about the Pfc is that there's no way to give it more gas. So there's no way to increase your willpower, or decision making, understanding, memorizing or recall. What you can do is approach those five functions as if they are precious resources because they are and plan your day in a way that uses them carefully. By creating more automation or habits so that you aren't using your decision making and willpower as often.

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Engineering autonomy in healthcare solutions must be a human-centered process. Engineers should not conflate automation, where AI can consistently repeat a task, with AI having autonomy. Humans must remain in the loop.

Good and Bad Habits Habits can be compared to macros in an Excel sheet. If we have tasks we wish to repeat in multiple cells, we can record a macro to automate them and quickly apply the set of actions to selected cells. Habits are like macros in the brain. On receiving the given cue, the brain automatically performs the actions of its programming. However, there is a catch to it. The created macro does not care whether it was correctly designed or not. If correct, it saves time through automated processes. But if the macro itself is wrong, we end up with a messed up excel sheet. Likewise, habits too programme the brain for our benefit or harm. Here is an anecdotal tale about habits.

Any change to environments or the software that runs on them should be applied using an automated process from version control

Pharmaceutical Product Exporters in Ahmedabad India’s Leading Exporters of Quality Pharmaceutical Formulations in India. We offer Pharmaceutical Drug Formulation and Pharmaceutical Marketers. Research Pvt. Ltd. is top manufacturing the anti-infective Tablets. We have a wide range of pharmaceutical product exporters in Ahmedabad India. ERPL is one of the fastest growing pharmaceutical manufacturing company in India. Spread over 1,47,500 sq. ft. acres, this modernized manufacturing plant with ultra-modern facility is set up in North Indian State of Uttarakhand. The facilities are approved by cGMP, WHO-GMP, GLP, ISO 9001:2000 and other global regulatory bodies. ERPL has carved out a distinct place of its own in manufacturing of best quality pharmaceutical products. Our success is the mirror of the hard work and team spirit put up the excellence of the professionalism by our dedicated Directors to excel and concurred new horizons with the motive of “Commit to Quality and Customer Satisfaction” Manufacturing Facility Installed with latest technology, automated manufacturing and packing operation and best practices, ensuring the highest quality of healthcare. • Commitment and zero compromise on quality • Follow up WHO-GMP, CGMP and GLP • Instrument Lab • Primary Area • Analytical Lab • Production process • Quality Management system • Process and products design, Evolution & Validation of process.

Which company is best for using construction Project work? The Shree Siva Balaaji Steels project is a significant endeavor that encompasses the establishment and operation of a modern and advanced steel manufacturing facility. This project represents a fusion of innovation, cutting-edge technology, and industrial expertise, aimed at delivering high-quality steel products to meet the growing demands of various sectors. Key Features: State-of-the-Art Manufacturing Plant: The project involves the construction and operation of a state-of-the-art manufacturing plant equipped with the latest machinery, automation systems, and environmentally friendly processes. This allows for efficient production and reduced environmental impact. Diverse Product Range: Shree Siva Balaaji Steels aims to offer a diverse range of steel products to cater to different industries such as construction, automotive, infrastructure, and manufacturing. This versatility enables the company to meet the varying needs of clients and partners. Quality Assurance: A cornerstone of the project is its commitment to delivering high-quality steel products. The facility adheres to strict quality control measures and follows international standards to ensure that the end products are durable, reliable, and meet or exceed industry specifications. Sustainability Focus: The project places a strong emphasis on sustainability and environmentally conscious practices. Energy-efficient processes, recycling initiatives, and waste reduction strategies are integrated into the manufacturing process to minimize the ecological footprint. Employment Opportunities: Shree Siva Balaaji Steels contributes to local economies by creating employment opportunities across various skill levels, from skilled labor to technical experts. This helps stimulate economic growth in the region surrounding the manufacturing facility. Collaboration and Partnerships: The project fosters collaborations with suppliers, distributors, and clients, establishing strong relationships within the steel industry. This network facilitates efficient supply chain management and enables the company to provide tailored solutions to its customers. Innovation and Research: The project invests in research and development to constantly improve manufacturing processes, product quality, and the development of new steel products. This dedication to innovation positions the company at the forefront of the steel industry. Community Engagement: Shree Siva Balaaji Steels is committed to engaging with local communities and implementing corporate social responsibility initiatives. These efforts include supporting education, healthcare, and other community-centric projects, fostering goodwill and positive impact. Vision: The Shree Siva Balaaji Steels project envisions becoming a leading name in the steel manufacturing sector, renowned for its exceptional quality, technological innovation, and sustainability practices. By adhering to its core values of integrity, excellence, and environmental responsibility, the project strives to contribute positively to the industry and the communities it operates within.

Revalgo leads the way in transforming sales dynamics for distributors and manufacturers. Their cutting-edge AI-driven automation platform offers an array of solutions, spanning from sales automation software to supply chain optimization. By simplifying intricate sales procedures and automating order processing, Revalgo liberates sales teams from tedious manual tasks. Experience the future of business resilience with Revalgo's transformative solution today!

Joseph A. Schumpeter, the Harvard economist who in 1943 published the iconic Capitalism, Socialism, and Democracy. The seventh chapter of that work, entitled “The Process of Creative Destruction,” is for many academics a sacred text. “The process of creative destruction,” Schumpeter writes, “is the essential fact about capitalism. It is what capitalism consists in and what every capitalist concern has got to live in.” Creative destruction is an elegantly simple idea describing the industrial mutation of old structures into new ones. The department store evolves from and “creatively destructs” the country store; the auto industry evolves from and replaces the horse and buggy business, automation makes many factory and farm jobs obsolete but creates new jobs in information technology, engineering, healthcare, and biotech.

The scale shift of labour composition from the nineteenth to the twentieth centuries affected also the logic of automation, that is, the scientific paradigms involved in this transformation. The relatively simple industrial division of labour and its seemingly rectilinear assembly lines could easily be compared to a simple algorithm, a rulebased procedure with an ‘if/then’ structure which has its equivalent in the logical form of deduction. Deduction, not by coincidence, is the logical form that via Leibniz, Babbage, Shannon, and Turing innervated into electromechanical computation and eventually symbolic AI. Deductive logic is useful for modelling simple processes, but not systems with a multitude of autonomous agents, such as society, the market, or the brain.

When I was a certified small business and startup mentor for several Non-Profit Organizations (NPOs), after several months I came to realize that most business owners saw websites as "one and done" items rather than as company portals through which valuable and important processes could go through -while concurrently promoting their business online 24/7 through multiple online channels. Set your goals higher and ask yourself "how can we automate what takes up so much of our time and energy" and "how can we learn from and mirror the strategies of larger, more profitable competitors?

What is Directed Energy Deposition in 3D Printing Directed Energy Deposition (DED) is a term that encompasses technologies involving semi-automated powder spraying and wire welding for manufacturing. When applied to 3D shapes, DED is considered an additive manufacturing process. It typically results in a rougher surface compared to Powder Bed Fusion, due to the larger bead sizes and coarser powder used, which often necessitates additional machining. DED systems generally fall into two categories: deposition systems and hybrid systems that combine a DED head with traditional machining equipment. The main advantages of DED include faster deposition compared to powder bed fusion 3D printing and the ability to create functionally graded material structures, especially when using powder. Additionally, since the feedstock and energy source move together, DED systems can manufacture very large structures, unrestricted by the size limitations of a build box. In some cases, DED can be more effective than traditional manufacturing methods or powder bed fusion. Most DED systems consist of a deposition head that uses either wire or powder and is mounted on a robot or CNC system. Common energy sources include Arc, Laser, or Electron Beam, with lasers being the most frequently used for powder feedstock. The process involves offline programming to generate a tool path from a sliced CAD file. The motion system then follows this path, depositing material in layers to build the desired shape. DED is compatible with a variety of weldable alloys, such as aluminum, steel, nickel, and titanium. Depending on the chosen alloy and process, shielding gas may be applied locally or within an enclosed environment.

Does It “Really” Need to Be an Email? By this point, you’ve probably figured out that I love email. Well, in spite of my love for email marketing, not every communication needs to be an email. In fact, there are times when emails really aren’t the best solution. So, if not email, what else? Other solutions include: In-App messages like popups, sidebars, site notifications, chat messages, browser or push notifications, desktop notifications, text messages, and even product tours and onboarding flows. Email is great when the user isn’t currently using your product. It’s great to drive them back in, but when they are right there using your product, you can’t expect them to be checking their emails at the same time. Before setting up a new email campaign, ask yourself if email is the best way to achieve your objective and drive the user behavior you seek. Maybe a popup or site notification would be more effective. Users can’t typically unsubscribe from popups, sidebars, site notifications, chat messages, or onboarding flows. They are usually better embedded into your app and more contextual. Because of this, they tend to reach users more directly than email can. That means that they can often be more effective to influence user behaviors. Push notifications, desktop notifications, and text messages still have some novelty to them. They can also reach users in different contexts from email. Although sometimes it’s better to use a different communication type, sometimes combining email with other options is the best way to go. For this reason, it’s important to consider the mix. For example, an email followed on-site by an In-App message, or an onboarding flow followed by an email summing up the process may be more effective than a single email. It will allow you to follow up on user actions, and make it really clear what needs to get done. By breaking down the steps one at a time, there’s more chances for users to learn. At LANDR, we often followed feature launch emails on-site with In-App messages. This helped to keep communications simple and goal-focused (one goal per message). The email was about getting people in the product, while the In-App message was about getting them to engage with the product. This approach allows you to evaluate and optimize each step of the process independently. Automation platforms like Intercom, ActiveCampaign and HubSpot generally allow you to combine messaging types. If your platform doesn’t currently have site messaging or onboarding functionalities, you may have to use multiple tools in conjunction in order to maximize results. This will make it trickier to track pacing, sequencing, and goals but it isn’t impossible. You also need to consider tracking effort when adding new communication types to your mix. As your program becomes more complex, it can be easy to lose track of the overall user experience: Are your users getting spammed? Are you creating a disjointed customer experience? Test things from your users’ perspective. Keep an eye out for social media messages and support requests as you do. In the next chapter we will look at setting up automations to minimize issues and maximize outcomes.

there were days during the process of automating this routine that I was cranky, days when my head pounded like a jackhammer and mornings when I just wanted to keep sleeping. But once I locked and loaded getting up at 5 AM regularly, my days grew consistently—and vastly—better than anything I’ve ever experienced.

Observing this is like seeing a pinball machine game, bouncing from one impact to the other. It is a life of rebound and reaction - mixing comedy with tragedy - automation with the old spark of ignition or re-cognition. If there is no intervention there shall be chaos. If intervention is not subtle there will be madness. It is a procedure and process of caution and concise knowledge.

self-service portals, automation, business analytics, forecasting tools, workflow tools, governance tools and cloud services

Microsoft Dynamics 365 Supply Chain Management Solutions Optimize your supply chain with Dynamics 365 Supply Chain Management. Our Microsoft expertise ensures efficient supply chain management. Introduction to Dynamics 365 Supply Chain Management In today's fast-paced business environment, managing a supply chain efficiently is crucial for success. Microsoft Dynamics 365 Supply Chain Management offers a comprehensive solution designed to streamline and enhance your supply chain operations. With our expertise in Microsoft technologies, we can help you achieve operational excellence and meet your business goals. Key Features of Dynamics 365 Supply Chain Management End-to-End Visibility: Gain complete visibility into your supply chain processes, from procurement to delivery. Real-Time Insights: Utilize advanced analytics and AI to make data-driven decisions. Seamless Integration: Integrate seamlessly with other Microsoft Dynamics 365 applications and third-party systems. Scalability: Easily scale your operations as your business grows. Enhanced Collaboration: Improve collaboration across departments with a unified platform. Benefits of Using Dynamics 365 Supply Chain Management Increased Efficiency: Automate and optimize your supply chain processes to reduce manual efforts and errors. Cost Savings: Identify cost-saving opportunities through better inventory management and demand forecasting. Improved Customer Satisfaction: Ensure timely delivery and high-quality products to enhance customer satisfaction. Risk Management: Mitigate risks by monitoring and managing potential disruptions in real-time. Why Choose Us? With our extensive experience in Microsoft Dynamics 365, we are committed to providing top-notch supply chain management solutions tailored to your business needs. Our team of experts will work with you to implement and optimize Dynamics 365 Supply Chain Management, ensuring you get the most out of your investment. Get Started Today Transform your supply chain with Dynamics 365 Supply Chain Management. Contact us today to learn more about how we can help you achieve a more efficient and effective supply chain.

With process, your business will become more and more clear to you and everyone else in the organization, leaving your people with a sense of ownership and of knowing exactly what they’re responsible for; how and who to communicate to and for what; what steps are waste that can be eliminated, automated, or improved immediately; and how to ensure a great customer experience, each and every time.

With process, your business will become so clear to you and everyone else in the organization that people will know exactly what they’re responsible for, how and who to communicate to and for what, what steps are waste that can be eliminated or automated or improved immediately, and how to ensure a great customer experience each and every time. That’s the process solution.

Processes help you talk about your business seamlessly and improve the speed of decision- making for all leadership, including senior leaders, board members, advisors, and investors. Processes save time, enhance performance, and allow for simplistic streamlining and automation where appropriate.

The process is an automation opportunity.

In the fast-paced, technology-driven world of today, businesses and organizations face the constant challenge of adapting to ever-evolving technological landscapes. SAP, which stands for Systems, Applications, and Products, has risen to the forefront as a leader in enterprise software solutions. SAP offers a diverse range of tools and applications that help businesses streamline their processes, make informed decisions, and manage their resources efficiently. As the demand for SAP expertise grows, SAP training programs have become pivotal for individuals and organizations alike. In a world where data is the new currency, organizations are increasingly turning to SAP to digitize their operations. Whether it's finance, human resources, supply chain management, or customer relationship management, SAP provides comprehensive solutions that allow organizations to integrate and automate their processes.

People, process, and data are orchestration's most basic building blocks.

our businesses should work like neuroplastic brains—wiring and rewiring processes all the time.

If task‐based automation is microscale, straight‐through processing is system scale. The technology takes over the entire process across systems and is designed to require no manual intervention.

Orchestration coordinates people, software, and data in end‐to‐end processes to get jobs done. With orchestration, people—customers, employees, or partners—are still key components of the process where their strengths are needed most.

orchestrate every process ingredient from start to finish. The ingredients fall into three categories: people, process, and data.

Even if virtual worlds were tabula rasa, we are encumbered with a great deal of cognitive baggage. Our brains are hardwired with many mental shortcuts to help us make sense of the world. We simply do not have the time to carefully process every piece of information that comes our way. To cope with this inundation of information, our brains have developed automated heuristics that filter and preprocess this information for us. Thus, when we encounter new media and technological devices, we fall back on the existing rules and norms we know.