In supply chain ML is an algorithm that processes collected data to determine logistics patterns, streamline routine processes, and predict and decide on best practices for operational improvement without programming command sequence intervention !!
Machine learning (ML) and Artificial Intelligence (AI) are two sides of the same coin, the terminology is used interchangeably from one source to the next. Bottom line, they both refer to the application of computer programs that can automatically identify behavioral patterns and improve existing processes without being explicitly told by humans to do so, i.e., vice versa with passive legacy programs. It’s an IoT autonomous brainchild that applies across all three core pillars to create smart building blocks that talk to each other in both micro and macrocosm scale of things. In other words, it can think on it’s own feet as long as a pattern is identifiable and a solution is available for a predefined scenario. Beyond that, the call is simply forwarded to the call center’s answer machine. From one machine to another, no harms done.
All the same, ML is a multidisciplinary field that combines computer sciences, engineering, and particularly important, statistics. In the former, ML requires key upstream applications in data mining, natural language processing, image recognition, and smart systems, among others. It also links to downstream knowledge bases required for effective application, e.g., biology, chemistry, and surprisingly, marketing, along with other things. It can help industries discover hidden patterns and insights that can unveil potential market niches for the most conventional retailer in the age of accelerated competition.
As machines collect and analyze data faster and more accurately than humans, operational overhead will decrease significantly while productivity and overall efficiency is raised to optimum levels. You heard correctly: individual human productivity is enhanced so much to the point that human asset requirements is put under the spotlight for a new reality check. Humans, thus, are its own worst enemy. To Keynesians’ dismay, classical theories that unemployment is due to excessive wages is proved to be correct all along. Success of macroeconomics in the future, sooner than anyone thought, may no long be measured by the number of added jobs. AI, however, doesn’t simply expose the W and T in the SWOT analysis. It amplifies an enhanced S and O, as well, to an exponential degree. At the end of a day, it’s an easier, cheaper, leaner and greener economy to run, and with that is a better quality of life for everyone.
Let’s jump in to see how Machine Learning is expected to revolutionize supply chain:
Machine Learning vs. Smart Machines
As ML is able to measure and evaluate its own performance as well as improve efficiency in an automated fashion, it will eventually become Smart Machines that are familiar with so many patterns, having accumulated so much data over time, it will be able to think of ways to reduce costs and optimize production on its own. It will become the ultimate solution by streamlining manufacturing and logistics operations and also by effective testing and developing new products.
Better supply chain decisions
Predictability at every stage will become a key component in supply chain as it affects the company’s profitability and every decision made in the management of logistics affects overall performance of the industry itself as well as all other industries. The opportunity cost of operating with a system that takes too long to decide can be a make or break deal for the competitors. Given all key checkpoints checked, on the other hand, the machine can automate a go ahead faster than we can blink. It doesn’t need to re-verify already verified information over and over again. I can apply objective algorithms instead of going through countless-endless brainstorms full of subjective truths and argument fallacies that no one remembers after the previous. It can help us solve complex problems more easily and discover other insights we have never thought of before. Machines are better decision makers, nevertheless, for scenarios that require objectivity and logical conclusions. It can render better solutions because it is able store and process effectively vast amounts of properly formatted historical data accumulated over generations and centuries. The program is designed to be constantly re-evaluating possible outcomes and thus the most effective alternative at any given time, i.e., the delta is as certain as change itself.
Manufacturing Process vs. Scalability
The supply chain industry needs to be prepared for the rapidly changing and complex demands of today’s ever-growing retail business. The information that machine learning collects can help both management and operational task forces handle such fluctuations better, it be decision making in areas of warehouse, technological, new service requirements or optimizing transportation processes.
Cross-functionality vs. Shared Goals
Traditionally, most supply chains had either cross or vertical organizational matrix and a little bit of the other given work channeled through proper chains of command, and thus disconnected technological systems that reflect the structure. ML enables cross-departmental and cross-functional teams to work seamlessly to achieve common goals, effectively, by providing accurate insights and introducing simplified work processes that flow effortlessly to all teams involved. Every is on the same page. Everyone knows what the correct, same, course of action is. With seamless workflow across functionalities come effective joint departmental goal settings, as well.
Maintenance, Repair and Overhaul (MRO) is exactly what the name suggests plus an extended key role in helping the organization’s manufacturing and services processes run smoothly. Especially in today’s world where manufacturing and services require even more sophisticated equipment and machinery, a malfunction or breakdown that needs a replacement could hold up the entire process longer than usual and directly impact productivity and service performance. ML is able to aid MRO capacities by accurately calculating and predicting deterioration and replacements so management can prepare ahead of time, financially and operationally, with a dash of cost-effective accounting as things are taken into account and precluded before actual damage even takes shape. As a result, supply chain operations can be properly maintained and run with optimal performance and the leanest operation possible.
ML is designed to be constantly re-evaluating possible outcomes and thus the most effective solution at any given time. Speaking of possible outcomes, if you think today’s computing is fast wait until quantum computing hits the shelves. Theoretically, it will be able to compute at least 4 times faster than the most sophisticated contemporary supercomputer with even the best algorithms known to man. Blockchain technology, on the other hand, will optimize both data and system integrity, transparency and cyber security to a level unforeseen to-date. No one, or at least the very few willing to commit unrealistic resources, will have the power to erase the trails of inefficacy. Connecting the dots together and top it off with advancement in energy, robotics and transportation, the digital supply chain revolution is only in infancy stage.
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