Leveraging the capabilities of PowerBI and our Data Visualisation teams expertise, a data model was constructed in PowerBI that brought together data sets from several internal systems for analysis.
Complex parts management is simplified using an Inventory Management Dashboard. Komatsu Australia is a market leading supplier of earthmoving equipment, parts, and service for the mining, construction and utility industries.
They are limited by how much inventory they can physically hold, and need to understand how much stock they need of any given part to meet customer demand. This is a complex task as individual parts can be used on many different machines. Consequently they need the ability to accurately & efficiently analyse their inventory across all distribution centres & branches.
Komatsu Australia was analysing their inventory using data extracts from various systems that fed reports built by hand in Excel. These reports were time-consuming to build & run and the information provided by them didn’t provide the flexibility that could allow the management team to quickly generate insights. Komatsu needed to being able to put together these datasets coming from various systems and visually analyse the inventory.
By leveraging the capabilities of PowerBI and our Enterprise PowerBI teams expertise, a data model was constructed in PowerBI that brought together data sets from several internal systems and linked them to each other in a way that allowed slice and dice analysis. Visualisations answering specific questions about the inventory were then built using this data model as the source. The process was then optimised to be able to ingest data sources and refresh the visuals on demand.
Using the Inventory Management Dashboard, the management team was able to quickly & accurately review parts usage and inventory quantities & costs. They were also be able to visually break this down by warehouses, regions, part rankings and other key dimensions. This gave management the ability to answer key operational questions such as “which slow-moving but high-value parts are being stored in local warehouses” and “what are the parts that are at risk of being understocked”.
Power BI was used to build the data model and the visualisations. Within Power BI, Power Query was used to performing the ETL.
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