Promotion Of Digital Transformation
By making full use of digital technology, we will reconstruct the way of management and business model, and support the promotion of digital transformation of all businesses.
Promotion Of Digital Transformation
With the development of web-based technology, the amount and quality of data available have increased dramatically. However, data can be accumulated, it does not make sense without analysis. VIETIS will utilize these data to help you solve problems.
Promotion Of Digital Transformation
By making full use of digital technology, we will reconstruct the way of management and business model, and support the promotion of digital transformation of all businesses.

We Will Propose The Optimum Solution According To Your Data EnvironmentWe Will Propose The Optimum Solution According To Your Data Environment

Level 1
Building data collection platform Making proposal of simple BI tool
  • Get some extent of data
Level 2
Making proposal of BI tool for advanced data analysis Training about SQL, etc.
  • Data is stored in the database
  • Basic statistics by SQL etc. are made
  • Some insights can be gained from statistics
Level 3
Data analysis agency Dispatch of data scientists
  • Basic statistics are substantial and can be managed by BI tools
  • Manual exploratory data analysis can be performed using a pivot table, etc.
  • Measures can be planned from manual data mining
Level 4
Development of machine learning system Building system for stable operation
  • Exploratory data analysis using machine learning algorithms
  • Make data-based decisions using A/ B testing, etc.
Level 5
Building a deep learning environment
Providing infrastructure
  • There is an environment where measures can be automatically executed using machine learning, etc.
  • There is a mechanism to make stable profits by machine learning
Level 6
Build more advanced machine learning algorithms
  • Build more advanced machine learning algorithms (such as deep learning) to further improve profitability

Features Of The ServiceFeatures Of The Service

Full support from analysis to solution construction and operation

Experienced and proven
expert team

Advanced technology know-how and abundant staff

Analyze Accumulated Data To Derive Valuable Output

You have data available, but don’t know how to use it. You don’t have staff who can analyze the data. You want to carry out marketing activities based on data. VIETIS will help you solve “ Increasing sales” , “Expanding profit” and “Making speedy decisions”.

Issue

Investigate issues

  • Business analysis
  • Identifying and classifying issues
  • Prioritization
  • Data collection and preprocessing
  • Data cleansing

We perform market research and analyze business/ business flows to identify issues. After further classifying and prioritizing the extracted issues, we will collect sample data.

Data analysis

Design data model

  • Model building
  • Model performance evaluation
  • Parameter adjustment
  • Comparison and selection by multiple models

This is the process of creating a data model in order to clarify data requirements and determine the implementation range. Start with simple machine learning, verify performance, and decide which model to use.

Solve business
issues

Build system

  • Data collection platform building
  • Operation management tool building
  • Machine learning tuning
  • Production introduction.

We will introduce a system that automates the process of data collection and data preprocessing, as well as management tools for verifying and evaluating the results. At the same time, we will change the algorithm and build an environment that optimizes machine learning to improve the accuracy of data analysis.

Case StudyCase Study

Analyze about 20,000 DATA flights and establish aviation fuel-saving solution

Problem

The client spent about 77 billion yen on fuel costs in 2017. This accounts for 26.5% of total spending. In this project, we aimed to devise and implement a fuel-saving solution by data analysis.

Input

Fuel costs are affected by a variety of factors, including aircraft design, flight mode, load weight, and fuel volume.
The project team analyzed data from about 20,000 A350 aircraft for the two years 2017-2019, provided by many systems such as CFP, ACAR, and LOOMS.

Output

As a result of data analysis, it was discovered that there was a discrepancy between the amount of fuel required by the captain before the flight and the actual amount of consumption. As a result of changing the guidelines to require declaration when the fuel of 500 liters or more is required, almost no captain demands a fuel amount of 500 liters or more, and we succeeded in saving a lot of fuel.