Terms like Artificial Intelligence and Machine Learning are present wherever you go: quite rightly because their field of application is endless and technologies are rapidly developing, which even offers broad potential along your Supply Chain. Optimize and accelerate your processes by means of picture recognition, voice processing or a computer-based automation such as robot control or quality assurance. Artificial intelligence puts you ahead of your competitors.

What is Machine Learning?

Machine Learning and Artificial Intelligence (AI) are frequently used interchangeably, whereas Machine Learning only describes a part of AI. Artificial Intelligence represents a huge and elusive topic, namely the reproduction of what we humans think to be intelligence with artificial means - for example, the imitation of human decision making structures. While Machine Learning enables IT systems to draw conclusions from data without the need for explicitly programmed rules. Various Machine Leaning procedures enable the evaluation of large data amounts (Big Data) for diverse purposes, so answers to questions are given. Machine Learning is applied a lot nowadays and is over and over applied throughout our IT- projects.

How does Machine Learning work?

During Machine Learning, a computer independently generates knowledge from experience, analogue to learning structures of humans. One well-known example of Machine Learning are chess- or Go - computer. The breakthrough in this discipline, however, was just a few years ago when the self-learning algorithm AlphaZero achieved an outstanding play level in just 4 hours. The software additionally learnt the Chinese Go and the Japanese Shogi. In contrast to the majority of chess programs, the algorithm was not programmed for years to know the game, but learns in taking random moves and competing against itself.

This success is particularly impressing, because AlphaZero represents a so-called generalized Artificial Intelligence. Instead of being specialized to one single application (chess), AlphaZero is able to learn various applications. Generalized intelligence is also the declared goal for industry and logistics, e.g. for the programming of robots: instead of prolongedly programming a robot for just one single operation, the generalized intelligence would quickly provide robots for miscellaneous applications - in times of lotsize 1 and a maximum possible production flexibility, a crucial advantage.

Machine Learning can be used for different purposes. However, it is always about finding self-sufficient solutions for any problem by applying algorithms - based on Big Data. Whether they already contain a logic, which may be applied for a new data set or no enriched data for practicing purposes is available, various procedures are applied.

Supervised Learning

Supervised Learning describes the most common form of Machine Learning. Different factors (input) are compared with a known lotsize (output/logic). This is how, for example, a computer is taught to evaluate different factors so it is able to make a forecasts. Historized data is used as training basis.

Unsupervised Learning

This form of Machine Learning does not include output-data. Instead, algorithms are able to sort unstructured data. Similarity structures allow the formation of groups within large data sets. These algorithms can be used for the processing of images or in order to identify deviations within data stocks.

Reinforcement Learning

Reinforcement Learning represents the third form. This method is the closest to the learning strategies of human intelligence, because it is similar to the „Trial and Error“ principle. Successful actions are either rewarded or punished - in a metaphorical sense, since these mostly tend to be social reinforcements.

Reinforcement Learning is similar to this. Rewards foster successful behavior, punishments repress behavior that leads to unwanted results.
Even when research has made significant progress in this field, the full potential of Reinforcement Learning however, is expected to be fully tapped over the next coming years.

Advantages and chances of Machine Learning

The major advantage of Machine Learning is found in automation. Production - and logistics applications in particular, feature numerous application scenarios because a lot of optimization- and automation potential lays here:

  • Efficient- and prompt evaluation of large data sets (Big Data)
  • More precise execution of complex tasks (such as cancer detection)
  • Create accurate and valid forecasts

Machine Learning application along the Supply Chain

Along the entire Supply Chain, the optimization potential with Machine Learning is massive. SALT Solutions’ experts have realized outstanding projects, showing that Machine Learning is improving and automating processes. Next to prototypes and research projects in the in-house Innovation Lab, we include Machine Learning procedures for diverse solutions. These applications are extremely individual and can differ immensely. The following examples will give you an insight of which possibilities are already provided by Machine Learning and how you can benefit from them:

Image recognition

Picture recognition is one classic application scenario of Artificial Intelligence . Within the production, Machine Learning can, for example, be used to detect production defects without having to install extendious inspection methods. Picture recognition can also be the solution if different products are to be assigned to different shipping types for distribution on the basis of visual characteristics.

Natural Language Processing

The typical example of speech processing are speech assistants such as Alexa or Siri. The picking procedure Pick-by-Voice already applies speech processing. In order to have his or her hands available for operating machinery or processing tasks, the warehouse employee is guided through the picking process by voice. Tasks are then confirmed by voice input of the warehouse employee.

Robot programming

With the increasing degree of automation in modern production, robots are used for more and more complex tasks, which also results in an increase of programming effort. The programming of grabs gets more and more complex and time-consuming depending on how complicated the grab is constructed. Machine Learning however enables the robot to learn how to grab objects - no matter what form and without having to program each individual movement. The goal is to have a short learning phase for robots, which are then used as versatile as possible.

Predictive models

Machine Learning is also applied for Predictive Maintenance and Predictive Quality. With the help of historized data, a system learns to evaluate current data so predictions about future developments can be made. The system can now learn from past disturbances of machines and when the next disturbance is likely to occur again. The machine operator can now interfere early on and eliminate disturbance causes or schedule maintenance intervals on time.

Supply Chain Management

Similar predictive models can definetly be applied along the entire Supply Chain. Machine Leaning enables precise information about the development of warehouse stocks, orders, shipments and demands. The utilization of warehouses and transportation service providers can be estimated precisely. Wrong estimations are reduced and costs are optimized.

Artificial Intelligence for your IT solutions

Machine Learning procedure

Artificial Intelligence helps you to automate processes and evaluate data. We are on your side and assist you to implement Machine Learning wherever your employees and processes can be supported. You benefit due to our process expertise along the entire Supply Chain. We have realized projects around production applications and logistics processes for various customers.

Thanks to our long-term experience in the fields of Big Data solutions, we are equipped with all necessary method - and technology expertise to develop tailor-made applications for you. We support you with IT solutions, which optimize, automate and analyze our individual processes with the help of Machine Learning.

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