LIXIL was established in 2011. It makes pioneering water and housing products that make better homes a reality for everyone, everywhere. A better home is made up of surprisingly simple things – showers and faucets to experience water in new ways; kitchens that unleash creativity; toilets that provide cleanliness and comfort; doors and windows that connect you with the world outside; interiors and exteriors that bring spaces to life; baths to escape in after a long day.
Pluto7, an Supply Chain Solutions Builder, leverages the power of AI/ML providing streamlined Supply Chain management while improving customer experience to make your supply chains agile, adaptive and intelligent. We are experts in Smart Analytics, Machine Learning, and AI on Google Cloud. Our services and solutions deliver innovation and intelligence to build a data-driven future.
The customer is facing the challenge of extracting data manually from different sources. Currently, the POS planning process is managed manually, through excel files. Due to manual planning, the customers have limited capacity to maintain privacy leading to an unfriendly user interface and low forecast accuracy. Another challenge was that the updates in the planner were asynchronous and error-prone as different users could extract data from SAP BW and update the changes.
Pluto7 suggested the customer to set up a data foundation using BigQuery with Looker by developing a POS Planner tool using Custom UI. The team also developed data pipelines to read the latest POS data existing in .csv format from cloud storage stored in BigQuery. A demand forecasting model using the Cortex framework to showcase the forecasted output.
After successfully developing the custom UI of the data pipelines the data was accessible for the users to view the data in the POS planner or they could even extract the data in CSV format to do the analysis in MS Excel. Custom UI also helped in access management by which the admin can restrict the access to edit the data. To make changes in the available data only the authorized users were given the access to make changes in the current week or in the future weeks’ data.
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