Agile Data-Driven Management is leading to greater sustainability for companies and society

Takahiro Fukushige（Takahiro）: Our BU’s mission statement is: “Transform the World with Intelligence & Technology.” In other words, we aim to harness the power of data by combining intelligence and technology to create innovations that can change the world, thereby contributing to the sustainable prosperity of the world.

In 2021, the Fujitsu Purpose was established as: “Making the world more sustainable by building trust in society through innovation.” To achieve this, our BU is responsible for developing solutions that lead to a sustainable society and the creation of value from a data perspective.

Takahiro: Naturally it is important for companies to grow their businesses and generate profits through their business activities, but in addition to that, they are expected to contribute to a sustainable society, such as through ESG management. To do that, management must make sophisticated decisions by taking into consideration profitability and customer satisfaction, as well as contributing to a sustainable society, such as carbon neutrality.

So while taking a hard look at complicated and uncertain times, top executives have to optimize their way of doing business. But it is extremely difficult for them to make optimal decisions based only on their own experiences, no matter how competent they may be. What matters here is to make effective use of the data and make decisions in an agile manner with the help of AI and machine learning. As data form the foundation of such decision-making, by increasing the reliability of data, we believe we can help transform business management.

Takahiro: I think we can solve the issue of insufficient information to come up with optimal decisions. More specifically, we can scrutinize and integrate data that exist within companies. Imagine that you have data with unknown lineage and origins. For example, it is likely that data get lost when transferred by multiple departments and employees, so you don’t know who created the data or when. One of our jobs is to trace data to identify where they originally came from. Another is to organize data including errors or defects.
Uncertainty in data can cause top executives to make the wrong decisions. So we provide solutions to turn uncertain data into reliable data.
Data-Driven Management is one such solution, which is a cycle of adding value to data, creating new experiences, adding more data and connecting that data correctly.
By integrating data scattered across the company, it can drive process transformation and the creation of new systems and value across the organization.

Take a manufacturing customer for example, if it is found that there is a problem with a product after its launch, Data-Driven Management makes it faster to link data about a defective product, thereby accelerating the speed of finding a solution that prevents similar problems from occurring in the future. Data-Driven Management should also make it possible to optimize inventories, forecast demand and detect any problems with production lines in factories.

I believe that the ability to use data in an optimal manner so management can improve quality and reduce costs will benefit customers by eventually lowering product prices.

Takahiro: In addition to data scientists like me, the Digital Experience Platform BU has data engineers responsible for database integration and restructuring, business producers responsible for project management and consultation, and others. A total of about 300 staff members work for the organization.
Data scientists closely work with data engineers, business producers and other specialists by combining forces ranging from defining customer issues of coming up with solutions thereto.

In this process, we, as data scientists, play the role of translating data into knowledge, namely, by making effective use of statistics, machine learning and AI, to provide data in whatever form is necessary to help resolve customer issues.

Corporate data may sprawl across many departments, or have different databases established by different departments which may not be linked together. Some customers don’t know what data may be stored on respective databases according to what criteria to be met by data, or cannot remember how to access databases established more than a decade ago, which makes it very difficult for us to discover data.

Unraveling relations among data dormant within the company, putting them together and making data meaningful – this is what data scientists do.

To do that, we must deeply understand the business domain of each customer. We are not only familiar with professional terms but also understand the environment surrounding the industry the customer is in, grasp the essence of the customer’s issue and make proposals one step ahead of the customer. This is what data scientists are required to perform.

Takahiro: We are going forward with a project of putting in-house Data-Driven Management into practice to manage parts and components to repairs and maintenance of hardware, such as servers. If one of our servers has a problem, we need to quickly deliver necessary parts/components from a parts center in Japan to a customer making an inquiry about their system trouble, within a certain period of time upon receipt of the customer’s inquiry.

Before the project was launched, there had been no integration between data about repairs and maintenance management, including the number of parts/components stored by each parts center and the number of purchase orders received by the center, and data about sales, including contracts with customers. So, even though we received an inquiry from a customer in Osaka to send some parts, the parts were only available at a Tokyo parts center, requiring us to make a long-distance shipment of the parts, resulting in higher costs; such cases had occurred very often.

Data connection is required to not only provide quick support for customers but also visualize inventory information and streamline warehouse management. In this project, we, the data scientists, and data engineers worked together to integrate siloed data into one.

More specifically, we integrated sales information and repairs and maintenance management information to visualize sales contribution by warehouse. Connecting data led to solutions to the issues by reducing the number of long-distance shipments and optimizing operations of parts centers. Cost reductions and the ability of business planning were significant in operational transformation.

We have another project underway that we are considering such services by analyzing data collected, stored and visualized via Fujitsu’s product lifestyle management (PLM) / smart factory solution “COLMINA” and classifying issues about onsite manufacturing data into several types, thereby quickly delivering further value to customers.

Takahiro: The Fujitsu Group has a member company called Fujitsu General Limited manufacturing air conditioners. Recent product models with on-board AI in can collect data from air conditioners. For example, I am exploring ideas about how we can use such data to suppress the peak levels of power consumption.
If we can automatically control air conditioners without making people feel uncomfortable, it would benefit power companies and should sooner or later benefit people, too. Fujitsu systems are also responsible for managing operations of trucks and buses; I was also wondering if we could optimize their transportation routes by using such data.

Takahiro: That’s true. In my opinion, people with creative desires and strong curiosity can certainly become data scientists.
As one more attribute, I would like data scientists at Fujitsu to have two or more titles. For example, I would introduce as a person familiar with power plant, car and aircraft engineering.
If you build your career as a data scientist to the extent of acquiring professional knowledge about each of the industry sectors your customers are in and of being able to talk with consultants and decision-making executives, this should increase the range of your work and job opportunities. I would like to train my staff to become such data scientists.

Takahiro: My purpose is to “have many people feel happy” at the end of the day.
I want company workers and engineers to make their work more efficient and enjoyable and product users to feel comfortable in life. I would like to work to support the world which creates a circle of happiness by using data. This should lead to sustainability of society. My desire shares commonality with the Fujitsu Purpose. I am proud to work for this company.