Technology
Data science technology
Supporting the use of data and promoting DX in all business fields
Supporting data utilization in all business fields
To offer value to consumers with their various needs and to reform business operations to make them more efficient, accumulated data is invaluable. Data science is a technology that offers various ideas for increasing efficiency, achieving optimization based on data, and contributing to business reform in a manner friendly to workers and the environment.
Technology Overview
Konica Minolta’s data science technology has the following features.
1. Driven by troubles of customers and on-site workers, not data-driven!
We do not analyze data for no reason. We determine the data needed to solve customers’ issues and select optimal analysis techniques.
2. Analysis output that leads to action!
We do not conduct analysis for analysis’ sake. We design analyses by considering what kinds of output will lead to implementation of measures.
3. Start small with a grand vision!
We start by solving troubles faced by customers in their daily work. We strive to realize a grand vision through in-depth analysis and horizontal deployment.
DX in the production field based on data science
Realization of DX at production sites by using data based on the issue-driven approach
There are high expectations for using DX to improve production sites by using data. To realize DX, it is essential to correctly identify on-site issues because each site has unique characteristics. At Konica Minolta, experts who have in-depth knowledge of on-site operations, such as production and indirect operations, work with data scientists on how best to use data to tackle on-site issues. They work on about 70 topics, including data visualization, prediction, and optimization.
DX in the production field based on data science
Improvement in the assembly process based on a machine learning approach using frame detection data
To increase the productivity of the assembly process at plants, Konica Minolta improves the process by using quality control (QC) and industrial engineering (IE) techniques. Previously, personnel in charge of analysis directly observed the process to quantify how workers performed manual tasks at plants, which was very time-consuming. The “frame detection algorithm” of FORXAI Recognition has been used to quantify the behavior of workers and promote automation of output, including the distance moved by workers and indirect work time.
DX in nursing care by using data science
Modifying nursing care services by using HitomeQ data
HitomeQ Care Support is a service to reduce the workload of nursing care and improve the quality of care by using images, which are captured by behavior analysis sensors installed in rooms, and smartphones. Konica Minolta is developing data services that can be used to offer optimal care in line with residents’ needs and increase the work efficiency of staff by collecting, storing, and visualizing data gathered from the care support system. Presented data is optimized by using technologies, including feature engineering, data visualization, and change detection, to solve issues at respective facilities.
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