Hitachi and Daicel Utilizing IoT to Develop Management and Manufacturing Dashboards

Author photo: Shin Kai
ByShin Kai
Category:
Acquisition or Partnership

Hitachi, Ltd. announced that in collaboration with Daicel Corporation, it has developed a management and manufacturing dashboard utilizing IoT to integrally visualize KPIs from management information to manufacturing workplaces' situation. According to the company, this enables executives, plant managers and production line supervisors to make decisions more rapidly by grasping useful information quantitatively and in a timely manner to improve management and productivity.

The system was developed taking full advantage of 4M (Man, Machine, Material and Method) data in manufacturing workplaces, which has been collected through the image analysis system to detect signs of deviations in front-line workers' motions and facility failures as part of the Collaborative Creation projects carried out by Hitachi and Daicel.

Daicel has been introducing manufacturing dashboard in phases for production line supervisors since October 2017 in its Harima Plant (Tatsuno, Hyogo Prefecture), which manufactures core components for automobile airbags. Management dashboard for executives is also under consideration and is scheduled to be fully operational. In addition, the system will be introduced to six of Daicel's overseas plants, aiming to accelerate management decision-making from global perspective and to further improve manufacturing productivity and product quality. Hitachi aims to proactively expand the system to manufacturers worldwide as one of the solution cores for the industrial field of the IoT platform "Lumada".

Utilizing 4M data in manufacturing workplaces, this system shows KPIs chronologically in graph form, which is helpful for making decisions to improve management and productivity, for each managing layer of executives, plant managers and production line supervisors. For instance, as KPIs for executives, it indicates sales, profitability, cash flow, and operational availability by business and plant, and as KPIs for plant managers, it indicates production amount and operational availability by line in the responsible plant as well as other plants' information. As KPIs for production line supervisors, it indicates the cycle time and utilization rate by responsible line as well as other lines' information. This system makes it possible to accelerate the cycle from understanding of the situation to problem identification, evaluation analysis and improvement from the viewpoint of the overall optimization for each layer. In addition, the system supports the combination of information in manufacturing premises that operate globally (e.g. processing results, video of works, etc.), analyzing the causes of defects by utilizing big data analysis technology, and making improvement measure proposals that contribute to global product quality improvements by feeding the outcome back to manufacturing workplaces.

It can also provide useful KPIs that lead to improvement activities, prioritizing viewpoints at the manufacturing workplaces, by taking advantage of the achievements where Hitachi has provided solutions for a wide range of manufacturers and the OT knowledge that it has accumulated as a manufacturer, as well as its original KPI Tree Modeling Technology. For example, for achieving "ultimate cost management," one of Daicel's most important KPIs, it is possible to seamless do analysis from executives to manufacturing workplaces by collecting the work results, such as actual work hours and delays against the standard tact, setup time, and waiting time due to equipment, and then by connecting the work results with high class KPIs (e.g. operational availability and manufacturing costs). This system has an environment where visualization and analysis can be operated efficiently by adopting the functions of "Lumada", such as data integration infrastructure, which unifies the formats of collected data, and data lake, which efficiently organizes and stores a broad variety of big data.

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