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The main principles of the Lean methodology include:

Eliminating Waste
Amplifying Learning
Deciding as Late as Possible
Delivering as Fast as Possible
Empowering the Team
Building Integrity In
Seeing the Whole

Lean development eliminates waste by asking users to select only the truly valuable features for a system, prioritize those features, and then work to deliver them in small batches. It relies on rapid and reliable feedback between programmers and customers, emphasizing the speed and efficiency of development workflows. Lean uses the idea of a work product being “pulled” via customer request. It gives decision-making authority to individuals and small teams since this has been proven to be a faster and more efficient method than a hierarchical flow of control. Lean also concentrates on the efficient use of team resources, trying to ensure that everyone is as productive as possible for the maximum amount of time. It strongly recommends that automated unit tests be written at the same time the code is written.

They were wrong. It soon became obvious that employees, less than engaged, were more likely to roll their eyes than roll up their sleeves when the new change requirements were explained to them. "Change fatigue" was ubiquitous. After all, the current effort was just the latest in a long string of change efforts—few of which had succeeded.
Even more worrisome: proof of resistance was everywhere, confidence in the company's senior management was low, and there was little clarity concerning the factors by which employees were measured. Long story short: the changes went nowhere fast. In that respect, the energy company was no different from the myriads of other organizations that fail to do a proper job of delivering transformational changes.
Today, the company's story is quite different. The targeted savings have been achieved—and are being sustained. Many of the company's functions are cost-effective, thanks largely to motivated in-house teams of line managers. The key initiatives are explicitly defined and owned, and managers know which milestones the organization must achieve and when, as well as which course corrections they must make if a milestone is likely to be missed.
 
 

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WHAT IS TECHNOLOGY – MEANING OF TECHNOLOGY AND ITS USE
 

WHAT IS TECHNOLOGY – MEANING OF TECHNOLOGY AND ITS USE
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Technology acceptance model

 
 


Based on the theory of reasoned Action, Davis ( 1986 ) developed the Technology Acceptance Model which deals more specifically with the prediction of the acceptability of an information system. The purpose of this model is to predict the acceptability of a tool and to identify the modifications which must be brought to the system in order to make it acceptable to users. This model suggests that the acceptability of an information system is determined by two main factors: perceived usefulness and perceived ease of use.
Perceived usefulness is defined as being the degree to which a person believes that the use of a system will improve his performance. Perceived ease of use refers to the degree to which a person believes that the use of a system will be effortless. Several factorial analyses demonstrated that perceived usefulness and perceived ease of use can be considered as two different dimensions (Hauser et Shugan, 1980 ; Larcker et Lessig, 1980 ; Swanson, 1987).
As demonstrated in the theory of reasoned Action, the Technology Acceptance Model postulates that the use of an information system is determined by the behavioral intention, but on the other hand, that the behavioral intention is determined by the person’s attitude towards the use of the system and also by his perception of its utility. According to Davis, the attitude of an individual is not the only factor that determines his use of a system, but is also based on the impact which it may have on his performance. Therefore, even if an employee does not welcome an information system, the probability that he will use it is high if he perceives that the system will improve his performance at work. Besides, the Technology Acceptance Model hypothesizes a direct link between perceived usefulness and perceived ease of use. With two systems offering the same features, a user will find more useful the one that he finds easier to use (Dillon and Morris, on 1996).

Technology acceptance model

 
 


Based on the theory of reasoned Action, Davis ( 1986 ) developed the Technology Acceptance Model which deals more specifically with the prediction of the acceptability of an information system. The purpose of this model is to predict the acceptability of a tool and to identify the modifications which must be brought to the system in order to make it acceptable to users. This model suggests that the acceptability of an information system is determined by two main factors: perceived usefulness and perceived ease of use.
Perceived usefulness is defined as being the degree to which a person believes that the use of a system will improve his performance. Perceived ease of use refers to the degree to which a person believes that the use of a system will be effortless. Several factorial analyses demonstrated that perceived usefulness and perceived ease of use can be considered as two different dimensions (Hauser et Shugan, 1980 ; Larcker et Lessig, 1980 ; Swanson, 1987).
As demonstrated in the theory of reasoned Action, the Technology Acceptance Model postulates that the use of an information system is determined by the behavioral intention, but on the other hand, that the behavioral intention is determined by the person’s attitude towards the use of the system and also by his perception of its utility. According to Davis, the attitude of an individual is not the only factor that determines his use of a system, but is also based on the impact which it may have on his performance. Therefore, even if an employee does not welcome an information system, the probability that he will use it is high if he perceives that the system will improve his performance at work. Besides, the Technology Acceptance Model hypothesizes a direct link between perceived usefulness and perceived ease of use. With two systems offering the same features, a user will find more useful the one that he finds easier to use (Dillon and Morris, on 1996).

CNN is a multi-layered neural network with a unique architecture designed to extract increasingly complex features of the data at each layer to determine the output. CNN's are well suited for perceptual tasks.

CNN is mostly used when there is an unstructured data set (e.g., images) and the practitioners need to extract information from it
For instance, if the task is to predict an image caption:

The CNN receives an image of let's say a cat, this image, in computer term, is a collection of the pixel. Generally, one layer for the greyscale picture and three layers for a color picture.
During the feature learning (i.e., hidden layers), the network will identify unique features, for instance, the tail of the cat, the ear, etc.
When the network thoroughly learned how to recognize a picture, it can provide a probability for each image it knows. The label with the highest probability will become the prediction of the network.