Artificial intelligence and production scheduling

What is possible today thanks to artificial intelligence can be represented by the intersection of these three fields: reasoning, interaction and learning. CIOs and executives who want to evolve their companies must choose the cases that are at this intersection, to get the most out

of AI. 

Artificial intelligence (also known as Artificial Intelligence or AI) is a priority for CIOs in all sectors. Many companies plan to implement artificial intelligence internally in order to make their production department smarter, faster, more profitable and competitive. In fact, a study by the Fortune Knowledge Group reveals that“82% of executives plan to implement AI projects within their companies in the next three years.” However, to implement artificial intelligence effectively, we must first agree on its definition and what constitutes AI or not.

AI is an intersection between technologies that reason, interact and learn:

  • Reasoning: Reasoning enables AI technologies to extract critical information from large structured and unstructured datasets, perform clustering analysis, and use statistical inference. The level of analysis that has been reached today is increasingly close to human capabilities.
  • Interaction: Interaction allows AI technologies to use machine vision to see, conversational AI and computational linguistics to communicate, again approaching the capabilities of the human being.
  • Learning: What really distinguishes artificial intelligence from intelligent automation, however, is the ability of technology to learn and become smarter over time. Only AI has this third dimension. One of the reasons artificial intelligence is so promising is because it changes the paradigm in the way we’ve written software code. Instead of programming in every condition “if, then … except”, telling a processing engine what to do and how to do it, AI allows you to solve tasks without having to explicitly write specific code that explains how to solve that task and therefore the engine can learn and understand independently how to solve the various tasks. In doing so, AI addresses problems that traditional software programming has never been able to address.

To date, there are three different types of learning within AI:

  1. Supervised Learning: This is the most common form of learning currently; the system is provided with sample examples regarding both inputs and outputs and from these it performs a series of techniques based on statistics (but not only) useful to extrapolate the final logic that connects inputs and outputs. Once the system has defined the final logic, it is considered “trained” and can apply the function to any new set of information. For example, this type of machine learning can help predict how likely a business is to fail. By providing the system with a large amount of data on companies (input) indicating whether they have failed or not (output), in fact, the machine can extrapolate the underlying trends. Thanks to this, the system can distinguish the warning signs and therefore signal in time to a company whether it is at risk or not.
  1. Unsupervised Learning: Unassisted learning is when a machine is presented with a set of documents or data and then calculates things on its own (examples consist only of inputs and not outputs). For example, if a company wishes to classify a series of contracts, the machine could read each document and, depending on the context, automatically separate them into categories, for example clauses such as the one on intellectual property, the clause on limitation of liability, the indemnity clause, etc. Thus, the machine is able to define a rudimentary ontology without human input.
  1. Reinforcement Learning: Also known as “goal-oriented” learning, reinforcement learning is when a machine is presented with a set goal and then left free to do what it must, while respecting the constraints of the context – including adjustments and rework – until it finds ways to more effectively achieve thatgoal. The most popular examples of reinforcement learning are found in video games, in these cases the goal of the machine is to win the game. You can see how it can also be applied in production planning by learning to manage a multitude of complexities to support planners in achieving business objectives such as service level, warehouses and efficiency.

Artificial intelligence in production planning and scheduling: Advanced Planning and Scheduling Software

In manufacturing companies, production is the core business. Planning production in the best possible way, ensuring adaptability and flexibility, minimising costs and maximising profits, increasingly becomes the winning weapon. Forward-looking CIOS, when evaluating their software investments, must take into account that the platforms are prepared to accommodate AI. These carefully chosen software allow you to act in time with respect to the competition while maintaining and increasing your competitive advantage.

CyberPlan Web is the first Advanced Planning and Scheduling (APS) software built on a web platform that can maximize the use of AI.


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