Sales & Operations Planning Dashboard
This company's legacy systems and lack of visibility of their inventory management was impacting their ability to fulfill orders and they needed help.
The project was quick, lasting only 8 weeks developing the solution, using Snowflake, Power BI, Figma and Figjam along the process.
By the end, Oob had a scalable solution that would help them forecast and plan for peak seasons without all the manual intervention they needed before.
*all figures, measures, categories and names displayed here are based on fake mock data and not in any way representative of real performance or information from the client's data.
Decisive NZ approached Data Rocks with a request to help their client, Oob, to replace their complex legacy systems with Power BI.
Their systems were not able to accommodate their growing business and changing needs, leading to a reliance on manual data manipulation in Excel spreadsheets. Data quality was also a concern due to the need to merge multiple systems with different naming conventions and classifications. This lack of visibility and reliability resulted in frustration for the client and their customers. I was aware that this would be a challenging project.
With the aim to improve Oob's Sales and Operations planning, we had a kick-off meeting to define the problem and scope of work, and then spent about 8 weeks developing the solution, using Snowflake, Power BI, Figma and Figjam as tools. Weekly catch-ups helped us share progress, feedback and make sure the dashboard met their needs.
The dashboard was designed to be easy for users who were familiar with spreadsheets.
It included tables, matrix visuals, and basic form charts, like Lines and Bars, with carefully curated interactions that allowed them to seek further information without risk of overwhelm.
The front page summarized their product stock for the next 20 weeks, with dynamic alerts based on user input. The second page allowed users to investigate sales orders, purchase orders, and stock levels in detail. A sales forecast accuracy page and a data quality checks page were also included, to ensure the client could have full transparency and visibility of their data issues and caveats.
The project involved ingesting data from their CRM system into Snowflake and curating it in different tables, which were then consumed through Power BI, creating an easy-to-use interface for the end-users. The goal was to improve visibility, ease of maintenance, speed, and scalability.
If you're a Power BI nerd, too: I'm particularly proud of the weekly stock projection DAX, as it is done based on a weekly schedule (ISO week based)
By the end, Oob had a scalable solution that would help them forecast and plan for peak seasons without all the manual intervention they needed before. They could safely retire their older CRM system, and keep maintenance to a minimum of manual intervention, freeing up time for them to focus on improving data quality and taking actions for better forecast accuracy.
