Three steps of the Self - Learning Supply Chain - step 2: getting knowledge
Deep Machine Learning
Deep Machine Learning teaches computers to do what people and animals do naturally - learning from experience. Machine Learning algorithms use computational methods to "learn" information directly from data without relying on a predefined model. Algorithms are adaptively improving their performance with an increasing number of data. Algorithms analyze historical data from overdue production cycles, and identify hidden patterns in millions of data points that people are unable to capture, such as relationships between order characteristics and setup times and production cycle times or any other measurable data.
The system will include Deep Neural Convolutional Networks that simulate brain processes using an artificial neural network that has many nested layers where output from one layer node is a nonlinear combination (convolution) of all inputs from the previous layer.
We will connect Deep Machine Learning with Reinforcement Learning and create a system that will teach itself to recognize the subtle patterns in a large quantity of data coming from the supply chain and combine actions (for example, adjusting the time of the manufacturing operation) with results (such as timely delivered product to the customer). The software will have access to real-time up-to-date supply chain data and will be essentially told: "Get them to learn how to maximize resources utilization and productivity."
During Reinforcement Machine Learning, a software agent performs observations and takes action in the environment and receives rewards in return. Its goal is to learn how to maximize expected long-term rewards. In short, the agent works in the environment and learns by trial and error to maximize his rewards and minimize his loss.
An agent can be a program that tracks real-time up-to-date supply chain data and decides how to adjust, for example, the time of the production operation to earn a positive reward, for example, when approaching the target values of key KPI performance indicators and negative rewards when KPIs are below a certain minimum value.
The algorithm used by the software agent to determine his actions is called his strategy. For example, a strategy may be a neural network that receives real-time updated data from the supply chain and derives an action to be taken.